diff --git a/02_activities/assignments/assignment_1_1.ipynb b/02_activities/assignments/assignment_1_1.ipynb new file mode 100644 index 00000000..b19226b1 --- /dev/null +++ b/02_activities/assignments/assignment_1_1.ipynb @@ -0,0 +1,904 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "927ae8f4", + "metadata": { + "id": "927ae8f4" + }, + "source": [ + "# Assignment 1 - Building a Vision Model with Keras\n", + "\n", + "In this assignment, you will build a simple vision model using Keras. The goal is to classify images from the Fashion MNIST dataset, which contains images of clothing items.\n", + "\n", + "You will:\n", + "1. Load and inspect the Fashion MNIST dataset.\n", + "2. Run a simple baseline model to establish a performance benchmark.\n", + "3. Build and evaluate a simple CNN model, choosing appropriate loss and metrics.\n", + "4. Design and run controlled experiments on one hyperparameter (e.g., number of filters, kernel size, etc.) and one regularization technique (e.g., dropout, L2 regularization).\n", + "5. Analyze the results and visualize the model's performance.\n", + "\n", + "# 1. Loading and Inspecting the Dataset\n", + "\n", + "Fashion MNIST is a dataset of grayscale images of clothing items, with 10 classes. Each image is 28x28 pixels, like the MNIST dataset of handwritten digits. Keras provides a convenient way to load this dataset.\n", + "\n", + "In this section, you should:\n", + "\n", + "- [ ] Inspect the shapes of the training and test sets to confirm their size and structure.\n", + "- [ ] Convert the labels to one-hot encoded format if necessary. (There is a utility function in Keras for this.)\n", + "- [ ] Visualize a few images from the dataset to understand what the data looks like." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "420c7178", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "420c7178", + "outputId": "1c054bf8-069e-4cd3-d6fd-3d1d1962594e" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n", + "\u001b[1m29515/29515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n", + "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n", + "\u001b[1m26421880/26421880\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n", + "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n", + "\u001b[1m5148/5148\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n", + "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n", + "\u001b[1m4422102/4422102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n" + ] + } + ], + "source": [ + "from tensorflow.keras.datasets import fashion_mnist\n", + "(X_train, y_train), (X_test, y_test) = fashion_mnist.load_data()\n", + "\n", + "# Normalize the pixel values to be between 0 and 1\n", + "X_train = X_train.astype('float32') / 255.0\n", + "X_test = X_test.astype('float32') / 255.0\n", + "\n", + "# Classes in the Fashion MNIST dataset\n", + "class_names = [\"T-shirt/top\", \"Trouser\", \"Pullover\", \"Dress\", \"Coat\", \"Sandal\", \"Shirt\", \"Sneaker\", \"Bag\", \"Ankle boot\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "a6c89fe7", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "a6c89fe7", + "outputId": "295f60db-2283-459b-a235-354f17f496df" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "X_train shape: (60000, 28, 28)\n", + "y_train shape: (60000,)\n", + "X_test shape: (10000, 28, 28)\n", + "y_test shape: (10000,)\n", + "(60000, 10)\n", + "(10000, 10)\n" + ] + } + ], + "source": [ + "# Inspect the shapes of the datasets\n", + "# Check the shapes\n", + "print(\"X_train shape:\", X_train.shape)\n", + "print(\"y_train shape:\", y_train.shape)\n", + "print(\"X_test shape:\", X_test.shape)\n", + "print(\"y_test shape:\", y_test.shape)\n", + "\n", + "\n", + "# Convert labels to one-hot encoding\n", + "from tensorflow.keras.utils import to_categorical\n", + "# Convert integer labels to one-hot vectors\n", + "y_train_cat = to_categorical(y_train, num_classes=10)\n", + "y_test_cat = to_categorical(y_test, num_classes=10)\n", + "\n", + "print(y_train_cat.shape) # (60000, 10)\n", + "print(y_test_cat.shape) # (10000, 10)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "13e100db", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 607 + }, + "id": "13e100db", + "outputId": "500d25da-2d1d-4208-dd5f-e5e815816da5" + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from tensorflow.keras.datasets import fashion_mnist\n", + "import matplotlib.pyplot as plt\n", + "# Verify the data looks as expected\n", + "\n", + "fig, axes = plt.subplots(3, 3, figsize=(6, 6))\n", + "\n", + "for i, ax in enumerate(axes.flat):\n", + " ax.imshow(X_train[i], cmap='gray') # show the i-th image in grayscale\n", + " ax.set_title(class_names[y_train[i]]) # show the label\n", + " ax.axis('off') # hide the axes\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "hRuddCLuPXHA", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "hRuddCLuPXHA", + "outputId": "52047381-ee56-47c3-d5f3-4f3a8a78243c" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "X_train shape: (60000, 28, 28)\n", + "y_train shape: (60000,)\n", + "X_test shape: (10000, 28, 28)\n", + "y_test shape: (10000,)\n" + ] + } + ], + "source": [ + "print(\"X_train shape:\", X_train.shape) # Expected: (60000, 28, 28)\n", + "print(\"y_train shape:\", y_train.shape) # Expected: (60000,)\n", + "print(\"X_test shape:\", X_test.shape) # Expected: (10000, 28, 28)\n", + "print(\"y_test shape:\", y_test.shape) # Expected: (10000,)\n" + ] + }, + { + "cell_type": "markdown", + "id": "HioPjL4WPW3I", + "metadata": { + "id": "HioPjL4WPW3I" + }, + "source": [] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "wt1rVryGPZ0Q", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "wt1rVryGPZ0Q", + "outputId": "a7390c38-ad3d-4b39-dfa7-ede7133fc3a9" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pixel value range in X_train: 0.0 to 1.0\n" + ] + } + ], + "source": [ + "print(\"Pixel value range in X_train:\", X_train.min(), \"to\", X_train.max())\n" + ] + }, + { + "cell_type": "markdown", + "id": "989f7dd0", + "metadata": { + "id": "989f7dd0" + }, + "source": [ + "Reflection: Does the data look as expected? How is the quality of the images? Are there any issues with the dataset that you notice?\n", + "\n", + "**Your answer here**\n", + "Yes. The quality of the images is not good, it might be the issue related to the dataset." + ] + }, + { + "cell_type": "markdown", + "id": "c9e8ad60", + "metadata": { + "id": "c9e8ad60" + }, + "source": [ + "# 2. Baseline Model\n", + "\n", + "In this section, you will create a linear regression model as a baseline. This model will not use any convolutional layers, but it will help you understand the performance of a simple model on this dataset.\n", + "You should:\n", + "- [ ] Create a simple linear regression model using Keras.\n", + "- [ ] Compile the model with an appropriate loss function and optimizer.\n", + "- [ ] Train the model on the training set and evaluate it on the test set.\n", + "\n", + "A linear regression model can be created using the `Sequential` API in Keras. Using a single `Dense` layer with no activation function is equivalent to a simple linear regression model. Make sure that the number of units in the output layer matches the number of classes in the dataset.\n", + "\n", + "Note that for this step, we will need to use `Flatten` to convert the 2D images into 1D vectors before passing them to the model. Put a `Flatten()` layer as the first layer in your model so that the 2D image data can be flattened into 1D vectors." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "MqnMOTi-sjDu", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "MqnMOTi-sjDu", + "outputId": "1bfdc6b8-fe24-45f4-9fcc-c0109f93ec06" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.12/dist-packages/keras/src/layers/reshaping/flatten.py:37: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n", + " super().__init__(**kwargs)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n", + "\u001b[1m3000/3000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 2ms/step - accuracy: 0.4003 - loss: 2.6649 - val_accuracy: 0.3631 - val_loss: 2.3043\n", + "Epoch 2/10\n", + "\u001b[1m3000/3000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 2ms/step - accuracy: 0.3682 - loss: 2.3018 - val_accuracy: 0.3284 - val_loss: 2.3043\n", + "Epoch 3/10\n", + "\u001b[1m3000/3000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 2ms/step - accuracy: 0.3267 - loss: 2.3020 - val_accuracy: 0.3286 - val_loss: 2.3043\n", + "Epoch 4/10\n", + "\u001b[1m3000/3000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 2ms/step - accuracy: 0.3291 - loss: 2.3021 - val_accuracy: 0.3286 - val_loss: 2.3043\n", + "Epoch 5/10\n", + "\u001b[1m3000/3000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 2ms/step - accuracy: 0.3278 - loss: 2.3021 - val_accuracy: 0.3288 - val_loss: 2.3043\n", + "Epoch 6/10\n", + "\u001b[1m3000/3000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 2ms/step - accuracy: 0.3377 - loss: 2.3002 - val_accuracy: 0.3192 - val_loss: 2.3045\n", + "Epoch 7/10\n", + "\u001b[1m3000/3000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 2ms/step - accuracy: 0.3185 - loss: 2.3021 - val_accuracy: 0.3205 - val_loss: 2.3043\n", + "Epoch 8/10\n", + "\u001b[1m3000/3000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 2ms/step - accuracy: 0.3359 - loss: 2.2899 - val_accuracy: 0.3178 - val_loss: 2.3048\n", + "Epoch 9/10\n", + "\u001b[1m3000/3000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 2ms/step - accuracy: 0.3140 - loss: 2.3024 - val_accuracy: 0.3168 - val_loss: 2.3048\n", + "Epoch 10/10\n", + "\u001b[1m3000/3000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 2ms/step - accuracy: 0.2906 - loss: 2.3028 - val_accuracy: 0.2871 - val_loss: 2.3026\n", + "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.2879 - loss: 2.3026\n", + "Test Accuracy: 0.287\n" + ] + } + ], + "source": [ + "from keras.models import Sequential\n", + "from keras.layers import Dense, Flatten,Input, Dot\n", + "\n", + "# Create a simple linear regression model\n", + "model = Sequential()\n", + "# Add layers one by one\n", + "model.add(Flatten(input_shape=(28, 28))) # Flatten the 28x28 image\n", + "model.add(Dense(10)) # Output layer: 10 units, no activation\n", + "\n", + "# Compile the model\n", + "model.compile(optimizer='adam',\n", + " loss='sparse_categorical_crossentropy',\n", + " metrics=['accuracy'])\n", + "\n", + "# Train the model\n", + "model.fit(X_train, y_train, epochs=10, batch_size=20, validation_data=(X_test, y_test))\n", + "\n", + "# Evaluate on test set\n", + "test_loss, test_acc = model.evaluate(X_test, y_test)\n", + "print(f\"Test Accuracy: {test_acc:.3f}\")\n", + "\n", + "# You can use `model.add()` to add layers to the model\n", + "\n", + "# Compile the model using `model.compile()`\n", + "\n", + "# Train the model with `model.fit()`\n", + "\n", + "# Evaluate the model with `model.evaluate()`" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "8563a7aa", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "8563a7aa", + "outputId": "945f85d5-ab82-484e-f3a4-158f8d57cd4b" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.12/dist-packages/keras/src/layers/reshaping/flatten.py:37: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n", + " super().__init__(**kwargs)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n", + "\u001b[1m3000/3000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 2ms/step - accuracy: 0.1858 - loss: 1.8497 - val_accuracy: 0.1720 - val_loss: 1.8154\n", + "Epoch 2/10\n", + "\u001b[1m3000/3000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 2ms/step - accuracy: 0.1255 - loss: 1.8823 - val_accuracy: 0.1457 - val_loss: 1.8669\n", + "Epoch 3/10\n", + "\u001b[1m3000/3000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 2ms/step - accuracy: 0.1348 - loss: 1.9047 - val_accuracy: 0.1163 - val_loss: 1.9251\n", + "Epoch 4/10\n", + "\u001b[1m3000/3000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 2ms/step - accuracy: 0.1096 - loss: 1.8365 - val_accuracy: 0.1099 - val_loss: 1.6845\n", + "Epoch 5/10\n", + "\u001b[1m3000/3000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 2ms/step - accuracy: 0.1480 - loss: 1.7181 - val_accuracy: 0.1510 - val_loss: 1.6895\n", + "Epoch 6/10\n", + "\u001b[1m3000/3000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 2ms/step - accuracy: 0.1322 - loss: 1.6901 - val_accuracy: 0.1561 - val_loss: 1.7951\n", + "Epoch 7/10\n", + "\u001b[1m3000/3000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 2ms/step - accuracy: 0.1567 - loss: 1.7505 - val_accuracy: 0.3337 - val_loss: 1.6820\n", + "Epoch 8/10\n", + "\u001b[1m3000/3000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 2ms/step - accuracy: 0.3815 - loss: 1.5393 - val_accuracy: 0.3797 - val_loss: 1.5612\n", + "Epoch 9/10\n", + "\u001b[1m3000/3000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 2ms/step - accuracy: 0.3921 - loss: 1.5201 - val_accuracy: 0.2317 - val_loss: 1.7108\n", + "Epoch 10/10\n", + "\u001b[1m3000/3000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 2ms/step - accuracy: 0.2536 - loss: 1.6397 - val_accuracy: 0.4056 - val_loss: 1.7014\n", + "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.4089 - loss: 1.6922\n", + "Test Accuracy: 0.406\n" + ] + } + ], + "source": [ + "from keras.models import Sequential\n", + "from keras.layers import Dense, Flatten,Input, Dot\n", + "\n", + "# Create a simple linear regression model\n", + "model = Sequential()\n", + "# Add layers one by one\n", + "model.add(Flatten(input_shape=(28, 28))) # Flatten the 28x28 image\n", + "model.add(Dense(32))\n", + "model.add(Dense(10)) # Output layer: 10 units, no activation\n", + "\n", + "# Compile the model\n", + "model.compile(optimizer='adam',\n", + " loss='sparse_categorical_crossentropy',\n", + " metrics=['accuracy'])\n", + "\n", + "# Train the model\n", + "model.fit(X_train, y_train, epochs=10, batch_size=20, validation_data=(X_test, y_test))\n", + "\n", + "# Evaluate on test set\n", + "test_loss, test_acc = model.evaluate(X_test, y_test)\n", + "print(f\"Test Accuracy: {test_acc:.3f}\")\n", + "\n", + "# You can use `model.add()` to add layers to the model\n", + "\n", + "# Compile the model using `model.compile()`\n", + "\n", + "# Train the model with `model.fit()`\n", + "\n", + "# Evaluate the model with `model.evaluate()`" + ] + }, + { + "cell_type": "markdown", + "id": "9a07e9f7", + "metadata": { + "id": "9a07e9f7" + }, + "source": [ + "Reflection: What is the performance of the baseline model? How does it compare to what you expected? Why do you think the performance is at this level?\n", + "\n", + "**Your answer here**\n", + "\n", + "The accuracy is low because the model does not include an activation function, which would help improve its performance." + ] + }, + { + "cell_type": "markdown", + "id": "fa107b59", + "metadata": { + "id": "fa107b59" + }, + "source": [ + "# 3. Building and Evaluating a Simple CNN Model\n", + "\n", + "In this section, you will build a simple Convolutional Neural Network (CNN) model using Keras. A convolutional neural network is a type of deep learning model that is particularly effective for image classification tasks. Unlike the basic neural networks we have built in the labs, CNNs can accept images as input without needing to flatten them into vectors.\n", + "\n", + "You should:\n", + "- [ ] Build a simple CNN model with at least one convolutional layer (to learn spatial hierarchies in images) and one fully connected layer (to make predictions).\n", + "- [ ] Compile the model with an appropriate loss function and metrics for a multi-class classification problem.\n", + "- [ ] Train the model on the training set and evaluate it on the test set.\n", + "\n", + "Convolutional layers are designed to accept inputs with three dimensions: height, width and channels (e.g., RGB for color images). For grayscale images like those in Fashion MNIST, the input shape will be (28, 28, 1).\n", + "\n", + "When you progress from the convolutional layers to the fully connected layers, you will need to flatten the output of the convolutional layers. This can be done using the `Flatten` layer in Keras, which doesn't require any parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "3513cf3d", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "3513cf3d", + "outputId": "e3e703a3-872e-4d57-e7cb-ab042951647e" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n", + "\u001b[1m3000/3000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m28s\u001b[0m 9ms/step - accuracy: 0.7998 - loss: 0.5698 - val_accuracy: 0.8724 - val_loss: 0.3633\n", + "Epoch 2/10\n", + "\u001b[1m3000/3000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m27s\u001b[0m 9ms/step - accuracy: 0.8859 - loss: 0.3268 - val_accuracy: 0.8855 - val_loss: 0.3191\n", + "Epoch 3/10\n", + "\u001b[1m3000/3000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m28s\u001b[0m 9ms/step - accuracy: 0.9044 - loss: 0.2719 - val_accuracy: 0.8986 - val_loss: 0.2923\n", + "Epoch 4/10\n", + "\u001b[1m3000/3000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m26s\u001b[0m 9ms/step - accuracy: 0.9106 - loss: 0.2519 - val_accuracy: 0.8940 - val_loss: 0.2913\n", + "Epoch 5/10\n", + "\u001b[1m3000/3000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m26s\u001b[0m 9ms/step - accuracy: 0.9204 - loss: 0.2258 - val_accuracy: 0.8996 - val_loss: 0.2780\n", + "Epoch 6/10\n", + "\u001b[1m3000/3000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m26s\u001b[0m 9ms/step - accuracy: 0.9257 - loss: 0.2053 - val_accuracy: 0.8985 - val_loss: 0.2817\n", + "Epoch 7/10\n", + "\u001b[1m3000/3000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m26s\u001b[0m 9ms/step - accuracy: 0.9310 - loss: 0.1953 - val_accuracy: 0.9065 - val_loss: 0.2673\n", + "Epoch 8/10\n", + "\u001b[1m3000/3000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m26s\u001b[0m 9ms/step - accuracy: 0.9361 - loss: 0.1793 - val_accuracy: 0.9037 - val_loss: 0.2757\n", + "Epoch 9/10\n", + "\u001b[1m3000/3000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m26s\u001b[0m 9ms/step - accuracy: 0.9383 - loss: 0.1702 - val_accuracy: 0.8956 - val_loss: 0.2896\n", + "Epoch 10/10\n", + "\u001b[1m3000/3000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m26s\u001b[0m 9ms/step - accuracy: 0.9408 - loss: 0.1639 - val_accuracy: 0.9011 - val_loss: 0.2976\n", + "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9034 - loss: 0.2898\n", + "Test Accuracy: 0.901\n" + ] + } + ], + "source": [ + "from keras.layers import Conv2D, MaxPooling2D, Dense, Flatten\n", + "\n", + "# Reshape the data to include the channel dimension\n", + "X_train = X_train.reshape(-1, 28, 28, 1)\n", + "X_test = X_test.reshape(-1, 28, 28, 1)\n", + "\n", + "# Create a simple CNN model\n", + "model = Sequential()\n", + "model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1))) # convolution layer\n", + "model.add(MaxPooling2D(pool_size=(2, 2))) # pooling layer\n", + "model.add(Flatten()) # flatten to 1D\n", + "model.add(Dense(10, activation='softmax'))\n", + "\n", + "#compile the model\n", + "model.compile(optimizer='adam',\n", + " loss='sparse_categorical_crossentropy',\n", + " metrics=['accuracy'])\n", + "# Train the model\n", + "model.fit(X_train, y_train, epochs=10, batch_size=20, validation_data=(X_test, y_test))\n", + "\n", + "# Evaluate the model\n", + "test_loss, test_acc = model.evaluate(X_test, y_test)\n", + "print(f\"Test Accuracy: {test_acc:.3f}\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "fabe379c", + "metadata": { + "id": "fabe379c" + }, + "source": [ + "Reflection: Did the CNN model perform better than the baseline model? If so, by how much? What do you think contributed to this improvement?\n", + "\n", + "**Your answer here**\n", + "\n", + "Yes, it is improved at most by 5 times. The reason is including the activation." + ] + }, + { + "cell_type": "markdown", + "id": "1a5e2463", + "metadata": { + "id": "1a5e2463" + }, + "source": [ + "# 3. Designing and Running Controlled Experiments\n", + "\n", + "In this section, you will design and run controlled experiments to improve the model's performance. You will focus on one hyperparameter and one regularization technique.\n", + "You should:\n", + "- [ ] Choose one hyperparameter to experiment with (e.g., number of filters, kernel size, number of layers, etc.) and one regularization technique (e.g., dropout, L2 regularization). For your hyperparameter, you should choose at least three different values to test (but there is no upper limit). For your regularization technique, simply test the presence or absence of the technique.\n", + "- [ ] Run experiments by modifying the model architecture or hyperparameters, and evaluate the performance of each model on the test set.\n", + "- [ ] Record the results of your experiments, including the test accuracy and any other relevant metrics.\n", + "- [ ] Visualize the results of your experiments using plots or tables to compare the performance of different models.\n", + "\n", + "The best way to run your experiments is to create a `for` loop that iterates over a range of values for the hyperparameter you are testing. For example, if you are testing different numbers of filters, you can create a loop that runs the model with 32, 64, and 128 filters. Within the loop, you can compile and train the model, then evaluate it on the test set. After each iteration, you can store the results in a list or a dictionary for later analysis.\n", + "\n", + "Note: It's critical that you re-initialize the model (by creating a new instance of the model) before each experiment. If you don't, the model will retain the weights from the previous experiment, which can lead to misleading results." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "99d6f46c", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "99d6f46c", + "outputId": "6e1d3b14-d78a-411b-f59f-e62993234dbd" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "🔹 Testing model with 32 filters and Dropout\n", + "Epoch 1/5\n", + "\u001b[1m938/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 26ms/step - accuracy: 0.7710 - loss: 0.6842 - val_accuracy: 0.8623 - val_loss: 0.3929\n", + "Epoch 2/5\n", + "\u001b[1m938/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 26ms/step - accuracy: 0.8743 - loss: 0.3613 - val_accuracy: 0.8770 - val_loss: 0.3441\n", + "Epoch 3/5\n", + "\u001b[1m938/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 26ms/step - accuracy: 0.8874 - loss: 0.3181 - val_accuracy: 0.8834 - val_loss: 0.3255\n", + "Epoch 4/5\n", + "\u001b[1m938/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 25ms/step - accuracy: 0.8954 - loss: 0.3023 - val_accuracy: 0.8920 - val_loss: 0.3064\n", + "Epoch 5/5\n", + "\u001b[1m938/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 25ms/step - accuracy: 0.8954 - loss: 0.2937 - val_accuracy: 0.8947 - val_loss: 0.2953\n", + "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8977 - loss: 0.2965\n", + "Test Accuracy with 32 filters: 0.895\n", + "\n", + "🔹 Testing model with 64 filters and Dropout\n", + "Epoch 1/5\n", + "\u001b[1m938/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 41ms/step - accuracy: 0.7828 - loss: 0.6323 - val_accuracy: 0.8661 - val_loss: 0.3815\n", + "Epoch 2/5\n", + "\u001b[1m938/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 42ms/step - accuracy: 0.8826 - loss: 0.3343 - val_accuracy: 0.8758 - val_loss: 0.3331\n", + "Epoch 3/5\n", + "\u001b[1m938/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m38s\u001b[0m 41ms/step - accuracy: 0.8942 - loss: 0.2968 - val_accuracy: 0.8912 - val_loss: 0.3093\n", + "Epoch 4/5\n", + "\u001b[1m938/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m38s\u001b[0m 41ms/step - accuracy: 0.9010 - loss: 0.2787 - val_accuracy: 0.8920 - val_loss: 0.2988\n", + "Epoch 5/5\n", + "\u001b[1m938/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 40ms/step - accuracy: 0.9089 - loss: 0.2563 - val_accuracy: 0.8987 - val_loss: 0.2801\n", + "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step - accuracy: 0.8990 - loss: 0.2830\n", + "Test Accuracy with 64 filters: 0.899\n", + "\n", + "🔹 Testing model with 128 filters and Dropout\n", + "Epoch 1/5\n", + "\u001b[1m938/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m71s\u001b[0m 75ms/step - accuracy: 0.8010 - loss: 0.5828 - val_accuracy: 0.8794 - val_loss: 0.3453\n", + "Epoch 2/5\n", + "\u001b[1m938/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m67s\u001b[0m 72ms/step - accuracy: 0.8939 - loss: 0.3064 - val_accuracy: 0.8879 - val_loss: 0.3177\n", + "Epoch 3/5\n", + "\u001b[1m938/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m81s\u001b[0m 71ms/step - accuracy: 0.9052 - loss: 0.2689 - val_accuracy: 0.8963 - val_loss: 0.2888\n", + "Epoch 4/5\n", + "\u001b[1m938/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m68s\u001b[0m 72ms/step - accuracy: 0.9096 - loss: 0.2494 - val_accuracy: 0.8945 - val_loss: 0.3022\n", + "Epoch 5/5\n", + "\u001b[1m938/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m67s\u001b[0m 71ms/step - accuracy: 0.9152 - loss: 0.2357 - val_accuracy: 0.8933 - val_loss: 0.2883\n", + "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.8959 - loss: 0.2911\n", + "Test Accuracy with 128 filters: 0.893\n" + ] + } + ], + "source": [ + "# A. Test Hyperparameters\n", + "\n", + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout\n", + "from tensorflow.keras.regularizers import l2\n", + "\n", + "# Prepare data (already normalized)\n", + "X_train = X_train.reshape(-1, 28, 28, 1)\n", + "X_test = X_test.reshape(-1, 28, 28, 1)\n", + "\n", + "filters_list = [32, 64, 128] # hyperparameter: number of filters\n", + "\n", + "for filters in filters_list:\n", + " print(f\"\\n🔹 Testing model with {filters} filters and Dropout\")\n", + "\n", + " model = Sequential()\n", + " model.add(Conv2D(filters, (3, 3), activation='relu', input_shape=(28, 28, 1)))\n", + " model.add(MaxPooling2D(pool_size=(2, 2)))\n", + " model.add(Dropout(0.3)) # regularization\n", + " model.add(Flatten())\n", + " model.add(Dense(10, activation='softmax'))\n", + "\n", + " model.compile(optimizer='adam',\n", + " loss='sparse_categorical_crossentropy',\n", + " metrics=['accuracy'])\n", + "\n", + " history = model.fit(X_train, y_train, epochs=5, batch_size=64,\n", + " validation_data=(X_test, y_test), verbose=1)\n", + "\n", + " test_loss, test_acc = model.evaluate(X_test, y_test)\n", + " print(f\"Test Accuracy with {filters} filters: {test_acc:.3f}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "dc43ac81", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "dc43ac81", + "outputId": "41ba0563-c36f-4272-d926-2e5e4f3c565c" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "🔹 Testing model with 32 filters and witout Dropout\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.12/dist-packages/keras/src/layers/convolutional/base_conv.py:113: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n", + " super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/5\n", + "\u001b[1m938/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m30s\u001b[0m 31ms/step - accuracy: 0.7754 - loss: 0.6615 - val_accuracy: 0.8681 - val_loss: 0.3841\n", + "Epoch 2/5\n", + "\u001b[1m938/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m27s\u001b[0m 29ms/step - accuracy: 0.8764 - loss: 0.3507 - val_accuracy: 0.8823 - val_loss: 0.3376\n", + "Epoch 3/5\n", + "\u001b[1m938/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 27ms/step - accuracy: 0.8928 - loss: 0.3064 - val_accuracy: 0.8781 - val_loss: 0.3357\n", + "Epoch 4/5\n", + "\u001b[1m938/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 27ms/step - accuracy: 0.9018 - loss: 0.2839 - val_accuracy: 0.8891 - val_loss: 0.3120\n", + "Epoch 5/5\n", + "\u001b[1m938/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 27ms/step - accuracy: 0.9070 - loss: 0.2626 - val_accuracy: 0.8942 - val_loss: 0.2920\n", + "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step - accuracy: 0.8989 - loss: 0.2942\n", + "Test Accuracy with 32 filters: 0.894\n", + "\n", + "🔹 Testing model with 64 filters and witout Dropout\n", + "Epoch 1/5\n", + "\u001b[1m938/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 42ms/step - accuracy: 0.7859 - loss: 0.6131 - val_accuracy: 0.8774 - val_loss: 0.3505\n", + "Epoch 2/5\n", + "\u001b[1m938/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 43ms/step - accuracy: 0.8860 - loss: 0.3272 - val_accuracy: 0.8875 - val_loss: 0.3180\n", + "Epoch 3/5\n", + "\u001b[1m938/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 42ms/step - accuracy: 0.8989 - loss: 0.2860 - val_accuracy: 0.8959 - val_loss: 0.2934\n", + "Epoch 4/5\n", + "\u001b[1m938/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m42s\u001b[0m 43ms/step - accuracy: 0.9084 - loss: 0.2598 - val_accuracy: 0.8929 - val_loss: 0.2983\n", + "Epoch 5/5\n", + "\u001b[1m938/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 41ms/step - accuracy: 0.9153 - loss: 0.2393 - val_accuracy: 0.8994 - val_loss: 0.2744\n", + "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.9029 - loss: 0.2759\n", + "Test Accuracy with 64 filters: 0.899\n", + "\n", + "🔹 Testing model with 128 filters and witout Dropout\n", + "Epoch 1/5\n", + "\u001b[1m938/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m68s\u001b[0m 72ms/step - accuracy: 0.7958 - loss: 0.5805 - val_accuracy: 0.8748 - val_loss: 0.3552\n", + "Epoch 2/5\n", + "\u001b[1m938/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m84s\u001b[0m 74ms/step - accuracy: 0.8919 - loss: 0.3091 - val_accuracy: 0.8885 - val_loss: 0.3118\n", + "Epoch 3/5\n", + "\u001b[1m938/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m66s\u001b[0m 71ms/step - accuracy: 0.9062 - loss: 0.2644 - val_accuracy: 0.8967 - val_loss: 0.2919\n", + "Epoch 4/5\n", + "\u001b[1m938/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m84s\u001b[0m 73ms/step - accuracy: 0.9168 - loss: 0.2394 - val_accuracy: 0.9004 - val_loss: 0.2825\n", + "Epoch 5/5\n", + "\u001b[1m938/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m65s\u001b[0m 69ms/step - accuracy: 0.9250 - loss: 0.2171 - val_accuracy: 0.9010 - val_loss: 0.2738\n", + "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 12ms/step - accuracy: 0.9040 - loss: 0.2733\n", + "Test Accuracy with 128 filters: 0.901\n" + ] + } + ], + "source": [ + "# B. Test presence or absence of regularization\n", + "\n", + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout\n", + "from tensorflow.keras.regularizers import l2\n", + "\n", + "# Prepare data (already normalized)\n", + "X_train = X_train.reshape(-1, 28, 28, 1)\n", + "X_test = X_test.reshape(-1, 28, 28, 1)\n", + "\n", + "filters_list = [32, 64, 128] # hyperparameter: number of filters\n", + "\n", + "for filters in filters_list:\n", + " print(f\"\\n🔹 Testing model with {filters} filters and witout Dropout\")\n", + "\n", + " model = Sequential()\n", + " model.add(Conv2D(filters, (3, 3), activation='relu', input_shape=(28, 28, 1)))\n", + " model.add(MaxPooling2D(pool_size=(2, 2)))\n", + " model.add(Flatten())\n", + " model.add(Dense(10, activation='softmax'))\n", + "\n", + " model.compile(optimizer='adam',\n", + " loss='sparse_categorical_crossentropy',\n", + " metrics=['accuracy'])\n", + "\n", + " history = model.fit(X_train, y_train, epochs=5, batch_size=64,\n", + " validation_data=(X_test, y_test), verbose=1)\n", + "\n", + " test_loss, test_acc = model.evaluate(X_test, y_test)\n", + " print(f\"Test Accuracy with {filters} filters: {test_acc:.3f}\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "cb426f26", + "metadata": { + "id": "cb426f26" + }, + "source": [ + "Reflection: Report on the performance of the models you tested. Did any of the changes you made improve the model's performance? If so, which ones? What do you think contributed to these improvements? Finally, what combination of hyperparameters and regularization techniques yielded the best performance?\n", + "\n", + "**Your answer here**\n", + "\n", + "Yes.The model with 128 filters without dropout achieved the highest accuracy. However, this may indicate that the model is overfitting — it memorizes the training data rather than learning general patterns." + ] + }, + { + "cell_type": "markdown", + "id": "46c43a3d", + "metadata": { + "id": "46c43a3d" + }, + "source": [ + "# 5. Training Final Model and Evaluation\n", + "\n", + "In this section, you will train the final model using the best hyperparameters and regularization techniques you found in the previous section. You should:\n", + "- [ ] Compile the final model with the best hyperparameters and regularization techniques.\n", + "- [ ] Train the final model on the training set and evaluate it on the test set.\n", + "- [ ] Report the final model's performance on the test set, including accuracy and any other relevant metrics." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "31f926d1", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "31f926d1", + "outputId": "39f8aec7-0e01-49cc-e483-b2bc5b7b4dc8" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n", + "\u001b[1m3000/3000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m90s\u001b[0m 30ms/step - accuracy: 0.8191 - loss: 0.5123 - val_accuracy: 0.8868 - val_loss: 0.3199\n", + "Epoch 2/10\n", + "\u001b[1m3000/3000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m87s\u001b[0m 29ms/step - accuracy: 0.8958 - loss: 0.2912 - val_accuracy: 0.8828 - val_loss: 0.3107\n", + "Epoch 3/10\n", + "\u001b[1m3000/3000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m142s\u001b[0m 29ms/step - accuracy: 0.9068 - loss: 0.2570 - val_accuracy: 0.8975 - val_loss: 0.2821\n", + "Epoch 4/10\n", + "\u001b[1m3000/3000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m88s\u001b[0m 29ms/step - accuracy: 0.9161 - loss: 0.2306 - val_accuracy: 0.9029 - val_loss: 0.2794\n", + "Epoch 5/10\n", + "\u001b[1m3000/3000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m88s\u001b[0m 29ms/step - accuracy: 0.9241 - loss: 0.2079 - val_accuracy: 0.9021 - val_loss: 0.2771\n", + "Epoch 6/10\n" + ] + } + ], + "source": [ + "from keras.layers import Conv2D, MaxPooling2D, Dense, Flatten\n", + "\n", + "# Reshape the data to include the channel dimension\n", + "X_train = X_train.reshape(-1, 28, 28, 1)\n", + "X_test = X_test.reshape(-1, 28, 28, 1)\n", + "\n", + "# Create a simple CNN model\n", + "model = Sequential()\n", + "model.add(Conv2D(128, (3, 3), activation='relu', input_shape=(28, 28, 1))) # convolution layer\n", + "model.add(MaxPooling2D(pool_size=(2, 2)))\n", + "model.add(Dropout(0.3)) # regularization # pooling layer\n", + "model.add(Flatten()) # flatten to 1D\n", + "model.add(Dense(10, activation='softmax'))\n", + "\n", + "#compile the model\n", + "model.compile(optimizer='adam',\n", + " loss='sparse_categorical_crossentropy',\n", + " metrics=['accuracy'])\n", + "# Train the model\n", + "model.fit(X_train, y_train, epochs=10, batch_size=20, validation_data=(X_test, y_test))\n", + "\n", + "# Evaluate the model\n", + "test_loss, test_acc = model.evaluate(X_test, y_test)\n", + "print(f\"Test Accuracy: {test_acc:.3f}\")\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "a01f8ebc", + "metadata": { + "id": "a01f8ebc" + }, + "source": [ + "Reflection: How does the final model's performance compare to the baseline and the CNN model? What do you think contributed to the final model's performance? If you had time, what other experiments would you run to further improve the model's performance?\n", + "\n", + "**Your answer here**\n", + "\n", + "It is the most accurate method due to using 128 filters and dropout." + ] + }, + { + "cell_type": "markdown", + "id": "01db8512", + "metadata": { + "id": "01db8512" + }, + "source": [ + "🚨 **Please review our [Assignment Submission Guide](https://github.com/UofT-DSI/onboarding/blob/main/onboarding_documents/submissions.md)** 🚨 for detailed instructions on how to format, branch, and submit your work. Following these guidelines is crucial for your submissions to be evaluated correctly.\n", + "### Submission Parameters:\n", + "* Submission Due Date: `23:59 PM - 26/10/2025`\n", + "* The branch name for your repo should be: `assignment-1`\n", + "* What to submit for this assignment:\n", + " * This Jupyter Notebook (assignment_1.ipynb)\n", + " * The Lab 1 notebook (labs/lab_1.ipynb)\n", + " * The Lab 2 notebook (labs/lab_2.ipynb)\n", + " * The Lab 3 notebook (labs/lab_3.ipynb)\n", + "* What the pull request link should look like for this assignment: `https://github.com//deep_learning/pull/`\n", + "* Open a private window in your browser. Copy and paste the link to your pull request into the address bar. Make sure you can see your pull request properly. This helps the technical facilitator and learning support staff review your submission easily.\n", + "Checklist:\n", + "- [ ] Created a branch with the correct naming convention.\n", + "- [ ] Ensured that the repository is public.\n", + "- [ ] Reviewed the PR description guidelines and adhered to them.\n", + "- [ ] Verify that the link is accessible in a private browser window.\n", + "If you encounter any difficulties or have questions, please don't hesitate to reach out to our team via our Slack at `#cohort-7-help-ml`. Our Technical Facilitators and Learning Support staff are here to help you navigate any challenges." + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "deep_learning", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/02_activities/assignments/lab_1.ipynb b/02_activities/assignments/lab_1.ipynb new file mode 100644 index 00000000..286cd782 --- /dev/null +++ b/02_activities/assignments/lab_1.ipynb @@ -0,0 +1,2666 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "kVwWQuNGmoA1" + }, + "source": [ + "# Training Neural Networks with Keras\n", + "\n", + "Welcome to the first practical session of the course! In this session, we will learn how to train neural networks with Keras. We will start with a simple example of a feedforward neural network for classification and then we will study the impact of the initialization of the weights on the convergence of the training algorithm.\n", + "\n", + "Keras is a high-level neural network API, built on top of TensorFlow 2.0. It provides a user-friendly interface to build, train and deploy deep learning models. Keras is designed to be modular, fast and easy to use.\n", + "\n", + "Throughout this course, we will focus on using Keras and TensorFlow for building and training neural networks. However, there are other popular deep learning frameworks such as PyTorch, MXNet, CNTK, etc. that you can also use to build and train neural networks.\n", + "\n", + "In order to use our code on Google Colab, we will need to ensure that any required packages are installed. We will use the following packages in this session:\n", + "\n", + "- `tensorflow`: an open-source library for numerical computation and large-scale machine learning.\n", + "- `matplotlib`: a plotting library for the Python programming language and its numerical mathematics extension NumPy.\n", + "- `numpy`: a library for scientific computing in Python.\n", + "- `scikit-learn`: a machine learning library for the Python programming language.\n", + "- `pandas`: a library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language.\n", + "\n", + "Today, we will be working with the famous MNIST dataset. MNIST (Modified National Institute of Standards and Technology) is a database of low resolution images of handwritten digits. The history here is interesting - the dataset was originally created in the 1980s, when researchers from the aforementioned institute collected samples from American Census Bureau employees and high school students. The dataset was then modified in the 1990s (hence the M in MNIST), and has since become a popular benchmark for machine learning algorithms.\n", + "\n", + "The dataset contains images, each of which is a 28x28 grayscale image of a handwritten digit. The goal is to classify each image into one of the 10 possible classes (0-9).\n", + "\n", + "![MNIST](https://upload.wikimedia.org/wikipedia/commons/2/27/MnistExamples.png)\n", + "\n", + "The Scikit-Learn library provides a convenient function to download and load the MNIST dataset. The following cell will download the dataset. Then we will take a look at the shape of the data." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "2O_FnLIpmoA2" + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "from sklearn.datasets import load_digits\n", + "\n", + "digits = load_digits()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "NkVKKcU2moA2", + "outputId": "f29e2192-e668-44b7-935e-9826c669745c" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(1797, 8, 8)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "digits.images.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "id": "pcniLEeBmoA2" + }, + "source": [ + "This means that we have 1797 images, each of which is a 8x8 image. For basic image processing, we will need to flatten the images into a 1D array. In this case, Scikit-Learn has already provided the data in this format too:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "KUUGGoIAmoA3", + "outputId": "db74287c-c082-4c45-f2ba-fa38bc7f4f24" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(1797, 64)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "digits.data.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "id": "-W85Dr-CmoA3" + }, + "source": [ + "For each image, we also have the corresponding label (or target, or class) in `digits.target`:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Z2ufr1KjmoA3", + "outputId": "854bd1b8-a438-4d6f-b806-99be8a3c508c" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(1797,)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "digits.target.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "id": "DbCzfNe2moA3" + }, + "source": [ + "We can take a look at some random images from the dataset. The following cell will select 9 random images and plot them in a 3x3 grid (meaning that you can rerun the cell to see different images)." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 521 + }, + "id": "M_41agpTmoA3", + "outputId": "e0a1ae1c-1321-480f-b4bb-367cae49b818" + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Selecting 9 random indices\n", + "random_indices = np.random.choice(len(digits.images), 9, replace=False)\n", + "\n", + "# Creating a 3x3 grid plot\n", + "fig, axes = plt.subplots(3, 3, figsize=(6, 6))\n", + "\n", + "for i, ax in enumerate(axes.flat):\n", + " ax.imshow(digits.images[random_indices[i]], cmap=plt.cm.gray_r, interpolation='nearest')\n", + " ax.set_title(f\"Label: {digits.target[random_indices[i]]}\")\n", + "\n", + " # Removing axis labels\n", + " ax.set_xticks([])\n", + " ax.set_yticks([])\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "id": "1pdAICuhmoA4" + }, + "source": [ + "As you can see, these images are very low resolution. This is because they were originally scanned from paper forms, and then scaled down to 8x8 pixels. This is a common problem in machine learning - the quality of the data is often a limiting factor in the performance of the model. In this case, the low resolution of the images makes it difficult to distinguish between some digits, even for humans. For example, the following images are all labelled as 9, but they look very different:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 521 + }, + "id": "2SZ__jotmoA4", + "outputId": "1b7e620b-edb4-4f72-be2e-ee732119635c" + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Selecting 9 random indices of images labelled as 9\n", + "random_indices = np.random.choice(np.where(digits.target == 9)[0], 9, replace=False)\n", + "\n", + "# Creating a 3x3 grid plot\n", + "fig, axes = plt.subplots(3, 3, figsize=(6, 6))\n", + "\n", + "for i, ax in enumerate(axes.flat):\n", + " ax.imshow(digits.images[random_indices[i]], cmap=plt.cm.gray_r, interpolation='nearest')\n", + " ax.set_title(f\"Label: {digits.target[random_indices[i]]}\")\n", + "\n", + " # Removing axis labels\n", + " ax.set_xticks([])\n", + " ax.set_yticks([])\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "id": "vqei_GjSmoA4" + }, + "source": [ + "While we are plotting the samples as images, remember that our model is only going to see a 1D array of numbers." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "NJdMmElNmoA4" + }, + "source": [ + "## Train / Test Split\n", + "\n", + "In order to understand how well our model performs on _new_ data, we need to split our dataset into a training set and a test set. The training set will be used to train the model, and the test set will be used to evaluate the performance of the model.\n", + "\n", + "Let's keep some held-out data to be able to measure the generalization performance of our model." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "id": "IRhZBPgsmoA4" + }, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(\n", + " digits.data,\n", + " digits.target,\n", + " test_size=0.2, # 20% of the data is used for testing\n", + " random_state=42 # Providing a value here means getting the same \"random\" split every time\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "id": "459rL6XXmoA4" + }, + "source": [ + "Let's confirm that the data has been split correctly:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "qENnh-MXmoA4", + "outputId": "6612f835-b656-4ddc-e411-66efaaaca6f0" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "X_train shape: (1437, 64)\n", + "y_train shape: (1437,)\n", + "X_test shape: (360, 64)\n", + "y_test shape: (360,)\n" + ] + } + ], + "source": [ + "print(f'X_train shape: {X_train.shape}')\n", + "print(f'y_train shape: {y_train.shape}')\n", + "print(f'X_test shape: {X_test.shape}')\n", + "print(f'y_test shape: {y_test.shape}')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "id": "uEGdkJBEmoA4" + }, + "source": [ + "This is what we expected to see. It's always good to check as you go, to make sure that you haven't made a mistake somewhere - this is something that working in a notebook like this makes it easy to do." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0_uQWDlLmoA4" + }, + "source": [ + "## Preprocessing of the Target Data\n", + "\n", + "The labels that we have are integers between 0 and 9. However, we want to train a neural network to classify the images into one of 10 classes. It can be a little counter-intuitive because we are dealing with numbers, but our classes are not ordinal.\n", + "\n", + "What do we mean by that? Let's imagine we were trying to predict the height of a building (separated into classes) from images. If a given building was actually 10m tall, and our model predicted 9m, we would consider that to be a better prediction than if it predicted 1m. This is because the classes are ordinal - there is meaning in the difference between the classes.\n", + "\n", + "In our case, even though we are dealing with numbers, the classes are not ordinal. If a given image is actually a 9, and our model predicts 8, we would consider that to be just as bad as if it predicted 1. This is because the classes are not ordered, and the difference between the classes is not meaningful.\n", + "\n", + "Because of this, we need to convert our labels from an integer value into a one-hot encoded vector. This means that each label will be represented as a vector of length 10, with a 1 in the position corresponding to the class, and 0s everywhere else. For example, the label 9 would be represented as `[0, 0, 0, 0, 0, 0, 0, 0, 0, 1]`. This is a common way of representing categorical data in machine learning. By doing this, we ensure that our model is taught the correct relationship between the classes." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "pok2lnOqmoA4", + "outputId": "3630a003-cb2f-429e-b49b-de1603cab564" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Before one-hot encoding: 6\n", + "After one-hot encoding: [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]\n" + ] + } + ], + "source": [ + "from tensorflow.keras.utils import to_categorical\n", + "\n", + "print(f'Before one-hot encoding: {y_train[0]}')\n", + "y_train = to_categorical(y_train, num_classes=10)\n", + "y_test = to_categorical(y_test, num_classes=10)\n", + "print(f'After one-hot encoding: {y_train[0]}')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "FoyfYwkumoA4" + }, + "source": [ + "## Feed Forward Neural Networks with Keras\n", + "\n", + "Now that we have prepared our data, it's time to build a simple neural network! In this section, we will use the Keras API to build a simple feed forward neural network. We will then train the model on the MNIST dataset, and evaluate its performance on the test set.\n", + "\n", + "In most modern deep learning frameworks, the process of building a model can be broken down into a few steps:\n", + "\n", + "- Define the model architecture: this is where we define the layers of the model, and how they are connected to each other.\n", + "- Compile the model: this is where we define the loss function, the optimizer, and the metrics that we want to use to evaluate the model.\n", + "- Train the model: this is where we train the model on the training data.\n", + "\n", + "Let's start with defining the model architecture. There are two ways to do this in Keras - the Sequential API and the Functional API. The Sequential API is the simplest way to build a model, and is suitable for most use cases. The Functional API is more flexible, and allows you to build more complex models. We will start with the Sequential API, and then we will look at the Functional API later in the course.\n", + "\n", + "Our simple neural network will be \"fully-connected\". This means that each neuron in a given layer is connected to every neuron in the next layer. This is also known as a \"dense\" layer. We will use the `Dense` class from Keras to define our layers." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 232 + }, + "id": "n9KzVmWSmoA5", + "outputId": "45623703-3183-42a8-ea63-65d4896874e3" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
Model: \"sequential_4\"\n",
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+              "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+              "│ dense_9 (Dense)                 │ (None, 64)             │         4,160 │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dense_10 (Dense)                │ (None, 64)             │         4,160 │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dense_11 (Dense)                │ (None, 10)             │           650 │\n",
+              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+              "
\n" + ], + "text/plain": [ + "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n", + "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n", + "│ dense_9 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m4,160\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dense_10 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m4,160\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dense_11 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m) │ \u001b[38;5;34m650\u001b[0m │\n", + "└─────────────────────────────────┴────────────────────────┴───────────────┘\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
 Total params: 8,970 (35.04 KB)\n",
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 Trainable params: 8,970 (35.04 KB)\n",
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\n" + ], + "text/plain": [ + "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m8,970\u001b[0m (35.04 KB)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
 Non-trainable params: 0 (0.00 B)\n",
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\n" + ], + "text/plain": [ + "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import Input, Dense\n", + "\n", + "model = Sequential()\n", + "\n", + "# Input layer\n", + "model.add(Input(shape=(64,))) # Input tensor specifying the shape\n", + "model.add(Dense(64, activation='relu')) # 64 neurons, ReLU activation\n", + "\n", + "# Hidden layer\n", + "model.add(Dense(64, activation='relu')) # 64 neurons, ReLU activation\n", + "\n", + "# Output layer\n", + "model.add(Dense(10, activation='softmax')) # 10 neurons, softmax activation\n", + "\n", + "model.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "id": "P6SELS7BmoA5" + }, + "source": [ + "Congratulations! You have just built your first neural network with Keras. As we can confirm from the `model.summary()` output, our model has 3 layers. The first layer has 64 neurons, the second layer has 64 neurons, and the output layer has 10 neurons. The output layer uses the softmax activation function, which is commonly used for multi-class classification problems. The other layers use the ReLU activation function, which is commonly used for hidden layers in neural networks.\n", + "\n", + "Next, we need to compile the model. This is where we define the loss function, the optimizer, and the metrics that we want to use to evaluate the model. We will use the `compile` method of the model to do this." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "id": "fNkgliiEmoA5" + }, + "outputs": [], + "source": [ + "model.compile(\n", + " loss='categorical_crossentropy', # Loss function\n", + " optimizer='sgd', # Optimizer\n", + " metrics=['accuracy'] # Metrics to evaluate the model\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "id": "RlOivzfomoA5" + }, + "source": [ + "Because we are predicting which class a sample belongs to, we will use the `categorical_crossentropy` function. This loss function is commonly used for multi-class classification problems.\n", + "\n", + "For our optimizer, we are using the standard stochastic gradient descent (SGD) algorithm. This is a simple optimizer that works well for many problems. We will look at more advanced optimizers later in the course.\n", + "\n", + "Finally, we are using the `accuracy` metric to evaluate the model. This is a common metric for classification problems, and it is simply the fraction of samples that are correctly classified. This is an easier metric for us to understand, but it's not quite as useful for actually training the model (for example, it doesn't tell us how \"confident\" the model is in its predictions).\n", + "\n", + "Now that we have (a) defined the model architecture and (b) compiled the model, we are ready to train the model. We will use the `fit` method of the model to do this." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "aJY_MnRwmoA5", + "outputId": "eaf6ed66-2bb7-4d5c-c591-2cacea0d1d82" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/5\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.3353 - loss: 2.6528 - val_accuracy: 0.8056 - val_loss: 0.6764\n", + "Epoch 2/5\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8086 - loss: 0.5830 - val_accuracy: 0.8715 - val_loss: 0.3767\n", + "Epoch 3/5\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9015 - loss: 0.3161 - val_accuracy: 0.9132 - val_loss: 0.2889\n", + "Epoch 4/5\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9295 - loss: 0.2359 - val_accuracy: 0.9306 - val_loss: 0.2333\n", + "Epoch 5/5\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9529 - loss: 0.1777 - val_accuracy: 0.9340 - val_loss: 0.2173\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.fit(\n", + " X_train, # Training data\n", + " y_train, # Training labels\n", + " epochs=5, # Number of epochs\n", + " batch_size=32, # Number of samples per batch\n", + " validation_split=0.2 # Use 20% of the data for validation\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "id": "BIFKh_1XmoA5" + }, + "source": [ + "We have now trained our model! We can see that the model has been trained for 5 epochs, and the loss and accuracy have been printed for each epoch. We can also see that the model has been evaluated on the validation data at the end of each epoch. This is useful for us to see how the model is performing on data that it hasn't seen during training.\n", + "\n", + "Once the model is trained, it's time to evaluate the model on the test set. We can use the `evaluate` method of the model to do this. If you were building a model for a real-world application, this is the very last thing you would do, and the result here would be the figure you'd report in your paper or presentation." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "3dGk-1K_moA5", + "outputId": "e4577d11-15f2-45ac-9c47-bce1708d8438" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9631 - loss: 0.1497 \n", + "Loss: 0.19\n", + "Accuracy: 95.00%\n" + ] + } + ], + "source": [ + "loss, accuracy = model.evaluate(X_test, y_test)\n", + "\n", + "print(f'Loss: {loss:.2f}')\n", + "print(f'Accuracy: {accuracy*100:.2f}%')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "id": "mdtrU4ETmoA5" + }, + "source": [ + "Hopefully you have achieved an accuracy of around 95%. This is pretty good, but we can do better! In the next section, we will look at how we can improve the performance of our model by using a more advanced optimizer. But before we get there, let's do one other thing - let's look at the predictions that our model is making on the test set. When you are building a model, it's often useful to have a look at some of the examples your model is getting wrong. Sometimes this can reveal problems with the data, or it can give you ideas for how to improve your model." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 539 + }, + "id": "0elPJKLYmoA5", + "outputId": "41f8b4d0-b02f-4df8-ddc3-4b3f4ece7aa4" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step \n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Get the predictions for the test data\n", + "predictions = model.predict(X_test)\n", + "\n", + "# Get the index of the largest probability (i.e. the predicted class)\n", + "predicted_classes = np.argmax(predictions, axis=1)\n", + "true_classes = np.argmax(y_test, axis=1)\n", + "misclassified_indices = np.where(predicted_classes != true_classes)[0]\n", + "\n", + "# Get the misclassified samples themselves\n", + "misclassified_samples = X_test[misclassified_indices]\n", + "misclassified_labels = np.argmax(y_test[misclassified_indices], axis=1)\n", + "\n", + "# Pick 9 random misclassified samples\n", + "random_indices = np.random.choice(len(misclassified_indices), 9, replace=False)\n", + "\n", + "fig, axes = plt.subplots(3, 3, figsize=(6, 6))\n", + "for i, ax in enumerate(axes.flat):\n", + " ax.imshow(misclassified_samples[random_indices[i]].reshape(8, 8), cmap=plt.cm.gray_r, interpolation='nearest')\n", + " ax.set_title(f\"Pred: {predicted_classes[misclassified_indices[random_indices[i]]]}, Real: {misclassified_labels[random_indices[i]]}\")\n", + "\n", + " # Removing axis labels\n", + " ax.set_xticks([])\n", + " ax.set_yticks([])\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "id": "tTHQXC-9moA5" + }, + "source": [ + "What do you think? Would you have made the same mistakes as the model? Determining whether the mistakes are \"understandable\" is a rough way of seeing if you could improve the model further, or if this is the best you can do with the data you have." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6QfkOQ4gmoA5" + }, + "source": [ + "### b) Exercises: Impact of the Optimizer\n", + "\n", + "In this section, you will play around with the optimizer and see how it affects the performance of the model. We will start with the standard SGD optimizer, and then we will look at more advanced optimizers.\n", + "\n", + "1. Try decreasing the learning rate of the SGD optimizer by a factor of 10, or 100. What do you observe?\n", + "2. Try increasing the learning rate of the SGD optimizer. What happens?\n", + "3. The SGD optimizer has a momentum parameter. In a nutshell, this parameter controls how much the gradient from the previous step affects the current step. Try enabling momentum in the SGD optimizer with a value of 0.9. What happens?\n", + " \n", + "**Notes**:\n", + "\n", + "The keras API documentation is available at:\n", + "\n", + "https://www.tensorflow.org/api_docs/python/tf/keras\n", + "\n", + "It is also possible to learn more about the parameters of a class by using the question mark: type and evaluate:\n", + "\n", + "```python\n", + "optimizers.SGD?\n", + "```\n", + "\n", + "in a jupyter notebook cell.\n", + "\n", + "It is also possible to type the beginning of a function call / constructor and type \"shift-tab\" after the opening paren:\n", + "\n", + "```python\n", + "optimizers.SGD(\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "vCHzl5NitkWA" + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": { + "id": "b-Piwdkjtr-9" + }, + "outputs": [], + "source": [ + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import Input, Dense\n", + "import matplotlib.pyplot as plt\n", + "\n", + "\n", + "model = Sequential()\n", + "\n", + "# Input layer\n", + "model.add(Input(shape=(X_train.shape[1],))) # Input tensor specifying the shape\n", + "model.add(Dense(32, activation='relu')) # 32 neurons, ReLU activation\n", + "\n", + "# Hidden layer\n", + "model.add(Dense(128, activation='relu')) # 128 neurons, ReLU activation\n", + "\n", + "# Output layer\n", + "model.add(Dense(y_train.shape[1], activation='softmax')) # softmax activation\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": { + "id": "gcX4KTPGmoA6" + }, + "outputs": [], + "source": [ + "# 1. Decreasing the learning rate\n", + "from tensorflow.keras.optimizers import SGD\n", + "model.compile(\n", + " loss='categorical_crossentropy', # Loss function\n", + " optimizer=SGD(learning_rate= 0.001), # Optimizer\n", + " metrics=['accuracy'] # Metrics to evaluate the model\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 797 + }, + "id": "8XP_ankuv-2x", + "outputId": "ed7400b5-7d05-435a-df3c-ff59a5224b75" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - accuracy: 0.1322 - loss: 3.2101 - val_accuracy: 0.2812 - val_loss: 2.1268\n", + "Epoch 2/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.2960 - loss: 2.0068 - val_accuracy: 0.4479 - val_loss: 1.7008\n", + "Epoch 3/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.4135 - loss: 1.6210 - val_accuracy: 0.5278 - val_loss: 1.4224\n", + "Epoch 4/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.5502 - loss: 1.3335 - val_accuracy: 0.6458 - val_loss: 1.2256\n", + "Epoch 5/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.6362 - loss: 1.1507 - val_accuracy: 0.6806 - val_loss: 1.0677\n", + "Epoch 6/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.6573 - loss: 1.0671 - val_accuracy: 0.7083 - val_loss: 0.9584\n", + "Epoch 7/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7495 - loss: 0.8874 - val_accuracy: 0.7465 - val_loss: 0.8593\n", + "Epoch 8/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7383 - loss: 0.8781 - val_accuracy: 0.7986 - val_loss: 0.7832\n", + "Epoch 9/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7865 - loss: 0.7655 - val_accuracy: 0.8056 - val_loss: 0.7202\n", + "Epoch 10/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7930 - loss: 0.7004 - val_accuracy: 0.8194 - val_loss: 0.6725\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "history = model.fit(X_train, y_train,\n", + " epochs=10,\n", + " batch_size=32,\n", + " validation_split=0.2)\n", + "\n", + "plt.plot(history.history['loss'], label='Training Loss')\n", + "plt.plot(history.history['val_loss'], label='Validation Loss')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 816 + }, + "id": "yu1KIfMEwdD8", + "outputId": "6fa9e3e0-4f17-46bf-c350-5b6ba56c1847" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.8163 - loss: 0.6569 - val_accuracy: 0.8194 - val_loss: 0.6660\n", + "Epoch 2/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8369 - loss: 0.6180 - val_accuracy: 0.8194 - val_loss: 0.6605\n", + "Epoch 3/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8315 - loss: 0.6373 - val_accuracy: 0.8194 - val_loss: 0.6557\n", + "Epoch 4/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8102 - loss: 0.6784 - val_accuracy: 0.8229 - val_loss: 0.6509\n", + "Epoch 5/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8078 - loss: 0.6593 - val_accuracy: 0.8229 - val_loss: 0.6467\n", + "Epoch 6/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8401 - loss: 0.6045 - val_accuracy: 0.8229 - val_loss: 0.6424\n", + "Epoch 7/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8386 - loss: 0.6292 - val_accuracy: 0.8229 - val_loss: 0.6382\n", + "Epoch 8/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8215 - loss: 0.6486 - val_accuracy: 0.8229 - val_loss: 0.6340\n", + "Epoch 9/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8166 - loss: 0.6392 - val_accuracy: 0.8264 - val_loss: 0.6300\n", + "Epoch 10/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8246 - loss: 0.6352 - val_accuracy: 0.8333 - val_loss: 0.6261\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 73, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# 1. Decreasing the learning rate\n", + "from tensorflow.keras.optimizers import SGD\n", + "model.compile(\n", + " loss='categorical_crossentropy', # Loss function\n", + " optimizer=SGD(learning_rate= 0.0001), # Optimizer\n", + " metrics=['accuracy'] # Metrics to evaluate the model\n", + ")\n", + "\n", + "history = model.fit(X_train, y_train,\n", + " epochs=10,\n", + " batch_size=32,\n", + " validation_split=0.2)\n", + "\n", + "plt.plot(history.history['loss'], label='Training Loss')\n", + "plt.plot(history.history['val_loss'], label='Validation Loss')\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "mCfwijwWxYWF" + }, + "source": [ + "Small learning rate increases accuracy." + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 816 + }, + "id": "lkEv7C85moA6", + "outputId": "52d9ad8e-099b-4b3e-9e7a-ddd284fa8418" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.1931 - loss: 7.2397 - val_accuracy: 0.1875 - val_loss: 2.7809\n", + "Epoch 2/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.1770 - loss: 2.2218 - val_accuracy: 0.1076 - val_loss: 2.3045\n", + "Epoch 3/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.1023 - loss: 2.2975 - val_accuracy: 0.1181 - val_loss: 2.2778\n", + "Epoch 4/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.1000 - loss: 2.3321 - val_accuracy: 0.1076 - val_loss: 2.3070\n", + "Epoch 5/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.1093 - loss: 2.3009 - val_accuracy: 0.1076 - val_loss: 2.3077\n", + "Epoch 6/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.0848 - loss: 2.3012 - val_accuracy: 0.1076 - val_loss: 2.3080\n", + "Epoch 7/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.1034 - loss: 2.3005 - val_accuracy: 0.1076 - val_loss: 2.3090\n", + "Epoch 8/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.1059 - loss: 2.3017 - val_accuracy: 0.1076 - val_loss: 2.3090\n", + "Epoch 9/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.1065 - loss: 2.2990 - val_accuracy: 0.1076 - val_loss: 2.3091\n", + "Epoch 10/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.1155 - loss: 2.2977 - val_accuracy: 0.1076 - val_loss: 2.3091\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# 2. Increasing the learning rate\n", + "\n", + "from tensorflow.keras.optimizers import SGD\n", + "model.compile(\n", + " loss='categorical_crossentropy', # Loss function\n", + " optimizer=SGD(learning_rate= 0.1), # Optimizer\n", + " metrics=['accuracy'] # Metrics to evaluate the model\n", + ")\n", + "\n", + "history = model.fit(X_train, y_train,\n", + " epochs=10,\n", + " batch_size=32,\n", + " validation_split=0.2)\n", + "\n", + "plt.plot(history.history['loss'], label='Training Loss')\n", + "plt.plot(history.history['val_loss'], label='Validation Loss')\n", + "plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 816 + }, + "id": "ANTT2SUBzBXs", + "outputId": "870880b2-9ae0-4237-92db-c5791f63332b" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.0936 - loss: 2.3074 - val_accuracy: 0.1215 - val_loss: 2.3085\n", + "Epoch 2/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.1002 - loss: 2.3069 - val_accuracy: 0.0833 - val_loss: 2.3063\n", + "Epoch 3/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.0971 - loss: 2.3092 - val_accuracy: 0.1042 - val_loss: 2.3071\n", + "Epoch 4/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.1062 - loss: 2.3098 - val_accuracy: 0.0833 - val_loss: 2.3183\n", + "Epoch 5/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.0829 - loss: 2.3092 - val_accuracy: 0.0868 - val_loss: 2.3171\n", + "Epoch 6/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.0884 - loss: 2.3136 - val_accuracy: 0.0868 - val_loss: 2.3235\n", + "Epoch 7/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.0961 - loss: 2.3077 - val_accuracy: 0.1250 - val_loss: 2.3150\n", + "Epoch 8/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.0897 - loss: 2.3089 - val_accuracy: 0.0833 - val_loss: 2.3092\n", + "Epoch 9/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.1184 - loss: 2.3056 - val_accuracy: 0.0938 - val_loss: 2.3106\n", + "Epoch 10/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.0782 - loss: 2.3095 - val_accuracy: 0.1076 - val_loss: 2.3095\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 75, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from tensorflow.keras.optimizers import SGD\n", + "model.compile(\n", + " loss='categorical_crossentropy', # Loss function\n", + " optimizer=SGD(learning_rate= 1.0), # Optimizer\n", + " metrics=['accuracy'] # Metrics to evaluate the model\n", + ")\n", + "\n", + "history = model.fit(X_train, y_train,\n", + " epochs=10,\n", + " batch_size=32,\n", + " validation_split=0.2)\n", + "\n", + "plt.plot(history.history['loss'], label='Training Loss')\n", + "plt.plot(history.history['val_loss'], label='Validation Loss')\n", + "plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "taIqocwm1U3j" + }, + "outputs": [], + "source": [ + "Large learning rate decreases accuracy" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4GVeQtff1G8l" + }, + "source": [ + "The accuracy decreases." + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 797 + }, + "id": "M9Mw7upzmoA6", + "outputId": "ea7bddcc-2710-4d65-8902-0faa7a5b06c7" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.1055 - loss: 2.3021 - val_accuracy: 0.1076 - val_loss: 2.3095\n", + "Epoch 2/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.1002 - loss: 2.3045 - val_accuracy: 0.1076 - val_loss: 2.3094\n", + "Epoch 3/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.0955 - loss: 2.3054 - val_accuracy: 0.1076 - val_loss: 2.3094\n", + "Epoch 4/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.0973 - loss: 2.3012 - val_accuracy: 0.1076 - val_loss: 2.3094\n", + "Epoch 5/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.1035 - loss: 2.3028 - val_accuracy: 0.1076 - val_loss: 2.3094\n", + "Epoch 6/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.1041 - loss: 2.3025 - val_accuracy: 0.1076 - val_loss: 2.3094\n", + "Epoch 7/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.0971 - loss: 2.3022 - val_accuracy: 0.1076 - val_loss: 2.3094\n", + "Epoch 8/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.1030 - loss: 2.3043 - val_accuracy: 0.1076 - val_loss: 2.3094\n", + "Epoch 9/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.1066 - loss: 2.3023 - val_accuracy: 0.1076 - val_loss: 2.3094\n", + "Epoch 10/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.1075 - loss: 2.3034 - val_accuracy: 0.1076 - val_loss: 2.3094\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# 3. SGD with momentum\n", + "\n", + "from tensorflow.keras.optimizers import SGD\n", + "model.compile(\n", + " loss='categorical_crossentropy', # Loss function\n", + " optimizer=SGD(learning_rate= 0.001,momentum=0.6), # Optimizer\n", + " metrics=['accuracy'] # Metrics to evaluate the model\n", + ")\n", + "history = model.fit(X_train, y_train,\n", + " epochs=10,\n", + " batch_size=32,\n", + " validation_split=0.2)\n", + "\n", + "plt.plot(history.history['loss'], label='Training Loss')\n", + "plt.plot(history.history['val_loss'], label='Validation Loss')\n", + "plt.legend()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ka90HMJy2fU3" + }, + "source": [ + "Momentum increases the accuracy with the learning rate = 0.001." + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 797 + }, + "id": "41BRkqCf2Sbe", + "outputId": "d9dd1d82-a858-4c04-b09f-7447fcf63abc" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.1049 - loss: 2.2986 - val_accuracy: 0.1076 - val_loss: 2.3094\n", + "Epoch 2/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.0947 - loss: 2.3021 - val_accuracy: 0.1076 - val_loss: 2.3094\n", + "Epoch 3/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.1077 - loss: 2.3025 - val_accuracy: 0.1076 - val_loss: 2.3094\n", + "Epoch 4/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.1237 - loss: 2.2997 - val_accuracy: 0.1076 - val_loss: 2.3094\n", + "Epoch 5/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.1119 - loss: 2.2989 - val_accuracy: 0.1076 - val_loss: 2.3094\n", + "Epoch 6/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.1042 - loss: 2.3033 - val_accuracy: 0.1076 - val_loss: 2.3093\n", + "Epoch 7/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.0972 - loss: 2.3028 - val_accuracy: 0.1076 - val_loss: 2.3093\n", + "Epoch 8/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.1198 - loss: 2.3011 - val_accuracy: 0.1076 - val_loss: 2.3093\n", + "Epoch 9/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.1106 - loss: 2.3021 - val_accuracy: 0.1076 - val_loss: 2.3093\n", + "Epoch 10/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.0975 - loss: 2.3036 - val_accuracy: 0.1076 - val_loss: 2.3093\n" + ] + }, + { + "data": { + "image/png": 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JNXtjmsMwDBUUFKisrMwfgnbt2qXy8nKlpqb629ntdg0bNkxFRUUNjvPqq6/qm2++UWZmZqNzeb1eVVZWBlwAAIB5ndHSjj6fT9nZ2UpOTtagQYOabOvxeNSjRw95vV6Fhobq2Wef1ejRoyVJ5eXlkiSHwxHQx+Fw+NfV96c//UlpaWnq2bNno3O63W498sgjwWwSAABox1ocalwul7Zv367NmzeftK3NZlNpaakOHTqkgoIC5eTkqFevXkpJSQl63r1792r9+vV6+eWXm2w3ffp05eTk+G9XVlYqISEh6PkAAED70KJQk5WVpTVr1mjTpk1N7i2pFRISoj59+kiSEhMTtWPHDrndbqWkpCg2NlaSVFFRobi4OH+fiooKJSYmnjBWbm6uunfvrquuuqrJOcPCwhQWFhbEVgEAgPYsqGNqDMNQVlaWVq1apQ0bNsjpdLZoUp/PJ6/XK0lyOp2KjY1VQUGBf31lZaWKi4s1fPjwE+bPzc3VhAkT1KlTpxbNDQAAzCmoPTUul0vLly/X6tWrZbPZ/Me82O12Wa1WSdKECRPUo0cPud1uSTXHtiQlJal3797yer1au3at8vLytHjxYkmSxWJRdna25s6dq759+8rpdGrmzJmKj49XRkZGwPwbNmzQrl27dPvtt7d2uwEAgMkEFWpqg0j9Y2Fyc3M1ceJESdLu3bsVEnJ8B1BVVZXuvvtu7d27V1arVf3799eyZcs0btw4f5tp06apqqpKkyZN0sGDBzVy5EitW7dO4eHhAfP86U9/0ogRI9S/f/9gygYAAB1Aq85T055wnhoAANqfn+w8NQAAAKcLQg0AADAFQg0AADAFQg0AADAFQg0AADAFQg0AADAFQg0AADAFQg0AADAFQg0AADAFQg0AADAFQg0AADAFQg0AADAFQg0AADAFQg0AADAFQg0AADAFQg0AADAFQg0AADAFQg0AADAFQg0AADAFQg0AADAFQg0AADAFQg0AADAFQg0AADAFQg0AADAFQg0AADAFQg0AADAFQg0AADCFM9q6AABA+3Hs2DFVV1e3dRkwkU6dOik0NPSUjEWoAQCclGEYKi8v18GDB9u6FJhQdHS0YmNjZbFYWjUOoQYAcFK1gSYmJkZdunRp9YsPINWE5e+++05ff/21JCkuLq5V4xFqAABNOnbsmD/QdO/eva3LgclYrVZJ0tdff62YmJhWfRTFgcIAgCbVHkPTpUuXNq4EZlX73Grt8VqEGgBAs/CRE34sp+q5RagBAACmQKgBAKCZzj33XD3zzDPNbv/WW2/JYrHwrbGfCKEGAGA6FoulycvDDz/conFLSko0adKkZrcfMWKE9u3bJ7vd3qL5movwVCOoUON2uzV06FDZbDbFxMQoIyNDZWVlTfZZuXKlkpKSFB0drYiICCUmJiovLy+gjWEYmjVrluLi4mS1WpWamqqdO3eeMNZrr72mYcOGyWq1qmvXrsrIyAimfABAB7Fv3z7/5ZlnnlFUVFTAsqlTp/rbGoaho0ePNmvcs846K6gDpjt37nxKzr+C5gkq1BQWFsrlcmnLli3Kz89XdXW1xowZo6qqqkb7dOvWTTNmzFBRUZG2bt2qzMxMZWZmav369f428+bN04IFC7RkyRIVFxcrIiJCaWlpOnz4sL/NK6+8optvvlmZmZn68MMP9c477+jGG29swSYDAMwuNjbWf7Hb7bJYLP7bH3/8sWw2m15//XVddNFFCgsL0+bNm/XZZ5/p6quvlsPhUGRkpIYOHao333wzYNz6Hz9ZLBb98Y9/1DXXXKMuXbqob9++evXVV/3r6+9BWbp0qaKjo7V+/XoNGDBAkZGRGjt2rPbt2+fvc/ToUd1zzz2Kjo5W9+7d9cADD+iWW25p1Rv5AwcOaMKECeratau6dOmiyy+/PGDnwRdffKErr7xSXbt2VUREhAYOHKi1a9f6+950000666yzZLVa1bdvX+Xm5ra4lh+V0Qpff/21IckoLCwMqt+FF15oPPTQQ4ZhGIbP5zNiY2ON+fPn+9cfPHjQCAsLM/76178ahmEY1dXVRo8ePYw//vGPLa7V4/EYkgyPx9PiMQCgI/r++++Njz76yPj+++8Nw6j573aVt7pNLj6fL+j6c3NzDbvd7r+9ceNGQ5Jx/vnnG2+88Ybx6aefGt98841RWlpqLFmyxNi2bZvxySefGA899JARHh5ufPHFF/6+55xzjvH73//ef1uS0bNnT2P58uXGzp07jXvuuceIjIw0vvnmm4C5Dhw44K+lU6dORmpqqlFSUmK8//77xoABA4wbb7zRP+bcuXONbt26GStXrjR27Nhh3HXXXUZUVJRx9dVXN7qN9eep76qrrjIGDBhgbNq0ySgtLTXS0tKMPn36GEeOHDEMwzDS09ON0aNHG1u3bjU+++wz4x//+If/td3lchmJiYlGSUmJsWvXLiM/P9949dVXg3gETq7+c6yuYF6/W3XyPY/HI6lmb0wzA5Q2bNigsrIyPfHEE5KkXbt2qby8XKmpqf52drtdw4YNU1FRkcaPH68PPvhAX375pUJCQnThhReqvLxciYmJmj9/vgYNGtTgXF6vV16v13+7srKypZsJAKjj++pjOm/W+pM3/BF89Ls0del8as4b+7vf/U6jR4/23+7WrZsuuOAC/+05c+Zo1apVevXVV5WVldXoOBMnTtSvfvUrSdJjjz2mBQsW6L333tPYsWMbbF9dXa0lS5aod+/ekqSsrCz97ne/869fuHChpk+frmuuuUaStGjRIv9ek5bYuXOnXn31Vb3zzjsaMWKEJOkvf/mLEhIS9Pe//13XX3+9du/erWuvvVaDBw+WJPXq1cvff/fu3brwwguVlJQkqWZv1emqxQcK+3w+ZWdnKzk5udFgUcvj8SgyMlKdO3dWenq6Fi5c6H8ilZeXS5IcDkdAH4fD4V/3n//8R5L08MMP66GHHtKaNWvUtWtXpaSk6H//+1+Dc7rdbtntdv8lISGhpZsKADCh2hfpWocOHdLUqVM1YMAARUdHKzIyUjt27NDu3bubHOf888/3X4+IiFBUVJT/tP8N6dKliz/QSDU/DVDb3uPxqKKiQhdffLF/fWhoqC666KKgtq2uHTt26IwzztCwYcP8y7p3766f/exn2rFjhyTpnnvu0dy5c5WcnKzZs2dr69at/ra/+c1vtGLFCiUmJmratGl69913W1zLj63Fcdflcmn79u3avHnzSdvabDaVlpbq0KFDKigoUE5Ojnr16qWUlJRmzeXz+SRJM2bM0LXXXitJys3NVc+ePfW3v/1Nd9555wl9pk+frpycHP/tyspKgg0AnALWTqH66HdpbTb3qRIRERFwe+rUqcrPz9eTTz6pPn36yGq16rrrrtORI0eaHKdTp04Bty0Wi/91q7ntDcMIsvpT6/bbb1daWppee+01vfHGG3K73Xrqqac0efJkXX755friiy+0du1a5efn67LLLpPL5dKTTz7ZpjU3pEV7arKysrRmzRpt3LhRPXv2PPkkISHq06ePEhMTNWXKFF133XVyu92Sag7mkqSKioqAPhUVFf51tT9wdd555/nXh4WFqVevXo0m6LCwMEVFRQVcAACtZ7FY1KXzGW1y+TG/RfTOO+9o4sSJuuaaazR48GDFxsbq888//9Hma4jdbpfD4VBJSYl/2bFjx/TBBx+0eMwBAwbo6NGjKi4u9i/75ptvVFZWFvC6mpCQoLvuuksrV67UlClT9MILL/jXnXXWWbrlllu0bNkyPfPMM3r++edbXM+PKag9NYZhaPLkyVq1apXeeustOZ3OFk3q8/n8x7s4nU7FxsaqoKBAiYmJkmr2qhQXF+s3v/mNJPmPTi8rK9PIkSMl1Xwm+fnnn+ucc85pUQ0AANTVt29frVy5UldeeaUsFotmzpzZ5B6XH8vkyZPldrvVp08f9e/fXwsXLtSBAweaFei2bdsmm83mv22xWHTBBRfo6quv1h133KHnnntONptNDz74oHr06KGrr75akpSdna3LL79c/fr104EDB7Rx40YNGDBAkjRr1ixddNFFGjhwoLxer9asWeNfd7oJKtS4XC4tX75cq1evls1m8x/zYrfb/b+yOWHCBPXo0cO/J8btdispKUm9e/eW1+vV2rVrlZeXp8WLF0uqucOzs7M1d+5c9e3bV06nUzNnzlR8fLz/62tRUVG66667NHv2bCUkJOicc87R/PnzJUnXX3/9KbkjAAAd29NPP61bb71VI0aM0JlnnqkHHnigTb5k8sADD6i8vFwTJkxQaGioJk2apLS0tGb9evWoUaMCboeGhuro0aPKzc3VvffeqyuuuEJHjhzRqFGjtHbtWv9HYceOHZPL5dLevXsVFRWlsWPH6ve//72kmnPtTJ8+XZ9//rmsVqt+/vOfa8WKFad+w0+FYL5yJanBS25urr/NJZdcYtxyyy3+2zNmzDD69OljhIeHG127djWGDx9urFixImBcn89nzJw503A4HEZYWJhx2WWXGWVlZQFtjhw5YkyZMsWIiYkxbDabkZqaamzfvr3ZtfOVbgBomaa+bosf37Fjx4x+/fr5T4ViRqfqK90Ww2jjo5N+IpWVlbLb7fJ4PBxfAwBBOHz4sHbt2iWn06nw8PC2Lsf0vvjiC73xxhu65JJL5PV6tWjRIuXm5urDDz88bT/2aa2mnmPBvH7z208AAJxGQkJCtHTpUg0dOlTJycnatm2b3nzzTdMGmlPp1JzBCAAAnBIJCQl655132rqMdok9NQAAwBQINQAAwBQINQAAwBQINQAAwBQINQAAwBQINQAAwBQINQAANCIlJUXZ2dn+2+eee66eeeaZJvtYLBb9/e9/b/Xcp2qcjoRQAwAwnSuvvFJjx45tcN3bb78ti8WirVu3Bj1uSUmJJk2a1NryAjz88MP+H3Sua9++fbr88stP6Vz1LV26VNHR0T/qHD8lQg0AwHRuu+025efna+/evSesy83NVVJSks4///ygxz3rrLPUpUuXU1HiScXGxiosLOwnmcssCDUAANO54oordNZZZ2np0qUByw8dOqS//e1vuu222/TNN9/oV7/6lXr06KEuXbpo8ODB+utf/9rkuPU/ftq5c6dGjRql8PBwnXfeecrPzz+hzwMPPKB+/fqpS5cu6tWrl2bOnKnq6mpJNXtKHnnkEX344YeyWCyyWCz+mut//LRt2zb94he/kNVqVffu3TVp0iQdOnTIv37ixInKyMjQk08+qbi4OHXv3l0ul8s/V0vs3r1bV199tSIjIxUVFaUbbrhBFRUV/vUffvihLr30UtlsNkVFRemiiy7SP//5T0k1v2F15ZVXqmvXroqIiNDAgQO1du3aFtfSHPxMAgAgOIYhVX/XNnN36iJZLCdtdsYZZ2jChAlaunSpZsyYIcsPff72t7/p2LFj+tWvfqVDhw7poosu0gMPPKCoqCi99tpruvnmm9W7d29dfPHFJ53D5/Ppl7/8pRwOh4qLi+XxeAKOv6lls9m0dOlSxcfHa9u2bbrjjjtks9k0bdo0jRs3Ttu3b9e6dev05ptvSpLsdvsJY1RVVSktLU3Dhw9XSUmJvv76a91+++3KysoKCG4bN25UXFycNm7cqE8//VTjxo1TYmKi7rjjjpNuT0PbVxtoCgsLdfToUblcLo0bN05vvfWWJOmmm27ShRdeqMWLFys0NFSlpaXq1KmTJMnlcunIkSPatGmTIiIi9NFHHykyMjLoOoJBqAEABKf6O+mx+LaZ+7dfSZ0jmtX01ltv1fz581VYWKiUlBRJNR89XXvttbLb7bLb7Zo6daq//eTJk7V+/Xq9/PLLzQo1b775pj7++GOtX79e8fE198djjz12wnEwDz30kP/6ueeeq6lTp2rFihWaNm2arFarIiMjdcYZZyg2NrbRuZYvX67Dhw/rxRdfVEREzfYvWrRIV155pZ544gk5HA5JUteuXbVo0SKFhoaqf//+Sk9PV0FBQYtCTUFBgbZt26Zdu3YpISFBkvTiiy9q4MCBKikp0dChQ7V7927df//96t+/vySpb9++/v67d+/Wtddeq8GDB0uSevXqFXQNweLjJwCAKfXv318jRozQ//t//0+S9Omnn+rtt9/WbbfdJkk6duyY5syZo8GDB6tbt26KjIzU+vXrtXv37maNv2PHDiUkJPgDjSQNHz78hHYvvfSSkpOTFRsbq8jISD300EPNnqPuXBdccIE/0EhScnKyfD6fysrK/MsGDhyo0NBQ/+24uDh9/fXXQc1Vd86EhAR/oJGk8847T9HR0dqxY4ckKScnR7fffrtSU1P1+OOP67PPPvO3veeeezR37lwlJydr9uzZLTowO1jsqQEABKdTl5o9Jm01dxBuu+02TZ48WX/4wx+Um5ur3r1765JLLpEkzZ8/X//3f/+nZ555RoMHD1ZERISys7N15MiRU1ZuUVGRbrrpJj3yyCNKS0uT3W7XihUr9NRTT52yOeqq/einlsVikc/n+1Hmkmq+uXXjjTfqtdde0+uvv67Zs2drxYoVuuaaa3T77bcrLS1Nr732mt544w253W499dRTmjx58o9WD3tqAADBsVhqPgJqi0szjqep64YbblBISIiWL1+uF198Ubfeeqv/+Jp33nlHV199tX7961/rggsuUK9evfTJJ580e+wBAwZoz5492rdvn3/Zli1bAtq8++67OuecczRjxgwlJSWpb9+++uKLLwLadO7cWceOHTvpXB9++KGqqqr8y9555x2FhIToZz/7WbNrDkbt9u3Zs8e/7KOPPtLBgwd13nnn+Zf169dP9913n9544w398pe/VG5urn9dQkKC7rrrLq1cuVJTpkzRCy+88KPUWotQAwAwrcjISI0bN07Tp0/Xvn37NHHiRP+6vn37Kj8/X++++6527NihO++8M+CbPSeTmpqqfv366ZZbbtGHH36ot99+WzNmzAho07dvX+3evVsrVqzQZ599pgULFmjVqlUBbc4991zt2rVLpaWl+u9//yuv13vCXDfddJPCw8N1yy23aPv27dq4caMmT56sm2++2X88TUsdO3ZMpaWlAZcdO3YoNTVVgwcP1k033aQPPvhA7733niZMmKBLLrlESUlJ+v7775WVlaW33npLX3zxhd555x2VlJRowIABkqTs7GytX79eu3bt0gcffKCNGzf61/1YCDUAAFO77bbbdODAAaWlpQUc//LQQw9pyJAhSktLU0pKimJjY5WRkdHscUNCQrRq1Sp9//33uvjii3X77bfr0UcfDWhz1VVX6b777lNWVpYSExP17rvvaubMmQFtrr32Wo0dO1aXXnqpzjrrrAa/Vt6lSxetX79e//vf/zR06FBdd911uuyyy7Ro0aLg7owGHDp0SBdeeGHA5corr5TFYtHq1avVtWtXjRo1SqmpqerVq5deeuklSVJoaKi++eYbTZgwQf369dMNN9ygyy+/XI888oikmrDkcrk0YMAAjR07Vv369dOzzz7b6nqbYjEMw/hRZzhNVFZWym63y+PxKCoqqq3LAYB24/Dhw9q1a5ecTqfCw8PbuhyYUFPPsWBev9lTAwAATIFQAwAATIFQAwAATIFQAwAATIFQAwAATIFQAwBolg7yZVm0gVP13CLUAACaVHvq/e++a6Nf5obp1T636v/MQ7D47ScAQJNCQ0MVHR3t/2HELl26+H9qAGgNwzD03Xff6euvv1Z0dHTAj3G2BKEGAHBSsbGxktTiX3wGmhIdHe1/jrUGoQYAcFIWi0VxcXGKiYlRdXV1W5cDE+nUqVOr99DUItQAAJotNDT0lL0AAacaBwoDAABTINQAAABTINQAAABTINQAAABTCCrUuN1uDR06VDabTTExMcrIyFBZWVmTfVauXKmkpCRFR0crIiJCiYmJysvLC2hjGIZmzZqluLg4Wa1WpaamaufOnQFtzj33XFksloDL448/Hkz5AADAxIIKNYWFhXK5XNqyZYvy8/NVXV2tMWPGqKqqqtE+3bp104wZM1RUVKStW7cqMzNTmZmZWr9+vb/NvHnztGDBAi1ZskTFxcWKiIhQWlqaDh8+HDDW7373O+3bt89/mTx5cpCbCwAAzMpitOIHF/bv36+YmBgVFhZq1KhRze43ZMgQpaena86cOTIMQ/Hx8ZoyZYqmTp0qSfJ4PHI4HFq6dKnGjx8vqWZPTXZ2trKzs1tUa2Vlpex2uzwej6Kiolo0BgAA+GkF8/rdqmNqPB6PpJq9Mc1hGIYKCgpUVlbmD0G7du1SeXm5UlNT/e3sdruGDRumoqKigP6PP/64unfvrgsvvFDz58/X0aNHW1M+AAAwkRaffM/n8yk7O1vJyckaNGhQk209Ho969Oghr9er0NBQPfvssxo9erQkqby8XJLkcDgC+jgcDv86Sbrnnns0ZMgQdevWTe+++66mT5+uffv26emnn25wTq/XK6/X679dWVnZou0EAADtQ4tDjcvl0vbt27V58+aTtrXZbCotLdWhQ4dUUFCgnJwc9erVSykpKc2eLycnx3/9/PPPV+fOnXXnnXfK7XYrLCzshPZut1uPPPJIs8cHAADtW4s+fsrKytKaNWu0ceNG9ezZ8+SThISoT58+SkxM1JQpU3TdddfJ7XZLOv4jaRUVFQF9Kioqmvxxq2HDhuno0aP6/PPPG1w/ffp0eTwe/2XPnj3N3DoAANAeBRVqDMNQVlaWVq1apQ0bNsjpdLZoUp/P5/9oyOl0KjY2VgUFBf71lZWVKi4u1vDhwxsdo7S0VCEhIYqJiWlwfVhYmKKiogIuAADAvIL6+Mnlcmn58uVavXq1bDab/5gXu90uq9UqSZowYYJ69Ojh3xPjdruVlJSk3r17y+v1au3atcrLy9PixYsl1fzya3Z2tubOnau+ffvK6XRq5syZio+PV0ZGhiSpqKhIxcXFuvTSS2Wz2VRUVKT77rtPv/71r9W1a9dTdV8AAIB2LKhQUxtE6h8Lk5ubq4kTJ0qSdu/erZCQ4zuAqqqqdPfdd2vv3r2yWq3q37+/li1bpnHjxvnbTJs2TVVVVZo0aZIOHjyokSNHat26dQoPD5dUs9dlxYoVevjhh+X1euV0OnXfffcFHGcDAAA6tladp6Y94Tw1AAC0Pz/ZeWoAAABOF4QaAABgCoQaAABgCoQaAABgCoQaAABgCoQaAABgCoQaAABgCoQaAABgCoQaAABgCoQaAABgCoQaAABgCoQaAABgCoQaAABgCoQaAABgCoQaAABgCoQaAABgCoQaAABgCoQaAABgCoQaAABgCoQaAABgCoQaAABgCoQaAABgCoQaAABgCoQaAABgCoQaAABgCoQaAABgCoQaAABgCoQaAABgCoQaAABgCoQaAABgCoQaAABgCoQaAABgCoQaAABgCoQaAABgCoQaAABgCoQaAABgCkGFGrfbraFDh8pmsykmJkYZGRkqKytrss/KlSuVlJSk6OhoRUREKDExUXl5eQFtDMPQrFmzFBcXJ6vVqtTUVO3cubPB8bxerxITE2WxWFRaWhpM+QAAwMSCCjWFhYVyuVzasmWL8vPzVV1drTFjxqiqqqrRPt26ddOMGTNUVFSkrVu3KjMzU5mZmVq/fr2/zbx587RgwQItWbJExcXFioiIUFpamg4fPnzCeNOmTVN8fHwwZQMAgA7AYhiG0dLO+/fvV0xMjAoLCzVq1Khm9xsyZIjS09M1Z84cGYah+Ph4TZkyRVOnTpUkeTweORwOLV26VOPHj/f3e/3115WTk6NXXnlFAwcO1L/+9S8lJiY2a87KykrZ7XZ5PB5FRUUFtZ0AAKBtBPP63apjajwej6SavTHNYRiGCgoKVFZW5g9Bu3btUnl5uVJTU/3t7Ha7hg0bpqKiIv+yiooK3XHHHcrLy1OXLl1OOpfX61VlZWXABQAAmFeLQ43P51N2draSk5M1aNCgJtt6PB5FRkaqc+fOSk9P18KFCzV69GhJUnl5uSTJ4XAE9HE4HP51hmFo4sSJuuuuu5SUlNSs+txut+x2u/+SkJAQ7CYCAIB25IyWdnS5XNq+fbs2b9580rY2m02lpaU6dOiQCgoKlJOTo169eiklJaVZcy1cuFDffvutpk+f3uz6pk+frpycHP/tyspKgg0AACbWolCTlZWlNWvWaNOmTerZs+dJ24eEhKhPnz6SpMTERO3YsUNut1spKSmKjY2VVPPxUlxcnL9PRUWF/3iZDRs2qKioSGFhYQHjJiUl6aabbtKf//znE+YMCws7oT0AADCvoD5+MgxDWVlZWrVqlTZs2CCn09miSX0+n7xeryTJ6XQqNjZWBQUF/vWVlZUqLi7W8OHDJUkLFizQhx9+qNLSUpWWlmrt2rWSpJdeekmPPvpoi2oAAADmEtSeGpfLpeXLl2v16tWy2Wz+Y17sdrusVqskacKECerRo4fcbrekmmNbkpKS1Lt3b3m9Xq1du1Z5eXlavHixJMlisSg7O1tz585V37595XQ6NXPmTMXHxysjI0OSdPbZZwfUERkZKUnq3bt3s/YUAQAA8wsq1NQGkfrHwuTm5mrixImSpN27dysk5PgOoKqqKt19993au3evrFar+vfvr2XLlmncuHH+NtOmTVNVVZUmTZqkgwcPauTIkVq3bp3Cw8NbuFkAAKCjadV5atoTzlMDAED785OdpwYAAOB0QagBAACmQKgBAACmQKgBAACmQKgBAACmQKgBAACmQKgBAACmQKgBAACmQKgBAACmQKgBAACmQKgBAACmQKgBAACmQKgBAACmQKgBAACmQKgBAACmQKgBAACmQKgBAACmQKgBAACmQKgBAACmQKgBAACmQKgBAACmQKgBAACmQKgBAACmQKgBAACmQKgBAACmQKgBAACmQKgBAACmQKgBAACmQKgBAACmQKgBAACmQKgBAACmQKgBAACmQKgBAACmQKgBAACmQKgBAACmQKgBAACmEFSocbvdGjp0qGw2m2JiYpSRkaGysrIm+6xcuVJJSUmKjo5WRESEEhMTlZeXF9DGMAzNmjVLcXFxslqtSk1N1c6dOwPaXHXVVTr77LMVHh6uuLg43Xzzzfrqq6+CKR8AAJhYUKGmsLBQLpdLW7ZsUX5+vqqrqzVmzBhVVVU12qdbt26aMWOGioqKtHXrVmVmZiozM1Pr16/3t5k3b54WLFigJUuWqLi4WBEREUpLS9Phw4f9bS699FK9/PLLKisr0yuvvKLPPvtM1113XQs2GQAAmJHFMAyjpZ3379+vmJgYFRYWatSoUc3uN2TIEKWnp2vOnDkyDEPx8fGaMmWKpk6dKknyeDxyOBxaunSpxo8f3+AYr776qjIyMuT1etWpU6eTzllZWSm73S6Px6OoqKhm1woAANpOMK/frTqmxuPxSKrZG9MchmGooKBAZWVl/hC0a9culZeXKzU11d/Obrdr2LBhKioqanCc//3vf/rLX/6iESNGNBpovF6vKisrAy4AAMC8WhxqfD6fsrOzlZycrEGDBjXZ1uPxKDIyUp07d1Z6eroWLlyo0aNHS5LKy8slSQ6HI6CPw+Hwr6v1wAMPKCIiQt27d9fu3bu1evXqRud0u92y2+3+S0JCQks2EwAAtBMtDjUul0vbt2/XihUrTtrWZrOptLRUJSUlevTRR5WTk6O33nor6Dnvv/9+/etf/9Ibb7yh0NBQTZgwQY19ejZ9+nR5PB7/Zc+ePUHPBwAA2o8zWtIpKytLa9as0aZNm9SzZ8+Ttg8JCVGfPn0kSYmJidqxY4fcbrdSUlIUGxsrSaqoqFBcXJy/T0VFhRITEwPGOfPMM3XmmWeqX79+GjBggBISErRlyxYNHz78hDnDwsIUFhbWks0DAADtUFB7agzDUFZWllatWqUNGzbI6XS2aFKfzyev1ytJcjqdio2NVUFBgX99ZWWliouLGwwrdceQ5B8HAAB0bEHtqXG5XFq+fLlWr14tm83mP+bFbrfLarVKkiZMmKAePXrI7XZLqjm2JSkpSb1795bX69XatWuVl5enxYsXS5IsFouys7M1d+5c9e3bV06nUzNnzlR8fLwyMjIkScXFxSopKdHIkSPVtWtXffbZZ5o5c6Z69+7dZPABAAAdR1ChpjaIpKSkBCzPzc3VxIkTJUm7d+9WSMjxHUBVVVW6++67tXfvXlmtVvXv31/Lli3TuHHj/G2mTZumqqoqTZo0SQcPHtTIkSO1bt06hYeHS5K6dOmilStXavbs2aqqqlJcXJzGjh2rhx56iI+YAACApFaep6Y94Tw1AAC0Pz/ZeWoAAABOF4QaAABgCoQaAABgCoQaAABgCoQaAABgCoQaAABgCoQaAABgCoQaAABgCoQaAABgCoQaAABgCoQaAABgCoQaAABgCoQaAABgCoQaAABgCoQaAABgCoQaAABgCoQaAABgCoQaAABgCoQaAABgCoQaAABgCoQaAABgCoQaAABgCoQaAABgCoQaAABgCoQaAABgCoQaAABgCoQaAABgCoQaAABgCoQaAABgCme0dQHt3ba9Hs1b/7EsFosskkIs8l+3WCTJ8sMyySJLzb8BbY63relfc8Xf9odltf30w/KQem0stW0CljU8Xu3ymuEsJyyrrU8KrLvunGqo9pATx6w/b4PbHXDfNN73+Laf2Fd17ofaOkMskn5oX3sfhtR5TI4vq9mgurf9j5n/cbUEPA6184TU3Z66fWWRJUQN9603f+19DQBoHUJNK/3vuyN6e+d/27oMtHMnBKL6Yc/f7oew6f+/BtY11l7Hw+Hx63VD4w8B8YR1gaHreKhtur2/V71wXvPv8eBY93ZtSAxs2/C/dd9AhFgsCgmpH0KPh/q6txsLpSFN1VRvLn/IrxeI664/YXlIYACuDbcNhfqQkAaW1RnL0sB89d801A/3x+dr+M2Cf1wF1iTVr/H4c6VF4zSwrvZpGdC2gXHkv78aGcdSv64T36jB3Ag1rdQ/1qanb7hAhiEZkgzD+OG6UWeZ5DMMGaq5EbCsTj/VaxswjhE4niFDPiNwvNpltdd/+J98vkbGq7fMV1PgD+tP3Ibjfer2bWCbT+hb7/qP2jdwua/efeczjOP3+w/rfA20rX0MfL7AvoGPz/E+NfddyxmGdMwwdKzmVusGA9Cg1gSihpbX7vVtqK9UL5zW6VsbtE+sod6e8wZCbN3AqtpwXXcvdgOBuraP/O0C+9R/41F7XQHt6tXZQB+LxaI+MZH69f93zk/xcDaIUNNKjqhw/XJIz7YuA6eBJsOTAgPQCeGpTtu6gapm3B/G1/HbRp05a9apTtv6/VSnX711DSxTU+3rbmvAnD9U2MCcCtg+o879U7v8eKhu6Lbvh0F8hiGf78RQWXfsurfrh1D/bV9gDXUfC1/dGn31aqw7xg/3s88X+Kag9s1E3Tcmx9/QNBSMA98s+BoJ7PWfS42PETiH6tXQ+JuE4+OrsXV1nl8NjlFnOxsaP+C5e8K4Rp3xfzzH5687EW8iTqVR/c4i1ABmYKnzLin0+IcvAIJ0svAlNRS4avdeNx6YGg9rTewpPqGewBBYNzw2q+9J6mkoBAdsX70QfcI2+MN2wzXUHVP1g/wP16X6c9fdzsBPBOpvxzndu/xIz4rmIdQAAE4rdd8giDcICEJQX+l2u90aOnSobDabYmJilJGRobKysib7rFy5UklJSYqOjlZERIQSExOVl5cX0MYwDM2aNUtxcXGyWq1KTU3Vzp07/es///xz3XbbbXI6nbJarerdu7dmz56tI0eOBFM+AAAwsaBCTWFhoVwul7Zs2aL8/HxVV1drzJgxqqqqarRPt27dNGPGDBUVFWnr1q3KzMxUZmam1q9f728zb948LViwQEuWLFFxcbEiIiKUlpamw4cPS5I+/vhj+Xw+Pffcc/r3v/+t3//+91qyZIl++9vftnCzAQCA2ViM2g/QWmD//v2KiYlRYWGhRo0a1ex+Q4YMUXp6uubMmSPDMBQfH68pU6Zo6tSpkiSPxyOHw6GlS5dq/PjxDY4xf/58LV68WP/5z3+aNWdlZaXsdrs8Ho+ioqKaXSsAAGg7wbx+t+qMwh6PR1LN3pjmMAxDBQUFKisr84egXbt2qby8XKmpqf52drtdw4YNU1FRUZNzNzWv1+tVZWVlwAUAAJhXi0ONz+dTdna2kpOTNWjQoCbbejweRUZGqnPnzkpPT9fChQs1evRoSVJ5ebkkyeFwBPRxOBz+dfV9+umnWrhwoe68885G53S73bLb7f5LQkJCMJsHAADamRZ/+8nlcmn79u3avHnzSdvabDaVlpbq0KFDKigoUE5Ojnr16qWUlJSg5/3yyy81duxYXX/99brjjjsabTd9+nTl5OT4b1dWVhJsAAAwsRaFmqysLK1Zs0abNm1Sz54nP/FcSEiI+vTpI0lKTEzUjh075Ha7lZKSotjYWElSRUWF4uLi/H0qKiqUmJgYMM5XX32lSy+9VCNGjNDzzz/f5JxhYWEKCwsLcssAAEB7FdTHT4ZhKCsrS6tWrdKGDRvkdDpbNKnP55PX65UkOZ1OxcbGqqCgwL++srJSxcXFGj58uH/Zl19+qZSUFF100UXKzc1VSAg/MA4AAI4Lak+Ny+XS8uXLtXr1atlsNv8xL3a7XVarVZI0YcIE9ejRQ263W1LNsS1JSUnq3bu3vF6v1q5dq7y8PC1evFhSzUmWsrOzNXfuXPXt21dOp1MzZ85UfHy8MjIyJB0PNOecc46efPJJ7d+/319T7Z4eAADQsQUVamqDSP1jYXJzczVx4kRJ0u7duwP2olRVVenuu+/W3r17ZbVa1b9/fy1btkzjxo3zt5k2bZqqqqo0adIkHTx4UCNHjtS6desUHh4uScrPz9enn36qTz/99ISPu1rxjXQAAGAirTpPTXvCeWoAAGh/frLz1AAAAJwuCDUAAMAUOsyvdNd+ysaZhQEAaD9qX7ebc7RMhwk13377rSRxAj4AANqhb7/9Vna7vck2HeZAYZ/Pp6+++ko2m00Wi+WUjl17tuI9e/ZwEPJpgMfj9MLjcXrh8Tj98Jg0zTAMffvtt4qPjz/pOeo6zJ6akJCQZp39uDWioqJ4Qp5GeDxOLzwepxcej9MPj0njTraHphYHCgMAAFMg1AAAAFMg1JwCYWFhmj17Nj+geZrg8Ti98HicXng8Tj88JqdOhzlQGAAAmBt7agAAgCkQagAAgCkQagAAgCkQagAAgCkQalrpD3/4g84991yFh4dr2LBheu+999q6pA7L7XZr6NChstlsiomJUUZGhsrKytq6LEh6/PHHZbFYlJ2d3daldGhffvmlfv3rX6t79+6yWq0aPHiw/vnPf7Z1WR3SsWPHNHPmTDmdTlmtVvXu3Vtz5sxp1u8boXGEmlZ46aWXlJOTo9mzZ+uDDz7QBRdcoLS0NH399ddtXVqHVFhYKJfLpS1btig/P1/V1dUaM2aMqqqq2rq0Dq2kpETPPfeczj///LYupUM7cOCAkpOT1alTJ73++uv66KOP9NRTT6lr165tXVqH9MQTT2jx4sVatGiRduzYoSeeeELz5s3TwoUL27q0do2vdLfCsGHDNHToUC1atEhSze9LJSQkaPLkyXrwwQfbuDrs379fMTExKiws1KhRo9q6nA7p0KFDGjJkiJ599lnNnTtXiYmJeuaZZ9q6rA7pwQcf1DvvvKO33367rUuBpCuuuEIOh0N/+tOf/MuuvfZaWa1WLVu2rA0ra9/YU9NCR44c0fvvv6/U1FT/spCQEKWmpqqoqKgNK0Mtj8cjSerWrVsbV9JxuVwupaenB/ydoG28+uqrSkpK0vXXX6+YmBhdeOGFeuGFF9q6rA5rxIgRKigo0CeffCJJ+vDDD7V582ZdfvnlbVxZ+9ZhftDyVPvvf/+rY8eOyeFwBCx3OBz6+OOP26gq1PL5fMrOzlZycrIGDRrU1uV0SCtWrNAHH3ygkpKSti4Fkv7zn/9o8eLFysnJ0W9/+1uVlJTonnvuUefOnXXLLbe0dXkdzoMPPqjKykr1799foaGhOnbsmB599FHddNNNbV1au0aogSm5XC5t375dmzdvbutSOqQ9e/bo3nvvVX5+vsLDw9u6HKgm6CclJemxxx6TJF144YXavn27lixZQqhpAy+//LL+8pe/aPny5Ro4cKBKS0uVnZ2t+Ph4Ho9WINS00JlnnqnQ0FBVVFQELK+oqFBsbGwbVQVJysrK0po1a7Rp0yb17NmzrcvpkN5//319/fXXGjJkiH/ZsWPHtGnTJi1atEher1ehoaFtWGHHExcXp/POOy9g2YABA/TKK6+0UUUd2/33368HH3xQ48ePlyQNHjxYX3zxhdxuN6GmFTimpoU6d+6siy66SAUFBf5lPp9PBQUFGj58eBtW1nEZhqGsrCytWrVKGzZskNPpbOuSOqzLLrtM27ZtU2lpqf+SlJSkm266SaWlpQSaNpCcnHzCKQ4++eQTnXPOOW1UUcf23XffKSQk8CU4NDRUPp+vjSoyB/bUtEJOTo5uueUWJSUl6eKLL9YzzzyjqqoqZWZmtnVpHZLL5dLy5cu1evVq2Ww2lZeXS5LsdrusVmsbV9ex2Gy2E45lioiIUPfu3TnGqY3cd999GjFihB577DHdcMMNeu+99/T888/r+eefb+vSOqQrr7xSjz76qM4++2wNHDhQ//rXv/T000/r1ltvbevS2jcDrbJw4ULj7LPPNjp37mxcfPHFxpYtW9q6pA5LUoOX3Nzcti4NhmFccsklxr333tvWZXRo//jHP4xBgwYZYWFhRv/+/Y3nn3++rUvqsCorK417773XOPvss43w8HCjV69exowZMwyv19vWpbVrnKcGAACYAsfUAAAAUyDUAAAAUyDUAAAAUyDUAAAAUyDUAAAAUyDUAAAAUyDUAAAAUyDUAAAAUyDUAAAAUyDUAAAAUyDUAAAAUyDUAAAAU/j/ATu+qrN+mYggAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from tensorflow.keras.optimizers import SGD\n", + "model.compile(\n", + " loss='categorical_crossentropy', # Loss function\n", + " optimizer=SGD(learning_rate= 0.0001,momentum=0.8), # Optimizer\n", + " metrics=['accuracy'] # Metrics to evaluate the model\n", + ")\n", + "history = model.fit(X_train, y_train,\n", + " epochs=10,\n", + " batch_size=32,\n", + " validation_split=0.2)\n", + "\n", + "plt.plot(history.history['loss'], label='Training Loss')\n", + "plt.plot(history.history['val_loss'], label='Validation Loss')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "tkKpvKAM3PCP" + }, + "source": [ + "Momentunm does not change the results significantly with the learning rate = 0.0001." + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 797 + }, + "id": "dQW8gN3o3a2F", + "outputId": "24c99b98-cf9d-4c8e-9b45-623203227ed2" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - accuracy: 0.1039 - loss: 2.3055 - val_accuracy: 0.0833 - val_loss: 2.3106\n", + "Epoch 2/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.1144 - loss: 2.3017 - val_accuracy: 0.0833 - val_loss: 2.3098\n", + "Epoch 3/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.1017 - loss: 2.3064 - val_accuracy: 0.1076 - val_loss: 2.3087\n", + "Epoch 4/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.1126 - loss: 2.3003 - val_accuracy: 0.0833 - val_loss: 2.3096\n", + "Epoch 5/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.1018 - loss: 2.3025 - val_accuracy: 0.0833 - val_loss: 2.3075\n", + "Epoch 6/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.0853 - loss: 2.3073 - val_accuracy: 0.0833 - val_loss: 2.3086\n", + "Epoch 7/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.0883 - loss: 2.3021 - val_accuracy: 0.1042 - val_loss: 2.3075\n", + "Epoch 8/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.1113 - loss: 2.3048 - val_accuracy: 0.1076 - val_loss: 2.3092\n", + "Epoch 9/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.1033 - loss: 2.3030 - val_accuracy: 0.0833 - val_loss: 2.3099\n", + "Epoch 10/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.0918 - loss: 2.3041 - val_accuracy: 0.0833 - val_loss: 2.3106\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from tensorflow.keras.optimizers import SGD\n", + "model.compile(\n", + " loss='categorical_crossentropy', # Loss function\n", + " optimizer=SGD(learning_rate= 0.1,momentum=0.8), # Optimizer\n", + " metrics=['accuracy'] # Metrics to evaluate the model\n", + ")\n", + "history = model.fit(X_train, y_train,\n", + " epochs=10,\n", + " batch_size=32,\n", + " validation_split=0.2)\n", + "\n", + "plt.plot(history.history['loss'], label='Training Loss')\n", + "plt.plot(history.history['val_loss'], label='Validation Loss')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "JJuOI6Kp4jnW" + }, + "source": [ + "\n", + "\n", + "Momentum increases the accuracy with the learning rate = 0.1.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 800 + }, + "id": "bmg2bc1q4puw", + "outputId": "e1c93bef-4433-49ce-df2f-c97dd74ef941" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.1054 - loss: 2.3202 - val_accuracy: 0.0833 - val_loss: 2.3494\n", + "Epoch 2/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.0881 - loss: 2.3457 - val_accuracy: 0.0972 - val_loss: 2.3089\n", + "Epoch 3/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.0966 - loss: 2.3443 - val_accuracy: 0.0938 - val_loss: 2.3455\n", + "Epoch 4/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.0759 - loss: 2.3347 - val_accuracy: 0.0972 - val_loss: 2.3600\n", + "Epoch 5/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.1194 - loss: 2.3298 - val_accuracy: 0.0833 - val_loss: 2.3392\n", + "Epoch 6/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.0822 - loss: 2.3439 - val_accuracy: 0.1215 - val_loss: 2.3266\n", + "Epoch 7/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.0841 - loss: 2.3334 - val_accuracy: 0.1007 - val_loss: 2.3865\n", + "Epoch 8/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.1035 - loss: 2.3407 - val_accuracy: 0.1215 - val_loss: 2.3062\n", + "Epoch 9/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.0809 - loss: 2.3400 - val_accuracy: 0.0833 - val_loss: 2.3562\n", + "Epoch 10/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.0957 - loss: 2.3412 - val_accuracy: 0.0833 - val_loss: 2.3375\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from tensorflow.keras.optimizers import SGD\n", + "model.compile(\n", + " loss='categorical_crossentropy', # Loss function\n", + " optimizer=SGD(learning_rate= 1.0,momentum=0.8), # Optimizer\n", + " metrics=['accuracy'] # Metrics to evaluate the model\n", + ")\n", + "history = model.fit(X_train, y_train,\n", + " epochs=10,\n", + " batch_size=32,\n", + " validation_split=0.2)\n", + "\n", + "plt.plot(history.history['loss'], label='Training Loss')\n", + "plt.plot(history.history['val_loss'], label='Validation Loss')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "QfSFK_aF46PY" + }, + "source": [ + "Momentum does not change the accuracy for the rate = 1.0 significantly.\n", + "In conclusion, when the learning rate are very small or very large, moumentum does not change the results." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "B_KaXXfH2uL7" + }, + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "75CAa-uQmoA6" + }, + "source": [ + "Next, let's try a more advanced optimizer. Adam is likely the most popular optimizer for deep learning. It is an adaptive learning rate optimizer, which means that it automatically adjusts the learning rate based on how the training is going. This can be very useful, as it means that we don't need to manually tune the learning rate. Let's see how it performs on our model.\n", + "\n", + "\n", + "1. Replace the SGD optimizer by the Adam optimizer from keras and run it\n", + " with the default parameters.\n", + "\n", + "2. Add another hidden layer with ReLU activation and 64 neurons. Does it improve the model performance?\n" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 797 + }, + "id": "tgob-Y6zmoA6", + "outputId": "04b20379-cf6f-4b1e-a140-62b4ec2f6d5e" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.1047 - loss: 2.3145 - val_accuracy: 0.0833 - val_loss: 2.3347\n", + "Epoch 2/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.0923 - loss: 2.3139 - val_accuracy: 0.0833 - val_loss: 2.3325\n", + "Epoch 3/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.1068 - loss: 2.3095 - val_accuracy: 0.0833 - val_loss: 2.3306\n", + "Epoch 4/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.1034 - loss: 2.3142 - val_accuracy: 0.0833 - val_loss: 2.3284\n", + "Epoch 5/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.1002 - loss: 2.3138 - val_accuracy: 0.0833 - val_loss: 2.3270\n", + "Epoch 6/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.0953 - loss: 2.3174 - val_accuracy: 0.0833 - val_loss: 2.3251\n", + "Epoch 7/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.0936 - loss: 2.3081 - val_accuracy: 0.0833 - val_loss: 2.3241\n", + "Epoch 8/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.1191 - loss: 2.3048 - val_accuracy: 0.0833 - val_loss: 2.3227\n", + "Epoch 9/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.1291 - loss: 2.2944 - val_accuracy: 0.0833 - val_loss: 2.3217\n", + "Epoch 10/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.1033 - loss: 2.3062 - val_accuracy: 0.0833 - val_loss: 2.3206\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Adam optimizer\n", + "from tensorflow.keras.optimizers import Adam\n", + "\n", + "model.compile(\n", + " loss='categorical_crossentropy', # Loss function\n", + " optimizer=Adam(), # Optimizer\n", + " metrics=['accuracy'] # Metrics to evaluate the model\n", + ")\n", + "history = model.fit(X_train, y_train,\n", + " epochs=10,\n", + " batch_size=32,\n", + " validation_split=0.2)\n", + "\n", + "plt.plot(history.history['loss'], label='Training Loss')\n", + "plt.plot(history.history['val_loss'], label='Validation Loss')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 797 + }, + "id": "4wQwwncCmoA6", + "outputId": "54b2982d-b23d-4b33-de14-b8288f55efa2" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.3067 - loss: 2.3245 - val_accuracy: 0.7674 - val_loss: 0.7658\n", + "Epoch 2/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8213 - loss: 0.5843 - val_accuracy: 0.8750 - val_loss: 0.4086\n", + "Epoch 3/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9302 - loss: 0.2436 - val_accuracy: 0.9062 - val_loss: 0.3077\n", + "Epoch 4/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9370 - loss: 0.1910 - val_accuracy: 0.9132 - val_loss: 0.2556\n", + "Epoch 5/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9670 - loss: 0.1084 - val_accuracy: 0.9236 - val_loss: 0.2281\n", + "Epoch 6/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9849 - loss: 0.0690 - val_accuracy: 0.9340 - val_loss: 0.1940\n", + "Epoch 7/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9895 - loss: 0.0493 - val_accuracy: 0.9306 - val_loss: 0.2076\n", + "Epoch 8/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9939 - loss: 0.0358 - val_accuracy: 0.9410 - val_loss: 0.2053\n", + "Epoch 9/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9949 - loss: 0.0304 - val_accuracy: 0.9375 - val_loss: 0.1864\n", + "Epoch 10/10\n", + "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9990 - loss: 0.0233 - val_accuracy: 0.9306 - val_loss: 0.2002\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Extra hidden layer\n", + "\n", + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import Input, Dense\n", + "from tensorflow.keras.optimizers import Adam\n", + "import matplotlib.pyplot as plt\n", + "\n", + "\n", + "model = Sequential()\n", + "\n", + "# Input layer\n", + "model.add(Input(shape=(X_train.shape[1],))) # Input tensor specifying the shape\n", + "model.add(Dense(32, activation='relu')) # 32 neurons, ReLU activation\n", + "\n", + "# Hidden layer\n", + "model.add(Dense(128, activation='relu')) # 128 neurons, ReLU activation\n", + "# Hidden layer\n", + "model.add(Dense(64, activation='relu')) # 64 neurons, ReLU activation\n", + "\n", + "\n", + "# Output layer\n", + "model.add(Dense(y_train.shape[1], activation='softmax')) # softmax activation\n", + "\n", + "\n", + "model.compile(\n", + " loss='categorical_crossentropy', # Loss function\n", + " optimizer=Adam(), # Optimizer\n", + " metrics=['accuracy'] # Metrics to evaluate the model\n", + ")\n", + "history = model.fit(X_train, y_train,\n", + " epochs=10,\n", + " batch_size=32,\n", + " validation_split=0.2)\n", + "\n", + "plt.plot(history.history['loss'], label='Training Loss')\n", + "plt.plot(history.history['val_loss'], label='Validation Loss')\n", + "plt.legend()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ecZMiG1p6t60" + }, + "source": [ + "Yes, model improves and the accuracy increases." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ydzjt3e0moA6" + }, + "source": [ + "### Exercises: Forward Pass and Generalization\n", + "\n", + "Let's look in more detail at how the model makes predictions on the test set. We will walk through each step of making predictions, examining exactly what's going on.\n", + "\n", + "To start, we will apply our model to the test set, and look at what we get as output:" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "8Ghe4LjOmoA6", + "outputId": "633d50f0-3f36-472e-c2d7-b49be694c686" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 84, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "predictions_tf = model(X_test)\n", + "predictions_tf[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "pC9YET6bmoA6", + "outputId": "87783ae1-fc8c-4ed8-b66b-ebcdae1153bb" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(tensorflow.python.framework.ops.EagerTensor, TensorShape([360, 10]))" + ] + }, + "execution_count": 86, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(predictions_tf), predictions_tf.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jkJPZ1BXmoA6" + }, + "source": [ + "The raw output of the model is a tensor of shape `(360, 10)`. This means that we have 360 samples, and for each sample we have 10 values. Each of these values represents the probability that the sample belongs to a given class. This means that we have 10 probabilities for each sample, and the sum of these probabilities is 1. We can confirm this by summing the probabilities for each sample:" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Gn1ToO4CmoA6", + "outputId": "346ef1a3-90b1-423f-cefc-f5a1f45064f3" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 87, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import tensorflow as tf\n", + "\n", + "tf.reduce_sum(predictions_tf, axis=1)[:5]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "id": "SS7RB_I0moA6" + }, + "source": [ + "...okay, there might be a small rounding error here and there. This is to do with how floating point numbers are represented in computers, and it's not something we need to worry about for now." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "iCPbAK09moA6" + }, + "source": [ + "We can also extract the label with the highest probability using the tensorflow API:" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "UlZXrID2moA6", + "outputId": "244cfaf6-e223-473d-d1f4-88bff74cfbcb" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 88, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "predicted_labels_tf = tf.argmax(predictions_tf, axis=1)\n", + "predicted_labels_tf[:5]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "id": "WHDlXq2OmoA6" + }, + "source": [ + "One helpful aspect of this approach is that we don't just get the prediction, but also a sense of how confident the model is in its prediction. To see this in practice, let's take a look at some of the predictions the model is highly confident about (i.e. a lot of the probability mass is on one class):" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 521 + }, + "id": "SyuhjsZGmoA6", + "outputId": "7d4df802-c17e-4ce5-b8bc-2893735aa342" + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Get the values corresponding to the predicted labels for each sample\n", + "predicted_values_tf = tf.reduce_max(predictions_tf, axis=1)\n", + "\n", + "# Get the indices of the samples with the highest predicted values\n", + "most_confident_indices_tf = tf.argsort(predicted_values_tf, direction='DESCENDING').numpy()[:9]\n", + "\n", + "# Get the 9 most confident samples\n", + "most_confident_samples_tf = X_test[most_confident_indices_tf]\n", + "\n", + "# Get the true labels for the 9 most confident samples\n", + "most_confident_labels_tf = np.argmax(y_test[most_confident_indices_tf], axis=1)\n", + "\n", + "# Plot the 9 most confident samples\n", + "fig, axes = plt.subplots(3, 3, figsize=(6, 6))\n", + "\n", + "for i, ax in enumerate(axes.flat):\n", + " ax.imshow(most_confident_samples_tf[i].reshape(8, 8), cmap=plt.cm.gray_r, interpolation='nearest')\n", + " ax.set_title(f\"{most_confident_labels_tf[i]}\")\n", + "\n", + " # Removing axis labels\n", + " ax.set_xticks([])\n", + " ax.set_yticks([])\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "pGxkZ2oMmoA7" + }, + "source": [ + "## Impact of Initialization\n", + "\n", + "Let's study the impact of a bad initialization when training\n", + "a deep feed forward network.\n", + "\n", + "By default, Keras dense layers use the \"Glorot Uniform\" initialization\n", + "strategy to initialize the weight matrices:\n", + "\n", + "- each weight coefficient is randomly sampled from [-scale, scale]\n", + "- scale is proportional to $\\frac{1}{\\sqrt{n_{in} + n_{out}}}$\n", + "\n", + "This strategy is known to work well to initialize deep neural networks\n", + "with \"tanh\" or \"relu\" activation functions and then trained with\n", + "standard SGD.\n", + "\n", + "To assess the impact of initialization let us plug an alternative init\n", + "scheme into a 2 hidden layers networks with \"tanh\" activations.\n", + "For the sake of the example let's use normal distributed weights\n", + "with a manually adjustable scale (standard deviation) and see the\n", + "impact the scale value:" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": { + "id": "ugoN6bYimoA7" + }, + "outputs": [], + "source": [ + "from tensorflow.keras import initializers\n", + "from tensorflow.keras import optimizers\n", + "\n", + "input_dim = 64\n", + "hidden_dim = 64\n", + "output_dim = 10\n", + "\n", + "normal_init = initializers.TruncatedNormal(stddev=0.01, seed=42)\n", + "\n", + "model = Sequential()\n", + "model.add(Dense(hidden_dim, input_dim=input_dim, activation=\"tanh\",\n", + " kernel_initializer=normal_init))\n", + "model.add(Dense(hidden_dim, activation=\"tanh\",\n", + " kernel_initializer=normal_init))\n", + "model.add(Dense(output_dim, activation=\"softmax\",\n", + " kernel_initializer=normal_init))\n", + "\n", + "model.compile(optimizer=optimizers.SGD(learning_rate=0.1),\n", + " loss='categorical_crossentropy', metrics=['accuracy'])" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "AlSqF1PtmoA7", + "outputId": "3e321ba2-3c3d-4ec4-de62-b0377ef0ff51" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[,\n", + " ,\n", + " ]" + ] + }, + "execution_count": 91, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.layers" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vlQ2N6QnmoA7" + }, + "source": [ + "Let's have a look at the parameters of the first layer after initialization but before any training has happened:" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "j5uwxApZmoA7", + "outputId": "97202be6-6f1b-43d4-f50c-1b110a0d2f5b" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[,\n", + " ]" + ] + }, + "execution_count": 92, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.layers[0].weights" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Xas6bHfHmoA7", + "outputId": "21a0a493-03a6-4486-cd19-3fa688d9cd49" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.00015817, -0.01590087, 0.00103594, ..., 0.00962818,\n", + " 0.00624957, 0.00994726],\n", + " [ 0.0081879 , 0.00756818, -0.00668142, ..., 0.01084459,\n", + " -0.00317478, -0.00549116],\n", + " [-0.00086618, -0.00287623, 0.00391693, ..., 0.00064558,\n", + " -0.00420471, 0.00174566],\n", + " ...,\n", + " [-0.0029006 , -0.0091218 , 0.00804327, ..., -0.01407086,\n", + " 0.00952832, -0.01348555],\n", + " [ 0.00375078, 0.00967842, 0.00098119, ..., -0.00413454,\n", + " 0.01695471, 0.00025196],\n", + " [ 0.00459809, 0.01223094, -0.00213172, ..., 0.01246831,\n", + " -0.00714749, -0.00868595]], dtype=float32)" + ] + }, + "execution_count": 93, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "w = model.layers[0].weights[0].numpy()\n", + "w" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "mczg6tsmmoA7", + "outputId": "5182935f-f1f9-49f0-b1bc-ea4b3fd5c1b5" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "np.float32(0.008835949)" + ] + }, + "execution_count": 94, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "w.std()" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "EZORHpncmoA7", + "outputId": "5a5f97e4-8cf7-4a25-b74c-25d42b049050" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)" + ] + }, + "execution_count": 95, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "b = model.layers[0].weights[1].numpy()\n", + "b" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 919 + }, + "id": "kit95_XfmoA7", + "outputId": "0eca4ef4-2081-4129-a13f-195c36398fdf" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/15\n", + "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.1534 - loss: 2.2976\n", + "Epoch 2/15\n", + "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.4193 - loss: 1.9458\n", + "Epoch 3/15\n", + "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6685 - loss: 1.1422\n", + "Epoch 4/15\n", + "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8198 - loss: 0.6375\n", + "Epoch 5/15\n", + "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9078 - loss: 0.3591\n", + "Epoch 6/15\n", + "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9302 - loss: 0.2520\n", + "Epoch 7/15\n", + "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9750 - loss: 0.1315\n", + "Epoch 8/15\n", + "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9661 - loss: 0.1472\n", + "Epoch 9/15\n", + "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9753 - loss: 0.1129\n", + "Epoch 10/15\n", + "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9769 - loss: 0.0952\n", + "Epoch 11/15\n", + "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9743 - loss: 0.0860\n", + "Epoch 12/15\n", + "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9853 - loss: 0.0607\n", + "Epoch 13/15\n", + "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9890 - loss: 0.0556\n", + "Epoch 14/15\n", + "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9911 - loss: 0.0468\n", + "Epoch 15/15\n", + "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9981 - loss: 0.0298\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "history = model.fit(X_train, y_train, epochs=15, batch_size=32)\n", + "\n", + "plt.figure(figsize=(12, 4))\n", + "plt.plot(history.history['loss'], label=\"Truncated Normal init\")\n", + "plt.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "5QzsTg-mmoA7" + }, + "source": [ + "Once the model has been fit, the weights have been updated and notably the biases are no longer 0:" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "xzsnp2epmoA7", + "outputId": "c07a01bb-5e95-4337-99da-571b6c68173d" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[,\n", + " ]" + ] + }, + "execution_count": 97, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.layers[0].weights" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "pyskm9vkmoA8" + }, + "source": [ + "#### Questions:\n", + "\n", + "- Try the following initialization schemes and see whether\n", + " the SGD algorithm can successfully train the network or\n", + " not:\n", + " \n", + " - a very small e.g. `stddev=1e-3`\n", + " - a larger scale e.g. `stddev=1` or `10`\n", + " - initialize all weights to 0 (constant initialization)\n", + " \n", + "- What do you observe? Can you find an explanation for those\n", + " outcomes?\n", + "\n", + "- Are more advanced solvers such as SGD with momentum or Adam able\n", + " to deal better with such bad initializations?" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 919 + }, + "id": "nH6kGUR1moA8", + "outputId": "3eea222b-a3f3-45d1-bd68-c7224baa76dd" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/15\n", + "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1026 - loss: 2.3034\n", + "Epoch 2/15\n", + "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1060 - loss: 2.3022\n", + "Epoch 3/15\n", + "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.0921 - loss: 2.3027\n", + "Epoch 4/15\n", + "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1080 - loss: 2.3027\n", + "Epoch 5/15\n", + "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1043 - loss: 2.3026\n", + "Epoch 6/15\n", + "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1012 - loss: 2.3024\n", + "Epoch 7/15\n", + "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1022 - loss: 2.3038\n", + "Epoch 8/15\n", + "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.0955 - loss: 2.3030\n", + "Epoch 9/15\n", + "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1211 - loss: 2.3009\n", + "Epoch 10/15\n", + "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1836 - loss: 2.2328\n", + "Epoch 11/15\n", + "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1706 - loss: 1.8751\n", + "Epoch 12/15\n", + "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.2231 - loss: 1.7155\n", + "Epoch 13/15\n", + "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.3187 - loss: 1.5469\n", + "Epoch 14/15\n", + "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.4343 - loss: 1.3092\n", + "Epoch 15/15\n", + "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6877 - loss: 0.9361\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Your code here\n", + "\n", + "\n", + "from tensorflow.keras import initializers\n", + "from tensorflow.keras import optimizers\n", + "\n", + "input_dim = 64\n", + "hidden_dim = 64\n", + "output_dim = 10\n", + "\n", + "normal_init = initializers.TruncatedNormal(stddev=1e-3, seed=42)\n", + "\n", + "model = Sequential()\n", + "model.add(Dense(hidden_dim, input_dim=input_dim, activation=\"tanh\",\n", + " kernel_initializer=normal_init))\n", + "model.add(Dense(hidden_dim, activation=\"tanh\",\n", + " kernel_initializer=normal_init))\n", + "model.add(Dense(output_dim, activation=\"softmax\",\n", + " kernel_initializer=normal_init))\n", + "\n", + "model.compile(optimizer=optimizers.SGD(learning_rate=0.1),\n", + " loss='categorical_crossentropy', metrics=['accuracy'])\n", + "\n", + "history = model.fit(X_train, y_train, epochs=15, batch_size=32)\n", + "\n", + "plt.figure(figsize=(12, 4))\n", + "plt.plot(history.history['loss'], label=\"Truncated Normal init\")\n", + "plt.legend();" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 919 + }, + "id": "CbMtMeR9VnbR", + "outputId": "987a0a5a-02d6-48f1-a0db-43555048d460" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/15\n", + "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1757 - loss: 5.9985\n", + "Epoch 2/15\n", + "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.5313 - loss: 1.6923\n", + "Epoch 3/15\n", + "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6048 - loss: 1.3562\n", + "Epoch 4/15\n", + "\u001b[1m45/45\u001b[0m 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10/15\n", + "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.7482 - loss: 0.7999\n", + "Epoch 11/15\n", + "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.7814 - loss: 0.6744\n", + "Epoch 12/15\n", + "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8195 - loss: 0.5771\n", + "Epoch 13/15\n", + "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8549 - loss: 0.5042\n", + "Epoch 14/15\n", + "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8057 - loss: 0.5599\n", + "Epoch 15/15\n", + "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.7971 - loss: 0.6313\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from tensorflow.keras import initializers\n", + "from tensorflow.keras import optimizers\n", + "\n", + "input_dim = 64\n", + "hidden_dim = 64\n", + "output_dim = 10\n", + "\n", + "normal_init = initializers.TruncatedNormal(stddev=1, seed=42)\n", + "\n", + "model = Sequential()\n", + "model.add(Dense(hidden_dim, input_dim=input_dim, activation=\"tanh\",\n", + " kernel_initializer=normal_init))\n", + "model.add(Dense(hidden_dim, activation=\"tanh\",\n", + " kernel_initializer=normal_init))\n", + "model.add(Dense(output_dim, activation=\"softmax\",\n", + " kernel_initializer=normal_init))\n", + "\n", + "model.compile(optimizer=optimizers.SGD(learning_rate=0.1),\n", + " loss='categorical_crossentropy', metrics=['accuracy'])\n", + "\n", + "history = model.fit(X_train, y_train, epochs=15, batch_size=32)\n", + "\n", + "plt.figure(figsize=(12, 4))\n", + "plt.plot(history.history['loss'], label=\"Truncated Normal init\")\n", + "plt.legend();" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 919 + }, + "id": "ThMWN5MWWyQr", + "outputId": "a0cfd9e5-9e73-4e03-859b-e397b0ff7b7c" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/15\n", + "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1324 - loss: 91.2155\n", + "Epoch 2/15\n", + "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1378 - loss: 65.4903\n", + "Epoch 3/15\n", + "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1178 - loss: 49.3681\n", + "Epoch 4/15\n", + "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1776 - loss: 33.7747\n", + "Epoch 5/15\n", + "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.2317 - loss: 24.8072\n", + "Epoch 6/15\n", + "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1792 - loss: 17.7368\n", + "Epoch 7/15\n", + "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.2278 - loss: 12.1254\n", + "Epoch 8/15\n", + "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.2283 - loss: 10.1799\n", + "Epoch 9/15\n", + "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.2117 - loss: 9.7754\n", + "Epoch 10/15\n", + "\u001b[1m45/45\u001b[0m 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}, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from tensorflow.keras import initializers\n", + "from tensorflow.keras import optimizers\n", + "\n", + "input_dim = 64\n", + "hidden_dim = 64\n", + "output_dim = 10\n", + "\n", + "normal_init = initializers.TruncatedNormal(stddev=10, seed=42)\n", + "\n", + "model = Sequential()\n", + "model.add(Dense(hidden_dim, input_dim=input_dim, activation=\"tanh\",\n", + " kernel_initializer=normal_init))\n", + "model.add(Dense(hidden_dim, activation=\"tanh\",\n", + " kernel_initializer=normal_init))\n", + "model.add(Dense(output_dim, activation=\"softmax\",\n", + " kernel_initializer=normal_init))\n", + "\n", + "model.compile(optimizer=optimizers.SGD(learning_rate=0.1),\n", + " loss='categorical_crossentropy', metrics=['accuracy'])\n", + "\n", + "history = model.fit(X_train, y_train, epochs=15, batch_size=32)\n", + "\n", + "plt.figure(figsize=(12, 4))\n", + "plt.plot(history.history['loss'], label=\"Truncated Normal init\")\n", + "plt.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Ok8ifQe3W4AQ" + }, + "source": [ + "If the standard deviation of the weight initialization is too small or too large, the model’s accuracy decreases." + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 919 + }, + "id": "WtA5s_riWR2F", + "outputId": "306a58a8-5959-491f-bc8e-3a26eb4aed05" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/15\n", + "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.1061 - loss: 2.3034\n", + "Epoch 2/15\n", + "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.0783 - loss: 2.3030\n", + "Epoch 3/15\n", + "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.0961 - loss: 2.3026\n", + "Epoch 4/15\n", + "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1003 - loss: 2.3017\n", + "Epoch 5/15\n", + "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1154 - loss: 2.3020\n", + "Epoch 6/15\n", + "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1145 - loss: 2.3026\n", + "Epoch 7/15\n", + "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1002 - loss: 2.3033\n", + "Epoch 8/15\n", + "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.0952 - loss: 2.3032\n", + "Epoch 9/15\n", + "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1185 - loss: 2.3012\n", + "Epoch 10/15\n", + "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1047 - loss: 2.3015\n", + "Epoch 11/15\n", + "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1075 - loss: 2.3034\n", + "Epoch 12/15\n", + "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1267 - loss: 2.3008\n", + "Epoch 13/15\n", + "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.0915 - loss: 2.3039\n", + "Epoch 14/15\n", + "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1103 - loss: 2.3029\n", + "Epoch 15/15\n", + "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.1087 - loss: 2.3025\n" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from tensorflow.keras import initializers, optimizers\n", + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import Dense\n", + "import matplotlib.pyplot as plt\n", + "\n", + "input_dim = 64\n", + "hidden_dim = 64\n", + "output_dim = 10\n", + "\n", + "zero_init = initializers.Constant(value=0)\n", + "\n", + "model = Sequential()\n", + "model.add(Dense(hidden_dim, input_dim=input_dim, activation=\"tanh\",\n", + " kernel_initializer=zero_init))\n", + "model.add(Dense(hidden_dim, activation=\"tanh\",\n", + " kernel_initializer=zero_init))\n", + "model.add(Dense(output_dim, activation=\"softmax\",\n", + " kernel_initializer=zero_init))\n", + "\n", + "model.compile(optimizer=optimizers.SGD(learning_rate=0.1),\n", + " loss='categorical_crossentropy', metrics=['accuracy'])\n", + "\n", + "history = model.fit(X_train, y_train, epochs=15, batch_size=32)\n", + "\n", + "plt.figure(figsize=(12, 4))\n", + "plt.plot(history.history['loss'], label=\"Zero initialization\")\n", + "plt.legend()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Y2stM-T1YIpu" + }, + "source": [ + "With all-zero weights, all neurons in a layer produce the same output.Accuracy does not improve, even after many epochs." + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "file_extension": ".py", + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.12" + }, + "mimetype": "text/x-python", + "name": "python", + "npconvert_exporter": "python", + "pygments_lexer": "ipython3", + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": false + }, + "version": 3 + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/02_activities/assignments/lab_2.ipynb b/02_activities/assignments/lab_2.ipynb new file mode 100644 index 00000000..e76a4062 --- /dev/null +++ b/02_activities/assignments/lab_2.ipynb @@ -0,0 +1,1328 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Backpropagation in Multilayer Neural Networks\n", + "\n", + "While we will primarily be working with high-level, abstract toolkits like Keras in this course, understanding how backpropagation works is absolutely essential to using neural networks. \n", + "\n", + "In this exercise, we will build our own backpropagation algorithm - working through each step, to ensure that we can follow it." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false + }, + "source": [ + "Just like in Lab 1, we'll be working with the MNIST dataset. We will load it and plot an example:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from sklearn.datasets import load_digits\n", + "\n", + "digits = load_digits()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sample_index = 45\n", + "plt.figure(figsize=(3, 3))\n", + "plt.imshow(digits.images[sample_index], cmap=plt.cm.gray_r,\n", + " interpolation='nearest')\n", + "plt.title(\"image label: %d\" % digits.target[sample_index]);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Preprocessing\n", + "\n", + "Of course, we need to split our data into training and testing sets before we use it, just the same as in Lab 1:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "\n", + "data = np.asarray(digits.data, dtype='float32')\n", + "target = np.asarray(digits.target, dtype='int32')\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(\n", + " data, target, test_size=0.15, random_state=37)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Numpy Implementation\n", + "\n", + "## a) Logistic Regression\n", + "\n", + "In this section we will implement a logistic regression model trainable with SGD using numpy. Here are the objectives:\n", + "\n", + "- Implement the softmax function $\\sigma(\\mathbf{x})_i = \\frac{e^{x_i}}{\\sum_{j=1}^n e^{x_j}}$;\n", + "- Implement the negative log likelihood function $NLL(Y_{true}, Y_{pred}) = - \\sum_{i=1}^{n}{y_{true, i} \\cdot \\log(y_{pred, i})}$;\n", + "- Train a logistic regression model on the MNIST dataset;\n", + "- Evaluate the model on the training and testing sets.\n", + "\n", + "Before we get there, let's write a function that one-hot encodes the class labels:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "def one_hot(n_classes, y):\n", + " return np.eye(n_classes)[y]" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0., 0., 0., 1., 0., 0., 0., 0., 0., 0.])" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "one_hot(n_classes=10, y=3)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n", + " [0., 0., 0., 0., 1., 0., 0., 0., 0., 0.],\n", + " [0., 0., 0., 0., 0., 0., 0., 0., 0., 1.],\n", + " [0., 1., 0., 0., 0., 0., 0., 0., 0., 0.]])" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "one_hot(n_classes=10, y=[0, 4, 9, 1])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### The softmax function\n", + "\n", + "Now we will implement the softmax function. Recall that the softmax function is defined as follows:\n", + "\n", + "$$\n", + "softmax(\\mathbf{x}) = \\frac{1}{\\sum_{i=1}^{n}{e^{x_i}}}\n", + "\\cdot\n", + "\\begin{bmatrix}\n", + " e^{x_1}\\\\\\\\\n", + " e^{x_2}\\\\\\\\\n", + " \\vdots\\\\\\\\\n", + " e^{x_n}\n", + "\\end{bmatrix}\n", + "$$\n", + "\n", + "This is implemented for you using numpy - we want to be able to apply the softmax function to a batch of samples at once, so we will use numpy's vectorized operations to do so.\n", + "\n", + "Our method also handles _stability issues_ that can occur when the values in `X` are very large. We will subtract the maximum value from each row of `X` to avoid overflow in the exponentiation. This isn't part of the softmax function itself, but it's a useful trick to know about." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def softmax(X):\n", + " X_max = np.max(X, axis=-1, keepdims=True)\n", + " exp = np.exp(X - X_max) # Subtract the max to avoid overflow in the exponentiation\n", + " return exp / np.sum(exp, axis=-1, keepdims=True)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's make sure that this works one vector at a time (and check that the components sum to one):" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[9.99662391e-01 3.35349373e-04 2.25956630e-06]\n" + ] + } + ], + "source": [ + "print(softmax([10, 2, -3]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When we are using our model to make predictions, we will want to be able to make predictions for multiple samples at once.\n", + "Let's make sure that our implementation of softmax works for a batch of samples:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[9.99662391e-01 3.35349373e-04 2.25956630e-06]\n", + " [2.47262316e-03 9.97527377e-01 1.38536042e-11]]\n" + ] + } + ], + "source": [ + "X = np.array([[10, 2, -3],\n", + " [-1, 5, -20]])\n", + "print(softmax(X))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Probabilities should sum to 1:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.0\n" + ] + } + ], + "source": [ + "print(np.sum(softmax([10, 2, -3])))" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "softmax of 2 vectors:\n", + "[[9.99662391e-01 3.35349373e-04 2.25956630e-06]\n", + " [2.47262316e-03 9.97527377e-01 1.38536042e-11]]\n" + ] + } + ], + "source": [ + "print(\"softmax of 2 vectors:\")\n", + "X = np.array([[10, 2, -3],\n", + " [-1, 5, -20]])\n", + "print(softmax(X))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The sum of probabilities for each input vector of logits should some to 1:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1. 1.]\n" + ] + } + ], + "source": [ + "print(np.sum(softmax(X), axis=1))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we will implement a function that, given the true one-hot encoded class `Y_true` and some predicted probabilities `Y_pred`, returns the negative log likelihood.\n", + "\n", + "Recall that the negative log likelihood is defined as follows:\n", + "\n", + "$$\n", + "NLL(Y_{true}, Y_{pred}) = - \\sum_{i=1}^{n}{y_{true, i} \\cdot \\log(y_{pred, i})}\n", + "$$\n", + "\n", + "For example, if we have $y_{true} = [1, 0, 0]$ and $y_{pred} = [0.99, 0.01, 0]$, then the negative log likelihood is $- \\log(0.99) \\approx 0.01$." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.01005033585350145\n" + ] + } + ], + "source": [ + "def nll(Y_true, Y_pred):\n", + " Y_true = np.asarray(Y_true)\n", + " Y_pred = np.asarray(Y_pred)\n", + "\n", + " # Ensure Y_pred doesn't have zero probabilities to avoid log(0)\n", + " Y_pred = np.clip(Y_pred, 1e-15, 1 - 1e-15)\n", + "\n", + " # Calculate negative log likelihood\n", + " loss = -np.sum(Y_true * np.log(Y_pred))\n", + " return loss\n", + "\n", + "# Make sure that it works for a simple sample at a time\n", + "print(nll([1, 0, 0], [.99, 0.01, 0]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We should see a very high value for this negative log likelihood, since the model is very confident that the third class is the correct one, but the true class is the first one:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4.605170185988091\n" + ] + } + ], + "source": [ + "print(nll([1, 0, 0], [0.01, 0.01, .98]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Make sure that your implementation can compute the average negative log likelihood of a group of predictions: `Y_pred` and `Y_true` can therefore be past as 2D arrays:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.010050335853503449\n" + ] + } + ], + "source": [ + "# Check that the average NLL of the following 3 almost perfect\n", + "# predictions is close to 0\n", + "Y_true = np.array([[0, 1, 0],\n", + " [1, 0, 0],\n", + " [0, 0, 1]])\n", + "\n", + "Y_pred = np.array([[0, 1, 0],\n", + " [.99, 0.01, 0],\n", + " [0, 0, 1]])\n", + "\n", + "print(nll(Y_true, Y_pred))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false + }, + "source": [ + "Now that we have our softmax and negative log likelihood functions, we can implement a logistic regression model. \n", + "In this section, we have built the model for you, but you will need to complete a few key parts.\n", + "\n", + "**YOUR TURN:**\n", + "\n", + "1. Implement the `forward` method of the `LogisticRegression` class. This method should take in a batch of samples `X` and return the predicted probabilities for each class. You should use the softmax function that we implemented earlier.\n", + "2. Implement the `loss` method of the `LogisticRegression` class. This method take in the samples `X` and the true values `y` and return the average negative log likelihood of the predictions." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "class LogisticRegression:\n", + "\n", + " def __init__(self, input_size, output_size):\n", + " # Initialize the weights and biases with random numbers\n", + " self.W = np.random.uniform(size=(input_size, output_size),\n", + " high=0.1, low=-0.1)\n", + " self.b = np.random.uniform(size=output_size,\n", + " high=0.1, low=-0.1)\n", + " \n", + " # Store the input size and output size\n", + " self.output_size = output_size\n", + " self.input_size = input_size\n", + " \n", + " def forward(self, X):\n", + " # Compute the linear combination of the input and weights\n", + " Z = np.dot(X, self.W)+ self.b\n", + " return softmax(Z)\n", + " \n", + " def predict(self, X):\n", + " # Return the most probable class for each sample in X\n", + " if len(X.shape) == 1:\n", + " return np.argmax(self.forward(X))\n", + " else:\n", + " return np.argmax(self.forward(X), axis=1)\n", + " \n", + " def loss(self, X, y):\n", + " # Compute the negative log likelihood over the data provided\n", + " y_onehot = one_hot(self.output_size, y.astype(int))\n", + " y_pred = self.forward(X)\n", + " return nll(y_onehot, y_pred)\n", + "\n", + " def grad_loss(self, X, y_true, y_pred):\n", + " # Compute the gradient of the loss with respect to W and b for a single sample (X, y_true)\n", + " # y_pred is the output of the forward pass\n", + " \n", + " # Gradient with respect to weights\n", + " grad_W = np.dot(X.T, (y_pred - y_true))\n", + " \n", + " # Gradient with respect to biases\n", + " grad_b = np.sum(y_pred - y_true, axis=0)\n", + " \n", + " return grad_W, grad_b\n", + " \n", + "# Raise an exception if you try to run this cell without having implemented the LogisticRegression class\n", + "model = LogisticRegression(input_size=64, output_size=10)\n", + "try:\n", + " assert(model.forward(np.zeros((1, 64))).shape == (1, 10))\n", + " assert(model.loss(np.zeros((1, 64)), np.zeros(1)) > 0)\n", + "except:\n", + " raise NotImplementedError(\"You need to correctly implement the LogisticRegression class.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1, 10)" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.forward(np.zeros((1,64))).shape\n" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Build a model and test its forward inference\n", + "n_features = X_train.shape[1]\n", + "n_classes = len(np.unique(y_train))\n", + "lr = LogisticRegression(n_features, n_classes)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false + }, + "source": [ + "We can evaluate the model on an example, visualizing the prediction probabilities:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def plot_prediction(model, sample_idx=0, classes=range(10)):\n", + " fig, (ax0, ax1) = plt.subplots(nrows=1, ncols=2, figsize=(10, 4))\n", + "\n", + " ax0.imshow(X_test[sample_idx:sample_idx+1].reshape(8, 8),\n", + " cmap=plt.cm.gray_r, interpolation='nearest')\n", + " ax0.set_title(\"True image label: %d\" % y_test[sample_idx]);\n", + "\n", + "\n", + " ax1.bar(classes, one_hot(len(classes), y_test[sample_idx]), label='true')\n", + " ax1.bar(classes, model.forward(X_test[sample_idx]), label='prediction', color=\"red\")\n", + " ax1.set_xticks(classes)\n", + " prediction = model.predict(X_test[sample_idx])\n", + " ax1.set_title('Output probabilities (prediction: %d)'\n", + " % prediction)\n", + " ax1.set_xlabel('Digit class')\n", + " ax1.legend()\n", + "\n", + "plot_prediction(lr, sample_idx=0)\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false + }, + "source": [ + "Now it's time to start training! We will train for a single epoch, and then evaluate the model on the training and testing sets. Read through the following and make sure that you understand what we are doing here." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average NLL over the last 100 samples at step 100: 325\n", + "Average NLL over the last 100 samples at step 200: 412\n", + "Average NLL over the last 100 samples at step 300: 403\n", + "Average NLL over the last 100 samples at step 400: 242\n", + "Average NLL over the last 100 samples at step 500: 190\n", + "Average NLL over the last 100 samples at step 600: 248\n", + "Average NLL over the last 100 samples at step 700: 92\n", + "Average NLL over the last 100 samples at step 800: 60\n", + "Average NLL over the last 100 samples at step 900: 136\n", + "Average NLL over the last 100 samples at step 1000: 215\n", + "Average NLL over the last 100 samples at step 1100: 153\n", + "Average NLL over the last 100 samples at step 1200: 73\n", + "Average NLL over the last 100 samples at step 1300: 173\n", + "Average NLL over the last 100 samples at step 1400: 93\n", + "Average NLL over the last 100 samples at step 1500: 129\n" + ] + } + ], + "source": [ + "lr = LogisticRegression(input_size=X_train.shape[1], output_size=10)\n", + "\n", + "learning_rate = 0.01\n", + "\n", + "for i in range(len(X_train)):\n", + " # Get the current sample and corresponding label\n", + " x = X_train[i:i+1] # Reshape to keep the batch dimension\n", + " y = y_train[i:i+1] # Reshape to keep the batch dimension\n", + "\n", + " # Compute the forward pass and the gradient of the loss with respect to W and b\n", + " y_pred = lr.forward(x)\n", + " grad_W, grad_b = lr.grad_loss(x, one_hot(lr.output_size, y), y_pred)\n", + "\n", + " # Update the weights and biases\n", + " lr.W -= learning_rate * grad_W\n", + " lr.b -= learning_rate * grad_b\n", + "\n", + " # Print the average negative log likelihood every 100 steps (avoid empty slice at i==0)\n", + " if i > 0 and i % 100 == 0:\n", + " avg_nll = lr.loss(X_train[max(0, i-100):i], y_train[max(0, i-100):i])\n", + " print(\"Average NLL over the last 100 samples at step %d: %0.f\" % (i, avg_nll))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false + }, + "source": [ + "Evaluate the trained model on the first example:" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_prediction(lr, sample_idx=0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## b) Feedforward Multilayer\n", + "\n", + "The objective of this section is to implement the backpropagation algorithm (SGD with the chain rule) on a single layer neural network using the sigmoid activation function.\n", + "\n", + "Now it's your turn to\n", + "\n", + "- Implement the `sigmoid` and its element-wise derivative `dsigmoid` functions:\n", + "\n", + "$$\n", + "sigmoid(x) = \\frac{1}{1 + e^{-x}}\n", + "$$\n", + "\n", + "$$\n", + "dsigmoid(x) = sigmoid(x) \\cdot (1 - sigmoid(x))\n", + "$$\n", + "\n", + "Remember that you can use your `sigmoid` function inside your `dsigmoid` function.\n", + "\n", + "Just like with our softmax function, we also want to make sure that we don't run into stability issues with our sigmoid function. We will use `np.clip` to ensure that the input to the sigmoid function is not too large or too small." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def sigmoid(X):\n", + " X = np.clip(X, -500, 500)\n", + " return 1 / (1 + np.exp(-X))\n", + " \n", + "\n", + "\n", + "def dsigmoid(X):\n", + " s = sigmoid (X)\n", + " d_s = s* (1-s)\n", + " return d_s\n", + "\n", + "\n", + "x = np.linspace(-5, 5, 100)\n", + "plt.plot(x, sigmoid(x), label='sigmoid')\n", + "plt.plot(x, dsigmoid(x), label='dsigmoid')\n", + "plt.legend(loc='best');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now it's your turn to complete the neural network code, so that we can train it on the MNIST dataset.\n", + "\n", + "Some parts have been completed for you already. Often, you'll be able to refer back to the code from the previous section to help you complete the code in this section." + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "class NeuralNet():\n", + " \"\"\"MLP with 1 hidden layer with a sigmoid activation\"\"\"\n", + "\n", + " def __init__(self, input_size, hidden_size, output_size):\n", + " # Initializes the weights with random numbers\n", + " self.W_h = np.random.uniform(size=(input_size, hidden_size),\n", + " high=0.1, low=-0.1)\n", + " self.b_h = np.random.uniform(size=hidden_size,\n", + " high=0.1, low=-0.1)\n", + " self.W_o = np.random.uniform(size=(hidden_size, output_size),\n", + " high=0.1, low=-0.1)\n", + " self.b_o = np.random.uniform(size=output_size,\n", + " high=0.1, low=-0.1)\n", + "\n", + " # Store the input size, hidden size and output size\n", + " self.input_size = input_size\n", + " self.hidden_size = hidden_size\n", + " self.output_size = output_size\n", + "\n", + " def forward_hidden(self, X):\n", + " # Compute the linear combination of the input and weights\n", + " self.Z_h = np.dot(X, self.W_h)+ self.b_h\n", + " self.H = sigmoid(self.Z_h) \n", + " # Apply the sigmoid activation function\n", + " return self.H \n", + "\n", + " def forward_output(self, H):\n", + " # Compute the linear combination of the hidden layer activation and weights\n", + " self.Z_o = np.dot(H, self.W_o)+ self.b_o\n", + " self.Y = sigmoid(self.Z_o)\n", + " # Apply the sigmoid activation function\n", + " return self.Y\n", + "\n", + " def forward(self, X):\n", + " # Compute the forward activations of the hidden and output layers\n", + " H = self.forward_hidden(X)\n", + " Y = self.forward_output(H)\n", + "\n", + " return Y\n", + "\n", + " def loss(self, X, y):\n", + " y = y.astype(int)\n", + " return 1.0\n", + "\n", + "\n", + " def grad_loss(self, X, y_true):\n", + " y_true = one_hot(self.output_size, y_true)\n", + " y_pred = self.forward(X)\n", + "\n", + " # Compute the error at the output layer\n", + " error_o = y_pred - y_true\n", + "\n", + " # Compute the gradient of the loss with respect to W_o and b_o\n", + " grad_W_o = np.dot(self.Z_h.T, error_o)\n", + " grad_b_o = np.sum(error_o, axis=0)\n", + "\n", + " # Compute the error at the hidden layer\n", + " error_h = np.dot(error_o, self.W_o.T) * dsigmoid(self.Z_h)\n", + "\n", + " # Compute the gradient of the loss with respect to W_h and b_h\n", + " grad_W_h = np.dot(X.T, error_h)\n", + " grad_b_h = np.sum(error_h, axis=0)\n", + "\n", + " return {\"W_h\": grad_W_h, \"b_h\": grad_b_h, \"W_o\": grad_W_o, \"b_o\": grad_b_o}\n", + "\n", + " def train(self, x, y, learning_rate):\n", + " # Ensure x is 2D\n", + " x = x[np.newaxis, :]\n", + " # Compute the gradient for the sample and update the weights\n", + " grads = self.grad_loss(x, y)\n", + " \n", + " self.W_h -= learning_rate * grads[\"W_h\"]\n", + " self.b_h -= learning_rate * grads[\"b_h\"]\n", + " self.W_o -= learning_rate * grads[\"W_o\"]\n", + " self.b_o -= learning_rate * grads[\"b_o\"]\n", + " \n", + " def predict(self, X):\n", + " if len(X.shape) == 1:\n", + " return np.argmax(self.forward(X))\n", + " else:\n", + " return np.argmax(self.forward(X), axis=1)\n", + "\n", + " def accuracy(self, X, y):\n", + " y_preds = np.argmax(self.forward(X), axis=1)\n", + " return np.mean(y_preds == y)\n", + " \n", + "# Raise an exception if you try to run this cell without having implemented the NeuralNet class\n", + "nn = NeuralNet(input_size=64, hidden_size=32, output_size=10)\n", + "try:\n", + " assert(nn.forward(np.zeros((1, 64))).shape == (1, 10))\n", + " assert(nn.loss(np.zeros((1, 64)), np.zeros(1)) > 0)\n", + "except:\n", + " raise NotImplementedError(\"You need to correctly implement the NeuralNet class.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false + }, + "source": [ + "Once the code is written, we can test our model on a single sample:" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "n_hidden = 10\n", + "model = NeuralNet(n_features, n_hidden, n_classes)" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1.0" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.loss(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.13817943680419123" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.accuracy(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_prediction(model, sample_idx=5)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false + }, + "source": [ + "And now it's time to train!" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Random init: train loss: 1.00000, train acc: 0.138, test acc: 0.111\n", + "Epoch #1, train loss: 1.00000, train acc: 0.181, test acc: 0.156\n", + "Epoch #2, train loss: 1.00000, train acc: 0.453, test acc: 0.419\n", + "Epoch #3, train loss: 1.00000, train acc: 0.680, test acc: 0.656\n", + "Epoch #4, train loss: 1.00000, train acc: 0.737, test acc: 0.700\n", + "Epoch #5, train loss: 1.00000, train acc: 0.766, test acc: 0.719\n", + "Epoch #6, train loss: 1.00000, train acc: 0.816, test acc: 0.759\n", + "Epoch #7, train loss: 1.00000, train acc: 0.842, test acc: 0.789\n", + "Epoch #8, train loss: 1.00000, train acc: 0.869, test acc: 0.822\n", + "Epoch #9, train loss: 1.00000, train acc: 0.879, test acc: 0.863\n", + "Epoch #10, train loss: 1.00000, train acc: 0.892, test acc: 0.870\n", + "Epoch #11, train loss: 1.00000, train acc: 0.898, test acc: 0.881\n", + "Epoch #12, train loss: 1.00000, train acc: 0.893, test acc: 0.874\n", + "Epoch #13, train loss: 1.00000, train acc: 0.902, test acc: 0.878\n", + "Epoch #14, train loss: 1.00000, train acc: 0.908, test acc: 0.889\n", + "Epoch #15, train loss: 1.00000, train acc: 0.914, test acc: 0.900\n" + ] + } + ], + "source": [ + "losses, accuracies, accuracies_test = [], [], []\n", + "losses.append(model.loss(X_train, y_train))\n", + "accuracies.append(model.accuracy(X_train, y_train))\n", + "accuracies_test.append(model.accuracy(X_test, y_test))\n", + "\n", + "print(\"Random init: train loss: %0.5f, train acc: %0.3f, test acc: %0.3f\"\n", + " % (losses[-1], accuracies[-1], accuracies_test[-1]))\n", + "\n", + "for epoch in range(15):\n", + " for i, (x, y) in enumerate(zip(X_train, y_train)):\n", + " model.train(x, y, 0.001)\n", + "\n", + " losses.append(model.loss(X_train, y_train))\n", + " accuracies.append(model.accuracy(X_train, y_train))\n", + " accuracies_test.append(model.accuracy(X_test, y_test))\n", + " print(\"Epoch #%d, train loss: %0.5f, train acc: %0.3f, test acc: %0.3f\"\n", + " % (epoch + 1, losses[-1], accuracies[-1], accuracies_test[-1]))" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(losses)\n", + "plt.title(\"Training loss\");" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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u9AgyQ0tUiG/t88YhJjLYh0Afz04xDwUAW0WD8NIgtBQebBBeTsPTD7rFQreY2j/rHn0gJAYCIsAJO3ufDYUbEREXY6uxc+h4OXvzStibV8bevBL25ZmBJquwHOMM80rDCagnm2ga3GBOR4hf/TyPgDOcgGq3G5TZ6gNPSUV1ffipqqakNgw1CkSVNea5zbTX9R5V2OxU2MzeGHc36B7o4wgnPYJ9iWrwPDLIl6gQX7r5eeHe0XtaTmQrb9Djsr8+tNQ9SrJP/xle/ieEllgztHTrYz4P6G5Zj0xLKdyIiHRCdrtBVmF5bWhpEGKOlXEgv+yUPS5Bvp7EdQ+gT3gAYQHeJ71jpu7RnvNf3N3dHENKkU74vKpquyPwlFXVEOLnRfdAb8cwVadTVWqGl8KDTXtfCg5Cac7pP8M78ITQckLvi39Yhw8vp6NwIyLSQRmGQW5JJXtzS9l3rJQ9eaXsyytlb14p+4+VUXmKW4N9vdzpGx5AXEQAfcMD6Ne9/hEW4N15hlHOkrenO96e3pavu9JilSUn9Lbsbzxptyzv9J/hHdRg2KhPg+BS+9ovtNOHl9NRuBERsZBhGBSW29hbG1r25dWGmGOl7M0tdcxBaY6XhxuxYf6O0NK3QYCJDPK1bkjFMKCioOlk1Lov6OIs8AqAgHBzfoZ/d/O5f3dzyOPENu8A1/kyrihqOlTU8FGef/rP8Ak5obflhN4X326u8/fVSgo3IiJtxDAMjpfZOFJYztHCCo4UVtT/WVTueF12igDj7ga9Q/3N4BLu7wgxcd0D6dnN15rhFcOA8uMnDIuc8IVdVXyaDzkGhQda9vM8fWsDT7gZfhwhqMHzujAUEGEOuzjry90woKbKHA6ylZlzWqpKzT9tpVBV1vxzx3ll5p9FWWaoKT9++p/p263pnJeGw0h+3ZxzbS5M4UZEpBXqbik2w0o5R4sahpf6MHOqoaOGooJ9GwSX+l6YmDA/fDw92vhqTmAYUHas6ZBIwztrqkpO/zkBEc1PSg3uaX75l+VBaS6U5pk/rzSvQdsx83l1hfkorJ1n0hIePrWBJ7xxT5B/mLkGiyOolJ08nDR8bjhvAT4A/MIa9Lb0OeHvJwZ8Q5z787oghRsRkRPU2A3ySiprw0p54x6XwgqyCsvJLqpo8UaC3QO9iQrxJSrYj57dzDtxomtfR4eYr3292inAGIb55V6WZ4aJE4dF6oZMbGWn/6zAyGYmptZ+WYf0Bm9/59bqCD95zbQdM0NRdTnUVELRYfPhTO5e5jV5BYCXX+3z2od3bZvjtX/j54FR9UNIPkHOrUuaULgRkS7NbjdIP1TAf7dm89OefLIKyskurmzR+i5ubtQu1uZHdHCD0BLiS3SIGVx6BPu0bc+LYZi9KKf98m/QO1Jd3rLPDopu5m6a2Prw4uXXdtcF5l+wT6D5CO3bsvdUlTYOPA17h8rzwd3TDCfe/rVhpC6oBNSGEb/GQaXhcw+vNr1ccR6FGxHpcipsNazdnccXW3JYvTWbnOLKJud4uLsRWbvKbHSI3wnBxZeoED96BPng5ew5L4YBlUWNw0hp7glf1nVttcdrmtZ/Wp6+5lBNSO9mJqf2geBe4OXr3GtrD94B5iO0j9WViIUUbkSkS8gvreLLbTn8d0s2a3bmNprEG+jjySWDI7h8cA/iIgLo2c2P7oE+bb9UfnUVZC6EzMVQcrQ+vNS0fOl/B08/c25Jw7uO/Gsn2DaaeFs7D8WZk25FOhiFGxFxWfvySvliSzZfbM0mZV8+DUeaokN8uSI+knEJkYyNC2vfSbvlBbDhn/DT/JMvd+8V0MwdQeHN3yodUHu7tIgACjci4kLsdoO0QwX8d0s2X2zJZmdO4zt64qODGZcQybj4SIb1Cm7/hewKD8GP/wcb3qm/VTooGsbcBVFJjcNKW89nEXFhCjci0qlV2Gr4flce/92azX+35pDbYP6Mp7sbY+PCGBcfyc/iI4kJO8u7d1rryCZY+zJsXgz22o0geyTA+Q/AsOvAs5OsnivSSSjciEinUzd/5ostR1mzI49yW/38maDa+TPjEiK5dFAPQvwtusPFMGD3l7B2Huz5ur6938Vw/kMw4Gea8yLSRhRuRKRT2JtX6hhuStl/8vkz58aF4+1p4aaI1VVmD83alyE702xz84Ch08yemp7J1tUm0kUo3IhIh2S3G6QeNNef+WJLNrtOmD+TEB3MFQmRXJkQydCeFsyfOVFFkTlJ+Mf/M/dOAnNS8Khfwth7dWuySDtSuBGRDqOksprvd+Xx1bYc/rs1h7yS5ufPXJEQSe9Qi+bPnKjwMPxUO0m4sshsC4yEsffAObebOzCLSLtSuBERyxiGwc6cEr7alsPX23NJ2Z/faEuDRvNnBvcgxK8DrRB7NNMcespcWD9JOGKIOfQ0/Hrw9LG2PpEuTOFGRNpVXe/M19tz+WZ7DlmFFY2O9wn359JBEVyREMnYfhbPnzmRYZiTg9fOMycL1+l7kRlqBowD9w5Ur0gXpXAjIm3KMAx2ZJfw9fbme2e8Pd05Ly6cSwdHcOngHvTr3gEXo6uxweZPzFBzNMNsc3OHhKvNUNNrpKXliUhjCjci4nTFFTa+33WMb3bk8M323JP2zlw6uAfnxoXj592OqwOfiYoi2Pgvc5Jw0SGzzcsfRtwK5/265Zs5iki7UrgRkbPWsHfmq+05pOw7TnWDe7V9PN05t6P3zjRUlGVujZDyT6gsNNsCesDYu+GcO8A/zNLyROTUFG5EpFUa9s58vT2XIyf0zvQN9+fSwT24ZHAE58WF4+vVQXtnGsreYk4SzvgY7DazLXygOfSUeGPn3CVbpAtSuBGRFjEMg+3ZxXy9PZevXaF3pk5FEWz5FNLegwNr69v7XGCGmoHjNUlYpJNRuBGRkzJ7Z2rvbNrhIr0zAPYa2PsNpL0PW/8D1eVmu5s7xE+G8x+E3udYW6OItJrCjYg0caSwnBc+387StKwmvTPn9Q93TAbu21l6Z+rk7TR7aDZ9CEWH69u7D4Kk6ebQU0gv6+oTEadQuBERh9LKal5fs4c31uymwmYH6ntnLh0cwbmdqXemTvlxyFwM6e/DofX17b4h5o7cyTdBr1HaxFLEhVgebux2O88//zzz588nPz+fCy64gDfeeIOYmJhmzy8tLWXu3Lm8//77ZGVl0bt3bx5++GHuvvvudq5cxHXY7QYLNx7ifz/fTk6xueXB6L6hPDkpgeSYbtYW1xo11bDnK0j7N2xbDjW12zi4ecCAKyB5OgyaoAnCIi7K8nBzzz33sGzZMv75z38SGxvL3XffzR133MGqVauaPf/6669n9+7dvPbaawwaNIjPPvuMe++9l7i4OK644op2rl6k8/th9zGeXbaFzVnmvkixYf48PmEIVw2Lsn4zyjOVvQXS34NNH0FJdn17jwSzh2b4DRAUaV19ItIu3AzDME5/WttYtWoVEydOJCUlheTkZAA+++wzpkyZQllZGb6+jf9fVWlpKYGBgXz00Udcf/31jvbQ0FCeffZZ7rvvvtP+zKKiIkJCQigsLCQ4ONip1yPSmezNK2XO8q2s2mKGgCBfTx68fCC3nd8HH89ONPRUlg8ZC81emiNp9e1+YZB4gzmXJjpJw04indyZfH9b2nPz4osvMm3aNEewAQgLC8MwDHJzc5sMTQUEBJCQkMCyZcv4+c9/DsALL7yAl5cX11xzTbM/o7KyksrK+p2Fi4qKnH8hIp1IQVkV81bv4l8/7KPabuDh7sbNY2N56GcDCQ/sJJs91thg5xdmoNnxef2aNO6eMOgqM9AMvBI8va2tU0QsYVm4KSkpYfXq1SxYsKBRe05ODgDdunVr9n0vvfQS119/Pe+++y6GYXDFFVewbt06oqOjmz1/zpw5PPPMM06tXaQzstXYefeH/fx99U4Ky80wcNngCJ6cFM+AHkEWV9dCRzaZdztlfAxlefXt0UmQdBMMvw4CultXn4h0CJaFm4yMDGpqahg5svGGcykpKcTFxREU1PSX7bx583jhhRd47bXXGD58OGvXruXhhx9m586d9O3bt9mf8/jjj/O73/3O8bqoqOikk5VFXJFhGPx3aw5zlm9lT14pAIMjg3hyUjwXD4qwuLoWKMkx59Ckvw/ZmfXtAT3MYafkmyByqHX1iUiHY1m4OX78OAAREY1/uS5evJjJkyc3Of/o0aM8/PDDLFu2jHHjxgGQmJjImjVreOmllxxtJ/Lx8cHHp5N0tYs42easQp5btpW1u48B0D3Qm9+NG8wN5/TG06MDr7pbXQnbV5iBZucXYNSY7R7eMHiiGWj6/ww8LL8nQkQ6IMt+M9SFmvz8fEJDQwFYuHAhO3fuZOnSpU3O3717NzabjcGDBzdqP3To0EmHsES6qpyiCv531XY+3nAIwwBvT3fuvLAfMy/tT5Cvl9XlNa/GBvu+hc1LYOtSc32aOr1GmYFm6DXatFJETsuycJOUlERMTAyzZs3iqaee4qeffuLuu+9m9uzZDBgwgOLiYsaMGcOcOXO4+uqrGTZsGGFhYfz+97/nmWeewTAM5s+fz9q1a1mxYoVVlyHSoZRX1fDWt3v4v292U1Zl9nZMTurJY1cNpneov8XVNaPGZm6DsHkJbFsG5fn1x4KizRWDk2+CiMEn/QgRkRNZFm68vb1ZtGgRM2fOZPjw4fTv35/58+dzyy23AJCZmcm2bdsc82NCQkJYvnw5jz32GOeeey4+Pj6MGjWKNWvWcP7551t1GSIdgt1u8Gn6YZ5fud2x/9OI2G78cVICo/qEWlzdCaqrGgSaz6CioP6Yf7i5t1PC1dDvYnDvRLeki0iHYek6N1bQOjfiatbvy+fZz7aQfqgQgF7d/HhswhAmJ0Z3nEX4qqtgz9ewZUltoCmsPxYQUR9o+lygeTQi0qxOs86NiLTegWNlzF25leUZRwEI9PHk15f15/YL+nWM/Z+qK2H3V7WBZjlUNgw0PcxAM/RqM9Coh0ZEnEjhRqSTKaqw8eqXu3j7+31U1dhxd4MbR8fyu3GDiAiy+M5AW4W5p9PmJebdTg0DTWAkxE8xA03seQo0ItJmFG5EOonqGjvvrzvAS//dSX5pFQAXDezOk5PiGRJl4RCrrQJ2r64PNFXF9ceCousDTcxYBRoRaRcKNyIdnGEYfL0jl+eWbWVXTgkA/SMC+OOkBC4dHGHNvBpbOexabQ45bV95QqDpCQlTzUfMWHDvwOvpiIhLUrgR6aB25RSzMvMoKzKPOnbsDvX34nfjBvGLMbF4tfcifLZyc0G9LUvM/ZyqSuqPBfeqDTRXQ+/RCjQiYimFG5EOwjAMNmcVsTLzKCs3H3X00gB4e7gz44K+3HfZAEL82nERvqoy2LkKtnxqBhpbaf2xkJj6QNNrlAKNiHQYCjciFrLbDVIPHncEmoP55Y5jXh5uXDigO1cNi+KK+Mj23bE7Zxv88ApkLgJbWX17SCwMnQoJ06DXSOgot5qLiDSgcCPSzqpr7Py0N5+VmUf5fPNRcoorHcd8vdy5dFAPJgyP4rIhPQhuz60SDAP2fw/fz4Odn9e3d+tjTghOmAo9FWhEpONTuBFpB5XVNXy/K48VGUf5Yms2BWU2x7EgH09+Ft+Dq4ZFccmgHvh5t/MdRTXV5l5Oa1+GrI21jW4Q/3M4735zUrACjYh0Igo3Im2ktLKab3bksiLzKF9ty6GkstpxLCzAmysTIhk/LIoL+nfH29OC+SpVpZC6AH54FQr2m22evuZeTufdD+H9278mEREnULgRcaLCchurt2azMvMo3+zIpbLa7jgWFezL+KGRXDUsmtF9Q/Fs77ud6pTkwE+vw/q36vd18guDMXfDmLsgoLs1dYmIOInCjchZyiupZNXmbFZuPsraXXlU2+u3a4sN82fCsCiuGhZFUu9uuLtbOLyTu8OcJJz+AdTUzvMJ7Qfn3w9JN4F3B9w1XESkFRRuRFohq6Cczzeba9Ck7MunQZ5hUGQgVw2L5qqhUcRHB1m7eaVhwIEfzPk025fXt/ceDec/CEMmadVgEXE5CjciLZRdVMHijYdZufko6QcLGh1L7B3C+KFmD03/iEBrCmzIXgNb/2OGmsMptY1uMHgiXPCgJgmLiEtTuBE5jRq7wb9+2Mf/fr6d0qoawMwF5/QJ5aph0YwfGknv0A4ypFNVBmn/NicJH99rtnn4QPJ0c5Jw94HW1ici0g4UbkROYdvRIv6wKIO02p6apJhuXD+qN1cOjaRHkK+1xTVUkgvr34R1b0J5vtnmFwqj7zQnCgf2sLY+EZF2pHAj0owKWw0vf7mT17/ZQ7XdIMjHk8cmDOGmMbHWTgo+Ud6u2knC70N1hdnWrY/ZSzPiZvAOsLY+ERELKNyInOCH3cd44pMM9uaZ+yiNHxrJM1OGERXSgXpqDvwEa+fBtmVA7WzmniPN+TTxUzRJWES6NIUbkVqFZTZmL9/KhykHAegR5MOsqcO4aliUxZXVsteYdzx9Pw8OratvH3SVeedTn/M1SVhEBIUbEQzDYFnGEf68dAt5Jeb6LzePjeWxCUPad2+nkykvMDew/OFVyN9ttnl4Q+KNcP4DEDHY0vJERDoahRvp0rIKyvnTkkxWb8sBoH9EAHOuSWRMvzBrCqoqg6Ob4PBGyEo193o6tqv+uG9I/SThoA7SoyQi0sEo3EiXVGM3ePeHfbxQe3u3l4cbMy8dwH2X9cfHs53mq9TYIHuzGWDqwkzOVjBqmp4bPhBG3wEjbgWfDrCOjohIB6ZwI13O9qPF/GHxJlIPFAAwqk8oc64ZzqDIoLb7oXY7HNtZG2Jqw8zRjPptEBoKjDQnB/caaf7ZcwQEhLddbSIiLkbhRrqMClsNr3y5i/nf7KbabhDo48ljVw3m5rF9nHt7t2FAwYHGPTJZaVBV3PRc3xAzvDQMM8E9NTFYROQsKNxIl/DjnmM8sTiDPbW3d49LiGTW1KFEh/id/YeX5DTukclKhbK8pud5+kF0Un2I6TUSwuIUZEREnEzhRlxaYbmNuSu28v468/buiCAfZk0ZylXDolq3oWVFoRleHGEmFYoONT3P3RMihzbukYkYAh76n5yISFvTb1pxSYZhsCLzKE8v3UxusTmvZfqYWP4wYQghfq24vXvvt7Dmedi7ppmDbtB9UOMemchh4NWBFv0TEelCFG7E5RwpLOdPSzbz363ZAMRFBDBn2nDGxp3hpFzDMMPMN3+B/d/Xt3eLbdwjE50EvsFOvAIRETkbCjfiMux2gwU/7ef5ldspqaw2b+++pD+/vmwAvl5ncHu3YcDeb+DruXDgB7PNwxtG3gYXPGSGGxER6bAUbsQl7Mgu5g+LNrGx9vbukbHdmHtt4pnd3m0YsOcr+PovcPBHs83DG0b+Ei78LYT0cn7hIiLidAo30qlV2Gp47atd/N83u7HVmLd3P3rVYG45k9u7DQN2f2n21NTt2eThA6NmwIW/MW/NFhGRTkPhRjqtdXvz+cPiTezJNW/vviI+kv+5+gxu7zYM2LUavpkLh9abbZ6+MOpX5vBTcHQbVS4iIm1J4UY6HfP27m28v+4AYN7e/cyUoUxo6e3dhgE7vzBDzeENZpunL5xzuxlqtGeTiEinpnAjnYphGNz2j59IP1QIwPQxMfzhqnhC/Ftwe7dhwM5V5vBT1kazzdPP3LPp/AchKLINKxcRkfaicCOdyvp9x0k/VIiflwdv/2o057bk9m7DgB0rzVu6s1LNNi//+lAT2KNtixYRkXalcCOdyge1Q1GTk6JPH2wMA7YvN0PNkXSzzcsfRt9ZG2oi2rhaERGxgsKNdBqFZTaWZRwB4BdjTrHWjGHAtmXmnJqjGWabVwCMuQvOfwACurdDtSIiYhWFG+k0Pk0/TGW1ncGRQYyI6db0BLsdtn0G3zwP2bWhxjsQxtwN590PAWe4QrGIiHRKCjfSKRiG4dj88hdjYhrfFWW3w9alsOYFyM4027yDYGxtqPEPs6BiERGxisKNdAqbDhWy9UgR3p7uTBtRu1Kw3Q5bPzV7anK2mG3eQXDuvXDurxVqRES6KIUb6RQ+WG9OJJ4wLIpuvh6QuQi+eQFyt5on+ATDuTPNh1+ohZWKiIjVFG6kwyutrGZpWhYAvxgdCx/dZs6tAfAJqQ019yrUiIgIoHAjncBnm7IoraqhX/cAzg08agYbd0+4+FEYew/4dbO6RBER6UAUbqTDq5tIfOPoGNzS/p/ZOOgquPQxC6sSEZGOyt3qAkROZdvRItIOFuDp7sa1SZGw6UPzQPLN1hYmIiIdlsKNdGgf1PbaXBEfSUT2t1CaCwERMHCcxZWJiEhHpXAjHVaFrYbFGw8B5to2pC4wDyTeCB4t2ChTRES6JIUb6bBWZh6lqKKaXt38uKinm7n5JWhISkRETqlV4eaFF17AZrM5uxaRRt6v3STzhnNi8Ni8EOzV0HMERCZYXJmIiHRkrQo377zzDkOGDGHx4sXOrkcEgD25Jfy0Nx93N7j+nN6Q+m/zgHptRETkNFoVbtLT0/ntb3/LPffcw8UXX8zGjRudXZd0cR+uNycSXzIogp7lO82NMD28Ydi1FlcmIiIdXavCjYeHB/fffz+7du1i5MiRnH/++cyYMYOsrCxn1yddUFW1nUWOicSxkFbbazNkkvaLEhGR0zqrCcUhISH87W9/IyMjg2PHjjFo0CBmzZpFeXm5s+qTLmj11mzySqqICPLh8oHdYNNH5gENSYmISAu0OtxUV1ezceNGXn/9df7yl7+wf/9+ysvL+Z//+R8GDRqk+TjSau/XDkldP6o3XrtWQXk+BEVD/8strkxERDqDVoWbsWPHEhQUxDnnnMP//u//UllZyb333sv69espLS3l2WefZebMmcyePdvZ9YqLO5hfxrc7cwFzuwXHkFTijeDuYWFlIiLSWbRqb6krr7ySp556inPPPZfw8PAmx3/5y18ycOBAJk+ezBNPPHHWRUrX8XHKQQwDzu8fTh/vEtj5hXlgxC3WFiYiIp1Gq3puHnroIcaPH99ssKkzbNgwgoODT/tZdruduXPn0rdvX4KDg5kwYQIHDx485Xtyc3O5//77iY2NxcfHh379+rFy5cozvg7pWGrsBh+lNJhIvOlDMGqg9xjoPtDi6kREpLNoVbi54YYbTjvkFBwczN69e0/7Wffccw/z5s3jjTfeYN26dZSWlnLHHXec9Pz9+/dzzjnn4OnpyaJFi9i+fTuvv/46Awfqy6+z+2ZHDkeLKgj192J8Qo/6Iankm6wtTEREOpVWDUtt2LCBl19+uUn7c889R05ODn//+99b9DmrVq3i7bffJiUlheTkZAAeffRRpkyZQkVFBb6+vk3ec8stt3D77bfz9NNPO9r69u3bmsuQDub92k0yrxnZG5+cdMjdBp6+MOwaiysTEZHOpNV3S7m5uTVpGz9+PIsWLWrxZ7z44otMmzbNEWwAwsLCMAyD3NzcJud//fXXpKen4+fnR0JCAlFRUVx33XVaX8cF5BRV8OW2HAB+MTqmfkXi+MngG2JhZSIi0tm0KtxccMEFLFy4sEl7eHg4+fn5LfqMkpISVq9ezbXXNl5xNifH/ILr1q1bk/csWrSI8vJyPv/8c+bPn8+CBQv46aefuOuuu076cyorKykqKmr0kI7n4w2HqLEbjOoTysAwL8is/e9La9uIiMgZatWw1Jw5czjvvPNwc3PjiSeewMPDvEV36dKlLR4iysjIoKamhpEjRzZqT0lJIS4ujqCgoCbvSU1NpXfv3ixbtswxZPXAAw/wP//zP6es9ZlnnmnhlYkV7HbDsd3CL0bHwPZlUFEIwb2h3yUWVyciIp1Nq3pukpKSWLhwIa+++io9e/ZkwoQJnH/++fz+97/nkUceadFnHD9+HICIiIhG7YsXL2by5MnNvic3N5ef//znjebiuLu74+/vf9Kf8/jjj1NYWOh4nO5OLGl/P+w5xoH8MoJ8PJmUGN1gk8zp4H5Wi2iLiEgX1KqeG4CJEyeya9cuFi9eTFpaGl5eXjz33HNcdtllLXp/XajJz88nNDQUgIULF7Jz506WLl3a7HvCw8MxDKNR25IlSxg/fvxJf46Pjw8+Pj4tqkms8f66AwBMSe6Jf3k27P7SPKC7pEREpBVaHW4AAgMDue2227jtttvO+L1JSUnExMQwa9YsnnrqKX766SfuvvtuZs+ezYABAyguLmbMmDHMmTOHq6++GoCrr76auXPnMmXKFHr27MlLL73E1q1beffdd8/mMsRC+aVVrNqcDcD0MbGw6S3AgD4XQFictcWJiEin1Kpwc/jwYR5++GG2bdtG7969SU5OJjk5mREjRhAX17IvJG9vbxYtWsTMmTMZPnw4/fv3Z/78+dxyi7kSbWZmJtu2bSMmJsbxnt/+9rccO3aMX/3qV5SVlXHRRRfxww8/0K9fv9ZchnQAizceoqrGzrBewQzrGQyLtLaNiIicHTfjxHGeFpg0aRIFBQXMmDGDBx54gMTERDZu3IhhGAQFBVFQUNAGpTpHUVERISEhFBYWtmgFZWk7hmEw7qU17Mop4dmrh3FLz6Pw/64ErwB4eAf4BFpdooiIdBBn8v3dqtma3333HfPmzeOuu+7Cy8uLDz74gI0bNxIfH8/cuXNbVbR0PRv2H2dXTgl+Xh5MTe4JaQvMAwlTFWxERKTVWhVuPDw8HPtK+fv7U1JSQmJiIs888wyrV692aoHiuupWJJ6UGE2QexVkfmIeGKG1bUREpPVaFW6GDh3Kjh07AIiNjWXnzp0AjBo1ilWrVjmvOnFZheU2lmWYK0tPHxMDWz+DqmII7Qux51tbnIiIdGqtmlD8m9/8xrEp5rXXXsvzzz/PkCFD+PjjjwkICHBqgeKalqZnUWGzM7BHICNjQ+Hr2iGppJu0to2IiJyVVoWbhlsmPPjgg3zxxRcMHz4cLy8vXnvtNacVJ67rg9q1bX4xJha3ggOwdw3gZi7cJyIichbO+P8iV1dXk5iYyO7duwFzzs3q1avZv38/OTk53HHHHU4vUlxLxqFCNmcV4e3hzjUjekH6B+aBfhdDt1hrixMRkU7vjHtuPD09ycvLw263N2pvuB6NyKm8v97stRk/LIpQP09Iq1vbRhOJRUTk7LVqcsOdd97JO++84+xapAsoraxmaVrtROLRMXBgLRTsB+8giG9+TzEREZEz0ao5N3v27OGzzz6juLiYBx98kP79+zu7LnFRyzKOUFJZTZ9wf86NC4dPa3tthk0D75NvgCoiItJSreq58fLyol+/frz++usMGjSI3r17M3nyZJ566ikWL17s7BrFhdRNJL5xdAzuthLYssQ8kHyLdUWJiIhLaVXPzdtvvw2Yk4u3bNlCeno6aWlprF27lvnz53PNNdc4tUhxDTuyi9l4oAAPdzeuG9kbtiwEWxmED4CYMVaXJyIiLuKsdgX39PQkMTGRxMREbr31VmfVJC7q/dpem58N6UGPYF9IbbBJppubhZWJiIgraVW4SUxMJDk5maSkJMcjIiLC2bWJC6mw1fBJ6mEApo+JhWO7zcnEbu6QpLVtRETEeVo15+bXv/41/v7+fPTRR0ydOpWoqCiio6OZMGECjz32mLNrFBfw+eajFJTZ6Bniy8WDIiD9ffNA/8shuKe1xYmIiEtpVc/Nvffe63hut9vJyMjgvffe49VXX8XT86xGusRFfVC7Seb158TggR3SasNN8k0WViUiIq7orJOIu7u7Y2hq6NCh7Nmzxxl1iQvZl1fKD3uO4eYG15/T29xqoegQ+IbA4ElWlyciIi7GqTsU3nLLLXz44YfO/EhxAR+sN3ttLh4YQe9Q//oViYddB16+FlYmIiKuqFU9N+PHjycpKckxqTg+Ph53d3eys7M5evSos2uUTsxWY2fhhkMATB8TA+UFsPU/5sER2m5BREScr1XhZsSIEaSnp7NgwQKOHj2Kj48P/fv35/Dhw4wbN87ZNUontnprDnkllXQP9OFn8ZGQ+g5UV0BEPPQcaXV5IiLigloVbubOnet4npubS1paGjt27KB79+5awE8a+aB2k8zrRvXGy8O9wSaZWttGRETaRqvCzaJFiwgNDeXyyy8nIiKCcePGqcdGmjhcUM43O3IBc7sFcrfDofXg5gGJN1pcnYiIuKpWTSj+4x//SF5eXpP2H3/8kb179551UeIaPlp/EMOAc+PC6Nc9ANLeMw8MvBKCIq0tTkREXFarws3evXsZPXp0k/bdu3dz++23n3VR0vnV2A0+TjHvkpo+JhZqqiH9A/Og1rYREZE21KpwExERweHDh5u0jx07ltTU1LMuSjq/NTtzySqsIMTPi/FDo2DPV1ByFPzCYNBVVpcnIiIurFXhZvr06fz5z3/GZrM1ai8tLcXd3alL50gn9UHtJpnTRvTC18sDUheYBxJvAE9vCysTERFX16ok8vTTT5OVlUVSUhJLly4lOzubHTt28MQTTzBmzBhn1yidTE5xBau35gC1Q1Jl+bB9uXkwWWvbiIhI22rV3VIBAQH88MMPPP7449x0002Ul5cD0K9fP/7zn/84tUDpfBZuOES13WBEbDcGRwXBujehpgqihkN0otXliYiIi2v1GFJISAivvfYax44dIzU1lczMTLZv3058fLwz65NOxjAMPqzdbmH66FizsW5ISr02IiLSDloVbu644w7+9re/AeDj40NiYiLx8fF4eHg4szbphH7Yc4z9x8oI9PFkUmI0ZG+GI2ng7gXDb7C6PBER6QJaFW4+++wzzj333Cbtr7/+Om+//fZZFyWd1wfrzF6byUk9CfDxrF/bZvBVEBBuYWUiItJVtCrcFBUVERER0aR98ODB/OUvfznroqRzOl5axcpMc+PU6WNioMYGm2p3ideQlIiItJNWhZuhQ4fy/fffN2nv168fBw4cOOuipHP6JPUwVTV2EqKDGd4rBHZ+AaW5ENADBlxhdXkiItJFtCrcPPHEEzzyyCN88803jdo3bdpE9+7dnVKYdC6GYTg2yZw+JgY3N7f6TTITbwAPLwurExGRrqRVt4Jfc801pKSkcPnllzNlyhTOPfdcCgsLeeutt/jlL3/p7BqlE9h4oIAd2SX4erkzJbkXlOTCjpXmwRG3WFuciIh0Ka2+FXz27Nn88MMPhIWF8eGHH7J69WpmzpzJc88958z6pJOoW5F44vBoQvy8IONjsFdDz5HQQ8sDiIhI+2lVz01NTQ1vvfUW27ZtIyEhgZtuuonk5GTCw3U3TFdUXGHjs01HgNoViQ2jfkhKm2SKiEg7a1W4eeCBB1i4cCHjxo3j1VdfxcPDg6qqKnr16kVycjJLly51dp3SgS1Nz6LcVsOAHoGc0ycUjm6C7Ezw8IHh11ldnoiIdDGtGpZavHgx7777Lv/+97/x8fFh/fr1zJs3j4qKCvr06ePsGqWDq1vb5hejaycSp9b22gyZBH6hFlYmIiJdUat6bkpKSkhISADAy8sLDw8P7rvvPqqqqsjKynJqgdKxZR4uJONwIV4eblwzsjdUV0LGR+ZBrW0jIiIWaFXPTVxcnCPE9OrVi8OHDwMwefJkFixY4LzqpMOru/37yqFRhAV4m3dIlR+HoGjof5nF1YmISFfUqnBz/fXXs3KleZvvpZdeyv/7f/8PgC1btjh2CBfXV1ZVzaepZsit3ySzdkgq6Rfgrr3GRESk/bVqWOpPf/qT4/kjjzzCmDFjiIiIoKioiDvuuMNpxUnHtjzjKMWV1cSE+XF+/3AoPgq7vjAPJmttGxERsUarwk1DsbGxbN68meXLlxMWFsakSZOcUZd0AnVr2/xidCzu7m7mPlKGHWLGQvcBFlcnIiJd1VmHG4Dw8HBuvfVWZ3yUdBIH88tI2X8cNze4blRvc22bVK1tIyIi1mv1CsXStdXt/j2mbxiRwb5weCPkbQdPPxh6jcXViYhIV6ZwI62yLMNckXhSYrTZkFZ7l1zCFPANtqgqERERhRtphcMF5aQdLMDNDa4aGgW2cshYZB7UkJSIiFhM4UbOWN2Q1Og+YfQI9oVty6CyEEJioO/FFlcnIiJdncKNnLHltUNSE4ZHmQ11m2QmTQd3/SclIiLW0jeRnJGjhRVs2H8cgAnDoqHwEOz+yjyoISkREekAFG7kjKzINHttRvUJJSrEF9I/AAzocyGE9bO2OBERERRu5AytyDDn20wcHm2ubZOmtW1ERKRjUbiRFsspqmD9/nwAJgyLgoM/Qf4e8AqAhKkWVyciImJSuJEWW7n5KIYBI2K70bObH6TWrm0z9GrwCbS0NhERkToKN9JiyzaZ820mDos217bZvMQ8kHyzdUWJiIicQOFGWiS3uJJ1+2qHpIZHwc4voKrYXNsm9jyLqxMREamncCMtUjckldQ7hN6h/rB5sXlg6NVa20ZERDoUy7+V7HY7c+fOpW/fvgQHBzNhwgQOHjzYovd++eWXxMTEEB8f38ZVyorahfsmDo+GqlLY8bl5QJtkiohIB2N5uLnnnnuYN28eb7zxBuvWraO0tJQ77rjjtO9LSUkhPT0dX19fxo4d2w6Vdl15JZX8uOcYUBtudqwEWxmE9oWeI6wtTkRE5ASWhptVq1bx9ttvs3z5cq688kqGDBnCo48+yn//+18qKipO+r7t27fz1ltvceedd7J7926Fmza2anM2dgOG9wohJswfMuuGpK4BNzdrixMRETmBp5U//MUXX2TatGkkJyc72sLCwjAMg9zcXGJiYpq85/Dhwzz88MO8//77pKenYxjGKcNNZWUllZWVjtdFRUVOvYauoNFeUhVF5mRigGEakhIRkY7Hsp6bkpISVq9ezbXXXtuoPScnB4Bu3bo1eU9+fj433XQT8+bNIzAwkLS0NPz8/EhMTDzpz5kzZw4hISGOR3OBSU4uv7SKH+qGpIZFw/blUFMJ4QMhcpjF1YmIiDRlWbjJyMigpqaGkSNHNmpPSUkhLi6OoKCgRu1lZWVMnTqVp556in79zD2MUlNTGTlyJJ6eJ++AevzxxyksLHQ8WjpZWUyrNh+lxm6QEB1M3+4B9UNSw67VkJSIiHRIlg1LHT9u7iwdERHRqH3x4sVMnjy5yfmffPIJ3333HePHj3e02e12APz9/SksLMTLy6vJ+3x8fPDx8XFm6V3K8kxzL6lJidFQfhx2f2ke0JCUiIh0UJaFm7pQk5+fT2hoKAALFy5k586dLF26tMn5V155JRkZGY7XdrudUaNG8eyzz3L11Vc3G2zk7BSUVbF2Vx5Qu5fU1k/AboMeQyFisMXViYiINM+yYamkpCRiYmKYNWsWu3fv5r333mPGjBnMnj2bAQMGUFxcTHx8PEuWLAHMMDRs2DDHw8/Pj+rqai6//HIGD9YXbVtYtSWbarvBkKgg4iIC6xfuGzbN2sJEREROwbKeG29vbxYtWsTMmTMZPnw4/fv3Z/78+dxyyy0AZGZmsm3btpNOAN6yZQsAQ4YMabeau5rlDRfuK82DPd+YB7Rwn4iIdGCW3go+evRoUlJSmj123nnnYRjGSd87derUUx6Xs1NYZuP72iGpicOjYetHYNRAdBKE97e4OhERkZOzfIVi6Zi+2JqNrcZgUGQgA3oENl64T0REpANTuJFmNdpLqvgo7PvOPDBU821ERKRjU7iRJooqbHy7s8GQ1JZPAQN6nQOhfawtTkRE5DQUbqSJ1VuzqaqxM6BHIIMigxos3KchKRER6fgUbqSJ5Rnmwn0Th0VB4SE4+CPgpiEpERHpFBRupJHiChvf7MgFYGJiNGxeYh6IPQ+Ce1pXmIiISAsp3EgjX27LoaraTlz3AAZHBjVYuE9DUiIi0jko3EgjDRfucyvYD4c3gJs7JEy1uDIREZGWUbgRh9LKar7ebg5JTRgeBZs/MQ/0vRACe1hYmYiISMsp3IjDl9tyqKy20zfcn4ToYC3cJyIinZLCjTisyDSHpCYMj8bt2G44ugncPCB+isWViYiItJzCjQBQVlXNl9tyAJg0PLp+InHcpRAQbl1hIiIiZ0jhRgD4ensuFTY7MWF+DO0ZrIX7RESk01K4EQCWNbxLKncb5G4Fdy8YMsniykRERM6Mwo1QXlXDV7VDUhOHRdf32gz4GfiFWliZiIjImVO4Eb7ZkUNZVQ29uvmR2Cu4wcJ911pbmIiISCso3Ej9XlLDo3DLzoRju8DTFwZPsLgyERGRM6dw08VV2GpYvTUbMG8Bd/TaDBwHPkEWViYiItI6Cjdd3JoduZRW1dAzxJcRvUO0cJ+IiHR6CjddXN1eUlcNi8btSCoU7Acvfxg03uLKREREWkfhpgurrK7hv1trF+5LjKrvtRl0FXgHWFiZiIhI6yncdGHf7sijpLKaqODaIam6jTK1cJ+IiHRiCjdd2PLMuiGpKNwPr4eiw+AdBAPGWVyZiIhI6yncdFGV1TV8scW8S2ri8AYL9w2ZCF6+FlYmIiJydhRuuqi1u45RXFFNjyAfzokJhi1LzAO6S0pERDo5hZsuallGgyGpgz9ASTb4hkD/yy2uTERE5Owo3HRBVdV2Vm2uW5W4wcJ98ZPB09vCykRERM6ewk0XtHZ3HkUV1XQP9GF0bDBsWWoe0JCUiIi4AIWbLmhF7V5SVw2LxGP/t1CWB/7h0O8SiysTERE5ewo3XYytxs7nW2qHpIY1uEsqfgp4eFpYmYiIiHMo3HQxP+45RkGZjfAAb8bEBsLW/5gHtHCfiIi4CIWbLmZ57ZDUlUOj8Nz3DVQUQGAk9LnA2sJEREScROGmC6musfN57V1Skxou3JcwFdw9LKxMRETEeRRuupB1e/PJL60i1N+Lc2P9Ydsy84DukhIREReicNOF1C3cN35oFJ57voSqYgjqCTFjLa5MRETEeRRuuogau+EYkprQcOG+odPAXf8ZiIiI69C3Whexbm8+eSVVhPh5cX6sH2xfaR4Ydq21hYmIiDiZwk0XsSLTHJK6MiESr91fgK0UuvWBXiMtrkxERMS5FG66gBq7wYrM2oX7EhvcJTV0Gri5WViZiIiI8yncdAEb9h8nt7iSYF9PLujtAztXmQe0cJ+IiLgghZsuYHntXVLjEqLw3v05VFdAWH+ISrS4MhEREedTuHFxdrvhmG8zcXhU/ZDUsGs0JCUiIi5J4cbFbTxwnOyiSoJ8PLkwxgt2/dc8oIX7RETERSncuLi6vaSuSIjEZ+cKsNsgYghEJlhcmYiISNtQuHFhjYekGi7cp14bERFxXQo3LiztUAFHCisI9PHkol7usOdr84DukhIRERemcOPClm8ye21+Ft8D313LwF4NUcOh+0CLKxMREWk7CjcuyjDqF+6bMKzhwn3qtREREdemcOOi0g8VcrigHH9vDy7tZcC+b80DQ6dZW5iIiEgbU7hxUStqF+67fEgPfHd+BoYdeo6EsH4WVyYiItK2FG5ckGEYLKsNN5OGRzdeuE9ERMTFKdy4oMzDRRw6Xo6flweX9qyGAz+YBzQkJSIiXYDCjQta1mBIym/HfwADYsZCSG9rCxMREWkHCjcuxrxLygw3E4ZHaeE+ERHpchRuXMzmrCL2HyvD18udy6Mq4dB6wA0SplpdmoiISLtQuHExdb02lw7qgf/OpWZjnwsgONrCqkRERNqP5eHGbrczd+5c+vbtS3BwMBMmTODgwYMnPf+9995jwoQJREdHExgYyIUXXsj69evbseKOyzAMx0aZExN1l5SIiHRNloebe+65h3nz5vHGG2+wbt06SktLueOOO056/scff8y1117LihUrWLduHYGBgUycOJHy8vJ2rLpj2na0mL15pXh7uvOzHiVwJA3cPDQkJSIiXYqnlT981apVvP3226SkpJCcnAzAo48+ypQpU6ioqMDX17fJez755JNGrx9++GHGjRvHrl27GD58eHuU3WHVLdx3yaAIAuqGpPpdDAHdLaxKRESkfVkabl588UWmTZvmCDYAYWFhGIZBbm4uMTExp/2MNWvWEBQURL9+za+8W1lZSWVlpeN1UVHRWdfdETVZuO/H2hCoISkREeliLBuWKikpYfXq1Vx77bWN2nNycgDo1q3baT/j008/Ze7cufz1r38lMDCw2XPmzJlDSEiI49GSwNQZ7cwpYXduKd4e7lwRUQDZmeDuCUN+bnVpIiIi7cqycJORkUFNTQ0jR45s1J6SkkJcXBxBQUGnfP/LL7/M9OnT+b//+z/uvPPOk573+OOPU1hY6HicarJyZ7Zsk9lrc/Gg7gTWDUn1vxz8wyysSkREpP1ZNix1/PhxACIiIhq1L168mMmTJ5/0fdXV1dx///0sXLiQFStWcMkll5zy5/j4+ODj43P2BXdwjoX7hkbBj1q4T0REui7Lwk1dqMnPzyc0NBSAhQsXsnPnTpYuXdrse/Lz87nuuuvIzs5m3bp1xMXFtVu9HdmunGJ2ZJfg5eHGlRHHIG8HeHjDkIlWlyYiItLuLBuWSkpKIiYmhlmzZrF7927ee+89ZsyYwezZsxkwYADFxcXEx8ezZMkSAHbs2MHYsWMpLS3lk08+wd/fn6NHj3L06FHsdrtVl9Eh1K1tc+GA7gTtqg2GA8aBb4iFVYmIiFjDsp4bb29vFi1axMyZMxk+fDj9+/dn/vz53HLLLQBkZmaybds2xwTgl156iV27dgEwePBgx+f4+flRVFSEu7vlS/ZY4mB+GUtSDwMwcVgUrNXCfSIi0rW5GYZhWF1EeyoqKiIkJITCwkKCg4OtLqfV9uSW8NrXu1mSephqu0GAtwc/zggn6F9XgKcfPLILfJq/g0xERKSzOZPvb0vXuZEzt/VIEa9+tYtlGUeoi6UXDezOw1cOJmjri2bDoPEKNiIi0mUp3HQS6QcLeOWrXXyxJdvRdkV8D+67bAAjYkPBMGChFu4TERFRuOngftpzjFe+2sW3O/MAcHODicOjue/SAST0bNAtd2g9FB4E70AYeKVF1YqIiFhP4aYDMgyDb3fm8cqXu1i3Lx8AD3c3pib35NeXDmBAj2aGnOp2AB88Abz82rFaERGRjkXhpgOx2w3+uzWbV77axaZDhQB4e7hz3Tm9mXlJf2LC/E/2RtiyxHyuhftERKSLU7jpAGrsBsszjvDqV7vYdrQYAF8vd24a04e7L44jKqTp7uiNHPgBio+ATwgM+Fk7VCwiItJxKdxYyFZjZ0nqYf7v693sySsFINDHk9vO68PtF/aje2ALt43YXDskNWQSeLr+VhMiIiKnonBjgQpbDR9vOMT8r3dzuKAcgBA/L26/oB8zzu9LiL9Xyz6ouhJSF8Cmj8zXuktKRERE4aY9lVVV895PB3hjzR5yiisB6B7ozZ0XxXHLuX0I9GnhP0d1JWz8F3z3EhSZqxMTORziLm2bwkVERDoRhZt2UFRh490f9vOP7/aSX1oFQHSIL/dcHMcvxsTi6+XRsg+yVUDqu/DtX6E4y2wLioYLfwsjbwOPFvb4iIiIuDCFmzaUX1rF29/v5Z9r91FcUQ1AbJg/v760P9eM7I23Zwv3w7JVwMZ3zJ6a4iNmW1DP+lDjdZoJxyIiIl2Iwk0byCmu4K1v97Lgx/2UVdUAMKBHIPdfNoCfJ0bj6dHSUFMOG/4J3/0NSsydvwnuZYaaEbcq1IiIiDRD4caJDheU8/o3u/lg/UGqqu0ADO0ZzP2XDWD80Cjc3d1a9kG2ckh5G77/G5TUbrcQ3Bsu+h2MuEV3RImIiJyCwo2TrN+Xz/Q3fqTabu5mOSK2Gw9ePpBLB0fg5tbCUFNVBin/D77/O5TmmG0hMWaoSb5ZoUZERKQFFG6cJDmmG5HBvsSG+fPA5QM4r3/4GYSaUlj/D1g7D0pzzbZusXDR7yHpJvD0brvCRUREXIzCjZN4ebjznwcuJCzgDIJIVSmsfwu+nwdl5saYdIuFix+BpOm6+0lERKQVFG6cqMXBprIE1r8Ja1+GsmNmW2hfuOhhSPqFQo2IiMhZULhpT5XFsO4NWPsKlJu7fRPaz+ypSbxBoUZERMQJFG7aQ0WRGWp+eAXKj5ttYXFw8aMw/Hrw0D+DiIiIs+hbtS1VFMFPr5uhpqLAbAsfYPbUDLtOoUZERKQN6Nu1LVQUwo/z4cdXzecA4QPhkkdh2LXg3sLtFkREROSMKdw4U3kB/DQffnytPtR0H2yGmqHTFGpERETagcKNsxxcDwuuhcraUBMxxAw1CVcr1IiIiLQjhRtniRxqriAcHN8g1LRwDykRERFxGoUbZ/H2h9tXmrd2K9SIiIhYRuHGmcL7W12BiIhIl6cuBhEREXEpCjciIiLiUhRuRERExKUo3IiIiIhLUbgRERERl6JwIyIiIi5F4UZERERcisKNiIiIuBSFGxEREXEpCjciIiLiUhRuRERExKUo3IiIiIhLUbgRERERl6JwIyIiIi5F4UZERERcisKNiIiIuBSFGxEREXEpCjciIiLiUhRuRERExKUo3IiIiIhLUbgRERERl6JwIyIiIi5F4UZERERcisKNiIiIuBSFGxEREXEpCjciIiLiUhRuRERExKUo3IiIiIhLUbgRERERl6JwIyIiIi7F8nBjt9uZO3cuffv2JTg4mAkTJnDw4MFTvmfJkiWMHDkSf39/kpOTWbNmTTtVKyIiIh2d5eHmnnvuYd68ebzxxhusW7eO0tJS7rjjjpOev2DBAm688UbuuusuMjMzueSSS7jmmmsoKipqx6pFRESko3IzDMOw6oevWrWKiRMnkpKSQnJyMgCfffYZU6ZMoaysDF9f30bn5+Xl0b9/f5577jnuv/9+AEpKSggKCmLlypWMHz/+tD+zqKiIkJAQCgsLCQ4Odvo1iYiIiPOdyfe3ZzvV1KwXX3yRadOmOYINQFhYGIZhkJubS0xMTKPz33zzTfz9/bn33nsdbYGBgXh5eZGTk9Psz6isrKSystLxurCwEEA9PSIiIp1I3fd2S/pkLAs3JSUlrF69mgULFjRqrwsp3bp1a/KeJUuWMHXqVDw968suKCjAZrMRGhra7M+ZM2cOzzzzTJP2E4OTiIiIdHzFxcWEhISc8hzLwk1GRgY1NTWMHDmyUXtKSgpxcXEEBQU1eU96enqT+TgpKSkAJCUlNftzHn/8cX73u985XtvtdvLz8wkPD8fNze1sL6ORoqIiYmJiOHjwYJcY8tL1ujZdr+vrates6+3cDMOguLiYnj17nvZcy8LN8ePHAYiIiGjUvnjxYiZPntzk/LKyMiorK5s9Pykp6aQ9MT4+Pvj4+DRqa65XyJmCg4Nd4j+kltL1ujZdr+vrates6+28TtdjU8eycFMXUvLz8x1DSgsXLmTnzp0sXbq0yfn+/v4EBASQn5/vaDtw4ADvvPMOr732WvsULSIiIh2eZbeC1/W2zJo1i927d/Pee+8xY8YMZs+ezYABAyguLiY+Pp4lS5Y43jNp0iReeeUVMjIy+O6777jiiiu4/PLLufXWW626DBEREelgLAs33t7eLFq0iM2bNzN8+HDmzJnD/PnzeeSRRwDIzMxk27ZtjYab5s2bR2xsLBdccAHTp0/nxhtvZPHixbi7W75cD2AOgT399NNNhsFcla7Xtel6XV9Xu2Zdb9dh6To3IiIiIs7WMbo8RERERJxE4UZERERcisKNiIiIuBSFGxEREXEpCjdOYrfbmTt3Ln379iU4OJgJEyZw8OBBq8tqE3a7nVmzZnHuuecSGhpKWFgYN9xwA9nZ2VaX1i5ee+01vLy8mDlzptWltLm0tDSuvfZaunfvjp+fH0lJSWRlZVldltPt27ePX/7yl/Tq1YuAgADOO+881qxZY3VZTvPmm28ybtw4unfvjpubGzt27Gh0vKKigkceeYTo6GhCQ0P5xS9+4VhotTM61fUWFRXx+OOPM3LkSIKDg4mMjOTuu++mpKTEworP3un+jRt68skncXNz4y9/+Us7Vti+FG6c5J577mHevHm88cYbrFu3jtLS0iZbRbiKPXv2kJ6ezqOPPsqPP/7I0qVLWb9+vcteb0P/+Mc/CAoKorq6mrFjx1pdTptasWIF48eP5+KLL+brr79m06ZN/OEPf2iySnhnV1hYyPnnn09eXh5Lly4lLS2N+Ph4Jk2aREFBgdXlOUVxcTE33XQTt956K8HBwQwcONBxzG63M3XqVJYtW8bixYv56quv2LRpE4899piFFZ+dU11vamoqR44c4dlnn2Xjxo3885//ZOHChfzhD3+wsOKzd6prbmj27Nn06dMHwLV/hxly1j7//HPDw8PDSE1NdbT95z//Mdzc3Izy8nLrCmtHTz75pBEWFmZ1GW3q448/Nl555RXjs88+MwBjy5YtVpfUZnJzc42wsDDjyy+/tLqUNrds2TIDMHJychxtqampBmBkZmZaWJnz3XHHHcYll1zSqO311183goKCjMOHDzvaXn75ZSMqKqqdq3O+5q63OTfffLMxcuTIti+oHZzqml966SVjyZIlxiuvvGK4u7sbxcXF7VtcO1LPjRO8+OKLTJs2jeTkZEdbWFgYhmGQm5trXWHt6Ntvvz3p5qWuYPXq1axevZr77ruPtLQ0QkJCGDJkiNVltZlXXnmFgQMH8umnnxIbG0vfvn35/e9/j81ms7o0pxs6dCi+vr4sXrwYu91OdnY2f/7zn7nooouIj4+3ujyn2rBhA6NGjWrU9te//pV77rmn0WaEYWFh5OTktHd5Ttfc9Z6oqqqKH3/80WV+f53smt99911KSkqYOnUqaWlpDB06lMDAQAsqbB8KN2eppKSE1atXc+211zZqr/vF0NabdHYEjz76KBs3buSvf/2r1aW0iZSUFP7yl7/wt7/9DTDnoYwePdrpu8p3JIsWLWLDhg3Y7XYWL17M008/zd///nfH34EriY2N5c9//jMzZ87Ex8eHqKgo+vXrx8qVKzvM6ufOUFlZyebNmxt98W3dupXt27c3+/urbs+/zqq56z1RdXU1M2bMoLS0lFmzZrVjdW3jZNe8bNkyPvvsM5588knA/B3m0kNSKNyctYyMDGpqahg5cmSj9pSUFOLi4ggKCrKosrZXWVnJbbfdxrvvvsvq1asb9Vy5ih07dnDnnXfyzjvvOJYwT01NdelfDJWVlWzZsoXrrruOefPmcc455/CrX/2KK6+80qUm2QIYhsENN9zAkiVLWLlyJevXr+f+++/no48+cpn5NnUyMjKw2WyNvvjS0tJwc3NjxIgRjc5NSUnp9D0ZzV1vQwUFBUyaNIkNGzbw7bff0rt373au0Pmau+bvv/+eWbNm8Y9//AM3NzdqamrIzMx06d9hoHBz1uruKDhxkuXixYuZPHmyFSW1i+zsbC677DIyMjJYt24dY8aMsbqkNvHCCy+wadMmYmJi8PT0xNPTk927dzNnzhx+9rOfWV1em8jPz8dut3Pdddc1and3d8ff39+iqtrGJ598wvLly1mxYgVXXnklycnJvPzyywAsWLDA4uqca8OGDQQGBjaaaHr8+HGCgoIa7T1UWVnJ8uXLO/3vr+aut87OnTs599xzsdvt/PTTTwwYMMCCCp2vuWv+85//TEpKCt26dcPT0xMfHx8qKiq45557XPomEE+rC+js6kJNfn6+oxt34cKF7Ny5k6VLl1pZWptJT09nypQpnHPOOfzrX/8iICDA6pLazJNPPslDDz3keL1hwwZmzJjBF198waBBgyysrO2Ehobi7u6O0WDbuby8PL777juXG3rcunUrkZGRjYaPi4uLKSgowNPTtX49btiwgREjRjQaaouIiKC0tJSqqiq8vb0BePnllzEMgxkzZlhUqXM0d70AX375Jddddx3Tp0/n73//u0v9Ozd3za+99hqVlZWO1//5z394+umn2bBhA5GRkVaU2T6snc/c+VVWVhoxMTHGbbfdZuzatcv497//bQQEBBjPP/+81aW1iU8//dQICAgw7rjjDiMrK8s4cuSIceTIESM3N9fq0trFG2+8YQQEBBh2u93qUtrUhAkTjFGjRhnp6enG999/b4wdO9YYPXq0UVVVZXVpTvXNN98Ybm5uxrPPPmvs3r3bWLt2rXHZZZcZPXr0MI4cOWJ1eWetqqrKSE1NNVJTU43hw4cbN954o5Gammrs3r3bMAzDyMnJMQICAow//OEPxp49e4y///3vhpeXl/Hhhx9aXHnrnO56X3/9dcPLy8t46qmnHL+7jhw5YuTn51tceeud7ppP9MQTTxgJCQntXGX7U7hxgnXr1hmjRo0y/Pz8jGHDhhnvvvuu1SW1mSFDhhhAk8cVV1xhdWnt4je/+Y0xatQoq8toc0eOHDGuu+46IzQ01OjZs6fx61//2igoKLC6rDbxr3/9yxg6dKjh6+trxMTEGDNmzDD27dtndVlO8d133zX7v9c777zTcc7y5cuN+Ph4w8/PzxgzZoyxYsUKCys+O6e63pqaGiMgIOC0fx+dTUv+jRu6+uqrjWuvvbadq2x/bobRoO9ZREREpJPThGIRERFxKQo3IiIi4lIUbkRERMSlKNyIiIiIS1G4EREREZeicCMiIiIuReFGREREXIrCjYiIiLgUhRsRERFxKQo3IiIi4lIUbkRERMSlKNyIiIiIS/n/PFnPXrlWMBgAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(accuracies, label='train')\n", + "plt.plot(accuracies_test, label='test')\n", + "plt.ylim(0, 1.1)\n", + "plt.ylabel(\"accuracy\")\n", + "plt.legend(loc='best');" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_prediction(model, sample_idx=4)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## c) Exercises\n", + "\n", + "### Look at worst prediction errors\n", + "\n", + "- Use numpy to find test samples for which the model made the worst predictions,\n", + "- Use the `plot_prediction` to look at the model predictions on those,\n", + "- Would you have done any better?" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Your code here\n", + "import matplotlib.pyplot as plt\n", + "y_pred_probs = nn.forward(X_test) # Predicted probabilities for each class\n", + "y_pred_labels = np.argmax(y_pred_probs, axis=1) # Predicted class labels\n", + "true_class_probs = y_pred_probs[np.arange(len(y_test)), y_test]\n", + "true_probs = y_pred_probs[np.arange(len(y_test)), y_test] # probability assigned to the true class\n", + "worst_indices = np.argsort(true_probs)[:5] # indices of 5 samples with lowest confidence in true class\n", + "classes = np.arange(nn.output_size) # 0-9 for MNIST\n", + "\n", + "worst_idx = np.argsort(y_pred_probs[np.arange(len(y_test)), y_test])[:5]\n", + "\n", + "for idx in worst_idx:\n", + " fig, (ax0, ax1) = plt.subplots(1, 2, figsize=(8, 3))\n", + " ax0.imshow(X_test[idx].reshape(8, 8), cmap='gray')\n", + " ax0.set_title(f\"True: {y_test[idx]}\")\n", + " ax1.bar(classes, one_hot(len(classes), [y_test[idx]])[0], label='true')\n", + " ax1.bar(classes, nn.forward(X_test[idx]), label='pred', color='red')\n", + " ax1.set_xticks(classes); ax1.legend()\n", + " plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After visualizing the worst predictions, consider the following:\n", + "\n", + "Are the misclassified samples visually similar to other classes?\n", + "\n", + "Could the model be confusing certain classes due to their features?\n", + "\n", + "What improvements can be made to the model to reduce such errors?\n", + "\n", + "Potential improvements might include:\n", + "\n", + "Increasing the model's complexity (e.g., adding more layers or neurons).\n", + "\n", + "Using different activation functions like ReLU.\n", + "\n", + "Training for more epochs or using better initialization techniques.\n", + "\n", + "Implementing data augmentation to provide more diverse training examples." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Hyper parameters settings\n", + "\n", + "- Experiment with different hyperparameters:\n", + " - learning rate,\n", + " - size of hidden layer,\n", + " - implement the support for a second hidden layer.\n", + " - What is the best test accuracy you can get?" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Your code here\n", + "import numpy as np\n", + "\n", + "class NeuralNet2Hidden:\n", + " \"\"\"MLP with 2 hidden layers using sigmoid activation\"\"\"\n", + "\n", + " def __init__(self, input_size, hidden1_size, hidden2_size, output_size):\n", + " # Initialize weights and biases\n", + " self.W1 = np.random.uniform(-0.1, 0.1, (input_size, hidden1_size))\n", + " self.b1 = np.random.uniform(-0.1, 0.1, hidden1_size)\n", + " self.W2 = np.random.uniform(-0.1, 0.1, (hidden1_size, hidden2_size))\n", + " self.b2 = np.random.uniform(-0.1, 0.1, hidden2_size)\n", + " self.W3 = np.random.uniform(-0.1, 0.1, (hidden2_size, output_size))\n", + " self.b3 = np.random.uniform(-0.1, 0.1, output_size)\n", + "\n", + " self.input_size = input_size\n", + " self.hidden1_size = hidden1_size\n", + " self.hidden2_size = hidden2_size\n", + " self.output_size = output_size\n", + "\n", + " # Forward pass\n", + " def forward(self, X):\n", + " self.Z1 = np.dot(X, self.W1) + self.b1\n", + " self.H1 = sigmoid(self.Z1)\n", + "\n", + " self.Z2 = np.dot(self.H1, self.W2) + self.b2\n", + " self.H2 = sigmoid(self.Z2)\n", + "\n", + " self.Z3 = np.dot(self.H2, self.W3) + self.b3\n", + " self.Y = sigmoid(self.Z3)\n", + " return self.Y\n", + "\n", + " # Gradient (backpropagation)\n", + " def grad_loss(self, X, y_true):\n", + " y_true = one_hot(self.output_size, [y_true]) if np.isscalar(y_true) else one_hot(self.output_size, y_true)\n", + " y_pred = self.forward(X)\n", + "\n", + " # Output layer error\n", + " error3 = y_pred - y_true\n", + " grad_W3 = np.dot(self.H2.T, error3)\n", + " grad_b3 = np.sum(error3, axis=0)\n", + "\n", + " # Hidden layer 2 error\n", + " error2 = np.dot(error3, self.W3.T) * dsigmoid(self.Z2)\n", + " grad_W2 = np.dot(self.H1.T, error2)\n", + " grad_b2 = np.sum(error2, axis=0)\n", + "\n", + " # Hidden layer 1 error\n", + " error1 = np.dot(error2, self.W2.T) * dsigmoid(self.Z1)\n", + " grad_W1 = np.dot(X.T, error1)\n", + " grad_b1 = np.sum(error1, axis=0)\n", + "\n", + " return {\"W1\": grad_W1, \"b1\": grad_b1,\n", + " \"W2\": grad_W2, \"b2\": grad_b2,\n", + " \"W3\": grad_W3, \"b3\": grad_b3}\n", + "\n", + " # Single-sample training\n", + " def train(self, x, y, lr=0.01):\n", + " x = x[np.newaxis, :] if x.ndim == 1 else x\n", + " grads = self.grad_loss(x, y)\n", + " self.W1 -= lr * grads[\"W1\"]\n", + " self.b1 -= lr * grads[\"b1\"]\n", + " self.W2 -= lr * grads[\"W2\"]\n", + " self.b2 -= lr * grads[\"b2\"]\n", + " self.W3 -= lr * grads[\"W3\"]\n", + " self.b3 -= lr * grads[\"b3\"]\n", + "\n", + " # Prediction\n", + " def predict(self, X):\n", + " Y = self.forward(X)\n", + " return np.argmax(Y, axis=1) if X.ndim > 1 else np.argmax(Y)\n", + "\n", + " # Accuracy\n", + " def accuracy(self, X, y):\n", + " return np.mean(self.predict(X) == y)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "LR=0.001, Hidden=(32,16) -> Test Accuracy: 0.1185\n", + "LR=0.001, Hidden=(64,32) -> Test Accuracy: 0.0630\n", + "LR=0.01, Hidden=(32,16) -> Test Accuracy: 0.0889\n", + "LR=0.01, Hidden=(64,32) -> Test Accuracy: 0.0926\n", + "LR=0.05, Hidden=(32,16) -> Test Accuracy: 0.4778\n", + "LR=0.05, Hidden=(64,32) -> Test Accuracy: 0.6852\n", + "\n", + "Best Test Accuracy: 0.6852 with LR=0.05, Hidden=64,32\n" + ] + } + ], + "source": [ + "# Hyperparameters to try\n", + "learning_rates = [0.001, 0.01, 0.05]\n", + "hidden_configs = [(32, 16), (64, 32)] # (hidden1_size, hidden2_size)\n", + "best_acc = 0\n", + "best_params = None\n", + "\n", + "for lr in learning_rates:\n", + " for h1, h2 in hidden_configs:\n", + " # Initialize a new network for each hyperparameter set\n", + " nn = NeuralNet2Hidden(input_size=64, hidden1_size=h1, hidden2_size=h2, output_size=10)\n", + " \n", + " # Train over the dataset (example: 1 epoch for simplicity)\n", + " for i in range(len(X_train)):\n", + " nn.train(X_train[i], y_train[i], lr=lr)\n", + " \n", + " # Evaluate accuracy\n", + " acc = nn.accuracy(X_test, y_test)\n", + " print(f\"LR={lr}, Hidden=({h1},{h2}) -> Test Accuracy: {acc:.4f}\")\n", + " \n", + " if acc > best_acc:\n", + " best_acc = acc\n", + " best_params = (lr, h1, h2)\n", + "\n", + "print(f\"\\nBest Test Accuracy: {best_acc:.4f} with LR={best_params[0]}, Hidden={best_params[1]},{best_params[2]}\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "By experimenting with different learning rates and hidden layer sizes, the best test accuracy achieved was ~69% using a learning rate of 0.05, with 64 neurons in the first hidden layer and 32 neurons in the second. Adding a second hidden layer improved performance, while extreme learning rates either slowed training or caused instability." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "dsi_participant", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.7" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/02_activities/assignments/lab_3.ipynb b/02_activities/assignments/lab_3.ipynb new file mode 100644 index 00000000..cf44b6a1 --- /dev/null +++ b/02_activities/assignments/lab_3.ipynb @@ -0,0 +1,16993 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Recommender Systems\n", + "\n", + "In this lab, we'll be using Keras to build a recommender system. We'll be using the MovieLens dataset, a common benchmark dataset for recommender systems. \n", + "\n", + "MovieLens is a web-based recommender system and virtual community that recommends movies for its users to watch, based on their film preferences using collaborative filtering of members' movie ratings and movie reviews. You can check out the website here: https://movielens.org/\n", + "\n", + "We will download a subset of the dataset containing 100k ratings. There are tens of millions of ratings in the full dataset, spanning hundreds of thousands of users and movies. The subset we'll be using is a good example to demonstrate the concepts in this lab." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from zipfile import ZipFile\n", + "from urllib.request import urlretrieve\n", + "from pathlib import Path\n", + "\n", + "ML_100K_URL = \"http://files.grouplens.org/datasets/movielens/ml-100k.zip\"\n", + "ML_100K_FILENAME = Path(\"ml-100k.zip\")\n", + "ML_100K_FOLDER = Path(\"ml-100k\")\n", + "\n", + "if not ML_100K_FOLDER.exists():\n", + " if not ML_100K_FILENAME.exists():\n", + " urlretrieve(ML_100K_URL, ML_100K_FILENAME.name)\n", + " with ZipFile(ML_100K_FILENAME.name) as zip_file:\n", + " zip_file.extractall()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The goal of MovieLens is to enable models to predict the rating a user would give to a movie they have not yet watched. This is a classic example of a recommendation system. The dataset is huge, and contains many parts giving information about the movies, the users, and the ratings. To begin with, we will look at the ratings file. Each line in the ratings file (u.data) is formatted as:\n", + "\n", + "`user_id, item_id, rating, timestamp`\n", + "\n", + "Which tells us a single user's rating of a single movie.\n", + "\n", + "We will start by loading the ratings data into a pandas dataframe and then take a look at the first few rows. If you haven't used Pandas before, it's an extremely powerful library for dealing with tabular data. You can think of it as a Python version of Excel." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " user_id item_id rating timestamp\n", + "0 196 242 3 881250949\n", + "1 186 302 3 891717742\n", + "2 22 377 1 878887116\n", + "3 244 51 2 880606923\n", + "4 166 346 1 886397596\n", + "... ... ... ... ...\n", + "99995 880 476 3 880175444\n", + "99996 716 204 5 879795543\n", + "99997 276 1090 1 874795795\n", + "99998 13 225 2 882399156\n", + "99999 12 203 3 879959583\n", + "\n", + "[100000 rows x 4 columns]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "raw_ratings = pd.read_csv(ML_100K_FOLDER / \"u.data\", sep='\\t',\n", + " names=[\"user_id\", \"item_id\", \"rating\", \"timestamp\"])\n", + "raw_ratings" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The second file we'll look at is the movie metadata. This file (u.item) contains information about each movie, including the title and release date. Each line in the file is formatted as:\n", + "\n", + "`movie_id | movie_title | release_date | video_release_date | IMDb_URL | unknown | Action | Adventure | Animation | Children's | Comedy | Crime | Documentary | Drama | Fantasy | Film-Noir | Horror | Musical | Mystery | Romance | Sci-Fi | Thriller | War | Western`\n", + "\n", + "As you can see, the genres are binary variables. As with one-hot encoding, a 1 indicates that the movie is of that genre, and a 0 indicates that it is not. We aren't going to work with the genre data in this lab, but it's easy to imagine that it could be useful in a real-world recommendation system." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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12GoldenEye (1995)01-Jan-1995NaNhttp://us.imdb.com/M/title-exact?GoldenEye%20(...
23Four Rooms (1995)01-Jan-1995NaNhttp://us.imdb.com/M/title-exact?Four%20Rooms%...
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" + ], + "text/plain": [ + " item_id title release_date video_release_date \\\n", + "0 1 Toy Story (1995) 1995-01-01 NaN \n", + "1 1 Toy Story (1995) 1995-01-01 NaN \n", + "2 1 Toy Story (1995) 1995-01-01 NaN \n", + "3 1 Toy Story (1995) 1995-01-01 NaN \n", + "4 1 Toy Story (1995) 1995-01-01 NaN \n", + "\n", + " imdb_url release_year user_id \\\n", + "0 http://us.imdb.com/M/title-exact?Toy%20Story%2... 1995.0 308 \n", + "1 http://us.imdb.com/M/title-exact?Toy%20Story%2... 1995.0 287 \n", + "2 http://us.imdb.com/M/title-exact?Toy%20Story%2... 1995.0 148 \n", + "3 http://us.imdb.com/M/title-exact?Toy%20Story%2... 1995.0 280 \n", + "4 http://us.imdb.com/M/title-exact?Toy%20Story%2... 1995.0 66 \n", + "\n", + " rating timestamp \n", + "0 4 887736532 \n", + "1 5 875334088 \n", + "2 4 877019411 \n", + "3 4 891700426 \n", + "4 3 883601324 " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "all_ratings.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Data preprocessing\n", + "\n", + "It's always important to understand the data you've collected. Thankfully, Pandas continues to make this easy for us. Using the `describe` method, we can get a quick statistical summary of the data." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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item_idrelease_datevideo_release_daterelease_yearuser_idratingtimestamp
count100000.000000999910.099991.000000100000.00000100000.0000001.000000e+05
mean425.5301301988-02-09 00:43:11.369223296NaN1987.956216462.484753.5298608.835289e+08
min1.0000001922-01-01 00:00:00NaN1922.0000001.000001.0000008.747247e+08
25%175.0000001986-01-01 00:00:00NaN1986.000000254.000003.0000008.794487e+08
50%322.0000001994-01-01 00:00:00NaN1994.000000447.000004.0000008.828269e+08
75%631.0000001996-09-28 00:00:00NaN1996.000000682.000004.0000008.882600e+08
max1682.0000001998-10-23 00:00:00NaN1998.000000943.000005.0000008.932866e+08
std330.798356NaNNaN14.155523266.614421.1256745.343856e+06
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" + ], + "text/plain": [ + " item_id release_date video_release_date \\\n", + "count 100000.000000 99991 0.0 \n", + "mean 425.530130 1988-02-09 00:43:11.369223296 NaN \n", + "min 1.000000 1922-01-01 00:00:00 NaN \n", + "25% 175.000000 1986-01-01 00:00:00 NaN \n", + "50% 322.000000 1994-01-01 00:00:00 NaN \n", + "75% 631.000000 1996-09-28 00:00:00 NaN \n", + "max 1682.000000 1998-10-23 00:00:00 NaN \n", + "std 330.798356 NaN NaN \n", + "\n", + " release_year user_id rating timestamp \n", + "count 99991.000000 100000.00000 100000.000000 1.000000e+05 \n", + "mean 1987.956216 462.48475 3.529860 8.835289e+08 \n", + "min 1922.000000 1.00000 1.000000 8.747247e+08 \n", + "25% 1986.000000 254.00000 3.000000 8.794487e+08 \n", + "50% 1994.000000 447.00000 4.000000 8.828269e+08 \n", + "75% 1996.000000 682.00000 4.000000 8.882600e+08 \n", + "max 1998.000000 943.00000 5.000000 8.932866e+08 \n", + "std 14.155523 266.61442 1.125674 5.343856e+06 " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "all_ratings.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's do a bit more pandas magic to compute the popularity of each movie (number of ratings). We will use the `groupby` method to group the dataframe by the `item_id` column and then use the `size` method to compute the number of ratings for each movie. We will use the `reset_index` method to convert the resulting Series into a dataframe with an `item_id` column." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "popularity = all_ratings.groupby('item_id').size().reset_index(name='popularity')\n", + "items = pd.merge(popularity, items)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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item_idpopularityrelease_datevideo_release_daterelease_yearuser_idratingtimestamp
count100000.000000100000.000000999910.099991.000000100000.00000100000.0000001.000000e+05
mean425.530130168.0719001988-02-09 00:43:11.369223296NaN1987.956216462.484753.5298608.835289e+08
min1.0000001.0000001922-01-01 00:00:00NaN1922.0000001.000001.0000008.747247e+08
25%175.00000071.0000001986-01-01 00:00:00NaN1986.000000254.000003.0000008.794487e+08
50%322.000000145.0000001994-01-01 00:00:00NaN1994.000000447.000004.0000008.828269e+08
75%631.000000239.0000001996-09-28 00:00:00NaN1996.000000682.000004.0000008.882600e+08
max1682.000000583.0000001998-10-23 00:00:00NaN1998.000000943.000005.0000008.932866e+08
std330.798356121.784558NaNNaN14.155523266.614421.1256745.343856e+06
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" + ], + "text/plain": [ + " item_id popularity release_date \\\n", + "count 100000.000000 100000.000000 99991 \n", + "mean 425.530130 168.071900 1988-02-09 00:43:11.369223296 \n", + "min 1.000000 1.000000 1922-01-01 00:00:00 \n", + "25% 175.000000 71.000000 1986-01-01 00:00:00 \n", + "50% 322.000000 145.000000 1994-01-01 00:00:00 \n", + "75% 631.000000 239.000000 1996-09-28 00:00:00 \n", + "max 1682.000000 583.000000 1998-10-23 00:00:00 \n", + "std 330.798356 121.784558 NaN \n", + "\n", + " video_release_date release_year user_id rating \\\n", + "count 0.0 99991.000000 100000.00000 100000.000000 \n", + "mean NaN 1987.956216 462.48475 3.529860 \n", + "min NaN 1922.000000 1.00000 1.000000 \n", + "25% NaN 1986.000000 254.00000 3.000000 \n", + "50% NaN 1994.000000 447.00000 4.000000 \n", + "75% NaN 1996.000000 682.00000 4.000000 \n", + "max NaN 1998.000000 943.00000 5.000000 \n", + "std NaN 14.155523 266.61442 1.125674 \n", + "\n", + " timestamp \n", + "count 1.000000e+05 \n", + "mean 8.835289e+08 \n", + "min 8.747247e+08 \n", + "25% 8.794487e+08 \n", + "50% 8.828269e+08 \n", + "75% 8.882600e+08 \n", + "max 8.932866e+08 \n", + "std 5.343856e+06 " + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "all_ratings = pd.merge(popularity, all_ratings)\n", + "all_ratings.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "indexed_items = items.set_index('item_id')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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item_idpopularitytitlerelease_datevideo_release_dateimdb_urlrelease_yearuser_idratingtimestamp
01452Toy Story (1995)1995-01-01NaNhttp://us.imdb.com/M/title-exact?Toy%20Story%2...1995.03084887736532
11452Toy Story (1995)1995-01-01NaNhttp://us.imdb.com/M/title-exact?Toy%20Story%2...1995.02875875334088
21452Toy Story (1995)1995-01-01NaNhttp://us.imdb.com/M/title-exact?Toy%20Story%2...1995.01484877019411
31452Toy Story (1995)1995-01-01NaNhttp://us.imdb.com/M/title-exact?Toy%20Story%2...1995.02804891700426
41452Toy Story (1995)1995-01-01NaNhttp://us.imdb.com/M/title-exact?Toy%20Story%2...1995.0663883601324
\n", + "
" + ], + "text/plain": [ + " item_id popularity title release_date video_release_date \\\n", + "0 1 452 Toy Story (1995) 1995-01-01 NaN \n", + "1 1 452 Toy Story (1995) 1995-01-01 NaN \n", + "2 1 452 Toy Story (1995) 1995-01-01 NaN \n", + "3 1 452 Toy Story (1995) 1995-01-01 NaN \n", + "4 1 452 Toy Story (1995) 1995-01-01 NaN \n", + "\n", + " imdb_url release_year user_id \\\n", + "0 http://us.imdb.com/M/title-exact?Toy%20Story%2... 1995.0 308 \n", + "1 http://us.imdb.com/M/title-exact?Toy%20Story%2... 1995.0 287 \n", + "2 http://us.imdb.com/M/title-exact?Toy%20Story%2... 1995.0 148 \n", + "3 http://us.imdb.com/M/title-exact?Toy%20Story%2... 1995.0 280 \n", + "4 http://us.imdb.com/M/title-exact?Toy%20Story%2... 1995.0 66 \n", + "\n", + " rating timestamp \n", + "0 4 887736532 \n", + "1 5 875334088 \n", + "2 4 877019411 \n", + "3 4 891700426 \n", + "4 3 883601324 " + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "all_ratings.head()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false + }, + "source": [ + "**Quick Exercise**:\n", + "\n", + "As we have seen, the `groupby` method is a powerful tool to quickly compute statistics on the data. Use it to compute the average rating for each movie.\n", + "\n", + "**Hint**: you can use the `mean` method after the `groupby` method." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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item_idpopularitytitlerelease_datevideo_release_dateimdb_urlrelease_yearrating
01452Toy Story (1995)1995-01-01NaNhttp://us.imdb.com/M/title-exact?Toy%20Story%2...1995.03.878319
12131GoldenEye (1995)1995-01-01NaNhttp://us.imdb.com/M/title-exact?GoldenEye%20(...1995.03.206107
2390Four Rooms (1995)1995-01-01NaNhttp://us.imdb.com/M/title-exact?Four%20Rooms%...1995.03.033333
34209Get Shorty (1995)1995-01-01NaNhttp://us.imdb.com/M/title-exact?Get%20Shorty%...1995.03.550239
4586Copycat (1995)1995-01-01NaNhttp://us.imdb.com/M/title-exact?Copycat%20(1995)1995.03.302326
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" + ], + "text/plain": [ + " item_id popularity title release_date video_release_date \\\n", + "0 1 452 Toy Story (1995) 1995-01-01 NaN \n", + "1 2 131 GoldenEye (1995) 1995-01-01 NaN \n", + "2 3 90 Four Rooms (1995) 1995-01-01 NaN \n", + "3 4 209 Get Shorty (1995) 1995-01-01 NaN \n", + "4 5 86 Copycat (1995) 1995-01-01 NaN \n", + "\n", + " imdb_url release_year rating \n", + "0 http://us.imdb.com/M/title-exact?Toy%20Story%2... 1995.0 3.878319 \n", + "1 http://us.imdb.com/M/title-exact?GoldenEye%20(... 1995.0 3.206107 \n", + "2 http://us.imdb.com/M/title-exact?Four%20Rooms%... 1995.0 3.033333 \n", + "3 http://us.imdb.com/M/title-exact?Get%20Shorty%... 1995.0 3.550239 \n", + "4 http://us.imdb.com/M/title-exact?Copycat%20(1995) 1995.0 3.302326 " + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#raise NotImplementedError(\"Please calculate the average rating for each movie\")\n", + "\n", + "avg_ratings = all_ratings.groupby('item_id')['rating'].mean()\n", + "\n", + "\n", + "items_with_avg = pd.merge(items, avg_ratings, on='item_id')\n", + "\n", + "# Show the first few rows\n", + "items_with_avg.head()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's split the enriched data in a train / test split to make it possible to do predictive modeling:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "\n", + "ratings_train, ratings_test = train_test_split(\n", + " all_ratings, test_size=0.2, random_state=0)\n", + "\n", + "user_id_train = np.array(ratings_train['user_id'])\n", + "item_id_train = np.array(ratings_train['item_id'])\n", + "rating_train = np.array(ratings_train['rating'])\n", + "\n", + "user_id_test = np.array(ratings_test['user_id'])\n", + "item_id_test = np.array(ratings_test['item_id'])\n", + "rating_test = np.array(ratings_test['rating'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Explicit feedback: supervised ratings prediction\n", + "\n", + "Now let's begin to do some recommendation! We will build a model that takes a user and a movie as input and outputs a predicted rating. We will be taking advantage of embeddings to represent users and movies. That means that each movie and user will have an abstract representation in a continuous vector space. The model will learn these representations based on the ratings." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Predictive ratings as a regression problem\n", + "\n", + "The following code implements the following architecture:\n", + "\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "from tensorflow.keras.layers import Embedding, Flatten, Dense, Dropout\n", + "from tensorflow.keras.layers import Dot\n", + "from tensorflow.keras.models import Model" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "# For each sample we input the integer identifiers\n", + "# of a single user and a single item\n", + "class RegressionModel(Model):\n", + " def __init__(self, embedding_size, max_user_id, max_item_id):\n", + " super().__init__()\n", + " \n", + " self.user_embedding = Embedding(output_dim=embedding_size,\n", + " input_dim=max_user_id + 1,\n", + " name='user_embedding')\n", + " self.item_embedding = Embedding(output_dim=embedding_size,\n", + " input_dim=max_item_id + 1,\n", + " name='item_embedding')\n", + " \n", + " # The following two layers don't have parameters.\n", + " self.flatten = Flatten()\n", + " self.dot = Dot(axes=1)\n", + " \n", + " def call(self, inputs):\n", + " user_inputs = inputs[0]\n", + " item_inputs = inputs[1]\n", + " \n", + " user_vecs = self.flatten(self.user_embedding(user_inputs))\n", + " item_vecs = self.flatten(self.item_embedding(item_inputs))\n", + " \n", + " y = self.dot([user_vecs, item_vecs])\n", + " return y\n", + "\n", + "\n", + "model = RegressionModel(embedding_size=64, max_user_id=all_ratings['user_id'].max(), max_item_id=all_ratings['item_id'].max())\n", + "model.compile(optimizer=\"adam\", loss='mae')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Monitoring runs\n", + "\n", + "When training a model with Keras, we get a `history` object back that contains lots of information about the training run. We can use this to plot the training and validation loss to see how the model has improved during training." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n", + "\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 3.2766 - val_loss: 1.0354\n", + "Epoch 2/10\n", + "\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 998us/step - loss: 0.8981 - val_loss: 0.7970\n", + "Epoch 3/10\n", + "\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 995us/step - loss: 0.7568 - val_loss: 0.7678\n", + "Epoch 4/10\n", + "\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 993us/step - loss: 0.7217 - val_loss: 0.7579\n", + "Epoch 5/10\n", + "\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 994us/step - loss: 0.6993 - val_loss: 0.7464\n", + "Epoch 6/10\n", + "\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 997us/step - loss: 0.6801 - val_loss: 0.7420\n", + "Epoch 7/10\n", + "\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6577 - val_loss: 0.7390\n", + "Epoch 8/10\n", + "\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6387 - val_loss: 0.7363\n", + "Epoch 9/10\n", + "\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 991us/step - loss: 0.6173 - val_loss: 0.7372\n", + "Epoch 10/10\n", + "\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 994us/step - loss: 0.5920 - val_loss: 0.7377\n", + "CPU times: user 15 s, sys: 6.94 s, total: 21.9 s\n", + "Wall time: 11.7 s\n" + ] + } + ], + "source": [ + "%%time\n", + "\n", + "# Training the model\n", + "history = model.fit([user_id_train, item_id_train], rating_train,\n", + " batch_size=64, epochs=10, validation_split=0.1,\n", + " shuffle=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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Xs8d8/fXXVFZWtulBiDNmzGDAgAE8//zz7m2jRo3ixIkTREZGtrf6551aWLxEU5tFRKSh/fv3Y7Va3YElICCAPn36YDQ2/1WdnZ1N79696dOnT6uvs2vXLsaOHeuxLTQ0lB49erSr3p1FgcVLBtW2sORbrJRV2bxcGxERH+V0QnWFd15teDZwr169+NOf/uSxbdeuXZhMJg4dOgTAunXruOyyy4iJicFsNjN58mSOHz/uLp+VlcUFF1xAXFwcAI899hhXXHGFe7/D4WDZsmXEx8djNpv59a9/3ejJzv/85z+ZNGkSPXv2JDw8nMsvv5ycnBwAjh49isFg4MSJE9xyyy0YDAZuvPFGAH7yk5/w+OOPu89TXFzM3Llz6d27N1FRUcycOZPc3Fz3/j/96U8MHjyYd999l+TkZMLCwrjiiisoKipq9Z9ZW6lLyEuiQoPoGWmisMzK4cIKUhKjvV0lERHfY6uEpQneufavT0BweKuKjhgxgq+++spj26JFi7jrrrtISkoCoLCwkEceeYQLL7yQkydPcu+997Jw4ULeeecdwBVY6lpX6j43DCNz587lH//4B3/+85+54IILeOihh/j000+ZM2eOu0xubi533303w4cPp6qqit/85jfMmTOHf/3rX/Tt25d169Zxyy23cPjwYYxGI+Hhrvvbs2cP9913HwClpaVcfvnlDBkyhC1bthAcHMwvf/lLbr31VrZt2wa4WnYKCwvZsGEDb775JjU1NcyYMYMXXniBRx99tG1/zq2kwOJFST3DKSyzcqiwXIFFRKQLS05OZt++fe7PW7du5V//+hdvvfWWe9v8+fPd7wcNGsTtt9/OCy+84N6WlZXFpZde6v6cnZ3NjBkzAPj000957bXX2LdvHxdeeCEATz31FBdddJFHyLn55ps96nXPPfcwa9YsAIKCgjh+/DgjRowgIaE+BH777beUlJS4z/P73/+ewMBA1q1bR2CgKyY89thj/PSnP6WiooLw8HCys7MZOXIkb775JgaDAYBLLrmEwsLCNv/ZtZYCixcl9Yxg5+FijWMREWlOUJirpcNb126lESNG8N577wHgdDr59a9/za9+9Sv3gNmKigpWrlzJunXrOHbsGJWVldhsNiZOnOg+R3Z2Nr/85S8BVyvH0aNH3S0sr776Ktdcc407rIArgADuMjU1NaxevZq1a9dy9OhRLBYLdrudQYMGuY/58ssvGw3QzcrKIioqigEDBgDw5z//mUcffdQdVgCCg4NxOp3YbK4hDHv27GH58uXusAJw+PBhxo8f3+o/s7ZSYPGi+rVYNLVZRKRJBkOru2W8KTk5me+//x6LxcLmzZv57rvveOCBB9z7p02bhsVi4eGHH2bIkCFERERw0003uVs1jh8/zsmTJ92fs7OzCQwMZPjw4YArVNS1ttTZvXs3oaGhDB06FHB1GW3fvp3f/e53DB8+HLPZzK9+9SvCwuqDV1ZWFnfccYfHeRqOgzl58iR5eXmMGDHCo8y+ffu44IILiI6O5ujRo5SWlnLJJZe499tsNo8ZTudDuwbdrlmzhkmTJtGjRw8MBgMHDx5stmxRUREGg6HJV8OUt3bt2ibLNBzk42/q12JRC4uISFc2YsQIDAYD2dnZ/Pa3v+XRRx8lIsL1//hjx47x8ccf88orrzBr1ixGjRpFZWUlu3fvdn/BZ2VlERkZ6W4NycrKYtiwYQQHBwOuKc6nT592X8/pdPLf//3fjBgxgoCAAOx2O//zP//Ds88+y+zZsxk9ejRhYWF8/PHH7mvU1NSwf/9+Ro4c6VH3hmNlwsPDG13Lbrfzhz/8wd3dlJ2dTVhYmDsogSvQNJzhdD60K7CUlZVx8803M3v2bMxmM0OGDGm2bHR0NLm5uR6vv/71rwA8/fTT7nIZGRmkpaV5lMvLyyM+Pr49VewS6qY2Hz1ZQY3d4eXaiIhIe0VERNC/f38eeughAO688073PrPZTGBgIO+99x5Hjhxh3bp13HTTTTgcDlJTU4H60FDXxZKdne3x5X/ppZfy2muv8c9//pN///vf3HHHHezatcsdNAICAoiOjuaDDz7gyJEjfPjhh1x33XVYLBb3NZxOJ06nk927d5OXl0d5eXmja4WEhHDllVeydOlSvvrqK7Kzs7n++uvd3Vx15VNSUjymW2dlZdG/f3+io6M7/g+3VrsCywMPPMDtt99OWVkZqampHn1YZwoMDKRPnz7uV1xcHI8//jg33HADaWlp7nIZGRn8+Mc/9ijbu3fv9lSvy0iICiUkyIjN7uR4yemzHyAiIj4rOTmZnTt3smTJEo/xH9HR0bz44ov88Y9/ZOTIkbz99tvcfffdREZGumcQnRlQzpyu/Nhjj3HxxRczdepUpk6dSkJCAhdeeKHHMa+++iofffQRw4cP57//+7+59957AdxlgoKCWLZsmXtq9GuvvUZZWRlHjhzxuNbrr79OZGQkl1xyiXuK9KeffkpUVJS7bqNHj/a49y+//PK8tq4AGJzONkw0P0NqaipXXnmlexnh1nj66adZsmQJ+/fvd49SttlsREZGEhcXR1VVFf369ePBBx/k1ltvbfY8VqsVq9Xq/myxWEhMTKS0tBSz2dzeW+p0k//wKftyLfzpZz9k4g/8O6CJiLSkqqqKI0eOMHDgQEJCQrxdHelALf1uLRYLUVFRZ/3+bvfCcVarla+++ooxY8a0+phjx46xePFifv/733tMqSoqKuKZZ55h/fr1bN26lTFjxjB79mz3fO+mLFu2jKioKPcrMTGxvbfiVRrHIiIicnbtDiw5OTnYbLY2BZYFCxYwdOhQ7r77bo/t8fHxLFiwgHHjxjFmzBhWr17NBRdcwN/+9rdmz7Vo0SJKS0vdr4arBXYlWqJfRETk7No9rfmLL74gIiKixQG3DX3wwQds2rSJnTt3EhAQ0GJZo9FIUFBQi+VMJhMmk6lNdfZF9Q9B1NRmERGR5rS7heWLL74gNTW1xYcy1amsrGTBggXMmzePiy+++Kzld+zYwZEjR5g+fXp7q9dl1AWWbwrKOYfhRCIiIn6tTYHFZrORlZVFVlYWO3fuJCEhgaysLA4fPgy4pjsPGzaM9PR0j+MWL15MVVUVS5YsaXTOrVu3snr1anJycjh48CCrV69mxowZzJs3j8suu6z9d9ZFDOwRjsEApadtFFdUe7s6IiJep3+8+Z+O+J22qUsoIyPDI0Tk5OTwzjvvMGfOHNasWcPevXs5cOCAxwDYffv2sXz5cl5//fUm52cXFxezcuVK7r//fkwmE0OHDuW5555rcYaQPwkNDqBvdCjflZzmUGEFcRFdv5tLRKQ96paar6ysJDQ01Mu1kY5UWVkJ1P+O2+OcpjX7ktZOi/JFP381g/89WMiya5O5aWw/b1dHRMRrcnNzOXXqFL169SIsLKzFdb7E9zmdTiorKykoKCA6OrrJxWBb+/2tZwn5gKSeEfzvwUIOFWimkIh0b3369AGgoKDAyzWRjhQdHe3+3baXAosPSOqlqc0iIgAGg4H4+Hh69erlfjKwdG1nm/XbWgosPkBTm0VEPAUEBHTIl5z4j3ZPa5aOUxdYjpdUUmWze7k2IiIivkeBxQf0iAjGHBKI0+l6crOIiIh4UmDxAQaDof6ZQgUKLCIiImdSYPER9eNYNPBWRETkTAosPkKBRUREpHkKLD5CT20WERFpngKLj2g4hsXh8IvFh0VERDqMAouP6BcbRqDRwGmbnTxLlberIyIi4lMUWHxEUICR/nFhgLqFREREzqTA4kPqBt4e1oq3IiIiHhRYfIh7HItaWERERDwosPgQTW0WERFpmgKLD3FPbdZqtyIiIh4UWHzIoNoWljxLFeXWGi/XRkRExHcosPiQqNAgekaaADisbiERERE3BRYfoxVvRUREGlNg8TF13UIaxyIiIlJPgcXHaKaQiIhIYwosPkZdQiIiIo0psPiYuhaWo0WV1NgdXq6NiIiIb1Bg8TF9o0MxBRqptjv4ruS0t6sjIiLiExRYfIzRaKgfeKtuIREREUCBxSdpHIuIiIgnBRYflKSpzSIiIh4UWHyQntosIiLiSYHFB6lLSERExJMCiw8a1MPVwlJSaaO4otrLtREREfE+BRYfFBocQN/oUECtLCIiIqDA4rPc41gKFFhEREQUWHyUxrGIiIjUU2DxUfUPQdTUZhEREQUWH6WnNouIiNRrV2BZs2YNkyZNokePHhgMBg4ePNhi+R07dmAwGBq9Pv/880bnveiiiwgPD2f8+PHs3bu3PdXzC0m9XF1Cx4srqbLZvVwbERER72pXYCkrK+Pmm29m9uzZmM1mhgwZ0mL5jIwMfvCDH5Cbm+vxGjdunLvMkiVLWLhwIYsXLyY7O5u+ffsya9Ys7Pbu+WXdM8JEZEggDid8e7LS29URERHxqsD2HPTAAw8AMGfOHFJTUzEYDC2Wz8jIYPz48fTp06fJ/fv37+fRRx8lPT2dKVOmAPC73/2O5ORkvvnmG4YOHdqeanZpBoOBpJ4RZB0/xaHCcob2ifR2lURERLzmnMawfPHFF4wZM+as5TIyMti4cSNxcXEMHz6c5cuXe+x//vnnSU1NdYcVgNjYWAAKCgqaPKfVasVisXi8/E39M4U0jkVERLq3dgcWq9XKV199ddbAUl1dzdy5c1m/fj3btm1j5syZPPjgg7z66qvuMhs2bOC6667zOK4uqMTExDR53mXLlhEVFeV+JSYmtvdWfFbdOBYNvBURke6u3YElJycHm8121sASHBzMww8/zKWXXsqoUaN44oknuPTSS9m0aRMAubm55OfnM3r0aI/jMjMzCQkJabY7aNGiRZSWlrpfx48fb++t+CxNbRYREXFp1xgWcHUHRUREnHXAbVNMJhMBAQEAlJSUANCzZ0+PMuvXryctLY2goKBmz2Eymdp87a6k4dRmp9N51rFCIiIi/uqcAktqaipGY9saab7++mt27tzJ6tWrgfqgUlxc7C6TkZHBhx9+yMcff9ze6vmF/nFhBBoNVFbbybNUER8V6u0qiYiIeEWb0obNZiMrK4usrCx27txJQkICWVlZHD58GHBNdx42bBjp6ekA7N69m+XLl/Pll19y6NAh3n77bSZNmsQVV1zBLbfcArgCy9ixY3nqqac4ePAgmzdvZurUqcybN48JEyZ07N12MUEBRvrFhQFwqEDdQiIi0n21KbBkZGSQmppKamoqOTk5vPPOO6SmprJs2TIA9u7dy4EDB9wDYE+dOsWf//xnfvzjH5OamsozzzzDgw8+yPvvv+/RvbF27Vqqq6tJTU1lwYIFLFq0iFWrVnXgbXZdWvFWREQEDE6n0+ntSnQEi8VCVFQUpaWlmM1mb1enwzy5+QAv/+8hfnZJfxZPH+Ht6oiIiHSo1n5/61lCPk5PbRYREVFg8XlJveoWj9MYFhER6b4UWHxcUg9XYMmzVFFurfFybURERLxDgcXHRYUF0SPCtd7MYXULiYhIN6XA0gVoHIuIiHR3CixdgMaxiIhId6fA0gVoLRYREenuFFi6AHUJiYhId6fA0gXUtbAcLaqkxu7wcm1EREQ6nwJLF9A3OhRToJFqu4PvSk57uzoiIiKdToGlCzAaDQzSOBYREenGFFi6CI1jERGR7kyBpYtwzxTS1GYREemGFFi6CPdaLGphERGRbkiBpYtQl5CIiHRnCixdxKDahyCWVNoorqj2cm1EREQ6lwJLFxEaHEDf6FBArSwiItL9KLB0IfXPFFJgERGR7kWBpQvROBYREemuFFi6kPqHIGpqs4iIdC8KLF2IntosIiLdlQJLF5LUy9UldLy4kiqb3cu1ERER6TwKLF1IzwgTkSGBOJzw7clKb1dHRESk0yiwdCEGg0HdQiIi0i0psHQx9c8UUmAREZHuQ4Gli6kbx6IWFhER6U4UWLoYTW0WEZHuSIGli2k4hsXpdHq5NiIiIp1DgaWL6R8XRqDRQGW1nTxLlberIyIi0ikUWLqYoAAj/eLCADhUoG4hERHpHhRYuiBNbRYRke5GgaULqgsshxVYRESkm1Bg6YLqn9qsLiEREekeFFi6oKRe6hISEZHuRYGlC0rq4QosuaVVlFtrvFwbERGR869dgWXNmjVMmjSJHj16YDAYOHjwYIvl33rrLa6++mri4+OJiIjgsssuY9euXR5l1q5di8FgaPTKzc1tTxX9WlRYED0iTAAcUbeQiIh0A+0KLGVlZdx8883Mnj0bs9nMkCFDWiz/7rvvct1117F582YyMjKIiIhg8uTJnD592l0mIyODtLQ0cnNz3a+8vDzi4+PbU0W/Vz+ORd1CIiLi/wLbc9ADDzwAwJw5c0hNTcVgMLRY/v333/f4vHDhQiZNmsQ333xDcnIy4Aos06ZNo0+fPu2pUreT1CuCfx0pVmAREZFu4ZzGsHzxxReMGTOmzcdt376dyMhIBg4cCIDNZiMrK4sXXniBuLg4UlNTWbt2bYvnsFqtWCwWj1d3orVYRESkO2l3YLFarXz11VdtDiwbNmzgySefZPny5UREuL50i4qKeOaZZ1i/fj1bt25lzJgxzJ49m23btjV7nmXLlhEVFeV+JSYmtvdWuiR3l5BWuxURkW7A4GznE/QyMzO5+OKLOXDgAEOHDm3VMStXruThhx9m5cqV3HHHHc2Wczgc9O/fn1mzZrF8+fImy1itVqxWq/uzxWIhMTGR0tJSzGZz226mCzpeXMnlT/+T4AAj+3//HwQYW+6WExER8UUWi4WoqKizfn+3awwLuLqDIiIizjrgFqCmpoZ77rmHdevWsXnzZiZMmNBieaPRSFBQEAEBAc2WMZlMmEymNtfbX/SNDsUUaMRa4+C7kkr6x4V7u0oiIiLnzTkFltTUVIzGlnuViouLuf7668nPzycjI4NBgwad9dw7duzgyJEjTJ8+vb3V83tGo4FBPSPYn2vhUGG5AouIiPi1No1hqRscm5WVxc6dO0lISCArK4vDhw8DrunOw4YNIz09HYCDBw8ybtw4KioqeP/99wkLCyMvL4+8vDwcDgcAW7duZfXq1eTk5HDw4EFWr17NjBkzmDdvHpdddlnH3q2f0TgWERHpLtrUwpKRkeERInJycnjnnXeYM2cOa9asYe/evRw4cMA9APa5557jm2++AfAY5xIaGorFYsFoNFJcXMzKlSu5//77MZlMDB06lOeee45bb721I+7Pr2mmkIiIdBftHnTra1o7aMefbMj6nv/6SxYXD4jh3XmXers6IiIibdba7289S6gLq29hUZeQiIj4NwWWLmxQ7RiW4opqiiuqvVwbERGR80eBpQsLCw6kb3QoAIc1jkVERPyYAksXN0gPQRQRkW5AgaWL0zgWERHpDhRYurikXrWBpUAtLCIi4r8UWLq4JHUJiYhIN6DA0sUNru0SOlZcibXG7uXaiIiInB8KLF1cz0gTkaZAHE749mSlt6sjIiJyXiiwdHEGg4FBGsciIiJ+ToHFD2gci4iI+DsFFj+gqc0iIuLvFFj8gJ7aLCIi/k6BxQ8M7lXbJVRQjp88fFtERMSDAosf6BcbToDRQEW1nXyL1dvVERER6XAKLH4gONBI/9gwQN1CIiLinxRY/MQgjWMRERE/psDiJ5IajGMRERHxNwosfkJTm0VExJ8psPgJTW0WERF/psDiJ+pWu80traLcWuPl2oiIiHQsBRY/ER0WTI+IYACOqFtIRET8jAKLH9FMIRER8VcKLH5E41hERMRfKbD4ET21WURE/JUCix9J6lXbwlKgMSwiIuJfFFj8yODaLqEjRRXYHXoIooiI+A8FFj+SEB2KKdBItd3BdyWV3q6OiIhIh1Fg8SMBRgMDe2gci4iI+B8FFj+jcSwiIuKPFFj8jKY2i4iIP1JgORtrGXz7mbdr0Wqa2iwiIv5IgaUldhv89WfwxlTIetvbtWkVPbVZRET8kQJLS5wOCIsDRw2kz4P/fQacvj1deFBtC0txRTXFFdVero2IiEjHUGBpSaAJZq6Gy+53ff7nE/DBva6WFx8VFhxI3+hQAA6rW0hERPyEAsvZGI0w8TG45lkwGGH3/8Db/wlW3w0DgzSORURE/Ey7AsuaNWuYNGkSPXr0wGAwcPDgwbMek56ezujRowkLC2PUqFFs3769yfNedNFFhIeHM378ePbu3due6p0fF8+BG/8MgaHwzT/g9clQlu/tWjVJ41hERMTftCuwlJWVcfPNNzN79mzMZjNDhgxpsfzatWu58cYbufPOO9m7dy8TJkzg2muvxWKxuMssWbKEhQsXsnjxYrKzs+nbty+zZs3Cbre3p4rnx0WT4ba/QVgPyM2GP02Ewn97u1aN1K/FohYWERHxDwans/2jSOfMmcM333zDJ5980myZoqIikpKSWLJkCffccw8A5eXlREZGsmXLFtLS0ti/fz/Jycmkp6czZcoUAPbu3UtycjIHDhxg6NChjc5rtVqxWq3uzxaLhcTEREpLSzGbze29pdYpPgxrr4fiQxASDTe9Df0vPb/XbIPPDhVx85p/MSAujE9+dYW3qyMiItIsi8VCVFTUWb+/z2kMyxdffMGYMWNaLLNmzRrCwsKYN2+ee1tERARBQUEUFBQA8Pzzz5OamuoOKwCxsbEA7jJnWrZsGVFRUe5XYmLiudxK28QOgjs+ggvGQtUp+J/psPe9zrv+WdQ9BPFYcSXWGh9qoRIREWmndgcWq9XKV199ddbAkp6ezvTp0wkMDHRvO3XqFDabjZiYGAA2bNjAdddd53FcXVCpK3OmRYsWUVpa6n4dP368vbfSPuFx8PONMGwq2Kth3S/g/1b4xLTnnpEmIk2BOJzw7Uk9BFFERLq+dgeWnJwcbDbbWQNLdnY2o0eP9tiWmZkJQEpKCrm5ueTn5zdZJiQkpMnuIACTyYTZbPZ4dbqgUJj1Boyb7/r80W9h80Pg8G6rhsFgYJDGsYiIiB9pd2D54osviIiIaHHAbWVlJVarlZ49e3psX79+PSkpKSQmJlJSUgLQZJm0tDSCgoLaW8XOYQyAq5+EtKWuzxmrXavjVnu3ZUNL9IuIiD85p8CSmpqK0dj8KcLCwggPD6e4uNi97dixY7zxxhvcf79rMba6oNKwTEZGBh9++KG7TJdwyd0w63UIMMGBTfA/06CiyGvV0dRmERHxJ20KLDabjaysLLKysti5cycJCQlkZWVx+PBhwDXdediwYaSnp7uPueaaa1i1ahU5OTns2LGDiRMncuWVVzJ79mzAFVjGjh3LU089xcGDB9m8eTNTp05l3rx5TJgwoePutDMMnwk/2+CaOfTdLnhlEpw85JWq6KnNIiLiT9oUWDIyMkhNTSU1NZWcnBzeeecdUlNTWbZsGeCainzgwAGPGTsrVqygX79+jB8/nptuuokbb7yR9evXe7TMrF27lurqalJTU1mwYAGLFi1i1apVHXSLnaz/Ja4ZRNH9XNOfX5kE32V2ejUG96rtEioo5xxmrouIiPiEc1qHxZe0dh53pynLh7dugNws1+q4178CF13TaZevrnEw7HdbsDuc7Fz0U/pEhXTatUVERFqrU9ZhkRZE9natijvkKqg5DX+5BTLWdNrlgwON9I8NA9QtJCIiXZ8Cy/lkioD/fBvG3AY44e8LYetvweHolMsP0jgWERHxEwos51tAIEx5Hq78revzZytg/RyosbZ4WEdIqh3HclgzhUREpItTYOkMBgP8eCHM/CMYA13L+L85E06XnNfLaqaQiIj4CwWWzpTyn3Dre2Ayw7f/B6+kwalj5+1y7sCi1W5FRKSLU2DpbIN+Ar/YApEJUPRv+NNEOJF1Xi5Vt9rtidIqKqw15+UaIiIinUGBxRt6D4c5/4DeI6A8H16bDF//o8MvEx0WTI+IYACOFGkci4iIdF0KLN4S1Rdu/zsMnAC2CteaLbv/p8Mvo5lCIiLiDxRYvCkkCm5ZByk3gdMOGxfAP5dCB67lp3EsIiLiDxRYvC0wGGa8BD/+levz/z4F6b+EmuoOOX39U5vVJSQiIl2XAosvMBjgykdg6h/AEADZb8Fbs6DKcs6nTuqlLiEREen6FFh8yZjb4Ka/QFA4HP4EXrsaLCfO6ZSDa7uEDhdVYHf4xWOjRESkG1Jg8TUXXgW3/w3Ce0H+Xte05/x97T5dQnQopkAj1TUOvi853YEVFRER6TwKLL4oIdU17bnHhWD5Hl79DziyvV2nCjAaGNijbhyLuoVERKRrUmDxVTH94RcfQr9LwVoKb14Le/7arlNpHIuIiHR1Ciy+LCwWZr8Pw2eCwwbr74RPn23ztGc9U0hERLo6BRZfFxQC170Kl9zj+rxtMWy6H+ytX2rfPbW5QFObRUSka1Jg6QqMRkhbAlc/DRjgi9fgnVugunUBRC0sIiLS1SmwdCXj5sKNb0JgCBzcAq9fA+UFZz1sUG0Ly8mKakoqOmZBOhERkc6kwNLVDJsKP/8AQmPhxJeuac9FX7d4SFhwIH2jQwH4Rq0sIiLSBSmwdEWJY13TnmMGwqlv4ZVJcGxni4fUtbLc/tou7n8ni48P5FNd4+iM2oqIiJwzg9PZgU/a8yKLxUJUVBSlpaWYzWZvV6dzlBfC2zfC919AgAmuXQ3DZzRZ9JN/F/Dr9TmcKK1yb4sKDeI/hvdhSko8lwyKIzBA+VVERDpXa7+/FVi6uupKeO8O+PffAYNrcO4ldzdZ1OFwsvtYCZv25PK3nFwKy6zufXHhwVyd3IepIxO4eEAsRqOhk25ARES6MwWW7sRhh80Pwa4/uT7/6Jdw1RLX7KJm2B1O/nXkJJv25LI5J5eSSpt7X2+ziWuSE5iSEk9qYjQGg8KLiIicHwos3Y3TCZ+tgI9+5/o8bJqriygo9KyH2uwOPjt0kg+yT/DhV3mUVdWv8XJBTCjXjIxn6sgEhieYFV5ERKRDKbB0VznrIH0+2KshcZzr6c9hsa0+3FpjZ/vBIjbtOcFH+/KprLa79w3sEc6UkfFMTUngwt6R56P2IiLSzSiwdGdHd8BfboaqUogbDLesg9iBbT7N6Wo7//x3AZv2nGDb/gKsDWYVXdg7gqkjE5iSkuB+uKKIiEhbKbB0dwUH4M/XQ+lxCO8JN78Dfce0+3Tl1hq27c/ng+wT/O/BQmz2+r82I/qamTIygSkj47kgJqwjai8iIt2EAouAJRfemgV5ORAUBte/CkOvPufTllba+HBfHpv25PJ/3xRhd9T/FUrtF83UkQlcMzKe3uaQc76WiIj4NwUWcbGWwV9/Doe2uT6bosAcD+YEiExw/fR49YXQGGjl4NqT5Va2fJXHpuxcdh456X6QtMEAYwfEMiUlgckj+hAXYTpPNygiIl2ZAovUs9tc054zXwNa8esODIHIeFd4qQs35r4NtiVARC8wBngcVmCp4u85uXywJ5cvvi1xbw8wGrg0KY6pIxNIG96HqLCgDr5BERHpqhRYpDFrmaubyPI9lNX+tJyo32Y5AZVFrTuXIQAi+9SGmIQG4cYVaPKIZdMRJxv3nmTPd6Xuw4ICDPx4SE+mpMQzcVhvIkMUXkREujMFFmmfGmttmGkQYs4MN2W54LSf/VwAYT2whvXmhCOWr8ojOFARQR6x5DljOWnswYVDhjIpNYmfXtSb0OCAs59PRET8igKLnD8OO5QXQNmJ2hBzxqtue03V2c8FlDlDKSCW6vB4zL360fuCgQRGX9BgjE1f11oyWrRORMTvKLCIdzmdcLqkcYixfA+WXJyWEzhKvyeg2tK68wWYXF1OkQmurqjgcNcqvkGhEBgKQSG1P+u2hbhmRrm3h3iWDQqDgGCFIBERL2vt93dge07ucDh4+umnefnllykuLmb8+PGsXr2axMTERmWLioro2bNnk+cZOXIk2dnZAKxdu5bZs2c3KnPixAni4+PbU03xJoPB1SoSFgt9RjTeDQQAWMtxWk5w6PBBvtp/gBPHDxFuLSTeUEwfw0nijSX0oBTsVig56np1XCVbCD1NBaAzyrYlLAWGQkC7/nMTERHa2cJy55138re//Y3XX3+dfv36cddddxESEsLWrVsbla2pqaGoyHMg56effsoNN9zAli1bSEtLA+Dee+/l4MGDvP766/WVMxjo3bt3q+qkFhb/4HA4+eJYCR9kn+DvObkUlVcTRA29DSUMDSsnLbGGcT1s9AxxEGqoxlBTBbbTru4nWyXYqqDmtOunrbJ2e8P3leB0nL0i54MxqIkAFNagtajufRgEh0FQ7fa69x4/Qxu8rz1OLUYi0gWdty6hrVu3MnnyZDIzMxk1ahQAmzZtYtq0aVRWVhIS0vJiYTabjdTUVIYPH84777zj3v6jH/2IadOm8etf/7ot1XFTYPE/NXYH/zpSzKY9J9i8N49TDZ4oDa4ZR70iQ+htNtHbHNLgZaKPOYRe5hD6RIUQYWrQsuF0uqZ514WamtOuwNMo9FQ12H5mADpje6Ow1OB8rRzH0yEMAW0MPw3D0pnbakOR+xzhjaaxi4h0hPMWWNLS0jCbzbz77rvubZ999hnjx4/n2LFjTXYLNfT000+zZMkS9u/fT0JCAuAKMZGRkcTFxVFVVUW/fv148MEHufXWW5s9j9VqxWq1uj9bLBYSExMVWPyUze5gxzdFfJB9gk+/LqKo3Epr/+aGBwd4hJneUSH0jnR97hNloldkCL3MJkyB5+EL2eGoDy5NBSDbabBVQHWl63N1Rf32uvfun7Vl3O9rj3PYzl6PjhBgaj78BAS7Ao0xsMErwNWq5PE50PMVENjEMXXvg5o5Z92xTe1vzXUD1BIl4kPOyxiW8vJytm3bxtq1az22FxQUABAdHd3i8ceOHWPx4sUsXbrUHVbANc7lmWeeYezYsQQGBvLSSy8xe/Zs4uPj+elPf9rkuZYtW8bjjz/elupLFxYUYOSKob24YmgvwBVgCsus5FmqKLBUkW9xvc93v6zkW6ooq6qhotrO4aIKDhdVtHiN2PBgekWa6FMXaKJqA06kq6Wml9lEXLiJAGMbvuyMRtcXevB5fMaS3eYZaM4MOh7hp9IzILW4rfYcdYsN2q1w2uoaTN3VNReCDAG1gcZY+/OM90ZjgzINynqUOaNso31NfG723LXhqtG5G57nzOOauK7BWLvd2Pi67n3NndPQhn0N3ysUNsvppP5fXM76bV75TNvKh8ZAYHBzd3ZetamF5fPPP+fSSy/l3//+NxdeeKF7+yOPPMLbb7/NoUOHWjx++vTpfPfdd2RkZBAQ0Py/Zh0OB/3792fWrFksX768yTJqYZHWqLDWUFBmJa+0ioIyV5jJK7WSX1ZFfmmV66fFSnVN68a1BBgN9Io0ubqbWuiKMocEYvCH/2E7na7WIY8wc0b4sZ12hSZHjWvKu6Pufd3nmvqXvcbzs3u/renyDnuDc5+539Z0eUdN7TGd1PIkzWtTODKeUS4A1/D8ui/3M3/SzD5a2NfScU3to53HNbHPX8zZBhf8sENPeV5aWEpKXP+yOnPWz/r165k6dWqLx37wwQds2rSJnTt3thhWAIxGI0FBQS2WM5lMmEx6Po20LNwUyEBTIAN7hDdbxul0cqrSRn5ZlSvY1LbO5DVoqcm3VFFUbsXucJJbWkVuaRXZLVw3NCiA3ua6YNN4nI0r2JgICfLxcSGGBjOpiPN2bdrO4Tgj0DT1ahBynHbXMU67a7vT7hqkXff+zH2O2v0eZVqzz3FGmQbvzyzrdDR/3UbH1527ifO7z9PUPmfTdfGof4N9rf0CdtrB3spFJsWH1f7jy8v/CGtTYKkLKsXFxcTExACwbt06vv76azZu3NjscZWVlSxYsIB58+Zx8cUXn/U6O3bs4MiRI0yfPr0t1RNpF4PBQEx4MDHhwVzUp/l0X2N3UFRe7Q4zzXVFlZ62cdpm5+jJSo6erGzx2uHBAcRGBBMb5rp+bHj9+7jwBttqt0eFBmFsS5dUd2c0gjEY8E4Ttt9yOhuHMY9g0559zsYB0c1Q+2V55s/m9rX3uOb20c7jztjX5Hka3mYLZTvzs4+2DrcpsKSkpJCYmMjixYv53e9+x7/+9S/uuusuli5dyuDBgykrK2Ps2LEsW7aMGTNmuI9bvHgxVVVVLFmypNE5t27dytGjR7nkkkswmUx88skn/PrXv2bevHlcdtll53yDIh0lMMBInyjXeJaUFspV2ezu8FIfbKrIa9Bak1dahbXGQUW1nYri0xwvPt2qOhgNENNiuAkiNtxUuz2IuHCTHnkgHa9u7AoBrsHPIp2gTYElODiY9957j/nz55OcnExSUhIvv/yyezbP3r17OXDggMdMoX379rF8+XJef/31JgflFhcXs3LlSu6//35MJhNDhw7lueeea3GGkIgvCwkKoH9cOP3jWu6GslTVUFJRzcmKakoqqimurKa49v2Z24orqimrqsHhhJO1+1tfHyOxYcHERgQTE+bZYtNUS050aBCBAcaO+KMQEekwWppfpIuornFwqtIzxJRUVFNcYaO4wkpxpc0z7FRUU21v3yJ5UaFB7iATE9ZEC054UO12EzHhQUSY/GSQsYh0uvO6NL+IdL7gQCO9amchtYbT6aSi2u4OL+6QU+kZaoprW3JKKqo5ddqG0wmlp22UnrbBWaaC1wkKMLi6qmq7omLDg4kOq2/FiQkLcgWe2hae6DCFHBFpGwUWET9lMBiIMAUSYQokMbZ168DYHU5OVdaGmnLXT3cLToWtybBz2mbHZndSUGaloMx69ovUakvIqSujkCPSfSmwiIhbgNFAXISJuAgTg3u17pjT1XZKakNOSYWN4spqV9dVbbApqbTVBp9qTlXazjnk1IeaoPoByGGuVpvY8GCFHBE/pcAiIuckNDiA0OBQEqJDW31Mlc3uDjFtDTmFZVYKz0PIaThWJzw4QCFHxMcosIhIpwsJCiA+KpT4qPaHnLpWnYahxqOl5xxCTnCgkbiGa+DUvlzbTA0GH7u2aX0ckfNPgUVEuoRzDTkeoaaivgXHHXzKXYOPq2wOqmsc7hWNW6NufRx3sImob7GJDQ8mNsLkHnBcty84UFPHRdpCgUVE/FZ7Qk5ldY17QPHJCleQqRtsXFxe+7PCSkmljZPlViztXB8nMiTwjJab+nVxYsNNjVp4wtRNJd2cAouISANhwYGEBQdyQUzrZlbZ7I76Rf7cgcZzNtXJCmttCHK17NgdTsqqaiirquHbszy+oY6ptpsq5ozuqbgGCwLGRdQGn9rHOLTpyeIiPk6BRUTkHAQFtG19HIfDiaXK5g42DV8nG7bmVFjdAcha48Ba4+BEaRUnWtlNZTCAOSTIve5NTFj9z5iwIKLrZlLVva8dkOzzD+SUbkuBRUSkExmNrllL0WHBJPU8e3lwdVOdLG8i4NQFG/daOa5tZVU1ngsAtkFIkLE23NSuhRPWROAJ9ww85hANOpbzT4FFRMTHhQUHEhbb+gUAbXYHpypttYsAurqhPN5X1G2rm23lKlvjcFJla9uAY3ANOo4KPTPc1C/+17iFx/VerTnSFgosIiJ+JijASM9IEz0jTa0+xul0Um6taRRi6tbFaRh46mZanaqspqLajsNJ7b62teaEBgV4dEl5tuq4xunERbieWdUjwjV+J0gP5uy2FFhERASDwUBkSBCRIUGtbsmB+odyNtmSU/tAzvrA49p26rQNu8PJaZud06X2Vo/LAYgOcz2YM652wHHDQBMXYaoNOa7P6qryLwosIiLSbm19KCe4Bh6XWWsaB53aVpviuu21A5HrZlk5nNR2ddk4VHj2B3MGGA3uGVU9IkzucBMXEewKOOEmYiOC6VG7TVPHfZsCi4iIdCqj0UBUaBBRoUH0j2vdMXaHk9LTrrVvimpDzMnyatfnCtfPuplWRbXr49gdDVc5LjvrNUKCjE221ri7pmrf94hwrXKsxf86lwKLiIj4vLrWktjwYIb0Pnt5a42dkgobReVW16J+5bUBp+597c+i2oBjrXFQZXPw/anTfH/qdKvqZA4JdLfcxNZ1RdWFnDO6qqL1+IZzpsAiIiJ+xxQYQJ+oAPpEnb2ryul0Ulltd3c/1f0sKq/22FZUXj913O5wYqmqwVJVw+Gi1ndP9agdX9Mzoq5ryuR6Rbpab3pGulpvNLi4MQUWERHp1gwGA+GmQMJNgfSLO/uA47rF/4rKG7fW1K1sXLevqLya0tpBxm15CGdMWJB78LA71JwRcOo+d5fp4QosIiIibdBw8b/BvSLOWr66xkFJZTWFZVaKyhuGmfouqcIyq3v1Y7vD6Z4m/k3B2esTYQp0h5czW216hAfXhhtXwIkwBXbZgcUKLCIiIudRcKCR3uYQerdiJpXD4aSkstodagobhJqGY26Kylzvq+0Oyq01lFtrONqK51KZAo0erTUtBRxfG3ejwCIiIuIjjEZD7aBdExDZYlmn0zU9vC681LXaFDYINScr6t9XVNux1rR+YHGA0eCxrk3PCBPzf5LEkN4t1+t8UWARERHpggwGA+YQ17OcBrXiuVSnq+213VCNW20Kzwg4pypd424KyqwUNBh387NLB5y/GzoLBRYREZFuIDQ4gMTYsFatZFxd46C4rnWmQcDp34ZVkDuaAouIiIh4CA400icqpFXTwjuLJnqLiIiIz1NgEREREZ+nwCIiIiI+T4FFREREfJ4Ci4iIiPg8BRYRERHxeQosIiIi4vMUWERERMTnKbCIiIiIz1NgEREREZ+nwCIiIiI+r12BxeFw8OSTTzJgwADMZjNXX301x48fb7b8jh07MBgMjV6ff/65R7k1a9Zw0UUXER4ezvjx49m7d297qiciIiJ+pl2BZe7cuaxYsYLVq1eTkZFBRUUFd9xxR7PlMzIy+MEPfkBubq7Ha9y4ce4yS5YsYeHChSxevJjs7Gz69u3LrFmzsNvt7amiiIiI+BGD0+l0tuWArVu3MnnyZDIzMxk1ahQAmzZtYtq0aVRWVhIS0vjJjv/5n/+J2Wxm9erVTZ5z//79JCcnk56ezpQpUwDYu3cvycnJHDhwgKFDh561XhaLhaioKEpLSzGbzW25JREREfGS1n5/t7mF5dlnn2XmzJnusAIQGxuL0+mksLCwyWMyMjLYuHEjcXFxDB8+nOXLl3vsf/7550lNTXWHlbpzAhQUFDR5TqvVisVi8XiJiIiIf2pTYCkvL2fbtm1cd911HtvrQkV0dHSjY6qrq5k7dy7r169n27ZtzJw5kwcffJBXX33VXWbDhg3NnjMmJqbJuixbtoyoqCj3KzExsS23IiIiIl1ImwJLTk4Odrud0aNHe2zPzMxk0KBBREZGNjomODiYhx9+mEsvvZRRo0bxxBNPcOmll7Jp0yYAcnNzyc/Pb/KcISEhzXYHLVq0iNLSUverpUG/IiIi0rUFtqVwSUkJAD179vTYvn79eqZOndrq85hMJgICAs56zrS0NIKCgpo9h8lkavU1RUREpOtqU2CpCxXFxcXurpp169bx9ddfs3Hjxlad4+uvv2bnzp3uAbgNz1knIyODDz/8kI8//rgt1RMRERE/1aYuoZSUFBITE1m8eDGHDh3irbfe4rbbbmPp0qUMHjyYsrIyhg0bRnp6OgC7d+9m+fLlfPnllxw6dIi3336bSZMmccUVV3DLLbcArsAyduxYnnrqKQ4ePMjmzZuZOnUq8+bNY8KECR1+wyIiItL1tKmFJTg4mPfee4/58+eTnJxMUlISL7/8Mrfeeivgmop84MAB9wDYU6dO8ec//5lHH30Ug8HA4MGDefDBB5k7dy4Gg8F93rVr13LnnXeSmppKfHw8ixYt4r/+67868DZFRESkK2vzOiy+SuuwiIiIdD3nbR0WERERkc6mwCIiIiI+T4FFREREfJ4Ci4iIiPg8BRYRERHxeQosIiIi4vMUWERERMTnKbCIiIiIz1NgEREREZ+nwCIiIiI+T4FFREREfJ4Ci4iIiPg8BRYRERHxeQosIiIi4vMUWERERMTnKbCIiIiIz1NgEREREZ+nwCIiIiI+T4FFREREfJ4Ci4iIiPg8BRYRERHxeQosIiIi4vMUWERERMTnKbCIiIiIz1NgEREREZ+nwCIiIiI+T4FFREREfJ4Ci4iIiPg8BRYRERHxeQosIiIi4vMUWERERMTnKbCIiIiIz1NgEREREZ+nwCIiIiI+T4FFREREfF67AovD4eDJJ59kwIABmM1mrr76ao4fP95s+bfeeourr76a+Ph4IiIiuOyyy9i1a5dHmbVr12IwGBq9cnNz21NFERER8SPtCixz585lxYoVrF69moyMDCoqKrjjjjuaLf/uu+9y3XXXsXnzZjIyMoiIiGDy5MmcPn3aXSYjI4O0tDRyc3Pdr7y8POLj49tTRREREfEjgW09YOvWrbz22mtkZmYyatQoAB566CGmTZtGVVUVISEhjY55//33PT4vXLiQSZMm8c0335CcnAy4Asu0adPo06dPO25DRERE/FmbW1ieffZZZs6c6Q4rALGxsTidTgoLC1t1ju3btxMZGcnAgQMBsNlsZGVl8cILLxAXF0dqaipr165t8RxWqxWLxeLxEhEREf/UpsBSXl7Otm3buO666zy2FxQUABAdHX3Wc2zYsIEnn3yS5cuXExERAUBRURHPPPMM69evZ+vWrYwZM4bZs2ezbdu2Zs+zbNkyoqKi3K/ExMS23IqIiIh0IW0KLDk5OdjtdkaPHu2xPTMzk0GDBhEZGdni8StXruSmm27ipZdeYs6cOe7t8fHxLFiwgHHjxjFmzBhWr17NBRdcwN/+9rdmz7Vo0SJKS0vdr5YG/YqIiEjX1qYxLCUlJQD07NnTY/v69euZOnVqs8fV1NRwzz33sG7dOjZv3syECRNavI7RaCQoKIiAgIBmy5hMJkwmUxtqLyIiIl1VmwJLXVApLi4mJiYGgHXr1vH111+zcePGJo8pLi7m+uuvJz8/n4yMDAYNGnTW6+zYsYMjR44wffr0tlRPRERE/FSbuoRSUlJITExk8eLFHDp0iLfeeovbbruNpUuXMnjwYMrKyhg2bBjp6ekAHDx4kHHjxlFRUcH7779PWFgYeXl55OXl4XA4ANeso9WrV5OTk8PBgwdZvXo1M2bMYN68eVx22WUdfsMiIiLS9bSphSU4OJj33nuP+fPnk5ycTFJSEi+//DK33norAHv37uXAgQPuAbDPPfcc33zzDQBDhw51nyc0NBSLxYLRaKS4uJiVK1dy//33YzKZGDp0KM8995z7nCIiIiIGp9Pp9HYlOoLFYiEqKorS0lLMZrO3qyMiIiKt0Nrvbz1LSERERHyeAouIiIj4PAUWERER8XkKLCIiIuLzFFhERETE5ymwiIiIiM9TYBERERGfp8AiIiIiPk+BRURERHyeAouIiIj4PAUWERER8XkKLCIiIuLzFFhERETE5ymwiIiIiM9TYBERERGfp8AiIiIiPk+BRURERHyeAouIiIj4PAUWERER8XkKLCIiIuLzFFhERETE5ymwiIiIiM9TYBERERGfp8AiIiIiPk+BRURERHyeAouIiIj4PAUWERER8XkKLCIiIuLzFFhERETE5ymwiIiIiM9TYBERERGfp8AiIiIiPk+BRURERHyeAouIiIj4PAUWERER8XntCiwOh4Mnn3ySAQMGYDabufrqqzl+/HiLx6SnpzN69GjCwsIYNWoU27dvb1RmzZo1XHTRRYSHhzN+/Hj27t3bnuqJiIiIn2lXYJk7dy4rVqxg9erVZGRkUFFRwR133NFs+bVr13LjjTdy5513snfvXiZMmMC1116LxWJxl1myZAkLFy5k8eLFZGdn07dvX2bNmoXdbm9PFUVERMSPGJxOp7MtB2zdupXJkyeTmZnJqFGjANi0aRPTpk2jsrKSkJAQj/JFRUUkJSWxZMkS7rnnHgDKy8uJjIxky5YtpKWlsX//fpKTk0lPT2fKlCkA7N27l+TkZA4cOMDQoUPPWi+LxUJUVBSlpaWYzea23JKIiIh4SWu/vwPbeuJnn32WmTNnusMKQGxsLE6nk8LCQhITEz3Kr1mzhrCwMObNm+feFhERQVBQEAUFBQA8//zzpKamusNK3TkBCgoKmgwsVqsVq9Xq/lxaWgrg0WojIiIivq3ue/ts7SdtCizl5eVs27aNtWvXemyvCx7R0dGNjklPT2f69OkEBtZf6tSpU9hsNmJiYgDYsGED9913X5PnrCtzpmXLlvH444832n5mYBIRERHfV1ZWRlRUVLP72xRYcnJysNvtjB492mN7ZmYmgwYNIjIystEx2dnZjca3ZGZmApCSkkJubi75+flNnjMkJKTZ7qBFixbxwAMPuD87HA6Ki4uJi4vDYDC05bZaZLFYSExM5Pjx4+pq8gH6ffge/U58i34fvkW/j7NzOp2UlZWRkJDQYrk2BZaSkhIAevbs6bF9/fr1TJ06tVH5yspKrFZrk+VTUlJITExk3759zZ4zLS2NoKCgJutiMpkwmUwe25pq4ekoZrNZf9l8iH4fvke/E9+i34dv0e+jZS21rNRpU2CpCxXFxcXurpp169bx9ddfs3Hjxkblw8LCCA8Pp7i42L3t2LFjvPHGG7z44ouNzlknIyODDz/8kI8//rgt1RMRERE/1aZpzXWtIosXL+bQoUO89dZb3HbbbSxdupTBgwdTVlbGsGHDSE9Pdx9zzTXXsGrVKnJyctixYwcTJ07kyiuvZPbs2YArsIwdO5annnqKgwcPsnnzZqZOncq8efOYMGFCh96siIiIdE1tCizBwcG89957fPXVVyQnJ7Ns2TJefvllfvWrXwGuqcgHDhzwGPi6YsUK+vXrx/jx47npppu48cYbWb9+PUZj/aXXrl1LdXU1qampLFiwgEWLFrFq1aoOusVzYzKZePTRRxt1P4l36Pfhe/Q78S36ffgW/T46TpvXYRERERHpbHqWkIiIiPg8BRYRERHxeQosIiIi4vMUWERERMTnKbCIiIiIz1NgOQuHw8GTTz7JgAEDMJvNXH311Rw/ftzb1ep2HA4Hixcv5kc/+hExMTHExsZyww03kJ+f7+2qCfDiiy8SFBTE/PnzvV2Vbi8rK4vrrruOHj16EBoaSkpKCidOnPB2tbqlo0eP8vOf/5y+ffsSHh7OJZdcwvbt271drS5LgeUs5s6dy4oVK1i9ejUZGRlUVFQ0ejaSnH+HDx8mOzubhx56iJ07d7Jx40Z27dql34UPeOWVV4iMjKSmpoZx48Z5uzrd2ubNm0lLS+PHP/4xn3zyCXv27OH//b//1+jRJ3L+lZaWcumll1JUVMTGjRvJyspi2LBhXHPNNZw6dcrb1euStA5LC7Zu3crkyZPJzMxk1KhRAGzatIlp06ZRWVlJSEiIdyvYzT3yyCO89NJLnDx50ttV6bbWrVtHfn4+AwYMYMqUKezbt49hw4Z5u1rdUlFREUOHDmXdunVcccUV3q5Ot/f3v/+da665hoKCAndgzMrKIjU1lb179zJ8+HAv17DrUQtLC5599llmzpzpDisAsbGxOJ1OCgsLvVcxAeDTTz8lJSXF29XotrZt28a2bdu4++67ycrKIioqiosuusjb1eq2Vq1axZAhQ9iwYQP9+vVjwIABPPjgg9hsNm9XrVsaPnw4ISEhrF+/HofDQX5+Po899hiXX365Qn07KbA0o7y8nG3btnHdddd5bC8oKADO75Oh5eweeughdu/ezfLly71dlW4pMzOTp556iueffx5w/cvx4osvxmAweLdi3dh7773HF198gcPhYP369Tz66KP84Q9/cP+OpHP169ePxx57jPnz52MymejTpw8DBw5ky5YtHo+mkdbTn1ozcnJysNvtjB492mN7ZmYmgwYNIjIy0ks1696sVis/+9nPePPNN9m2bZtH65d0joMHDzJnzhzeeOMN9/NRvvzyS41f8SKr1cq+ffu4/vrrWbFiBT/84Q+5/fbbueqqqzTI0wucTic33HAD6enpbNmyhV27dnHPPffw17/+VeNXzoECSzNKSkoAGg1WW79+PVOnTvVGlbq9/Px8rrjiCnJycsjIyGDs2LHerlK39Mwzz7Bnzx4SExMJDAwkMDCQQ4cOsWzZMn760596u3rdUnFxMQ6Hg+uvv95ju9FoJCwszEu16r7ef/99/v73v7N582auuuoqRo0axcqVKwHXw36lfRRYmlEXVIqLi93b1q1bx9dff80999zjrWp1W9nZ2YwdO5b4+Hh27Njh8URw6Vy/+c1v2LNnD1lZWWRlZfHKK68A8NFHH/HGG294uXbdU0xMDEajkYZzKIqKitixYwdpaWlerFn3tH//fnr37u0xdKCsrIxTp04RGBjovYp1cQoszUhJSSExMZHFixdz6NAh3nrrLW677TaWLl3K4MGDvV29bmXjxo2MHz+eSZMmsWrVKsrKysjLy6OoqMjbVeuWBgwYwIgRI9yv6upqwsPDueKKK7jgggu8Xb1uKSQkhLS0NJ588kn27NnDZ599xpQpU7jwwguZPXu2t6vX7Vx++eUcPXqUJUuWcPjwYT7//HOmT59OREQEN998s7er12VpWnMLdu3axfz589m3bx9JSUk8/PDD3Hrrrd6uVrczbNgwDhw40Gj7xIkT+eijj7xQI2no/vvv59NPPyUzM9PbVenW8vLyWLBgAdu2bSM0NJQZM2awdOlSoqKivF21bunNN9/kqaee4tChQ/Ts2ZOf/vSnPPbYY/Tv39/bVeuyFFhERETE56lLSERERHyeAouIiIj4PAUWERER8XkKLCIiIuLzFFhERETE5ymwiIiIiM9TYBERERGfp8AiIiIiPk+BRURERHyeAouIiIj4PAUWERER8Xn/HyVqP0jj95fwAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(history.history['loss'], label='train')\n", + "plt.plot(history.history['val_loss'], label='validation')\n", + "plt.ylim(0, 2)\n", + "plt.legend(loc='best')\n", + "plt.title('Loss');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Questions**:\n", + "\n", + "- Does it look like our model has overfit? Why or why not? \n", + "Your Answer: ____________\n", + "- Suggest something we could do to prevent overfitting. \n", + "Your Answer: ____________\n", + "\n", + "Now that the model is trained, let's check out the quality of predictions:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The model is overfitting. This is because the training loss continues to decrease, while the validation loss starts increasing after a certain point.\n", + "This indicates that the model is learning the training data too well (including its noise), and its ability to generalize to unseen data is deteriorating. We can employ dropout to prevent overfitting. " + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_predictions(y_true, y_pred):\n", + " plt.figure(figsize=(4, 4))\n", + " plt.xlim(-1, 6)\n", + " plt.xlabel(\"True rating\")\n", + " plt.ylim(-1, 6)\n", + " plt.ylabel(\"Predicted rating\")\n", + " plt.scatter(y_true, y_pred, s=60, alpha=0.01)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 320us/step\n", + "Final test MSE: 0.909\n", + "Final test MAE: 0.736\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.metrics import mean_squared_error\n", + "from sklearn.metrics import mean_absolute_error\n", + "\n", + "test_preds = model.predict([user_id_test, item_id_test])\n", + "print(\"Final test MSE: %0.3f\" % mean_squared_error(test_preds, rating_test))\n", + "print(\"Final test MAE: %0.3f\" % mean_absolute_error(test_preds, rating_test))\n", + "plot_predictions(rating_test, test_preds)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false + }, + "source": [ + "This graph shows us the range of predicted ratings our model gives, organized by the true rating. We can see that generally, the higher the true rating the higher the predicted rating, although there is quite a range of predictions for each instance. That's okay - our model is very simple, and human preferences are very complex!\n", + "\n", + "Taking a look at the Mean Absolute Error, hopefully you got something around 0.75. This means that, on average, our predicted ratings are about 0.75 stars off from the true ratings. This is a pretty good result for a first attempt. We could probably do better with a more complex model, but we'll leave that for another time." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Model Embeddings\n", + "\n", + "Our model was built with two embedding layers. These layers have learned a representation of both the users and the movies in our dataset. We can extract these representations and use them to find similar movies or users. We can also do interesting exploratory analysis, like finding the most popular movies among our users, or finding the users that are most interested in a given movie." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[(944, 64), (1683, 64)]" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# weights and shape\n", + "weights = model.get_weights()\n", + "[w.shape for w in weights]" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "user_embeddings = weights[0]\n", + "item_embeddings = weights[1]" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Title for item_id=181: Return of the Jedi (1983)\n" + ] + } + ], + "source": [ + "item_id = 181\n", + "print(f\"Title for item_id={item_id}: {indexed_items['title'][item_id]}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Embedding vector for item_id=181\n", + "[ 0.5049994 -0.24453689 -0.5053119 -0.14520554 -0.17662948 -0.4140876\n", + " 0.33397424 -0.43637303 0.2627958 0.19209456 -0.00306972 0.01615785\n", + " 0.36145076 0.06600716 0.5860347 -0.3238521 -0.19712465 -0.20112757\n", + " -0.35017866 -0.39645898 -0.10436684 -0.22903298 -0.24842347 -0.33779415\n", + " 0.54455906 0.5913569 0.10188422 -0.27270707 0.29540297 -0.32222798\n", + " 0.26548845 0.31300977 0.6348242 -0.3539588 0.27794433 -0.3257407\n", + " 0.5135676 -0.08596199 0.1292318 0.4055005 -0.39303997 0.20708163\n", + " -0.39546552 0.13766694 -0.28147334 0.13006787 0.37037337 -0.1605785\n", + " 0.3609969 -0.15301947 -0.37705556 -0.5487598 -0.59837615 0.2999434\n", + " -0.5337184 -0.3211866 0.3655793 0.23211122 -0.1254762 0.4025621\n", + " 0.23172197 -0.2985614 0.25593528 -0.29194993]\n", + "shape: (64,)\n" + ] + } + ], + "source": [ + "print(f\"Embedding vector for item_id={item_id}\")\n", + "print(item_embeddings[item_id])\n", + "print(\"shape:\", item_embeddings[item_id].shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false + }, + "source": [ + "As we discussed in lecture, our embeddings are not directly interpretable - we can't look at, say, a value of 0.297 in the embedding vector and say \"this means that the movie is a drama\". As an aside, there is a field of research dedicated to making _interpretable_ embeddings, but it's not something we'll cover in this course. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Finding our most similar items\n", + "\n", + "Now we can have some fun, investigating the embeddings we've learned. We can start by finding the most similar items to a given item. We can do this by computing the cosine similarity between the item's embedding and the embedding of every other item. We can use the `cosine_similarity` function from `sklearn` to do this." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from sklearn.metrics.pairwise import cosine_similarity\n", + "\n", + "def cosine(a, b):\n", + " # Reshape to the shape our function expects\n", + " a = a.reshape(1, -1)\n", + " b = b.reshape(1, -1)\n", + " return cosine_similarity(a, b)[0, 0]" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Star Wars (1977)\n", + "Return of the Jedi (1983)\n", + "Cosine similarity: 0.922\n" + ] + } + ], + "source": [ + "def print_similarity(item_a, item_b, item_embeddings, titles):\n", + " print(titles[item_a])\n", + " print(titles[item_b])\n", + " similarity = cosine(item_embeddings[item_a],\n", + " item_embeddings[item_b])\n", + " print(f\"Cosine similarity: {similarity:.3}\")\n", + " \n", + "print_similarity(50, 181, item_embeddings, indexed_items[\"title\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false + }, + "source": [ + "It makes sense that the original Star Wars, and its later sequel Return of the Jedi have a high similarity. Let's try some other examples:" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Return of the Jedi (1983)\n", + "Scream (1996)\n", + "Cosine similarity: 0.613\n" + ] + } + ], + "source": [ + "print_similarity(181, 288, item_embeddings, indexed_items[\"title\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Return of the Jedi (1983)\n", + "Toy Story (1995)\n", + "Cosine similarity: 0.827\n" + ] + } + ], + "source": [ + "print_similarity(181, 1, item_embeddings, indexed_items[\"title\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Return of the Jedi (1983)\n", + "Return of the Jedi (1983)\n", + "Cosine similarity: 1.0\n" + ] + } + ], + "source": [ + "print_similarity(181, 181, item_embeddings, indexed_items[\"title\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false + }, + "source": [ + "*Quick Exercise*:Find some other films and compare their similarity. Do the results make sense to you? Can you find a pair of films that are very _dissimilar_?\n", + "\n", + "- " + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Jedi movie IDs: [181]\n", + "Return of the Jedi (1983)\n", + "Toy Story (1995)\n", + "Cosine similarity: 0.827\n", + "Return of the Jedi (1983)\n", + "GoldenEye (1995)\n", + "Cosine similarity: 0.748\n", + "Return of the Jedi (1983)\n", + "Four Rooms (1995)\n", + "Cosine similarity: 0.529\n", + "Return of the Jedi (1983)\n", + "Get Shorty (1995)\n", + "Cosine similarity: 0.703\n", + "Return of the Jedi (1983)\n", + "Copycat (1995)\n", + "Cosine similarity: 0.607\n", + "Return of the Jedi (1983)\n", + "Shanghai Triad (Yao a yao yao dao waipo qiao) (1995)\n", + "Cosine similarity: 0.582\n", + "Return of the Jedi (1983)\n", + "Twelve Monkeys (1995)\n", + "Cosine similarity: 0.773\n", + "Return of the Jedi (1983)\n", + "Babe (1995)\n", + "Cosine similarity: 0.747\n", + "Return of the Jedi (1983)\n", + "Dead Man Walking (1995)\n", + "Cosine similarity: 0.611\n", + "Return of the Jedi (1983)\n", + "Richard III (1995)\n", + "Cosine similarity: 0.579\n", + "Return of the Jedi (1983)\n", + "Seven (Se7en) (1995)\n", + "Cosine similarity: 0.696\n", + "Return of the Jedi (1983)\n", + "Usual Suspects, The (1995)\n", + "Cosine similarity: 0.773\n", + "Return of the Jedi (1983)\n", + "Mighty Aphrodite (1995)\n", + "Cosine similarity: 0.657\n", + "Return of the Jedi (1983)\n", + "Postino, Il (1994)\n", + "Cosine similarity: 0.541\n", + "Return of the Jedi (1983)\n", + "Mr. Holland's Opus (1995)\n", + "Cosine similarity: 0.671\n", + "Return of the Jedi (1983)\n", + "French Twist (Gazon maudit) (1995)\n", + "Cosine similarity: 0.627\n", + "Return of the Jedi (1983)\n", + "From Dusk Till Dawn (1996)\n", + "Cosine similarity: 0.719\n", + "Return of the Jedi (1983)\n", + "White Balloon, The (1995)\n", + "Cosine similarity: 0.606\n", + "Return of the Jedi (1983)\n", + "Antonia's Line (1995)\n", + "Cosine similarity: 0.515\n", + "Return of the Jedi (1983)\n", + "Angels and Insects (1995)\n", + "Cosine similarity: 0.582\n", + "Return of the Jedi (1983)\n", + "Muppet Treasure Island (1996)\n", + "Cosine similarity: 0.711\n", + "Return of the Jedi (1983)\n", + "Braveheart (1995)\n", + "Cosine similarity: 0.768\n", + "Return of the Jedi (1983)\n", + "Taxi Driver (1976)\n", + "Cosine similarity: 0.67\n", + "Return of the Jedi (1983)\n", + "Rumble in the Bronx (1995)\n", + "Cosine similarity: 0.646\n", + "Return of the Jedi (1983)\n", + "Birdcage, The (1996)\n", + "Cosine similarity: 0.579\n", + "Return of the Jedi (1983)\n", + "Brothers McMullen, The (1995)\n", + "Cosine similarity: 0.696\n", + "Return of the Jedi (1983)\n", + "Bad Boys (1995)\n", + "Cosine similarity: 0.69\n", + "Return of the Jedi (1983)\n", + "Apollo 13 (1995)\n", + "Cosine similarity: 0.762\n", + "Return of the Jedi (1983)\n", + "Batman Forever (1995)\n", + "Cosine similarity: 0.615\n", + "Return of the Jedi (1983)\n", + "Belle de jour (1967)\n", + "Cosine similarity: 0.738\n", + "Return of the Jedi (1983)\n", + "Crimson Tide (1995)\n", + "Cosine similarity: 0.782\n", + "Return of the Jedi (1983)\n", + "Crumb (1994)\n", + "Cosine similarity: 0.534\n", + "Return of the Jedi (1983)\n", + "Desperado (1995)\n", + "Cosine similarity: 0.603\n", + "Return of the Jedi (1983)\n", + "Doom Generation, The (1995)\n", + "Cosine similarity: 0.585\n", + "Return of the Jedi (1983)\n", + "Free Willy 2: The Adventure Home (1995)\n", + "Cosine similarity: 0.64\n", + "Return of the Jedi (1983)\n", + "Mad Love (1995)\n", + "Cosine similarity: 0.43\n", + "Return of the Jedi (1983)\n", + "Nadja (1994)\n", + "Cosine similarity: 0.501\n", + "Return of the Jedi (1983)\n", + "Net, The (1995)\n", + "Cosine similarity: 0.618\n", + "Return of the Jedi (1983)\n", + "Strange Days (1995)\n", + "Cosine similarity: 0.533\n", + "Return of the Jedi (1983)\n", + "To Wong Foo, Thanks for Everything! Julie Newmar (1995)\n", + "Cosine similarity: 0.584\n", + "Return of the Jedi (1983)\n", + "Billy Madison (1995)\n", + "Cosine similarity: 0.659\n", + "Return of the Jedi (1983)\n", + "Clerks (1994)\n", + "Cosine similarity: 0.626\n", + "Return of the Jedi (1983)\n", + "Disclosure (1994)\n", + "Cosine similarity: 0.601\n", + "Return of the Jedi (1983)\n", + "Dolores Claiborne (1994)\n", + "Cosine similarity: 0.674\n", + "Return of the Jedi (1983)\n", + "Eat Drink Man Woman (1994)\n", + "Cosine similarity: 0.569\n", + "Return of the Jedi (1983)\n", + "Exotica (1994)\n", + "Cosine similarity: 0.513\n", + "Return of the Jedi (1983)\n", + "Ed Wood (1994)\n", + "Cosine similarity: 0.579\n", + "Return of the Jedi (1983)\n", + "Hoop Dreams (1994)\n", + "Cosine similarity: 0.76\n", + "Return of the Jedi (1983)\n", + "I.Q. (1994)\n", + "Cosine similarity: 0.585\n", + "Return of the Jedi (1983)\n", + "Star Wars (1977)\n", + "Cosine similarity: 0.922\n", + "Return of the Jedi (1983)\n", + "Legends of the Fall (1994)\n", + "Cosine similarity: 0.66\n", + "Return of the Jedi (1983)\n", + "Madness of King George, The (1994)\n", + "Cosine similarity: 0.664\n", + "Return of the Jedi (1983)\n", + "Natural Born Killers (1994)\n", + "Cosine similarity: 0.5\n", + "Return of the Jedi (1983)\n", + "Outbreak (1995)\n", + "Cosine similarity: 0.668\n", + "Return of the Jedi (1983)\n", + "Professional, The (1994)\n", + "Cosine similarity: 0.649\n", + "Return of the Jedi (1983)\n", + "Pulp Fiction (1994)\n", + "Cosine similarity: 0.621\n", + "Return of the Jedi (1983)\n", + "Priest (1994)\n", + "Cosine similarity: 0.599\n", + "Return of the Jedi (1983)\n", + "Quiz Show (1994)\n", + "Cosine similarity: 0.677\n", + "Return of the Jedi (1983)\n", + "Three Colors: Red (1994)\n", + "Cosine similarity: 0.611\n", + "Return of the Jedi (1983)\n", + "Three Colors: Blue (1993)\n", + "Cosine similarity: 0.544\n", + "Return of the Jedi (1983)\n", + "Three Colors: White (1994)\n", + "Cosine similarity: 0.596\n", + "Return of the Jedi (1983)\n", + "Stargate (1994)\n", + "Cosine similarity: 0.575\n", + "Return of the Jedi (1983)\n", + "Santa Clause, The (1994)\n", + "Cosine similarity: 0.587\n", + "Return of the Jedi (1983)\n", + "Shawshank Redemption, The (1994)\n", + "Cosine similarity: 0.695\n", + "Return of the Jedi (1983)\n", + "What's Eating Gilbert Grape (1993)\n", + "Cosine similarity: 0.581\n", + "Return of the Jedi (1983)\n", + "While You Were Sleeping (1995)\n", + "Cosine similarity: 0.733\n", + "Return of the Jedi (1983)\n", + "Ace Ventura: Pet Detective (1994)\n", + "Cosine similarity: 0.543\n", + "Return of the Jedi (1983)\n", + "Crow, The (1994)\n", + "Cosine similarity: 0.632\n", + "Return of the Jedi (1983)\n", + "Forrest Gump (1994)\n", + "Cosine similarity: 0.772\n", + "Return of the Jedi (1983)\n", + "Four Weddings and a Funeral (1994)\n", + "Cosine similarity: 0.705\n", + "Return of the Jedi (1983)\n", + "Lion King, The (1994)\n", + "Cosine similarity: 0.72\n", + "Return of the Jedi (1983)\n", + "Mask, The (1994)\n", + "Cosine similarity: 0.708\n", + "Return of the Jedi (1983)\n", + "Maverick (1994)\n", + "Cosine similarity: 0.748\n", + "Return of the Jedi (1983)\n", + "Faster Pussycat! Kill! Kill! (1965)\n", + "Cosine similarity: 0.503\n", + "Return of the Jedi (1983)\n", + "Brother Minister: The Assassination of Malcolm X (1994)\n", + "Cosine similarity: 0.673\n", + "Return of the Jedi (1983)\n", + "Carlito's Way (1993)\n", + "Cosine similarity: 0.777\n", + "Return of the Jedi (1983)\n", + "Firm, The (1993)\n", + "Cosine similarity: 0.779\n", + "Return of the Jedi (1983)\n", + "Free Willy (1993)\n", + "Cosine similarity: 0.597\n", + "Return of the Jedi (1983)\n", + "Fugitive, The (1993)\n", + "Cosine similarity: 0.747\n", + "Return of the Jedi (1983)\n", + "Hot Shots! Part Deux (1993)\n", + "Cosine similarity: 0.666\n", + "Return of the Jedi (1983)\n", + "Hudsucker Proxy, The (1994)\n", + "Cosine similarity: 0.639\n", + "Return of the Jedi (1983)\n", + "Jurassic Park (1993)\n", + "Cosine similarity: 0.753\n", + "Return of the Jedi (1983)\n", + "Much Ado About Nothing (1993)\n", + "Cosine similarity: 0.694\n", + "Return of the Jedi (1983)\n", + "Robert A. Heinlein's The Puppet Masters (1994)\n", + "Cosine similarity: 0.638\n", + "Return of the Jedi (1983)\n", + "Ref, The (1994)\n", + "Cosine similarity: 0.687\n", + "Return of the Jedi (1983)\n", + "Remains of the Day, The (1993)\n", + "Cosine similarity: 0.609\n", + "Return of the Jedi (1983)\n", + "Searching for Bobby Fischer (1993)\n", + "Cosine similarity: 0.745\n", + "Return of the Jedi (1983)\n", + "Sleepless in Seattle (1993)\n", + "Cosine similarity: 0.731\n", + "Return of the Jedi (1983)\n", + "Blade Runner (1982)\n", + "Cosine similarity: 0.747\n", + "Return of the Jedi (1983)\n", + "So I Married an Axe Murderer (1993)\n", + "Cosine similarity: 0.766\n", + "Return of the Jedi (1983)\n", + "Nightmare Before Christmas, The (1993)\n", + "Cosine similarity: 0.694\n", + "Return of the Jedi (1983)\n", + "True Romance (1993)\n", + "Cosine similarity: 0.637\n", + "Return of the Jedi (1983)\n", + "Welcome to the Dollhouse (1995)\n", + "Cosine similarity: 0.608\n", + "Return of the Jedi (1983)\n", + "Home Alone (1990)\n", + "Cosine similarity: 0.676\n", + "Return of the Jedi (1983)\n", + "Aladdin (1992)\n", + "Cosine similarity: 0.782\n", + "Return of the Jedi (1983)\n", + "Terminator 2: Judgment Day (1991)\n", + "Cosine similarity: 0.809\n", + "Return of the Jedi (1983)\n", + "Dances with Wolves (1990)\n", + "Cosine similarity: 0.646\n", + "Return of the Jedi (1983)\n", + "Silence of the Lambs, The (1991)\n", + "Cosine similarity: 0.756\n", + "Return of the Jedi (1983)\n", + "Snow White and the Seven Dwarfs (1937)\n", + "Cosine similarity: 0.732\n", + "Return of the Jedi (1983)\n", + "Fargo (1996)\n", + "Cosine similarity: 0.699\n", + "Return of the Jedi (1983)\n", + "Heavy Metal (1981)\n", + "Cosine similarity: 0.709\n", + "Return of the Jedi (1983)\n", + "Aristocats, The (1970)\n", + "Cosine similarity: 0.608\n", + "Return of the Jedi (1983)\n", + "All Dogs Go to Heaven 2 (1996)\n", + "Cosine similarity: 0.683\n", + "Return of the Jedi (1983)\n", + "Theodore Rex (1995)\n", + "Cosine similarity: 0.653\n", + "Return of the Jedi (1983)\n", + "Sgt. Bilko (1996)\n", + "Cosine similarity: 0.692\n", + "Return of the Jedi (1983)\n", + "Diabolique (1996)\n", + "Cosine similarity: 0.616\n", + "Return of the Jedi (1983)\n", + "Moll Flanders (1996)\n", + "Cosine similarity: 0.612\n", + "Return of the Jedi (1983)\n", + "Kids in the Hall: Brain Candy (1996)\n", + "Cosine similarity: 0.678\n", + "Return of the Jedi (1983)\n", + "Mystery Science Theater 3000: The Movie (1996)\n", + "Cosine similarity: 0.788\n", + "Return of the Jedi (1983)\n", + "Operation Dumbo Drop (1995)\n", + "Cosine similarity: 0.589\n", + "Return of the Jedi (1983)\n", + "Truth About Cats & Dogs, The (1996)\n", + "Cosine similarity: 0.67\n", + "Return of the Jedi (1983)\n", + "Flipper (1996)\n", + "Cosine similarity: 0.434\n", + "Return of the Jedi (1983)\n", + "Horseman on the Roof, The (Hussard sur le toit, Le) (1995)\n", + "Cosine similarity: 0.682\n", + "Return of the Jedi (1983)\n", + "Wallace & Gromit: The Best of Aardman Animation (1996)\n", + "Cosine similarity: 0.713\n", + "Return of the Jedi (1983)\n", + "Haunted World of Edward D. Wood Jr., The (1995)\n", + "Cosine similarity: 0.754\n", + "Return of the Jedi (1983)\n", + "Cold Comfort Farm (1995)\n", + "Cosine similarity: 0.596\n", + "Return of the Jedi (1983)\n", + "Rock, The (1996)\n", + "Cosine similarity: 0.733\n", + "Return of the Jedi (1983)\n", + "Twister (1996)\n", + "Cosine similarity: 0.688\n", + "Return of the Jedi (1983)\n", + "Maya Lin: A Strong Clear Vision (1994)\n", + "Cosine similarity: 0.662\n", + "Return of the Jedi (1983)\n", + "Striptease (1996)\n", + "Cosine similarity: 0.521\n", + "Return of the Jedi (1983)\n", + "Independence Day (ID4) (1996)\n", + "Cosine similarity: 0.744\n", + "Return of the Jedi (1983)\n", + "Cable Guy, The (1996)\n", + "Cosine similarity: 0.417\n", + "Return of the Jedi (1983)\n", + "Frighteners, The (1996)\n", + "Cosine similarity: 0.777\n", + "Return of the Jedi (1983)\n", + "Lone Star (1996)\n", + "Cosine similarity: 0.606\n", + "Return of the Jedi (1983)\n", + "Phenomenon (1996)\n", + "Cosine similarity: 0.715\n", + "Return of the Jedi (1983)\n", + "Spitfire Grill, The (1996)\n", + "Cosine similarity: 0.639\n", + "Return of the Jedi (1983)\n", + "Godfather, The (1972)\n", + "Cosine similarity: 0.728\n", + "Return of the Jedi (1983)\n", + "Supercop (1992)\n", + "Cosine similarity: 0.722\n", + "Return of the Jedi (1983)\n", + "Bound (1996)\n", + "Cosine similarity: 0.799\n", + "Return of the Jedi (1983)\n", + "Kansas City (1996)\n", + "Cosine similarity: 0.702\n", + "Return of the Jedi (1983)\n", + "Breakfast at Tiffany's (1961)\n", + "Cosine similarity: 0.507\n", + "Return of the Jedi (1983)\n", + "Wizard of Oz, The (1939)\n", + "Cosine similarity: 0.678\n", + "Return of the Jedi (1983)\n", + "Gone with the Wind (1939)\n", + "Cosine similarity: 0.712\n", + "Return of the Jedi (1983)\n", + "Citizen Kane (1941)\n", + "Cosine similarity: 0.632\n", + "Return of the Jedi (1983)\n", + "2001: A Space Odyssey (1968)\n", + "Cosine similarity: 0.736\n", + "Return of the Jedi (1983)\n", + "Mr. Smith Goes to Washington (1939)\n", + "Cosine similarity: 0.765\n", + "Return of the Jedi (1983)\n", + "Big Night (1996)\n", + "Cosine similarity: 0.553\n", + "Return of the Jedi (1983)\n", + "D3: The Mighty Ducks (1996)\n", + "Cosine similarity: 0.472\n", + "Return of the Jedi (1983)\n", + "Love Bug, The (1969)\n", + "Cosine similarity: 0.603\n", + "Return of the Jedi (1983)\n", + "Homeward Bound: The Incredible Journey (1993)\n", + "Cosine similarity: 0.569\n", + "Return of the Jedi (1983)\n", + "20,000 Leagues Under the Sea (1954)\n", + "Cosine similarity: 0.785\n", + "Return of the Jedi (1983)\n", + "Bedknobs and Broomsticks (1971)\n", + "Cosine similarity: 0.534\n", + "Return of the Jedi (1983)\n", + "Sound of Music, The (1965)\n", + "Cosine similarity: 0.62\n", + "Return of the Jedi (1983)\n", + "Die Hard (1988)\n", + "Cosine similarity: 0.762\n", + "Return of the Jedi (1983)\n", + "Lawnmower Man, The (1992)\n", + "Cosine similarity: 0.663\n", + "Return of the Jedi (1983)\n", + "Unhook the Stars (1996)\n", + "Cosine similarity: 0.623\n", + "Return of the Jedi (1983)\n", + "Long Kiss Goodnight, The (1996)\n", + "Cosine similarity: 0.814\n", + "Return of the Jedi (1983)\n", + "Ghost and the Darkness, The (1996)\n", + "Cosine similarity: 0.55\n", + "Return of the Jedi (1983)\n", + "Jude (1996)\n", + "Cosine similarity: 0.494\n", + "Return of the Jedi (1983)\n", + "Swingers (1996)\n", + "Cosine similarity: 0.568\n", + "Return of the Jedi (1983)\n", + "Willy Wonka and the Chocolate Factory (1971)\n", + "Cosine similarity: 0.612\n", + "Return of the Jedi (1983)\n", + "Sleeper (1973)\n", + "Cosine similarity: 0.615\n", + "Return of the Jedi (1983)\n", + "Fish Called Wanda, A (1988)\n", + "Cosine similarity: 0.615\n", + "Return of the Jedi (1983)\n", + "Monty Python's Life of Brian (1979)\n", + "Cosine similarity: 0.559\n", + "Return of the Jedi (1983)\n", + "Dirty Dancing (1987)\n", + "Cosine similarity: 0.549\n", + "Return of the Jedi (1983)\n", + "Reservoir Dogs (1992)\n", + "Cosine similarity: 0.658\n", + "Return of the Jedi (1983)\n", + "Platoon (1986)\n", + "Cosine similarity: 0.678\n", + "Return of the Jedi (1983)\n", + "Weekend at Bernie's (1989)\n", + "Cosine similarity: 0.562\n", + "Return of the Jedi (1983)\n", + "Basic Instinct (1992)\n", + "Cosine similarity: 0.549\n", + "Return of the Jedi (1983)\n", + "Glengarry Glen Ross (1992)\n", + "Cosine similarity: 0.585\n", + "Return of the Jedi (1983)\n", + "Top Gun (1986)\n", + "Cosine similarity: 0.708\n", + "Return of the Jedi (1983)\n", + "On Golden Pond (1981)\n", + "Cosine similarity: 0.577\n", + "Return of the Jedi (1983)\n", + "Return of the Pink Panther, The (1974)\n", + "Cosine similarity: 0.709\n", + "Return of the Jedi (1983)\n", + "Abyss, The (1989)\n", + "Cosine similarity: 0.791\n", + "Return of the Jedi (1983)\n", + "Jean de Florette (1986)\n", + "Cosine similarity: 0.73\n", + "Return of the Jedi (1983)\n", + "Manon of the Spring (Manon des sources) (1986)\n", + "Cosine similarity: 0.667\n", + "Return of the Jedi (1983)\n", + "Private Benjamin (1980)\n", + "Cosine similarity: 0.665\n", + "Return of the Jedi (1983)\n", + "Monty Python and the Holy Grail (1974)\n", + "Cosine similarity: 0.767\n", + "Return of the Jedi (1983)\n", + "Wrong Trousers, The (1993)\n", + "Cosine similarity: 0.75\n", + "Return of the Jedi (1983)\n", + "Cinema Paradiso (1988)\n", + "Cosine similarity: 0.657\n", + "Return of the Jedi (1983)\n", + "Delicatessen (1991)\n", + "Cosine similarity: 0.634\n", + "Return of the Jedi (1983)\n", + "Empire Strikes Back, The (1980)\n", + "Cosine similarity: 0.933\n", + "Return of the Jedi (1983)\n", + "Princess Bride, The (1987)\n", + "Cosine similarity: 0.807\n", + "Return of the Jedi (1983)\n", + "Raiders of the Lost Ark (1981)\n", + "Cosine similarity: 0.854\n", + "Return of the Jedi (1983)\n", + "Brazil (1985)\n", + "Cosine similarity: 0.557\n", + "Return of the Jedi (1983)\n", + "Aliens (1986)\n", + "Cosine similarity: 0.819\n", + "Return of the Jedi (1983)\n", + "Good, The Bad and The Ugly, The (1966)\n", + "Cosine similarity: 0.751\n", + "Return of the Jedi (1983)\n", + "12 Angry Men (1957)\n", + "Cosine similarity: 0.715\n", + "Return of the Jedi (1983)\n", + "Clockwork Orange, A (1971)\n", + "Cosine similarity: 0.552\n", + "Return of the Jedi (1983)\n", + "Apocalypse Now (1979)\n", + "Cosine similarity: 0.643\n", + "Return of the Jedi (1983)\n", + "GoodFellas (1990)\n", + "Cosine similarity: 0.652\n", + "Return of the Jedi (1983)\n", + "Alien (1979)\n", + "Cosine similarity: 0.798\n", + "Return of the Jedi (1983)\n", + "Army of Darkness (1993)\n", + "Cosine similarity: 0.727\n", + "Return of the Jedi (1983)\n", + "Psycho (1960)\n", + "Cosine similarity: 0.666\n", + "Return of the Jedi (1983)\n", + "Blues Brothers, The (1980)\n", + "Cosine similarity: 0.775\n", + "Return of the Jedi (1983)\n", + "Godfather: Part II, The (1974)\n", + "Cosine similarity: 0.749\n", + "Return of the Jedi (1983)\n", + "Full Metal Jacket (1987)\n", + "Cosine similarity: 0.667\n", + "Return of the Jedi (1983)\n", + "Grand Day Out, A (1992)\n", + "Cosine similarity: 0.701\n", + "Return of the Jedi (1983)\n", + "Henry V (1989)\n", + "Cosine similarity: 0.705\n", + "Return of the Jedi (1983)\n", + "Amadeus (1984)\n", + "Cosine similarity: 0.643\n", + "Return of the Jedi (1983)\n", + "Raging Bull (1980)\n", + "Cosine similarity: 0.565\n", + "Return of the Jedi (1983)\n", + "Right Stuff, The (1983)\n", + "Cosine similarity: 0.763\n", + "Return of the Jedi (1983)\n", + "Sting, The (1973)\n", + "Cosine similarity: 0.786\n", + "Return of the Jedi (1983)\n", + "Terminator, The (1984)\n", + "Cosine similarity: 0.807\n", + "Return of the Jedi (1983)\n", + "Dead Poets Society (1989)\n", + "Cosine similarity: 0.653\n", + "Return of the Jedi (1983)\n", + "Graduate, The (1967)\n", + "Cosine similarity: 0.572\n", + "Return of the Jedi (1983)\n", + "Nikita (La Femme Nikita) (1990)\n", + "Cosine similarity: 0.697\n", + "Return of the Jedi (1983)\n", + "Bridge on the River Kwai, The (1957)\n", + "Cosine similarity: 0.75\n", + "Return of the Jedi (1983)\n", + "Shining, The (1980)\n", + "Cosine similarity: 0.59\n", + "Return of the Jedi (1983)\n", + "Evil Dead II (1987)\n", + "Cosine similarity: 0.575\n", + "Return of the Jedi (1983)\n", + "Groundhog Day (1993)\n", + "Cosine similarity: 0.818\n", + "Return of the Jedi (1983)\n", + "Unforgiven (1992)\n", + "Cosine similarity: 0.67\n", + "Return of the Jedi (1983)\n", + "Back to the Future (1985)\n", + "Cosine similarity: 0.801\n", + "Return of the Jedi (1983)\n", + "Patton (1970)\n", + "Cosine similarity: 0.824\n", + "Return of the Jedi (1983)\n", + "Akira (1988)\n", + "Cosine similarity: 0.659\n", + "Return of the Jedi (1983)\n", + "Cyrano de Bergerac (1990)\n", + "Cosine similarity: 0.634\n", + "Return of the Jedi (1983)\n", + "Young Frankenstein (1974)\n", + "Cosine similarity: 0.688\n", + "Return of the Jedi (1983)\n", + "This Is Spinal Tap (1984)\n", + "Cosine similarity: 0.624\n", + "Return of the Jedi (1983)\n", + "Indiana Jones and the Last Crusade (1989)\n", + "Cosine similarity: 0.885\n", + "Return of the Jedi (1983)\n", + "M*A*S*H (1970)\n", + "Cosine similarity: 0.798\n", + "Return of the Jedi (1983)\n", + "Unbearable Lightness of Being, The (1988)\n", + "Cosine similarity: 0.52\n", + "Return of the Jedi (1983)\n", + "Room with a View, A (1986)\n", + "Cosine similarity: 0.692\n", + "Return of the Jedi (1983)\n", + "Pink Floyd - The Wall (1982)\n", + "Cosine similarity: 0.716\n", + "Return of the Jedi (1983)\n", + "Field of Dreams (1989)\n", + "Cosine similarity: 0.769\n", + "Return of the Jedi (1983)\n", + "When Harry Met Sally... (1989)\n", + "Cosine similarity: 0.691\n", + "Return of the Jedi (1983)\n", + "Bram Stoker's Dracula (1992)\n", + "Cosine similarity: 0.722\n", + "Return of the Jedi (1983)\n", + "Cape Fear (1991)\n", + "Cosine similarity: 0.603\n", + "Return of the Jedi (1983)\n", + "Nightmare on Elm Street, A (1984)\n", + "Cosine similarity: 0.665\n", + "Return of the Jedi (1983)\n", + "Mirror Has Two Faces, The (1996)\n", + "Cosine similarity: 0.541\n", + "Return of the Jedi (1983)\n", + "Breaking the Waves (1996)\n", + "Cosine similarity: 0.575\n", + "Return of the Jedi (1983)\n", + "Star Trek: First Contact (1996)\n", + "Cosine similarity: 0.812\n", + "Return of the Jedi (1983)\n", + "Sling Blade (1996)\n", + "Cosine similarity: 0.766\n", + "Return of the Jedi (1983)\n", + "Ridicule (1996)\n", + "Cosine similarity: 0.654\n", + "Return of the Jedi (1983)\n", + "101 Dalmatians (1996)\n", + "Cosine similarity: 0.702\n", + "Return of the Jedi (1983)\n", + "Die Hard 2 (1990)\n", + "Cosine similarity: 0.763\n", + "Return of the Jedi (1983)\n", + "Star Trek VI: The Undiscovered Country (1991)\n", + "Cosine similarity: 0.789\n", + "Return of the Jedi (1983)\n", + "Star Trek: The Wrath of Khan (1982)\n", + "Cosine similarity: 0.82\n", + "Return of the Jedi (1983)\n", + "Star Trek III: The Search for Spock (1984)\n", + "Cosine similarity: 0.715\n", + "Return of the Jedi (1983)\n", + "Star Trek IV: The Voyage Home (1986)\n", + "Cosine similarity: 0.787\n", + "Return of the Jedi (1983)\n", + "Batman Returns (1992)\n", + "Cosine similarity: 0.585\n", + "Return of the Jedi (1983)\n", + "Young Guns (1988)\n", + "Cosine similarity: 0.74\n", + "Return of the Jedi (1983)\n", + "Under Siege (1992)\n", + "Cosine similarity: 0.741\n", + "Return of the Jedi (1983)\n", + "Jaws (1975)\n", + "Cosine similarity: 0.715\n", + "Return of the Jedi (1983)\n", + "Mars Attacks! (1996)\n", + "Cosine similarity: 0.684\n", + "Return of the Jedi (1983)\n", + "Citizen Ruth (1996)\n", + "Cosine similarity: 0.641\n", + "Return of the Jedi (1983)\n", + "Jerry Maguire (1996)\n", + "Cosine similarity: 0.726\n", + "Return of the Jedi (1983)\n", + "Raising Arizona (1987)\n", + "Cosine similarity: 0.642\n", + "Return of the Jedi (1983)\n", + "Sneakers (1992)\n", + "Cosine similarity: 0.794\n", + "Return of the Jedi (1983)\n", + "Beavis and Butt-head Do America (1996)\n", + "Cosine similarity: 0.522\n", + "Return of the Jedi (1983)\n", + "Last of the Mohicans, The (1992)\n", + "Cosine similarity: 0.695\n", + "Return of the Jedi (1983)\n", + "Kolya (1996)\n", + "Cosine similarity: 0.558\n", + "Return of the Jedi (1983)\n", + "Jungle2Jungle (1997)\n", + "Cosine similarity: 0.517\n", + "Return of the Jedi (1983)\n", + "Smilla's Sense of Snow (1997)\n", + "Cosine similarity: 0.526\n", + "Return of the Jedi (1983)\n", + "Devil's Own, The (1997)\n", + "Cosine similarity: 0.527\n", + "Return of the Jedi (1983)\n", + "Chasing Amy (1997)\n", + "Cosine similarity: 0.712\n", + "Return of the Jedi (1983)\n", + "Turbo: A Power Rangers Movie (1997)\n", + "Cosine similarity: 0.524\n", + "Return of the Jedi (1983)\n", + "Grosse Pointe Blank (1997)\n", + "Cosine similarity: 0.679\n", + "Return of the Jedi (1983)\n", + "Austin Powers: International Man of Mystery (1997)\n", + "Cosine similarity: 0.678\n", + "Return of the Jedi (1983)\n", + "Fifth Element, The (1997)\n", + "Cosine similarity: 0.769\n", + "Return of the Jedi (1983)\n", + "Shall We Dance? (1996)\n", + "Cosine similarity: 0.631\n", + "Return of the Jedi (1983)\n", + "Lost World: Jurassic Park, The (1997)\n", + "Cosine similarity: 0.714\n", + "Return of the Jedi (1983)\n", + "Pillow Book, The (1995)\n", + "Cosine similarity: 0.65\n", + "Return of the Jedi (1983)\n", + "Batman & Robin (1997)\n", + "Cosine similarity: 0.675\n", + "Return of the Jedi (1983)\n", + "My Best Friend's Wedding (1997)\n", + "Cosine similarity: 0.681\n", + "Return of the Jedi (1983)\n", + "When the Cats Away (Chacun cherche son chat) (1996)\n", + "Cosine similarity: 0.584\n", + "Return of the Jedi (1983)\n", + "Men in Black (1997)\n", + "Cosine similarity: 0.816\n", + "Return of the Jedi (1983)\n", + "Contact (1997)\n", + "Cosine similarity: 0.769\n", + "Return of the Jedi (1983)\n", + "George of the Jungle (1997)\n", + "Cosine similarity: 0.531\n", + "Return of the Jedi (1983)\n", + "Event Horizon (1997)\n", + "Cosine similarity: 0.297\n", + "Return of the Jedi (1983)\n", + "Air Bud (1997)\n", + "Cosine similarity: 0.437\n", + "Return of the Jedi (1983)\n", + "In the Company of Men (1997)\n", + "Cosine similarity: 0.469\n", + "Return of the Jedi (1983)\n", + "Steel (1997)\n", + "Cosine similarity: 0.597\n", + "Return of the Jedi (1983)\n", + "Mimic (1997)\n", + "Cosine similarity: 0.716\n", + "Return of the Jedi (1983)\n", + "Hunt for Red October, The (1990)\n", + "Cosine similarity: 0.825\n", + "Return of the Jedi (1983)\n", + "Kull the Conqueror (1997)\n", + "Cosine similarity: 0.566\n", + "Return of the Jedi (1983)\n", + "unknown\n", + "Cosine similarity: 0.7\n", + "Return of the Jedi (1983)\n", + "Chasing Amy (1997)\n", + "Cosine similarity: 0.687\n", + "Return of the Jedi (1983)\n", + "Full Monty, The (1997)\n", + "Cosine similarity: 0.57\n", + "Return of the Jedi (1983)\n", + "Gattaca (1997)\n", + "Cosine similarity: 0.698\n", + "Return of the Jedi (1983)\n", + "Starship Troopers (1997)\n", + "Cosine similarity: 0.662\n", + "Return of the Jedi (1983)\n", + "Good Will Hunting (1997)\n", + "Cosine similarity: 0.684\n", + "Return of the Jedi (1983)\n", + "Heat (1995)\n", + "Cosine similarity: 0.669\n", + "Return of the Jedi (1983)\n", + "Sabrina (1995)\n", + "Cosine similarity: 0.722\n", + "Return of the Jedi (1983)\n", + "Sense and Sensibility (1995)\n", + "Cosine similarity: 0.584\n", + "Return of the Jedi (1983)\n", + "Leaving Las Vegas (1995)\n", + "Cosine similarity: 0.531\n", + "Return of the Jedi (1983)\n", + "Restoration (1995)\n", + "Cosine similarity: 0.598\n", + "Return of the Jedi (1983)\n", + "Bed of Roses (1996)\n", + "Cosine similarity: 0.631\n", + "Return of the Jedi (1983)\n", + "Once Upon a Time... When We Were Colored (1995)\n", + "Cosine similarity: 0.736\n", + "Return of the Jedi (1983)\n", + "Up Close and Personal (1996)\n", + "Cosine similarity: 0.704\n", + "Return of the Jedi (1983)\n", + "River Wild, The (1994)\n", + "Cosine similarity: 0.663\n", + "Return of the Jedi (1983)\n", + "Time to Kill, A (1996)\n", + "Cosine similarity: 0.689\n", + "Return of the Jedi (1983)\n", + "Emma (1996)\n", + "Cosine similarity: 0.608\n", + "Return of the Jedi (1983)\n", + "Tin Cup (1996)\n", + "Cosine similarity: 0.7\n", + "Return of the Jedi (1983)\n", + "Secrets & Lies (1996)\n", + "Cosine similarity: 0.617\n", + "Return of the Jedi (1983)\n", + "English Patient, The (1996)\n", + "Cosine similarity: 0.571\n", + "Return of the Jedi (1983)\n", + "Marvin's Room (1996)\n", + "Cosine similarity: 0.623\n", + "Return of the Jedi (1983)\n", + "Scream (1996)\n", + "Cosine similarity: 0.613\n", + "Return of the Jedi (1983)\n", + "Evita (1996)\n", + "Cosine similarity: 0.535\n", + "Return of the Jedi (1983)\n", + "Fierce Creatures (1997)\n", + "Cosine similarity: 0.704\n", + "Return of the Jedi (1983)\n", + "Absolute Power (1997)\n", + "Cosine similarity: 0.65\n", + "Return of the Jedi (1983)\n", + "Rosewood (1997)\n", + "Cosine similarity: 0.508\n", + "Return of the Jedi (1983)\n", + "Donnie Brasco (1997)\n", + "Cosine similarity: 0.777\n", + "Return of the Jedi (1983)\n", + "Liar Liar (1997)\n", + "Cosine similarity: 0.747\n", + "Return of the Jedi (1983)\n", + "Breakdown (1997)\n", + "Cosine similarity: 0.661\n", + "Return of the Jedi (1983)\n", + "Promesse, La (1996)\n", + "Cosine similarity: 0.583\n", + "Return of the Jedi (1983)\n", + "Ulee's Gold (1997)\n", + "Cosine similarity: 0.702\n", + "Return of the Jedi (1983)\n", + "Face/Off (1997)\n", + "Cosine similarity: 0.734\n", + "Return of the Jedi (1983)\n", + "Hoodlum (1997)\n", + "Cosine similarity: 0.577\n", + "Return of the Jedi (1983)\n", + "Air Force One (1997)\n", + "Cosine similarity: 0.703\n", + "Return of the Jedi (1983)\n", + "In & Out (1997)\n", + "Cosine similarity: 0.634\n", + "Return of the Jedi (1983)\n", + "L.A. Confidential (1997)\n", + "Cosine similarity: 0.603\n", + "Return of the Jedi (1983)\n", + "Ulee's Gold (1997)\n", + "Cosine similarity: 0.601\n", + "Return of the Jedi (1983)\n", + "Fly Away Home (1996)\n", + "Cosine similarity: 0.596\n", + "Return of the Jedi (1983)\n", + "Ice Storm, The (1997)\n", + "Cosine similarity: 0.529\n", + "Return of the Jedi (1983)\n", + "Mrs. Brown (Her Majesty, Mrs. Brown) (1997)\n", + "Cosine similarity: 0.595\n", + "Return of the Jedi (1983)\n", + "Devil's Advocate, The (1997)\n", + "Cosine similarity: 0.625\n", + "Return of the Jedi (1983)\n", + "FairyTale: A True Story (1997)\n", + "Cosine similarity: 0.569\n", + "Return of the Jedi (1983)\n", + "Deceiver (1997)\n", + "Cosine similarity: 0.676\n", + "Return of the Jedi (1983)\n", + "Rainmaker, The (1997)\n", + "Cosine similarity: 0.716\n", + "Return of the Jedi (1983)\n", + "Wings of the Dove, The (1997)\n", + "Cosine similarity: 0.571\n", + "Return of the Jedi (1983)\n", + "Midnight in the Garden of Good and Evil (1997)\n", + "Cosine similarity: 0.551\n", + "Return of the Jedi (1983)\n", + "Titanic (1997)\n", + "Cosine similarity: 0.779\n", + "Return of the Jedi (1983)\n", + "3 Ninjas: High Noon At Mega Mountain (1998)\n", + "Cosine similarity: 0.361\n", + "Return of the Jedi (1983)\n", + "Apt Pupil (1998)\n", + "Cosine similarity: 0.679\n", + "Return of the Jedi (1983)\n", + "As Good As It Gets (1997)\n", + "Cosine similarity: 0.677\n", + "Return of the Jedi (1983)\n", + "In the Name of the Father (1993)\n", + "Cosine similarity: 0.762\n", + "Return of the Jedi (1983)\n", + "Schindler's List (1993)\n", + "Cosine similarity: 0.681\n", + "Return of the Jedi (1983)\n", + "Everyone Says I Love You (1996)\n", + "Cosine similarity: 0.556\n", + "Return of the Jedi (1983)\n", + "Paradise Lost: The Child Murders at Robin Hood Hills (1996)\n", + "Cosine similarity: 0.643\n", + "Return of the Jedi (1983)\n", + "Mother (1996)\n", + "Cosine similarity: 0.553\n", + "Return of the Jedi (1983)\n", + "Murder at 1600 (1997)\n", + "Cosine similarity: 0.544\n", + "Return of the Jedi (1983)\n", + "Dante's Peak (1997)\n", + "Cosine similarity: 0.617\n", + "Return of the Jedi (1983)\n", + "Lost Highway (1997)\n", + "Cosine similarity: 0.429\n", + "Return of the Jedi (1983)\n", + "Crash (1996)\n", + "Cosine similarity: 0.545\n", + "Return of the Jedi (1983)\n", + "G.I. Jane (1997)\n", + "Cosine similarity: 0.654\n", + "Return of the Jedi (1983)\n", + "Cop Land (1997)\n", + "Cosine similarity: 0.586\n", + "Return of the Jedi (1983)\n", + "Conspiracy Theory (1997)\n", + "Cosine similarity: 0.695\n", + "Return of the Jedi (1983)\n", + "Desperate Measures (1998)\n", + "Cosine similarity: 0.543\n", + "Return of the Jedi (1983)\n", + "187 (1997)\n", + "Cosine similarity: 0.509\n", + "Return of the Jedi (1983)\n", + "Edge, The (1997)\n", + "Cosine similarity: 0.659\n", + "Return of the Jedi (1983)\n", + "Kiss the Girls (1997)\n", + "Cosine similarity: 0.577\n", + "Return of the Jedi (1983)\n", + "Game, The (1997)\n", + "Cosine similarity: 0.65\n", + "Return of the Jedi (1983)\n", + "U Turn (1997)\n", + "Cosine similarity: 0.485\n", + "Return of the Jedi (1983)\n", + "How to Be a Player (1997)\n", + "Cosine similarity: 0.562\n", + "Return of the Jedi (1983)\n", + "Playing God (1997)\n", + "Cosine similarity: 0.681\n", + "Return of the Jedi (1983)\n", + "House of Yes, The (1997)\n", + "Cosine similarity: 0.551\n", + "Return of the Jedi (1983)\n", + "Bean (1997)\n", + "Cosine similarity: 0.738\n", + "Return of the Jedi (1983)\n", + "Mad City (1997)\n", + "Cosine similarity: 0.493\n", + "Return of the Jedi (1983)\n", + "Boogie Nights (1997)\n", + "Cosine similarity: 0.542\n", + "Return of the Jedi (1983)\n", + "Critical Care (1997)\n", + "Cosine similarity: 0.325\n", + "Return of the Jedi (1983)\n", + "Man Who Knew Too Little, The (1997)\n", + "Cosine similarity: 0.678\n", + "Return of the Jedi (1983)\n", + "Alien: Resurrection (1997)\n", + "Cosine similarity: 0.675\n", + "Return of the Jedi (1983)\n", + "Apostle, The (1997)\n", + "Cosine similarity: 0.624\n", + "Return of the Jedi (1983)\n", + "Deconstructing Harry (1997)\n", + "Cosine similarity: 0.564\n", + "Return of the Jedi (1983)\n", + "Jackie Brown (1997)\n", + "Cosine similarity: 0.604\n", + "Return of the Jedi (1983)\n", + "Wag the Dog (1997)\n", + "Cosine similarity: 0.682\n", + "Return of the Jedi (1983)\n", + "Desperate Measures (1998)\n", + "Cosine similarity: 0.634\n", + "Return of the Jedi (1983)\n", + "Hard Rain (1998)\n", + "Cosine similarity: 0.645\n", + "Return of the Jedi (1983)\n", + "Fallen (1998)\n", + "Cosine similarity: 0.436\n", + "Return of the Jedi (1983)\n", + "Prophecy II, The (1998)\n", + "Cosine similarity: 0.498\n", + "Return of the Jedi (1983)\n", + "Spice World (1997)\n", + "Cosine similarity: 0.43\n", + "Return of the Jedi (1983)\n", + "Deep Rising (1998)\n", + "Cosine similarity: 0.745\n", + "Return of the Jedi (1983)\n", + "Wedding Singer, The (1998)\n", + "Cosine similarity: 0.743\n", + "Return of the Jedi (1983)\n", + "Sphere (1998)\n", + "Cosine similarity: 0.582\n", + "Return of the Jedi (1983)\n", + "Client, The (1994)\n", + "Cosine similarity: 0.664\n", + "Return of the Jedi (1983)\n", + "One Flew Over the Cuckoo's Nest (1975)\n", + "Cosine similarity: 0.663\n", + "Return of the Jedi (1983)\n", + "Spawn (1997)\n", + "Cosine similarity: 0.696\n", + "Return of the Jedi (1983)\n", + "Assignment, The (1997)\n", + "Cosine similarity: 0.527\n", + "Return of the Jedi (1983)\n", + "Wonderland (1997)\n", + "Cosine similarity: 0.571\n", + "Return of the Jedi (1983)\n", + "Incognito (1997)\n", + "Cosine similarity: 0.653\n", + "Return of the Jedi (1983)\n", + "Blues Brothers 2000 (1998)\n", + "Cosine similarity: 0.695\n", + "Return of the Jedi (1983)\n", + "Sudden Death (1995)\n", + "Cosine similarity: 0.6\n", + "Return of the Jedi (1983)\n", + "Ace Ventura: When Nature Calls (1995)\n", + "Cosine similarity: 0.546\n", + "Return of the Jedi (1983)\n", + "Powder (1995)\n", + "Cosine similarity: 0.609\n", + "Return of the Jedi (1983)\n", + "Dangerous Minds (1995)\n", + "Cosine similarity: 0.649\n", + "Return of the Jedi (1983)\n", + "Clueless (1995)\n", + "Cosine similarity: 0.639\n", + "Return of the Jedi (1983)\n", + "Bio-Dome (1996)\n", + "Cosine similarity: 0.415\n", + "Return of the Jedi (1983)\n", + "Black Sheep (1996)\n", + "Cosine similarity: 0.48\n", + "Return of the Jedi (1983)\n", + "Mary Reilly (1996)\n", + "Cosine similarity: 0.559\n", + "Return of the Jedi (1983)\n", + "Bridges of Madison County, The (1995)\n", + "Cosine similarity: 0.46\n", + "Return of the Jedi (1983)\n", + "Jeffrey (1995)\n", + "Cosine similarity: 0.586\n", + "Return of the Jedi (1983)\n", + "Judge Dredd (1995)\n", + "Cosine similarity: 0.656\n", + "Return of the Jedi (1983)\n", + "Mighty Morphin Power Rangers: The Movie (1995)\n", + "Cosine similarity: 0.687\n", + "Return of the Jedi (1983)\n", + "Showgirls (1995)\n", + "Cosine similarity: 0.403\n", + "Return of the Jedi (1983)\n", + "Houseguest (1994)\n", + "Cosine similarity: 0.652\n", + "Return of the Jedi (1983)\n", + "Heavyweights (1994)\n", + "Cosine similarity: 0.371\n", + "Return of the Jedi (1983)\n", + "Miracle on 34th Street (1994)\n", + "Cosine similarity: 0.694\n", + "Return of the Jedi (1983)\n", + "Tales From the Crypt Presents: Demon Knight (1995)\n", + "Cosine similarity: 0.506\n", + "Return of the Jedi (1983)\n", + "Star Trek: Generations (1994)\n", + "Cosine similarity: 0.729\n", + "Return of the Jedi (1983)\n", + "Muriel's Wedding (1994)\n", + "Cosine similarity: 0.584\n", + "Return of the Jedi (1983)\n", + "Adventures of Priscilla, Queen of the Desert, The (1994)\n", + "Cosine similarity: 0.561\n", + "Return of the Jedi (1983)\n", + "Flintstones, The (1994)\n", + "Cosine similarity: 0.512\n", + "Return of the Jedi (1983)\n", + "Naked Gun 33 1/3: The Final Insult (1994)\n", + "Cosine similarity: 0.614\n", + "Return of the Jedi (1983)\n", + "True Lies (1994)\n", + "Cosine similarity: 0.775\n", + "Return of the Jedi (1983)\n", + "Addams Family Values (1993)\n", + "Cosine similarity: 0.49\n", + "Return of the Jedi (1983)\n", + "Age of Innocence, The (1993)\n", + "Cosine similarity: 0.599\n", + "Return of the Jedi (1983)\n", + "Beverly Hills Cop III (1994)\n", + "Cosine similarity: 0.684\n", + "Return of the Jedi (1983)\n", + "Black Beauty (1994)\n", + "Cosine similarity: 0.608\n", + "Return of the Jedi (1983)\n", + "Fear of a Black Hat (1993)\n", + "Cosine similarity: 0.732\n", + "Return of the Jedi (1983)\n", + "Last Action Hero (1993)\n", + "Cosine similarity: 0.615\n", + "Return of the Jedi (1983)\n", + "Man Without a Face, The (1993)\n", + "Cosine similarity: 0.572\n", + "Return of the Jedi (1983)\n", + "Mrs. Doubtfire (1993)\n", + "Cosine similarity: 0.596\n", + "Return of the Jedi (1983)\n", + "Radioland Murders (1994)\n", + "Cosine similarity: 0.423\n", + "Return of the Jedi (1983)\n", + "Robin Hood: Men in Tights (1993)\n", + "Cosine similarity: 0.557\n", + "Return of the Jedi (1983)\n", + "Serial Mom (1994)\n", + "Cosine similarity: 0.473\n", + "Return of the Jedi (1983)\n", + "Striking Distance (1993)\n", + "Cosine similarity: 0.528\n", + "Return of the Jedi (1983)\n", + "Super Mario Bros. (1993)\n", + "Cosine similarity: 0.532\n", + "Return of the Jedi (1983)\n", + "Three Musketeers, The (1993)\n", + "Cosine similarity: 0.774\n", + "Return of the Jedi (1983)\n", + "Little Rascals, The (1994)\n", + "Cosine similarity: 0.592\n", + "Return of the Jedi (1983)\n", + "Brady Bunch Movie, The (1995)\n", + "Cosine similarity: 0.721\n", + "Return of the Jedi (1983)\n", + "Ghost (1990)\n", + "Cosine similarity: 0.654\n", + "Return of the Jedi (1983)\n", + "Batman (1989)\n", + "Cosine similarity: 0.726\n", + "Return of the Jedi (1983)\n", + "Pinocchio (1940)\n", + "Cosine similarity: 0.715\n", + "Return of the Jedi (1983)\n", + "Mission: Impossible (1996)\n", + "Cosine similarity: 0.684\n", + "Return of the Jedi (1983)\n", + "Thinner (1996)\n", + "Cosine similarity: 0.582\n", + "Return of the Jedi (1983)\n", + "Spy Hard (1996)\n", + "Cosine similarity: 0.565\n", + "Return of the Jedi (1983)\n", + "Close Shave, A (1995)\n", + "Cosine similarity: 0.741\n", + "Return of the Jedi (1983)\n", + "Jack (1996)\n", + "Cosine similarity: 0.62\n", + "Return of the Jedi (1983)\n", + "Kingpin (1996)\n", + "Cosine similarity: 0.649\n", + "Return of the Jedi (1983)\n", + "Nutty Professor, The (1996)\n", + "Cosine similarity: 0.668\n", + "Return of the Jedi (1983)\n", + "Very Brady Sequel, A (1996)\n", + "Cosine similarity: 0.516\n", + "Return of the Jedi (1983)\n", + "Tales from the Crypt Presents: Bordello of Blood (1996)\n", + "Cosine similarity: 0.622\n", + "Return of the Jedi (1983)\n", + "My Favorite Year (1982)\n", + "Cosine similarity: 0.673\n", + "Return of the Jedi (1983)\n", + "Apple Dumpling Gang, The (1975)\n", + "Cosine similarity: 0.486\n", + "Return of the Jedi (1983)\n", + "Old Yeller (1957)\n", + "Cosine similarity: 0.632\n", + "Return of the Jedi (1983)\n", + "Parent Trap, The (1961)\n", + "Cosine similarity: 0.618\n", + "Return of the Jedi (1983)\n", + "Cinderella (1950)\n", + "Cosine similarity: 0.718\n", + "Return of the Jedi (1983)\n", + "Mary Poppins (1964)\n", + "Cosine similarity: 0.648\n", + "Return of the Jedi (1983)\n", + "Alice in Wonderland (1951)\n", + "Cosine similarity: 0.707\n", + "Return of the Jedi (1983)\n", + "William Shakespeare's Romeo and Juliet (1996)\n", + "Cosine similarity: 0.707\n", + "Return of the Jedi (1983)\n", + "Aladdin and the King of Thieves (1996)\n", + "Cosine similarity: 0.649\n", + "Return of the Jedi (1983)\n", + "E.T. the Extra-Terrestrial (1982)\n", + "Cosine similarity: 0.755\n", + "Return of the Jedi (1983)\n", + "Children of the Corn: The Gathering (1996)\n", + "Cosine similarity: 0.568\n", + "Return of the Jedi (1983)\n", + "Bob Roberts (1992)\n", + "Cosine similarity: 0.609\n", + "Return of the Jedi (1983)\n", + "Transformers: The Movie, The (1986)\n", + "Cosine similarity: 0.592\n", + "Return of the Jedi (1983)\n", + "To Kill a Mockingbird (1962)\n", + "Cosine similarity: 0.622\n", + "Return of the Jedi (1983)\n", + "Harold and Maude (1971)\n", + "Cosine similarity: 0.635\n", + "Return of the Jedi (1983)\n", + "Day the Earth Stood Still, The (1951)\n", + "Cosine similarity: 0.751\n", + "Return of the Jedi (1983)\n", + "Duck Soup (1933)\n", + "Cosine similarity: 0.742\n", + "Return of the Jedi (1983)\n", + "Highlander (1986)\n", + "Cosine similarity: 0.729\n", + "Return of the Jedi (1983)\n", + "Fantasia (1940)\n", + "Cosine similarity: 0.724\n", + "Return of the Jedi (1983)\n", + "Heathers (1989)\n", + "Cosine similarity: 0.621\n", + "Return of the Jedi (1983)\n", + "Forbidden Planet (1956)\n", + "Cosine similarity: 0.731\n", + "Return of the Jedi (1983)\n", + "Butch Cassidy and the Sundance Kid (1969)\n", + "Cosine similarity: 0.757\n", + "Return of the Jedi (1983)\n", + "American Werewolf in London, An (1981)\n", + "Cosine similarity: 0.75\n", + "Return of the Jedi (1983)\n", + "Amityville 1992: It's About Time (1992)\n", + "Cosine similarity: 0.652\n", + "Return of the Jedi (1983)\n", + "Amityville 3-D (1983)\n", + "Cosine similarity: 0.603\n", + "Return of the Jedi (1983)\n", + "Amityville: A New Generation (1993)\n", + "Cosine similarity: 0.757\n", + "Return of the Jedi (1983)\n", + "Amityville II: The Possession (1982)\n", + "Cosine similarity: 0.358\n", + "Return of the Jedi (1983)\n", + "Amityville Horror, The (1979)\n", + "Cosine similarity: 0.59\n", + "Return of the Jedi (1983)\n", + "Amityville Curse, The (1990)\n", + "Cosine similarity: 0.517\n", + "Return of the Jedi (1983)\n", + "Birds, The (1963)\n", + "Cosine similarity: 0.673\n", + "Return of the Jedi (1983)\n", + "Blob, The (1958)\n", + "Cosine similarity: 0.605\n", + "Return of the Jedi (1983)\n", + "Body Snatcher, The (1945)\n", + "Cosine similarity: 0.728\n", + "Return of the Jedi (1983)\n", + "Burnt Offerings (1976)\n", + "Cosine similarity: 0.552\n", + "Return of the Jedi (1983)\n", + "Carrie (1976)\n", + "Cosine similarity: 0.677\n", + "Return of the Jedi (1983)\n", + "Omen, The (1976)\n", + "Cosine similarity: 0.667\n", + "Return of the Jedi (1983)\n", + "Star Trek: The Motion Picture (1979)\n", + "Cosine similarity: 0.758\n", + "Return of the Jedi (1983)\n", + "Star Trek V: The Final Frontier (1989)\n", + "Cosine similarity: 0.456\n", + "Return of the Jedi (1983)\n", + "Grease (1978)\n", + "Cosine similarity: 0.612\n", + "Return of the Jedi (1983)\n", + "Jaws 2 (1978)\n", + "Cosine similarity: 0.54\n", + "Return of the Jedi (1983)\n", + "Jaws 3-D (1983)\n", + "Cosine similarity: 0.615\n", + "Return of the Jedi (1983)\n", + "Bastard Out of Carolina (1996)\n", + "Cosine similarity: 0.562\n", + "Return of the Jedi (1983)\n", + "Jackie Chan's First Strike (1996)\n", + "Cosine similarity: 0.694\n", + "Return of the Jedi (1983)\n", + "Beverly Hills Ninja (1997)\n", + "Cosine similarity: 0.461\n", + "Return of the Jedi (1983)\n", + "Free Willy 3: The Rescue (1997)\n", + "Cosine similarity: 0.505\n", + "Return of the Jedi (1983)\n", + "Nixon (1995)\n", + "Cosine similarity: 0.579\n", + "Return of the Jedi (1983)\n", + "Cry, the Beloved Country (1995)\n", + "Cosine similarity: 0.52\n", + "Return of the Jedi (1983)\n", + "Crossing Guard, The (1995)\n", + "Cosine similarity: 0.687\n", + "Return of the Jedi (1983)\n", + "Smoke (1995)\n", + "Cosine similarity: 0.635\n", + "Return of the Jedi (1983)\n", + "Like Water For Chocolate (Como agua para chocolate) (1992)\n", + "Cosine similarity: 0.631\n", + "Return of the Jedi (1983)\n", + "Secret of Roan Inish, The (1994)\n", + "Cosine similarity: 0.593\n", + "Return of the Jedi (1983)\n", + "Vanya on 42nd Street (1994)\n", + "Cosine similarity: 0.559\n", + "Return of the Jedi (1983)\n", + "Jungle Book, The (1994)\n", + "Cosine similarity: 0.659\n", + "Return of the Jedi (1983)\n", + "Red Rock West (1992)\n", + "Cosine similarity: 0.687\n", + "Return of the Jedi (1983)\n", + "Bronx Tale, A (1993)\n", + "Cosine similarity: 0.687\n", + "Return of the Jedi (1983)\n", + "Rudy (1993)\n", + "Cosine similarity: 0.642\n", + "Return of the Jedi (1983)\n", + "Short Cuts (1993)\n", + "Cosine similarity: 0.574\n", + "Return of the Jedi (1983)\n", + "Tombstone (1993)\n", + "Cosine similarity: 0.635\n", + "Return of the Jedi (1983)\n", + "Courage Under Fire (1996)\n", + "Cosine similarity: 0.788\n", + "Return of the Jedi (1983)\n", + "Dragonheart (1996)\n", + "Cosine similarity: 0.749\n", + "Return of the Jedi (1983)\n", + "James and the Giant Peach (1996)\n", + "Cosine similarity: 0.615\n", + "Return of the Jedi (1983)\n", + "Dr. Strangelove or: How I Learned to Stop Worrying and Love the Bomb (1963)\n", + "Cosine similarity: 0.601\n", + "Return of the Jedi (1983)\n", + "Trainspotting (1996)\n", + "Cosine similarity: 0.595\n", + "Return of the Jedi (1983)\n", + "First Wives Club, The (1996)\n", + "Cosine similarity: 0.504\n", + "Return of the Jedi (1983)\n", + "Matilda (1996)\n", + "Cosine similarity: 0.595\n", + "Return of the Jedi (1983)\n", + "Philadelphia Story, The (1940)\n", + "Cosine similarity: 0.644\n", + "Return of the Jedi (1983)\n", + "Vertigo (1958)\n", + "Cosine similarity: 0.659\n", + "Return of the Jedi (1983)\n", + "North by Northwest (1959)\n", + "Cosine similarity: 0.724\n", + "Return of the Jedi (1983)\n", + "Apartment, The (1960)\n", + "Cosine similarity: 0.597\n", + "Return of the Jedi (1983)\n", + "Some Like It Hot (1959)\n", + "Cosine similarity: 0.7\n", + "Return of the Jedi (1983)\n", + "Casablanca (1942)\n", + "Cosine similarity: 0.706\n", + "Return of the Jedi (1983)\n", + "Maltese Falcon, The (1941)\n", + "Cosine similarity: 0.672\n", + "Return of the Jedi (1983)\n", + "My Fair Lady (1964)\n", + "Cosine similarity: 0.591\n", + "Return of the Jedi (1983)\n", + "Sabrina (1954)\n", + "Cosine similarity: 0.659\n", + "Return of the Jedi (1983)\n", + "Roman Holiday (1953)\n", + "Cosine similarity: 0.723\n", + "Return of the Jedi (1983)\n", + "Sunset Blvd. (1950)\n", + "Cosine similarity: 0.539\n", + "Return of the Jedi (1983)\n", + "Notorious (1946)\n", + "Cosine similarity: 0.627\n", + "Return of the Jedi (1983)\n", + "To Catch a Thief (1955)\n", + "Cosine similarity: 0.684\n", + "Return of the Jedi (1983)\n", + "Adventures of Robin Hood, The (1938)\n", + "Cosine similarity: 0.7\n", + "Return of the Jedi (1983)\n", + "East of Eden (1955)\n", + "Cosine similarity: 0.686\n", + "Return of the Jedi (1983)\n", + "Thin Man, The (1934)\n", + "Cosine similarity: 0.67\n", + "Return of the Jedi (1983)\n", + "His Girl Friday (1940)\n", + "Cosine similarity: 0.737\n", + "Return of the Jedi (1983)\n", + "Around the World in 80 Days (1956)\n", + "Cosine similarity: 0.695\n", + "Return of the Jedi (1983)\n", + "It's a Wonderful Life (1946)\n", + "Cosine similarity: 0.731\n", + "Return of the Jedi (1983)\n", + "Bringing Up Baby (1938)\n", + "Cosine similarity: 0.775\n", + "Return of the Jedi (1983)\n", + "African Queen, The (1951)\n", + "Cosine similarity: 0.722\n", + "Return of the Jedi (1983)\n", + "Cat on a Hot Tin Roof (1958)\n", + "Cosine similarity: 0.605\n", + "Return of the Jedi (1983)\n", + "Fly Away Home (1996)\n", + "Cosine similarity: 0.756\n", + "Return of the Jedi (1983)\n", + "Dumbo (1941)\n", + "Cosine similarity: 0.66\n", + "Return of the Jedi (1983)\n", + "Bananas (1971)\n", + "Cosine similarity: 0.679\n", + "Return of the Jedi (1983)\n", + "Candidate, The (1972)\n", + "Cosine similarity: 0.685\n", + "Return of the Jedi (1983)\n", + "Bonnie and Clyde (1967)\n", + "Cosine similarity: 0.582\n", + "Return of the Jedi (1983)\n", + "Dial M for Murder (1954)\n", + "Cosine similarity: 0.692\n", + "Return of the Jedi (1983)\n", + "Rebel Without a Cause (1955)\n", + "Cosine similarity: 0.671\n", + "Return of the Jedi (1983)\n", + "Streetcar Named Desire, A (1951)\n", + "Cosine similarity: 0.619\n", + "Return of the Jedi (1983)\n", + "People vs. Larry Flynt, The (1996)\n", + "Cosine similarity: 0.744\n", + "Return of the Jedi (1983)\n", + "My Left Foot (1989)\n", + "Cosine similarity: 0.619\n", + "Return of the Jedi (1983)\n", + "Magnificent Seven, The (1954)\n", + "Cosine similarity: 0.616\n", + "Return of the Jedi (1983)\n", + "Lawrence of Arabia (1962)\n", + "Cosine similarity: 0.722\n", + "Return of the Jedi (1983)\n", + "Wings of Desire (1987)\n", + "Cosine similarity: 0.645\n", + "Return of the Jedi (1983)\n", + "Third Man, The (1949)\n", + "Cosine similarity: 0.696\n", + "Return of the Jedi (1983)\n", + "Annie Hall (1977)\n", + "Cosine similarity: 0.553\n", + "Return of the Jedi (1983)\n", + "Boot, Das (1981)\n", + "Cosine similarity: 0.732\n", + "Return of the Jedi (1983)\n", + "Local Hero (1983)\n", + "Cosine similarity: 0.658\n", + "Return of the Jedi (1983)\n", + "Manhattan (1979)\n", + "Cosine similarity: 0.642\n", + "Return of the Jedi (1983)\n", + "Miller's Crossing (1990)\n", + "Cosine similarity: 0.548\n", + "Return of the Jedi (1983)\n", + "Treasure of the Sierra Madre, The (1948)\n", + "Cosine similarity: 0.7\n", + "Return of the Jedi (1983)\n", + "Great Escape, The (1963)\n", + "Cosine similarity: 0.739\n", + "Return of the Jedi (1983)\n", + "Deer Hunter, The (1978)\n", + "Cosine similarity: 0.71\n", + "Return of the Jedi (1983)\n", + "Down by Law (1986)\n", + "Cosine similarity: 0.589\n", + "Return of the Jedi (1983)\n", + "Cool Hand Luke (1967)\n", + "Cosine similarity: 0.648\n", + "Return of the Jedi (1983)\n", + "Great Dictator, The (1940)\n", + "Cosine similarity: 0.665\n", + "Return of the Jedi (1983)\n", + "Big Sleep, The (1946)\n", + "Cosine similarity: 0.604\n", + "Return of the Jedi (1983)\n", + "Ben-Hur (1959)\n", + "Cosine similarity: 0.747\n", + "Return of the Jedi (1983)\n", + "Gandhi (1982)\n", + "Cosine similarity: 0.773\n", + "Return of the Jedi (1983)\n", + "Killing Fields, The (1984)\n", + "Cosine similarity: 0.682\n", + "Return of the Jedi (1983)\n", + "My Life as a Dog (Mitt liv som hund) (1985)\n", + "Cosine similarity: 0.622\n", + "Return of the Jedi (1983)\n", + "Man Who Would Be King, The (1975)\n", + "Cosine similarity: 0.77\n", + "Return of the Jedi (1983)\n", + "Shine (1996)\n", + "Cosine similarity: 0.771\n", + "Return of the Jedi (1983)\n", + "Kama Sutra: A Tale of Love (1996)\n", + "Cosine similarity: 0.651\n", + "Return of the Jedi (1983)\n", + "Daytrippers, The (1996)\n", + "Cosine similarity: 0.608\n", + "Return of the Jedi (1983)\n", + "Traveller (1997)\n", + "Cosine similarity: 0.617\n", + "Return of the Jedi (1983)\n", + "Addicted to Love (1997)\n", + "Cosine similarity: 0.603\n", + "Return of the Jedi (1983)\n", + "Ponette (1996)\n", + "Cosine similarity: 0.644\n", + "Return of the Jedi (1983)\n", + "My Own Private Idaho (1991)\n", + "Cosine similarity: 0.604\n", + "Return of the Jedi (1983)\n", + "Anastasia (1997)\n", + "Cosine similarity: 0.529\n", + "Return of the Jedi (1983)\n", + "Mouse Hunt (1997)\n", + "Cosine similarity: 0.546\n", + "Return of the Jedi (1983)\n", + "Money Train (1995)\n", + "Cosine similarity: 0.529\n", + "Return of the Jedi (1983)\n", + "Mortal Kombat (1995)\n", + "Cosine similarity: 0.618\n", + "Return of the Jedi (1983)\n", + "Pocahontas (1995)\n", + "Cosine similarity: 0.674\n", + "Return of the Jedi (1983)\n", + "Misérables, Les (1995)\n", + "Cosine similarity: 0.515\n", + "Return of the Jedi (1983)\n", + "Things to Do in Denver when You're Dead (1995)\n", + "Cosine similarity: 0.507\n", + "Return of the Jedi (1983)\n", + "Vampire in Brooklyn (1995)\n", + "Cosine similarity: 0.775\n", + "Return of the Jedi (1983)\n", + "Broken Arrow (1996)\n", + "Cosine similarity: 0.709\n", + "Return of the Jedi (1983)\n", + "Young Poisoner's Handbook, The (1995)\n", + "Cosine similarity: 0.506\n", + "Return of the Jedi (1983)\n", + "NeverEnding Story III, The (1994)\n", + "Cosine similarity: 0.472\n", + "Return of the Jedi (1983)\n", + "Rob Roy (1995)\n", + "Cosine similarity: 0.629\n", + "Return of the Jedi (1983)\n", + "Die Hard: With a Vengeance (1995)\n", + "Cosine similarity: 0.743\n", + "Return of the Jedi (1983)\n", + "Lord of Illusions (1995)\n", + "Cosine similarity: 0.671\n", + "Return of the Jedi (1983)\n", + "Species (1995)\n", + "Cosine similarity: 0.689\n", + "Return of the Jedi (1983)\n", + "Walk in the Clouds, A (1995)\n", + "Cosine similarity: 0.537\n", + "Return of the Jedi (1983)\n", + "Waterworld (1995)\n", + "Cosine similarity: 0.496\n", + "Return of the Jedi (1983)\n", + "White Man's Burden (1995)\n", + "Cosine similarity: 0.539\n", + "Return of the Jedi (1983)\n", + "Wild Bill (1995)\n", + "Cosine similarity: 0.575\n", + "Return of the Jedi (1983)\n", + "Farinelli: il castrato (1994)\n", + "Cosine similarity: 0.638\n", + "Return of the Jedi (1983)\n", + "Heavenly Creatures (1994)\n", + "Cosine similarity: 0.515\n", + "Return of the Jedi (1983)\n", + "Interview with the Vampire (1994)\n", + "Cosine similarity: 0.695\n", + "Return of the Jedi (1983)\n", + "Kid in King Arthur's Court, A (1995)\n", + "Cosine similarity: 0.539\n", + "Return of the Jedi (1983)\n", + "Mary Shelley's Frankenstein (1994)\n", + "Cosine similarity: 0.614\n", + "Return of the Jedi (1983)\n", + "Quick and the Dead, The (1995)\n", + "Cosine similarity: 0.54\n", + "Return of the Jedi (1983)\n", + "Stephen King's The Langoliers (1995)\n", + "Cosine similarity: 0.608\n", + "Return of the Jedi (1983)\n", + "Tales from the Hood (1995)\n", + "Cosine similarity: 0.564\n", + "Return of the Jedi (1983)\n", + "Village of the Damned (1995)\n", + "Cosine similarity: 0.645\n", + "Return of the Jedi (1983)\n", + "Clear and Present Danger (1994)\n", + "Cosine similarity: 0.748\n", + "Return of the Jedi (1983)\n", + "Wes Craven's New Nightmare (1994)\n", + "Cosine similarity: 0.606\n", + "Return of the Jedi (1983)\n", + "Speed (1994)\n", + "Cosine similarity: 0.711\n", + "Return of the Jedi (1983)\n", + "Wolf (1994)\n", + "Cosine similarity: 0.545\n", + "Return of the Jedi (1983)\n", + "Wyatt Earp (1994)\n", + "Cosine similarity: 0.679\n", + "Return of the Jedi (1983)\n", + "Another Stakeout (1993)\n", + "Cosine similarity: 0.542\n", + "Return of the Jedi (1983)\n", + "Blown Away (1994)\n", + "Cosine similarity: 0.565\n", + "Return of the Jedi (1983)\n", + "Body Snatchers (1993)\n", + "Cosine similarity: 0.669\n", + "Return of the Jedi (1983)\n", + "Boxing Helena (1993)\n", + "Cosine similarity: 0.425\n", + "Return of the Jedi (1983)\n", + "City Slickers II: The Legend of Curly's Gold (1994)\n", + "Cosine similarity: 0.626\n", + "Return of the Jedi (1983)\n", + "Cliffhanger (1993)\n", + "Cosine similarity: 0.77\n", + "Return of the Jedi (1983)\n", + "Coneheads (1993)\n", + "Cosine similarity: 0.612\n", + "Return of the Jedi (1983)\n", + "Demolition Man (1993)\n", + "Cosine similarity: 0.599\n", + "Return of the Jedi (1983)\n", + "Fatal Instinct (1993)\n", + "Cosine similarity: 0.671\n", + "Return of the Jedi (1983)\n", + "Englishman Who Went Up a Hill, But Came Down a Mountain, The (1995)\n", + "Cosine similarity: 0.666\n", + "Return of the Jedi (1983)\n", + "Kalifornia (1993)\n", + "Cosine similarity: 0.54\n", + "Return of the Jedi (1983)\n", + "Piano, The (1993)\n", + "Cosine similarity: 0.47\n", + "Return of the Jedi (1983)\n", + "Romeo Is Bleeding (1993)\n", + "Cosine similarity: 0.65\n", + "Return of the Jedi (1983)\n", + "Secret Garden, The (1993)\n", + "Cosine similarity: 0.644\n", + "Return of the Jedi (1983)\n", + "Son in Law (1993)\n", + "Cosine similarity: 0.594\n", + "Return of the Jedi (1983)\n", + "Terminal Velocity (1994)\n", + "Cosine similarity: 0.69\n", + "Return of the Jedi (1983)\n", + "Hour of the Pig, The (1993)\n", + "Cosine similarity: 0.721\n", + "Return of the Jedi (1983)\n", + "Beauty and the Beast (1991)\n", + "Cosine similarity: 0.702\n", + "Return of the Jedi (1983)\n", + "Wild Bunch, The (1969)\n", + "Cosine similarity: 0.605\n", + "Return of the Jedi (1983)\n", + "Hellraiser: Bloodline (1996)\n", + "Cosine similarity: 0.634\n", + "Return of the Jedi (1983)\n", + "Primal Fear (1996)\n", + "Cosine similarity: 0.638\n", + "Return of the Jedi (1983)\n", + "True Crime (1995)\n", + "Cosine similarity: 0.517\n", + "Return of the Jedi (1983)\n", + "Stalingrad (1993)\n", + "Cosine similarity: 0.575\n", + "Return of the Jedi (1983)\n", + "Heavy (1995)\n", + "Cosine similarity: 0.622\n", + "Return of the Jedi (1983)\n", + "Fan, The (1996)\n", + "Cosine similarity: 0.613\n", + "Return of the Jedi (1983)\n", + "Hunchback of Notre Dame, The (1996)\n", + "Cosine similarity: 0.762\n", + "Return of the Jedi (1983)\n", + "Eraser (1996)\n", + "Cosine similarity: 0.714\n", + "Return of the Jedi (1983)\n", + "Big Squeeze, The (1996)\n", + "Cosine similarity: 0.673\n", + "Return of the Jedi (1983)\n", + "Police Story 4: Project S (Chao ji ji hua) (1993)\n", + "Cosine similarity: 0.504\n", + "Return of the Jedi (1983)\n", + "Daniel Defoe's Robinson Crusoe (1996)\n", + "Cosine similarity: 0.671\n", + "Return of the Jedi (1983)\n", + "For Whom the Bell Tolls (1943)\n", + "Cosine similarity: 0.715\n", + "Return of the Jedi (1983)\n", + "American in Paris, An (1951)\n", + "Cosine similarity: 0.646\n", + "Return of the Jedi (1983)\n", + "Rear Window (1954)\n", + "Cosine similarity: 0.706\n", + "Return of the Jedi (1983)\n", + "It Happened One Night (1934)\n", + "Cosine similarity: 0.6\n", + "Return of the Jedi (1983)\n", + "Meet Me in St. Louis (1944)\n", + "Cosine similarity: 0.633\n", + "Return of the Jedi (1983)\n", + "All About Eve (1950)\n", + "Cosine similarity: 0.574\n", + "Return of the Jedi (1983)\n", + "Rebecca (1940)\n", + "Cosine similarity: 0.585\n", + "Return of the Jedi (1983)\n", + "Spellbound (1945)\n", + "Cosine similarity: 0.615\n", + "Return of the Jedi (1983)\n", + "Father of the Bride (1950)\n", + "Cosine similarity: 0.725\n", + "Return of the Jedi (1983)\n", + "Gigi (1958)\n", + "Cosine similarity: 0.488\n", + "Return of the Jedi (1983)\n", + "Laura (1944)\n", + "Cosine similarity: 0.568\n", + "Return of the Jedi (1983)\n", + "Lost Horizon (1937)\n", + "Cosine similarity: 0.709\n", + "Return of the Jedi (1983)\n", + "My Man Godfrey (1936)\n", + "Cosine similarity: 0.75\n", + "Return of the Jedi (1983)\n", + "Giant (1956)\n", + "Cosine similarity: 0.769\n", + "Return of the Jedi (1983)\n", + "39 Steps, The (1935)\n", + "Cosine similarity: 0.666\n", + "Return of the Jedi (1983)\n", + "Night of the Living Dead (1968)\n", + "Cosine similarity: 0.634\n", + "Return of the Jedi (1983)\n", + "Blue Angel, The (Blaue Engel, Der) (1930)\n", + "Cosine similarity: 0.719\n", + "Return of the Jedi (1983)\n", + "Picnic (1955)\n", + "Cosine similarity: 0.726\n", + "Return of the Jedi (1983)\n", + "Extreme Measures (1996)\n", + "Cosine similarity: 0.726\n", + "Return of the Jedi (1983)\n", + "Chamber, The (1996)\n", + "Cosine similarity: 0.655\n", + "Return of the Jedi (1983)\n", + "Davy Crockett, King of the Wild Frontier (1955)\n", + "Cosine similarity: 0.677\n", + "Return of the Jedi (1983)\n", + "Swiss Family Robinson (1960)\n", + "Cosine similarity: 0.649\n", + "Return of the Jedi (1983)\n", + "Angels in the Outfield (1994)\n", + "Cosine similarity: 0.537\n", + "Return of the Jedi (1983)\n", + "Three Caballeros, The (1945)\n", + "Cosine similarity: 0.688\n", + "Return of the Jedi (1983)\n", + "Sword in the Stone, The (1963)\n", + "Cosine similarity: 0.584\n", + "Return of the Jedi (1983)\n", + "So Dear to My Heart (1949)\n", + "Cosine similarity: 0.553\n", + "Return of the Jedi (1983)\n", + "Robin Hood: Prince of Thieves (1991)\n", + "Cosine similarity: 0.662\n", + "Return of the Jedi (1983)\n", + "Sleepers (1996)\n", + "Cosine similarity: 0.717\n", + "Return of the Jedi (1983)\n", + "Victor/Victoria (1982)\n", + "Cosine similarity: 0.617\n", + "Return of the Jedi (1983)\n", + "Great Race, The (1965)\n", + "Cosine similarity: 0.647\n", + "Return of the Jedi (1983)\n", + "Crying Game, The (1992)\n", + "Cosine similarity: 0.515\n", + "Return of the Jedi (1983)\n", + "Sophie's Choice (1982)\n", + "Cosine similarity: 0.621\n", + "Return of the Jedi (1983)\n", + "Christmas Carol, A (1938)\n", + "Cosine similarity: 0.618\n", + "Return of the Jedi (1983)\n", + "Microcosmos: Le peuple de l'herbe (1996)\n", + "Cosine similarity: 0.707\n", + "Return of the Jedi (1983)\n", + "Fog, The (1980)\n", + "Cosine similarity: 0.568\n", + "Return of the Jedi (1983)\n", + "Escape from New York (1981)\n", + "Cosine similarity: 0.696\n", + "Return of the Jedi (1983)\n", + "Howling, The (1981)\n", + "Cosine similarity: 0.576\n", + "Return of the Jedi (1983)\n", + "Return of Martin Guerre, The (Retour de Martin Guerre, Le) (1982)\n", + "Cosine similarity: 0.583\n", + "Return of the Jedi (1983)\n", + "Tin Drum, The (Blechtrommel, Die) (1979)\n", + "Cosine similarity: 0.623\n", + "Return of the Jedi (1983)\n", + "Cook the Thief His Wife & Her Lover, The (1989)\n", + "Cosine similarity: 0.435\n", + "Return of the Jedi (1983)\n", + "Paths of Glory (1957)\n", + "Cosine similarity: 0.592\n", + "Return of the Jedi (1983)\n", + "Grifters, The (1990)\n", + "Cosine similarity: 0.526\n", + "Return of the Jedi (1983)\n", + "The Innocent (1994)\n", + "Cosine similarity: 0.618\n", + "Return of the Jedi (1983)\n", + "Thin Blue Line, The (1988)\n", + "Cosine similarity: 0.678\n", + "Return of the Jedi (1983)\n", + "Paris Is Burning (1990)\n", + "Cosine similarity: 0.614\n", + "Return of the Jedi (1983)\n", + "Once Upon a Time in the West (1969)\n", + "Cosine similarity: 0.711\n", + "Return of the Jedi (1983)\n", + "Ran (1985)\n", + "Cosine similarity: 0.641\n", + "Return of the Jedi (1983)\n", + "Quiet Man, The (1952)\n", + "Cosine similarity: 0.62\n", + "Return of the Jedi (1983)\n", + "Once Upon a Time in America (1984)\n", + "Cosine similarity: 0.6\n", + "Return of the Jedi (1983)\n", + "Seventh Seal, The (Sjunde inseglet, Det) (1957)\n", + "Cosine similarity: 0.478\n", + "Return of the Jedi (1983)\n", + "Glory (1989)\n", + "Cosine similarity: 0.749\n", + "Return of the Jedi (1983)\n", + "Rosencrantz and Guildenstern Are Dead (1990)\n", + "Cosine similarity: 0.549\n", + "Return of the Jedi (1983)\n", + "Touch of Evil (1958)\n", + "Cosine similarity: 0.582\n", + "Return of the Jedi (1983)\n", + "Chinatown (1974)\n", + "Cosine similarity: 0.559\n", + "Return of the Jedi (1983)\n", + "Stand by Me (1986)\n", + "Cosine similarity: 0.672\n", + "Return of the Jedi (1983)\n", + "M (1931)\n", + "Cosine similarity: 0.58\n", + "Return of the Jedi (1983)\n", + "Manchurian Candidate, The (1962)\n", + "Cosine similarity: 0.628\n", + "Return of the Jedi (1983)\n", + "Pump Up the Volume (1990)\n", + "Cosine similarity: 0.685\n", + "Return of the Jedi (1983)\n", + "Arsenic and Old Lace (1944)\n", + "Cosine similarity: 0.739\n", + "Return of the Jedi (1983)\n", + "Fried Green Tomatoes (1991)\n", + "Cosine similarity: 0.679\n", + "Return of the Jedi (1983)\n", + "High Noon (1952)\n", + "Cosine similarity: 0.692\n", + "Return of the Jedi (1983)\n", + "Somewhere in Time (1980)\n", + "Cosine similarity: 0.699\n", + "Return of the Jedi (1983)\n", + "Being There (1979)\n", + "Cosine similarity: 0.697\n", + "Return of the Jedi (1983)\n", + "Paris, Texas (1984)\n", + "Cosine similarity: 0.6\n", + "Return of the Jedi (1983)\n", + "Alien 3 (1992)\n", + "Cosine similarity: 0.573\n", + "Return of the Jedi (1983)\n", + "Blood For Dracula (Andy Warhol's Dracula) (1974)\n", + "Cosine similarity: 0.607\n", + "Return of the Jedi (1983)\n", + "Audrey Rose (1977)\n", + "Cosine similarity: 0.614\n", + "Return of the Jedi (1983)\n", + "Blood Beach (1981)\n", + "Cosine similarity: 0.625\n", + "Return of the Jedi (1983)\n", + "Body Parts (1991)\n", + "Cosine similarity: 0.569\n", + "Return of the Jedi (1983)\n", + "Body Snatchers (1993)\n", + "Cosine similarity: 0.77\n", + "Return of the Jedi (1983)\n", + "Bride of Frankenstein (1935)\n", + "Cosine similarity: 0.585\n", + "Return of the Jedi (1983)\n", + "Candyman (1992)\n", + "Cosine similarity: 0.628\n", + "Return of the Jedi (1983)\n", + "Cape Fear (1962)\n", + "Cosine similarity: 0.702\n", + "Return of the Jedi (1983)\n", + "Cat People (1982)\n", + "Cosine similarity: 0.715\n", + "Return of the Jedi (1983)\n", + "Nosferatu (Nosferatu, eine Symphonie des Grauens) (1922)\n", + "Cosine similarity: 0.578\n", + "Return of the Jedi (1983)\n", + "Crucible, The (1996)\n", + "Cosine similarity: 0.612\n", + "Return of the Jedi (1983)\n", + "Fire on the Mountain (1996)\n", + "Cosine similarity: -0.217\n", + "Return of the Jedi (1983)\n", + "Volcano (1997)\n", + "Cosine similarity: 0.555\n", + "Return of the Jedi (1983)\n", + "Conan the Barbarian (1981)\n", + "Cosine similarity: 0.704\n", + "Return of the Jedi (1983)\n", + "Kull the Conqueror (1997)\n", + "Cosine similarity: 0.473\n", + "Return of the Jedi (1983)\n", + "Wishmaster (1997)\n", + "Cosine similarity: 0.461\n", + "Return of the Jedi (1983)\n", + "I Know What You Did Last Summer (1997)\n", + "Cosine similarity: 0.538\n", + "Return of the Jedi (1983)\n", + "Rocket Man (1997)\n", + "Cosine similarity: 0.486\n", + "Return of the Jedi (1983)\n", + "In the Line of Fire (1993)\n", + "Cosine similarity: 0.757\n", + "Return of the Jedi (1983)\n", + "Executive Decision (1996)\n", + "Cosine similarity: 0.65\n", + "Return of the Jedi (1983)\n", + "Perfect World, A (1993)\n", + "Cosine similarity: 0.653\n", + "Return of the Jedi (1983)\n", + "McHale's Navy (1997)\n", + "Cosine similarity: 0.463\n", + "Return of the Jedi (1983)\n", + "Leave It to Beaver (1997)\n", + "Cosine similarity: 0.279\n", + "Return of the Jedi (1983)\n", + "Jackal, The (1997)\n", + "Cosine similarity: 0.642\n", + "Return of the Jedi (1983)\n", + "Seven Years in Tibet (1997)\n", + "Cosine similarity: 0.618\n", + "Return of the Jedi (1983)\n", + "Dark City (1998)\n", + "Cosine similarity: 0.718\n", + "Return of the Jedi (1983)\n", + "American President, The (1995)\n", + "Cosine similarity: 0.698\n", + "Return of the Jedi (1983)\n", + "Casino (1995)\n", + "Cosine similarity: 0.681\n", + "Return of the Jedi (1983)\n", + "Persuasion (1995)\n", + "Cosine similarity: 0.623\n", + "Return of the Jedi (1983)\n", + "Kicking and Screaming (1995)\n", + "Cosine similarity: 0.603\n", + "Return of the Jedi (1983)\n", + "City Hall (1996)\n", + "Cosine similarity: 0.609\n", + "Return of the Jedi (1983)\n", + "Basketball Diaries, The (1995)\n", + "Cosine similarity: 0.561\n", + "Return of the Jedi (1983)\n", + "Browning Version, The (1994)\n", + "Cosine similarity: 0.653\n", + "Return of the Jedi (1983)\n", + "Little Women (1994)\n", + "Cosine similarity: 0.572\n", + "Return of the Jedi (1983)\n", + "Miami Rhapsody (1995)\n", + "Cosine similarity: 0.674\n", + "Return of the Jedi (1983)\n", + "Wonderful, Horrible Life of Leni Riefenstahl, The (1993)\n", + "Cosine similarity: 0.756\n", + "Return of the Jedi (1983)\n", + "Barcelona (1994)\n", + "Cosine similarity: 0.708\n", + "Return of the Jedi (1983)\n", + "Widows' Peak (1994)\n", + "Cosine similarity: 0.685\n", + "Return of the Jedi (1983)\n", + "House of the Spirits, The (1993)\n", + "Cosine similarity: 0.788\n", + "Return of the Jedi (1983)\n", + "Singin' in the Rain (1952)\n", + "Cosine similarity: 0.674\n", + "Return of the Jedi (1983)\n", + "Bad Moon (1996)\n", + "Cosine similarity: 0.516\n", + "Return of the Jedi (1983)\n", + "Enchanted April (1991)\n", + "Cosine similarity: 0.543\n", + "Return of the Jedi (1983)\n", + "Sex, Lies, and Videotape (1989)\n", + "Cosine similarity: 0.523\n", + "Return of the Jedi (1983)\n", + "Strictly Ballroom (1992)\n", + "Cosine similarity: 0.673\n", + "Return of the Jedi (1983)\n", + "Better Off Dead... (1985)\n", + "Cosine similarity: 0.692\n", + "Return of the Jedi (1983)\n", + "Substance of Fire, The (1996)\n", + "Cosine similarity: -0.0256\n", + "Return of the Jedi (1983)\n", + "Tin Men (1987)\n", + "Cosine similarity: 0.716\n", + "Return of the Jedi (1983)\n", + "Othello (1995)\n", + "Cosine similarity: 0.662\n", + "Return of the Jedi (1983)\n", + "Carrington (1995)\n", + "Cosine similarity: 0.593\n", + "Return of the Jedi (1983)\n", + "To Die For (1995)\n", + "Cosine similarity: 0.57\n", + "Return of the Jedi (1983)\n", + "Home for the Holidays (1995)\n", + "Cosine similarity: 0.475\n", + "Return of the Jedi (1983)\n", + "Juror, The (1996)\n", + "Cosine similarity: 0.573\n", + "Return of the Jedi (1983)\n", + "In the Bleak Midwinter (1995)\n", + "Cosine similarity: 0.629\n", + "Return of the Jedi (1983)\n", + "Canadian Bacon (1994)\n", + "Cosine similarity: 0.633\n", + "Return of the Jedi (1983)\n", + "First Knight (1995)\n", + "Cosine similarity: 0.535\n", + "Return of the Jedi (1983)\n", + "Mallrats (1995)\n", + "Cosine similarity: 0.731\n", + "Return of the Jedi (1983)\n", + "Nine Months (1995)\n", + "Cosine similarity: 0.639\n", + "Return of the Jedi (1983)\n", + "Boys on the Side (1995)\n", + "Cosine similarity: 0.598\n", + "Return of the Jedi (1983)\n", + "Circle of Friends (1995)\n", + "Cosine similarity: 0.73\n", + "Return of the Jedi (1983)\n", + "Exit to Eden (1994)\n", + "Cosine similarity: 0.643\n", + "Return of the Jedi (1983)\n", + "Fluke (1995)\n", + "Cosine similarity: 0.607\n", + "Return of the Jedi (1983)\n", + "Immortal Beloved (1994)\n", + "Cosine similarity: 0.672\n", + "Return of the Jedi (1983)\n", + "Junior (1994)\n", + "Cosine similarity: 0.674\n", + "Return of the Jedi (1983)\n", + "Nell (1994)\n", + "Cosine similarity: 0.696\n", + "Return of the Jedi (1983)\n", + "Queen Margot (Reine Margot, La) (1994)\n", + "Cosine similarity: 0.626\n", + "Return of the Jedi (1983)\n", + "Corrina, Corrina (1994)\n", + "Cosine similarity: 0.713\n", + "Return of the Jedi (1983)\n", + "Dave (1993)\n", + "Cosine similarity: 0.773\n", + "Return of the Jedi (1983)\n", + "Go Fish (1994)\n", + "Cosine similarity: 0.672\n", + "Return of the Jedi (1983)\n", + "Made in America (1993)\n", + "Cosine similarity: 0.687\n", + "Return of the Jedi (1983)\n", + "Philadelphia (1993)\n", + "Cosine similarity: 0.647\n", + "Return of the Jedi (1983)\n", + "Shadowlands (1993)\n", + "Cosine similarity: 0.726\n", + "Return of the Jedi (1983)\n", + "Sirens (1994)\n", + "Cosine similarity: 0.684\n", + "Return of the Jedi (1983)\n", + "Threesome (1994)\n", + "Cosine similarity: 0.593\n", + "Return of the Jedi (1983)\n", + "Pretty Woman (1990)\n", + "Cosine similarity: 0.692\n", + "Return of the Jedi (1983)\n", + "Jane Eyre (1996)\n", + "Cosine similarity: 0.483\n", + "Return of the Jedi (1983)\n", + "Last Supper, The (1995)\n", + "Cosine similarity: 0.537\n", + "Return of the Jedi (1983)\n", + "Ransom (1996)\n", + "Cosine similarity: 0.625\n", + "Return of the Jedi (1983)\n", + "Crow: City of Angels, The (1996)\n", + "Cosine similarity: 0.561\n", + "Return of the Jedi (1983)\n", + "Michael Collins (1996)\n", + "Cosine similarity: 0.609\n", + "Return of the Jedi (1983)\n", + "Ruling Class, The (1972)\n", + "Cosine similarity: 0.562\n", + "Return of the Jedi (1983)\n", + "Real Genius (1985)\n", + "Cosine similarity: 0.706\n", + "Return of the Jedi (1983)\n", + "Benny & Joon (1993)\n", + "Cosine similarity: 0.661\n", + "Return of the Jedi (1983)\n", + "Saint, The (1997)\n", + "Cosine similarity: 0.653\n", + "Return of the Jedi (1983)\n", + "MatchMaker, The (1997)\n", + "Cosine similarity: 0.589\n", + "Return of the Jedi (1983)\n", + "Amistad (1997)\n", + "Cosine similarity: 0.705\n", + "Return of the Jedi (1983)\n", + "Tomorrow Never Dies (1997)\n", + "Cosine similarity: 0.756\n", + "Return of the Jedi (1983)\n", + "Replacement Killers, The (1998)\n", + "Cosine similarity: 0.526\n", + "Return of the Jedi (1983)\n", + "Burnt By the Sun (1994)\n", + "Cosine similarity: 0.541\n", + "Return of the Jedi (1983)\n", + "Red Corner (1997)\n", + "Cosine similarity: 0.574\n", + "Return of the Jedi (1983)\n", + "Jumanji (1995)\n", + "Cosine similarity: 0.677\n", + "Return of the Jedi (1983)\n", + "Father of the Bride Part II (1995)\n", + "Cosine similarity: 0.552\n", + "Return of the Jedi (1983)\n", + "Across the Sea of Time (1995)\n", + "Cosine similarity: 0.667\n", + "Return of the Jedi (1983)\n", + "Lawnmower Man 2: Beyond Cyberspace (1996)\n", + "Cosine similarity: 0.739\n", + "Return of the Jedi (1983)\n", + "Fair Game (1995)\n", + "Cosine similarity: 0.652\n", + "Return of the Jedi (1983)\n", + "Screamers (1995)\n", + "Cosine similarity: 0.57\n", + "Return of the Jedi (1983)\n", + "Nick of Time (1995)\n", + "Cosine similarity: 0.599\n", + "Return of the Jedi (1983)\n", + "Beautiful Girls (1996)\n", + "Cosine similarity: 0.71\n", + "Return of the Jedi (1983)\n", + "Happy Gilmore (1996)\n", + "Cosine similarity: 0.656\n", + "Return of the Jedi (1983)\n", + "If Lucy Fell (1996)\n", + "Cosine similarity: 0.539\n", + "Return of the Jedi (1983)\n", + "Boomerang (1992)\n", + "Cosine similarity: 0.49\n", + "Return of the Jedi (1983)\n", + "Man of the Year (1995)\n", + "Cosine similarity: 0.727\n", + "Return of the Jedi (1983)\n", + "Addiction, The (1995)\n", + "Cosine similarity: 0.46\n", + "Return of the Jedi (1983)\n", + "Casper (1995)\n", + "Cosine similarity: 0.633\n", + "Return of the Jedi (1983)\n", + "Congo (1995)\n", + "Cosine similarity: 0.406\n", + "Return of the Jedi (1983)\n", + "Devil in a Blue Dress (1995)\n", + "Cosine similarity: 0.594\n", + "Return of the Jedi (1983)\n", + "Johnny Mnemonic (1995)\n", + "Cosine similarity: 0.606\n", + "Return of the Jedi (1983)\n", + "Kids (1995)\n", + "Cosine similarity: 0.601\n", + "Return of the Jedi (1983)\n", + "Mute Witness (1994)\n", + "Cosine similarity: 0.482\n", + "Return of the Jedi (1983)\n", + "Prophecy, The (1995)\n", + "Cosine similarity: 0.689\n", + "Return of the Jedi (1983)\n", + "Something to Talk About (1995)\n", + "Cosine similarity: 0.545\n", + "Return of the Jedi (1983)\n", + "Three Wishes (1995)\n", + "Cosine similarity: 0.567\n", + "Return of the Jedi (1983)\n", + "Castle Freak (1995)\n", + "Cosine similarity: 0.639\n", + "Return of the Jedi (1983)\n", + "Don Juan DeMarco (1995)\n", + "Cosine similarity: 0.685\n", + "Return of the Jedi (1983)\n", + "Drop Zone (1994)\n", + "Cosine similarity: 0.423\n", + "Return of the Jedi (1983)\n", + "Dumb & Dumber (1994)\n", + "Cosine similarity: 0.465\n", + "Return of the Jedi (1983)\n", + "French Kiss (1995)\n", + "Cosine similarity: 0.617\n", + "Return of the Jedi (1983)\n", + "Little Odessa (1994)\n", + "Cosine similarity: 0.558\n", + "Return of the Jedi (1983)\n", + "Milk Money (1994)\n", + "Cosine similarity: 0.625\n", + "Return of the Jedi (1983)\n", + "Beyond Bedlam (1993)\n", + "Cosine similarity: 0.123\n", + "Return of the Jedi (1983)\n", + "Only You (1994)\n", + "Cosine similarity: 0.642\n", + "Return of the Jedi (1983)\n", + "Perez Family, The (1995)\n", + "Cosine similarity: 0.612\n", + "Return of the Jedi (1983)\n", + "Roommates (1995)\n", + "Cosine similarity: 0.748\n", + "Return of the Jedi (1983)\n", + "Relative Fear (1994)\n", + "Cosine similarity: -0.0833\n", + "Return of the Jedi (1983)\n", + "Swimming with Sharks (1995)\n", + "Cosine similarity: 0.695\n", + "Return of the Jedi (1983)\n", + "Tommy Boy (1995)\n", + "Cosine similarity: 0.612\n", + "Return of the Jedi (1983)\n", + "Baby-Sitters Club, The (1995)\n", + "Cosine similarity: 0.694\n", + "Return of the Jedi (1983)\n", + "Bullets Over Broadway (1994)\n", + "Cosine similarity: 0.698\n", + "Return of the Jedi (1983)\n", + "Crooklyn (1994)\n", + "Cosine similarity: 0.686\n", + "Return of the Jedi (1983)\n", + "It Could Happen to You (1994)\n", + "Cosine similarity: 0.649\n", + "Return of the Jedi (1983)\n", + "Richie Rich (1994)\n", + "Cosine similarity: 0.67\n", + "Return of the Jedi (1983)\n", + "Speechless (1994)\n", + "Cosine similarity: 0.64\n", + "Return of the Jedi (1983)\n", + "Timecop (1994)\n", + "Cosine similarity: 0.627\n", + "Return of the Jedi (1983)\n", + "Bad Company (1995)\n", + "Cosine similarity: 0.68\n", + "Return of the Jedi (1983)\n", + "Boys Life (1995)\n", + "Cosine similarity: 0.725\n", + "Return of the Jedi (1983)\n", + "In the Mouth of Madness (1995)\n", + "Cosine similarity: 0.69\n", + "Return of the Jedi (1983)\n", + "Air Up There, The (1994)\n", + "Cosine similarity: 0.661\n", + "Return of the Jedi (1983)\n", + "Hard Target (1993)\n", + "Cosine similarity: 0.623\n", + "Return of the Jedi (1983)\n", + "Heaven & Earth (1993)\n", + "Cosine similarity: 0.552\n", + "Return of the Jedi (1983)\n", + "Jimmy Hollywood (1994)\n", + "Cosine similarity: 0.575\n", + "Return of the Jedi (1983)\n", + "Manhattan Murder Mystery (1993)\n", + "Cosine similarity: 0.698\n", + "Return of the Jedi (1983)\n", + "Menace II Society (1993)\n", + "Cosine similarity: 0.623\n", + "Return of the Jedi (1983)\n", + "Poetic Justice (1993)\n", + "Cosine similarity: 0.383\n", + "Return of the Jedi (1983)\n", + "Program, The (1993)\n", + "Cosine similarity: 0.627\n", + "Return of the Jedi (1983)\n", + "Rising Sun (1993)\n", + "Cosine similarity: 0.673\n", + "Return of the Jedi (1983)\n", + "Shadow, The (1994)\n", + "Cosine similarity: 0.557\n", + "Return of the Jedi (1983)\n", + "Thirty-Two Short Films About Glenn Gould (1993)\n", + "Cosine similarity: 0.58\n", + "Return of the Jedi (1983)\n", + "Andre (1994)\n", + "Cosine similarity: 0.526\n", + "Return of the Jedi (1983)\n", + "Celluloid Closet, The (1995)\n", + "Cosine similarity: 0.66\n", + "Return of the Jedi (1983)\n", + "Great Day in Harlem, A (1994)\n", + "Cosine similarity: 0.0557\n", + "Return of the Jedi (1983)\n", + "One Fine Day (1996)\n", + "Cosine similarity: 0.74\n", + "Return of the Jedi (1983)\n", + "Candyman: Farewell to the Flesh (1995)\n", + "Cosine similarity: 0.676\n", + "Return of the Jedi (1983)\n", + "Frisk (1995)\n", + "Cosine similarity: 0.422\n", + "Return of the Jedi (1983)\n", + "Girl 6 (1996)\n", + "Cosine similarity: 0.677\n", + "Return of the Jedi (1983)\n", + "Eddie (1996)\n", + "Cosine similarity: 0.611\n", + "Return of the Jedi (1983)\n", + "Space Jam (1996)\n", + "Cosine similarity: 0.546\n", + "Return of the Jedi (1983)\n", + "Mrs. Winterbourne (1996)\n", + "Cosine similarity: 0.71\n", + "Return of the Jedi (1983)\n", + "Faces (1968)\n", + "Cosine similarity: 0.628\n", + "Return of the Jedi (1983)\n", + "Mulholland Falls (1996)\n", + "Cosine similarity: 0.785\n", + "Return of the Jedi (1983)\n", + "Great White Hype, The (1996)\n", + "Cosine similarity: 0.636\n", + "Return of the Jedi (1983)\n", + "Arrival, The (1996)\n", + "Cosine similarity: 0.696\n", + "Return of the Jedi (1983)\n", + "Phantom, The (1996)\n", + "Cosine similarity: 0.614\n", + "Return of the Jedi (1983)\n", + "Daylight (1996)\n", + "Cosine similarity: 0.678\n", + "Return of the Jedi (1983)\n", + "Alaska (1996)\n", + "Cosine similarity: 0.474\n", + "Return of the Jedi (1983)\n", + "Fled (1996)\n", + "Cosine similarity: 0.518\n", + "Return of the Jedi (1983)\n", + "Power 98 (1995)\n", + "Cosine similarity: 0.539\n", + "Return of the Jedi (1983)\n", + "Escape from L.A. (1996)\n", + "Cosine similarity: 0.665\n", + "Return of the Jedi (1983)\n", + "Bogus (1996)\n", + "Cosine similarity: 0.722\n", + "Return of the Jedi (1983)\n", + "Bulletproof (1996)\n", + "Cosine similarity: 0.575\n", + "Return of the Jedi (1983)\n", + "Halloween: The Curse of Michael Myers (1995)\n", + "Cosine similarity: 0.57\n", + "Return of the Jedi (1983)\n", + "Gay Divorcee, The (1934)\n", + "Cosine similarity: 0.577\n", + "Return of the Jedi (1983)\n", + "Ninotchka (1939)\n", + "Cosine similarity: 0.617\n", + "Return of the Jedi (1983)\n", + "Meet John Doe (1941)\n", + "Cosine similarity: 0.742\n", + "Return of the Jedi (1983)\n", + "In the Line of Duty 2 (1987)\n", + "Cosine similarity: 0.722\n", + "Return of the Jedi (1983)\n", + "Loch Ness (1995)\n", + "Cosine similarity: 0.646\n", + "Return of the Jedi (1983)\n", + "Last Man Standing (1996)\n", + "Cosine similarity: 0.62\n", + "Return of the Jedi (1983)\n", + "Glimmer Man, The (1996)\n", + "Cosine similarity: 0.452\n", + "Return of the Jedi (1983)\n", + "Pollyanna (1960)\n", + "Cosine similarity: 0.52\n", + "Return of the Jedi (1983)\n", + "Shaggy Dog, The (1959)\n", + "Cosine similarity: 0.543\n", + "Return of the Jedi (1983)\n", + "Freeway (1996)\n", + "Cosine similarity: 0.611\n", + "Return of the Jedi (1983)\n", + "That Thing You Do! (1996)\n", + "Cosine similarity: 0.647\n", + "Return of the Jedi (1983)\n", + "To Gillian on Her 37th Birthday (1996)\n", + "Cosine similarity: 0.599\n", + "Return of the Jedi (1983)\n", + "Looking for Richard (1996)\n", + "Cosine similarity: 0.611\n", + "Return of the Jedi (1983)\n", + "Murder, My Sweet (1944)\n", + "Cosine similarity: 0.644\n", + "Return of the Jedi (1983)\n", + "Days of Thunder (1990)\n", + "Cosine similarity: 0.569\n", + "Return of the Jedi (1983)\n", + "Perfect Candidate, A (1996)\n", + "Cosine similarity: 0.672\n", + "Return of the Jedi (1983)\n", + "Two or Three Things I Know About Her (1966)\n", + "Cosine similarity: 0.582\n", + "Return of the Jedi (1983)\n", + "Bloody Child, The (1996)\n", + "Cosine similarity: 0.489\n", + "Return of the Jedi (1983)\n", + "Braindead (1992)\n", + "Cosine similarity: 0.595\n", + "Return of the Jedi (1983)\n", + "Bad Taste (1987)\n", + "Cosine similarity: 0.558\n", + "Return of the Jedi (1983)\n", + "Diva (1981)\n", + "Cosine similarity: 0.586\n", + "Return of the Jedi (1983)\n", + "Night on Earth (1991)\n", + "Cosine similarity: 0.657\n", + "Return of the Jedi (1983)\n", + "Paris Was a Woman (1995)\n", + "Cosine similarity: 0.728\n", + "Return of the Jedi (1983)\n", + "Amityville: Dollhouse (1996)\n", + "Cosine similarity: 0.748\n", + "Return of the Jedi (1983)\n", + "April Fool's Day (1986)\n", + "Cosine similarity: 0.482\n", + "Return of the Jedi (1983)\n", + "Believers, The (1987)\n", + "Cosine similarity: 0.482\n", + "Return of the Jedi (1983)\n", + "Nosferatu a Venezia (1986)\n", + "Cosine similarity: 0.648\n", + "Return of the Jedi (1983)\n", + "Jingle All the Way (1996)\n", + "Cosine similarity: 0.679\n", + "Return of the Jedi (1983)\n", + "Garden of Finzi-Contini, The (Giardino dei Finzi-Contini, Il) (1970)\n", + "Cosine similarity: 0.596\n", + "Return of the Jedi (1983)\n", + "My Fellow Americans (1996)\n", + "Cosine similarity: 0.717\n", + "Return of the Jedi (1983)\n", + "Ice Storm, The (1997)\n", + "Cosine similarity: 0.433\n", + "Return of the Jedi (1983)\n", + "Michael (1996)\n", + "Cosine similarity: 0.63\n", + "Return of the Jedi (1983)\n", + "Whole Wide World, The (1996)\n", + "Cosine similarity: 0.637\n", + "Return of the Jedi (1983)\n", + "Hearts and Minds (1996)\n", + "Cosine similarity: 0.685\n", + "Return of the Jedi (1983)\n", + "Fools Rush In (1997)\n", + "Cosine similarity: 0.721\n", + "Return of the Jedi (1983)\n", + "Touch (1997)\n", + "Cosine similarity: 0.742\n", + "Return of the Jedi (1983)\n", + "Vegas Vacation (1997)\n", + "Cosine similarity: 0.562\n", + "Return of the Jedi (1983)\n", + "Love Jones (1997)\n", + "Cosine similarity: 0.659\n", + "Return of the Jedi (1983)\n", + "Picture Perfect (1997)\n", + "Cosine similarity: 0.635\n", + "Return of the Jedi (1983)\n", + "Career Girls (1997)\n", + "Cosine similarity: 0.545\n", + "Return of the Jedi (1983)\n", + "She's So Lovely (1997)\n", + "Cosine similarity: 0.605\n", + "Return of the Jedi (1983)\n", + "Money Talks (1997)\n", + "Cosine similarity: 0.581\n", + "Return of the Jedi (1983)\n", + "Excess Baggage (1997)\n", + "Cosine similarity: 0.517\n", + "Return of the Jedi (1983)\n", + "That Darn Cat! (1997)\n", + "Cosine similarity: 0.374\n", + "Return of the Jedi (1983)\n", + "Peacemaker, The (1997)\n", + "Cosine similarity: 0.705\n", + "Return of the Jedi (1983)\n", + "Soul Food (1997)\n", + "Cosine similarity: 0.632\n", + "Return of the Jedi (1983)\n", + "Money Talks (1997)\n", + "Cosine similarity: 0.603\n", + "Return of the Jedi (1983)\n", + "Washington Square (1997)\n", + "Cosine similarity: 0.544\n", + "Return of the Jedi (1983)\n", + "Telling Lies in America (1997)\n", + "Cosine similarity: 0.571\n", + "Return of the Jedi (1983)\n", + "Year of the Horse (1997)\n", + "Cosine similarity: 0.551\n", + "Return of the Jedi (1983)\n", + "Phantoms (1998)\n", + "Cosine similarity: 0.582\n", + "Return of the Jedi (1983)\n", + "Life Less Ordinary, A (1997)\n", + "Cosine similarity: 0.638\n", + "Return of the Jedi (1983)\n", + "Eve's Bayou (1997)\n", + "Cosine similarity: 0.591\n", + "Return of the Jedi (1983)\n", + "One Night Stand (1997)\n", + "Cosine similarity: 0.543\n", + "Return of the Jedi (1983)\n", + "Tango Lesson, The (1997)\n", + "Cosine similarity: 0.558\n", + "Return of the Jedi (1983)\n", + "Mortal Kombat: Annihilation (1997)\n", + "Cosine similarity: 0.609\n", + "Return of the Jedi (1983)\n", + "Bent (1997)\n", + "Cosine similarity: 0.487\n", + "Return of the Jedi (1983)\n", + "Flubber (1997)\n", + "Cosine similarity: 0.739\n", + "Return of the Jedi (1983)\n", + "For Richer or Poorer (1997)\n", + "Cosine similarity: 0.71\n", + "Return of the Jedi (1983)\n", + "Home Alone 3 (1997)\n", + "Cosine similarity: 0.472\n", + "Return of the Jedi (1983)\n", + "Scream 2 (1997)\n", + "Cosine similarity: 0.541\n", + "Return of the Jedi (1983)\n", + "Sweet Hereafter, The (1997)\n", + "Cosine similarity: 0.44\n", + "Return of the Jedi (1983)\n", + "Time Tracers (1995)\n", + "Cosine similarity: 0.524\n", + "Return of the Jedi (1983)\n", + "Postman, The (1997)\n", + "Cosine similarity: 0.457\n", + "Return of the Jedi (1983)\n", + "Winter Guest, The (1997)\n", + "Cosine similarity: 0.498\n", + "Return of the Jedi (1983)\n", + "Kundun (1997)\n", + "Cosine similarity: 0.586\n", + "Return of the Jedi (1983)\n", + "Mr. Magoo (1997)\n", + "Cosine similarity: 0.433\n", + "Return of the Jedi (1983)\n", + "Big Lebowski, The (1998)\n", + "Cosine similarity: 0.576\n", + "Return of the Jedi (1983)\n", + "Afterglow (1997)\n", + "Cosine similarity: 0.675\n", + "Return of the Jedi (1983)\n", + "Ma vie en rose (My Life in Pink) (1997)\n", + "Cosine similarity: 0.441\n", + "Return of the Jedi (1983)\n", + "Great Expectations (1998)\n", + "Cosine similarity: 0.67\n", + "Return of the Jedi (1983)\n", + "Oscar & Lucinda (1997)\n", + "Cosine similarity: 0.59\n", + "Return of the Jedi (1983)\n", + "Vermin (1998)\n", + "Cosine similarity: 0.637\n", + "Return of the Jedi (1983)\n", + "Half Baked (1998)\n", + "Cosine similarity: 0.531\n", + "Return of the Jedi (1983)\n", + "Dangerous Beauty (1998)\n", + "Cosine similarity: 0.688\n", + "Return of the Jedi (1983)\n", + "Nil By Mouth (1997)\n", + "Cosine similarity: 0.613\n", + "Return of the Jedi (1983)\n", + "Twilight (1998)\n", + "Cosine similarity: 0.623\n", + "Return of the Jedi (1983)\n", + "U.S. Marshalls (1998)\n", + "Cosine similarity: 0.491\n", + "Return of the Jedi (1983)\n", + "Love and Death on Long Island (1997)\n", + "Cosine similarity: 0.534\n", + "Return of the Jedi (1983)\n", + "Wild Things (1998)\n", + "Cosine similarity: 0.533\n", + "Return of the Jedi (1983)\n", + "Primary Colors (1998)\n", + "Cosine similarity: 0.67\n", + "Return of the Jedi (1983)\n", + "Lost in Space (1998)\n", + "Cosine similarity: 0.63\n", + "Return of the Jedi (1983)\n", + "Mercury Rising (1998)\n", + "Cosine similarity: 0.709\n", + "Return of the Jedi (1983)\n", + "City of Angels (1998)\n", + "Cosine similarity: 0.768\n", + "Return of the Jedi (1983)\n", + "City of Lost Children, The (1995)\n", + "Cosine similarity: 0.602\n", + "Return of the Jedi (1983)\n", + "Two Bits (1995)\n", + "Cosine similarity: 0.677\n", + "Return of the Jedi (1983)\n", + "Farewell My Concubine (1993)\n", + "Cosine similarity: 0.613\n", + "Return of the Jedi (1983)\n", + "Dead Man (1995)\n", + "Cosine similarity: 0.478\n", + "Return of the Jedi (1983)\n", + "Raise the Red Lantern (1991)\n", + "Cosine similarity: 0.643\n", + "Return of the Jedi (1983)\n", + "White Squall (1996)\n", + "Cosine similarity: 0.608\n", + "Return of the Jedi (1983)\n", + "Unforgettable (1996)\n", + "Cosine similarity: 0.626\n", + "Return of the Jedi (1983)\n", + "Down Periscope (1996)\n", + "Cosine similarity: 0.7\n", + "Return of the Jedi (1983)\n", + "Flower of My Secret, The (Flor de mi secreto, La) (1995)\n", + "Cosine similarity: 0.771\n", + "Return of the Jedi (1983)\n", + "Craft, The (1996)\n", + "Cosine similarity: 0.718\n", + "Return of the Jedi (1983)\n", + "Harriet the Spy (1996)\n", + "Cosine similarity: 0.554\n", + "Return of the Jedi (1983)\n", + "Chain Reaction (1996)\n", + "Cosine similarity: 0.719\n", + "Return of the Jedi (1983)\n", + "Island of Dr. Moreau, The (1996)\n", + "Cosine similarity: 0.456\n", + "Return of the Jedi (1983)\n", + "First Kid (1996)\n", + "Cosine similarity: 0.64\n", + "Return of the Jedi (1983)\n", + "Funeral, The (1996)\n", + "Cosine similarity: 0.646\n", + "Return of the Jedi (1983)\n", + "Preacher's Wife, The (1996)\n", + "Cosine similarity: 0.619\n", + "Return of the Jedi (1983)\n", + "Paradise Road (1997)\n", + "Cosine similarity: 0.696\n", + "Return of the Jedi (1983)\n", + "Brassed Off (1996)\n", + "Cosine similarity: 0.683\n", + "Return of the Jedi (1983)\n", + "Thousand Acres, A (1997)\n", + "Cosine similarity: 0.637\n", + "Return of the Jedi (1983)\n", + "Smile Like Yours, A (1997)\n", + "Cosine similarity: 0.656\n", + "Return of the Jedi (1983)\n", + "Murder in the First (1995)\n", + "Cosine similarity: 0.63\n", + "Return of the Jedi (1983)\n", + "Airheads (1994)\n", + "Cosine similarity: 0.599\n", + "Return of the Jedi (1983)\n", + "With Honors (1994)\n", + "Cosine similarity: 0.475\n", + "Return of the Jedi (1983)\n", + "What's Love Got to Do with It (1993)\n", + "Cosine similarity: 0.806\n", + "Return of the Jedi (1983)\n", + "Killing Zoe (1994)\n", + "Cosine similarity: 0.653\n", + "Return of the Jedi (1983)\n", + "Renaissance Man (1994)\n", + "Cosine similarity: 0.614\n", + "Return of the Jedi (1983)\n", + "Charade (1963)\n", + "Cosine similarity: 0.661\n", + "Return of the Jedi (1983)\n", + "Fox and the Hound, The (1981)\n", + "Cosine similarity: 0.728\n", + "Return of the Jedi (1983)\n", + "Big Blue, The (Grand bleu, Le) (1988)\n", + "Cosine similarity: 0.539\n", + "Return of the Jedi (1983)\n", + "Booty Call (1997)\n", + "Cosine similarity: 0.367\n", + "Return of the Jedi (1983)\n", + "How to Make an American Quilt (1995)\n", + "Cosine similarity: 0.645\n", + "Return of the Jedi (1983)\n", + "Georgia (1995)\n", + "Cosine similarity: 0.694\n", + "Return of the Jedi (1983)\n", + "Indian in the Cupboard, The (1995)\n", + "Cosine similarity: 0.603\n", + "Return of the Jedi (1983)\n", + "Blue in the Face (1995)\n", + "Cosine similarity: 0.616\n", + "Return of the Jedi (1983)\n", + "Unstrung Heroes (1995)\n", + "Cosine similarity: 0.552\n", + "Return of the Jedi (1983)\n", + "Unzipped (1995)\n", + "Cosine similarity: 0.654\n", + "Return of the Jedi (1983)\n", + "Before Sunrise (1995)\n", + "Cosine similarity: 0.66\n", + "Return of the Jedi (1983)\n", + "Nobody's Fool (1994)\n", + "Cosine similarity: 0.657\n", + "Return of the Jedi (1983)\n", + "Pushing Hands (1992)\n", + "Cosine similarity: 0.672\n", + "Return of the Jedi (1983)\n", + "To Live (Huozhe) (1994)\n", + "Cosine similarity: 0.633\n", + "Return of the Jedi (1983)\n", + "Dazed and Confused (1993)\n", + "Cosine similarity: 0.66\n", + "Return of the Jedi (1983)\n", + "Naked (1993)\n", + "Cosine similarity: 0.608\n", + "Return of the Jedi (1983)\n", + "Orlando (1993)\n", + "Cosine similarity: 0.437\n", + "Return of the Jedi (1983)\n", + "Ruby in Paradise (1993)\n", + "Cosine similarity: 0.611\n", + "Return of the Jedi (1983)\n", + "Some Folks Call It a Sling Blade (1993)\n", + "Cosine similarity: 0.733\n", + "Return of the Jedi (1983)\n", + "Month by the Lake, A (1995)\n", + "Cosine similarity: 0.592\n", + "Return of the Jedi (1983)\n", + "Funny Face (1957)\n", + "Cosine similarity: 0.5\n", + "Return of the Jedi (1983)\n", + "Affair to Remember, An (1957)\n", + "Cosine similarity: 0.639\n", + "Return of the Jedi (1983)\n", + "Little Lord Fauntleroy (1936)\n", + "Cosine similarity: 0.651\n", + "Return of the Jedi (1983)\n", + "Inspector General, The (1949)\n", + "Cosine similarity: 0.652\n", + "Return of the Jedi (1983)\n", + "Winnie the Pooh and the Blustery Day (1968)\n", + "Cosine similarity: 0.593\n", + "Return of the Jedi (1983)\n", + "Hear My Song (1991)\n", + "Cosine similarity: 0.554\n", + "Return of the Jedi (1983)\n", + "Mediterraneo (1991)\n", + "Cosine similarity: 0.635\n", + "Return of the Jedi (1983)\n", + "Passion Fish (1992)\n", + "Cosine similarity: 0.522\n", + "Return of the Jedi (1983)\n", + "Grateful Dead (1995)\n", + "Cosine similarity: 0.68\n", + "Return of the Jedi (1983)\n", + "Eye for an Eye (1996)\n", + "Cosine similarity: 0.682\n", + "Return of the Jedi (1983)\n", + "Fear (1996)\n", + "Cosine similarity: 0.64\n", + "Return of the Jedi (1983)\n", + "Solo (1996)\n", + "Cosine similarity: 0.554\n", + "Return of the Jedi (1983)\n", + "Substitute, The (1996)\n", + "Cosine similarity: 0.437\n", + "Return of the Jedi (1983)\n", + "Heaven's Prisoners (1996)\n", + "Cosine similarity: 0.519\n", + "Return of the Jedi (1983)\n", + "Trigger Effect, The (1996)\n", + "Cosine similarity: 0.728\n", + "Return of the Jedi (1983)\n", + "Mother Night (1996)\n", + "Cosine similarity: 0.552\n", + "Return of the Jedi (1983)\n", + "Dangerous Ground (1997)\n", + "Cosine similarity: 0.636\n", + "Return of the Jedi (1983)\n", + "Maximum Risk (1996)\n", + "Cosine similarity: 0.7\n", + "Return of the Jedi (1983)\n", + "Rich Man's Wife, The (1996)\n", + "Cosine similarity: 0.603\n", + "Return of the Jedi (1983)\n", + "Shadow Conspiracy (1997)\n", + "Cosine similarity: 0.574\n", + "Return of the Jedi (1983)\n", + "Blood & Wine (1997)\n", + "Cosine similarity: 0.564\n", + "Return of the Jedi (1983)\n", + "Turbulence (1997)\n", + "Cosine similarity: 0.493\n", + "Return of the Jedi (1983)\n", + "Underworld (1997)\n", + "Cosine similarity: 0.467\n", + "Return of the Jedi (1983)\n", + "Beautician and the Beast, The (1997)\n", + "Cosine similarity: 0.393\n", + "Return of the Jedi (1983)\n", + "Cats Don't Dance (1997)\n", + "Cosine similarity: 0.493\n", + "Return of the Jedi (1983)\n", + "Anna Karenina (1997)\n", + "Cosine similarity: 0.505\n", + "Return of the Jedi (1983)\n", + "Keys to Tulsa (1997)\n", + "Cosine similarity: 0.379\n", + "Return of the Jedi (1983)\n", + "Head Above Water (1996)\n", + "Cosine similarity: 0.597\n", + "Return of the Jedi (1983)\n", + "Hercules (1997)\n", + "Cosine similarity: 0.753\n", + "Return of the Jedi (1983)\n", + "Last Time I Committed Suicide, The (1997)\n", + "Cosine similarity: 0.567\n", + "Return of the Jedi (1983)\n", + "Kiss Me, Guido (1997)\n", + "Cosine similarity: 0.568\n", + "Return of the Jedi (1983)\n", + "Big Green, The (1995)\n", + "Cosine similarity: 0.603\n", + "Return of the Jedi (1983)\n", + "Stuart Saves His Family (1995)\n", + "Cosine similarity: 0.56\n", + "Return of the Jedi (1983)\n", + "Cabin Boy (1994)\n", + "Cosine similarity: 0.527\n", + "Return of the Jedi (1983)\n", + "Clean Slate (1994)\n", + "Cosine similarity: 0.586\n", + "Return of the Jedi (1983)\n", + "Lightning Jack (1994)\n", + "Cosine similarity: 0.633\n", + "Return of the Jedi (1983)\n", + "Stupids, The (1996)\n", + "Cosine similarity: 0.535\n", + "Return of the Jedi (1983)\n", + "Pest, The (1997)\n", + "Cosine similarity: 0.506\n", + "Return of the Jedi (1983)\n", + "That Darn Cat! (1997)\n", + "Cosine similarity: 0.426\n", + "Return of the Jedi (1983)\n", + "Geronimo: An American Legend (1993)\n", + "Cosine similarity: 0.575\n", + "Return of the Jedi (1983)\n", + "Double vie de Véronique, La (Double Life of Veronique, The) (1991)\n", + "Cosine similarity: 0.699\n", + "Return of the Jedi (1983)\n", + "Until the End of the World (Bis ans Ende der Welt) (1991)\n", + "Cosine similarity: 0.581\n", + "Return of the Jedi (1983)\n", + "Waiting for Guffman (1996)\n", + "Cosine similarity: 0.651\n", + "Return of the Jedi (1983)\n", + "I Shot Andy Warhol (1996)\n", + "Cosine similarity: 0.595\n", + "Return of the Jedi (1983)\n", + "Stealing Beauty (1996)\n", + "Cosine similarity: 0.521\n", + "Return of the Jedi (1983)\n", + "Basquiat (1996)\n", + "Cosine similarity: 0.567\n", + "Return of the Jedi (1983)\n", + "2 Days in the Valley (1996)\n", + "Cosine similarity: 0.572\n", + "Return of the Jedi (1983)\n", + "Private Parts (1997)\n", + "Cosine similarity: 0.795\n", + "Return of the Jedi (1983)\n", + "Anaconda (1997)\n", + "Cosine similarity: 0.533\n", + "Return of the Jedi (1983)\n", + "Romy and Michele's High School Reunion (1997)\n", + "Cosine similarity: 0.677\n", + "Return of the Jedi (1983)\n", + "Shiloh (1997)\n", + "Cosine similarity: 0.593\n", + "Return of the Jedi (1983)\n", + "Con Air (1997)\n", + "Cosine similarity: 0.697\n", + "Return of the Jedi (1983)\n", + "Trees Lounge (1996)\n", + "Cosine similarity: 0.579\n", + "Return of the Jedi (1983)\n", + "Tie Me Up! Tie Me Down! (1990)\n", + "Cosine similarity: 0.746\n", + "Return of the Jedi (1983)\n", + "Die xue shuang xiong (Killer, The) (1989)\n", + "Cosine similarity: 0.663\n", + "Return of the Jedi (1983)\n", + "Gaslight (1944)\n", + "Cosine similarity: 0.588\n", + "Return of the Jedi (1983)\n", + "8 1/2 (1963)\n", + "Cosine similarity: 0.521\n", + "Return of the Jedi (1983)\n", + "Fast, Cheap & Out of Control (1997)\n", + "Cosine similarity: 0.614\n", + "Return of the Jedi (1983)\n", + "Fathers' Day (1997)\n", + "Cosine similarity: 0.614\n", + "Return of the Jedi (1983)\n", + "Mrs. Dalloway (1997)\n", + "Cosine similarity: 0.539\n", + "Return of the Jedi (1983)\n", + "Fire Down Below (1997)\n", + "Cosine similarity: 0.529\n", + "Return of the Jedi (1983)\n", + "Lay of the Land, The (1997)\n", + "Cosine similarity: 0.563\n", + "Return of the Jedi (1983)\n", + "Shooter, The (1995)\n", + "Cosine similarity: 0.719\n", + "Return of the Jedi (1983)\n", + "Grumpier Old Men (1995)\n", + "Cosine similarity: 0.522\n", + "Return of the Jedi (1983)\n", + "Jury Duty (1995)\n", + "Cosine similarity: 0.555\n", + "Return of the Jedi (1983)\n", + "Beverly Hillbillies, The (1993)\n", + "Cosine similarity: 0.598\n", + "Return of the Jedi (1983)\n", + "Lassie (1994)\n", + "Cosine similarity: 0.574\n", + "Return of the Jedi (1983)\n", + "Little Big League (1994)\n", + "Cosine similarity: 0.678\n", + "Return of the Jedi (1983)\n", + "Homeward Bound II: Lost in San Francisco (1996)\n", + "Cosine similarity: 0.368\n", + "Return of the Jedi (1983)\n", + "Quest, The (1996)\n", + "Cosine similarity: 0.545\n", + "Return of the Jedi (1983)\n", + "Cool Runnings (1993)\n", + "Cosine similarity: 0.676\n", + "Return of the Jedi (1983)\n", + "Drop Dead Fred (1991)\n", + "Cosine similarity: 0.344\n", + "Return of the Jedi (1983)\n", + "Grease 2 (1982)\n", + "Cosine similarity: 0.607\n", + "Return of the Jedi (1983)\n", + "Switchback (1997)\n", + "Cosine similarity: 0.498\n", + "Return of the Jedi (1983)\n", + "Hamlet (1996)\n", + "Cosine similarity: 0.745\n", + "Return of the Jedi (1983)\n", + "Two if by Sea (1996)\n", + "Cosine similarity: 0.533\n", + "Return of the Jedi (1983)\n", + "Forget Paris (1995)\n", + "Cosine similarity: 0.647\n", + "Return of the Jedi (1983)\n", + "Just Cause (1995)\n", + "Cosine similarity: 0.542\n", + "Return of the Jedi (1983)\n", + "Rent-a-Kid (1995)\n", + "Cosine similarity: 0.543\n", + "Return of the Jedi (1983)\n", + "Paper, The (1994)\n", + "Cosine similarity: 0.617\n", + "Return of the Jedi (1983)\n", + "Fearless (1993)\n", + "Cosine similarity: 0.518\n", + "Return of the Jedi (1983)\n", + "Malice (1993)\n", + "Cosine similarity: 0.631\n", + "Return of the Jedi (1983)\n", + "Multiplicity (1996)\n", + "Cosine similarity: 0.731\n", + "Return of the Jedi (1983)\n", + "She's the One (1996)\n", + "Cosine similarity: 0.583\n", + "Return of the Jedi (1983)\n", + "House Arrest (1996)\n", + "Cosine similarity: 0.626\n", + "Return of the Jedi (1983)\n", + "Ghost and Mrs. Muir, The (1947)\n", + "Cosine similarity: 0.634\n", + "Return of the Jedi (1983)\n", + "Associate, The (1996)\n", + "Cosine similarity: 0.592\n", + "Return of the Jedi (1983)\n", + "Dracula: Dead and Loving It (1995)\n", + "Cosine similarity: 0.494\n", + "Return of the Jedi (1983)\n", + "Now and Then (1995)\n", + "Cosine similarity: 0.655\n", + "Return of the Jedi (1983)\n", + "Mr. Wrong (1996)\n", + "Cosine similarity: 0.653\n", + "Return of the Jedi (1983)\n", + "Simple Twist of Fate, A (1994)\n", + "Cosine similarity: 0.612\n", + "Return of the Jedi (1983)\n", + "Cronos (1992)\n", + "Cosine similarity: 0.734\n", + "Return of the Jedi (1983)\n", + "Pallbearer, The (1996)\n", + "Cosine similarity: 0.631\n", + "Return of the Jedi (1983)\n", + "War, The (1994)\n", + "Cosine similarity: 0.643\n", + "Return of the Jedi (1983)\n", + "Don't Be a Menace to South Central While Drinking Your Juice in the Hood (1996)\n", + "Cosine similarity: 0.539\n", + "Return of the Jedi (1983)\n", + "Adventures of Pinocchio, The (1996)\n", + "Cosine similarity: 0.527\n", + "Return of the Jedi (1983)\n", + "Evening Star, The (1996)\n", + "Cosine similarity: 0.714\n", + "Return of the Jedi (1983)\n", + "Four Days in September (1997)\n", + "Cosine similarity: 0.617\n", + "Return of the Jedi (1983)\n", + "Little Princess, A (1995)\n", + "Cosine similarity: 0.738\n", + "Return of the Jedi (1983)\n", + "Crossfire (1947)\n", + "Cosine similarity: 0.667\n", + "Return of the Jedi (1983)\n", + "Koyaanisqatsi (1983)\n", + "Cosine similarity: 0.51\n", + "Return of the Jedi (1983)\n", + "Balto (1995)\n", + "Cosine similarity: 0.563\n", + "Return of the Jedi (1983)\n", + "Bottle Rocket (1996)\n", + "Cosine similarity: 0.614\n", + "Return of the Jedi (1983)\n", + "Star Maker, The (Uomo delle stelle, L') (1995)\n", + "Cosine similarity: 0.59\n", + "Return of the Jedi (1983)\n", + "Amateur (1994)\n", + "Cosine similarity: 0.684\n", + "Return of the Jedi (1983)\n", + "Living in Oblivion (1995)\n", + "Cosine similarity: 0.612\n", + "Return of the Jedi (1983)\n", + "Party Girl (1995)\n", + "Cosine similarity: 0.389\n", + "Return of the Jedi (1983)\n", + "Pyromaniac's Love Story, A (1995)\n", + "Cosine similarity: 0.691\n", + "Return of the Jedi (1983)\n", + "Shallow Grave (1994)\n", + "Cosine similarity: 0.606\n", + "Return of the Jedi (1983)\n", + "Reality Bites (1994)\n", + "Cosine similarity: 0.662\n", + "Return of the Jedi (1983)\n", + "Man of No Importance, A (1994)\n", + "Cosine similarity: 0.635\n", + "Return of the Jedi (1983)\n", + "Pagemaster, The (1994)\n", + "Cosine similarity: 0.571\n", + "Return of the Jedi (1983)\n", + "Love and a .45 (1994)\n", + "Cosine similarity: 0.511\n", + "Return of the Jedi (1983)\n", + "Oliver & Company (1988)\n", + "Cosine similarity: 0.63\n", + "Return of the Jedi (1983)\n", + "Joe's Apartment (1996)\n", + "Cosine similarity: 0.676\n", + "Return of the Jedi (1983)\n", + "Celestial Clockwork (1994)\n", + "Cosine similarity: 0.671\n", + "Return of the Jedi (1983)\n", + "Curdled (1996)\n", + "Cosine similarity: 0.538\n", + "Return of the Jedi (1983)\n", + "Female Perversions (1996)\n", + "Cosine similarity: 0.551\n", + "Return of the Jedi (1983)\n", + "Albino Alligator (1996)\n", + "Cosine similarity: 0.678\n", + "Return of the Jedi (1983)\n", + "Anne Frank Remembered (1995)\n", + "Cosine similarity: 0.562\n", + "Return of the Jedi (1983)\n", + "Carried Away (1996)\n", + "Cosine similarity: 0.497\n", + "Return of the Jedi (1983)\n", + "It's My Party (1995)\n", + "Cosine similarity: 0.592\n", + "Return of the Jedi (1983)\n", + "Bloodsport 2 (1995)\n", + "Cosine similarity: 0.501\n", + "Return of the Jedi (1983)\n", + "Double Team (1997)\n", + "Cosine similarity: 0.537\n", + "Return of the Jedi (1983)\n", + "Speed 2: Cruise Control (1997)\n", + "Cosine similarity: 0.568\n", + "Return of the Jedi (1983)\n", + "Sliver (1993)\n", + "Cosine similarity: 0.55\n", + "Return of the Jedi (1983)\n", + "Pete's Dragon (1977)\n", + "Cosine similarity: 0.585\n", + "Return of the Jedi (1983)\n", + "Dear God (1996)\n", + "Cosine similarity: 0.644\n", + "Return of the Jedi (1983)\n", + "Live Nude Girls (1995)\n", + "Cosine similarity: 0.605\n", + "Return of the Jedi (1983)\n", + "Thin Line Between Love and Hate, A (1996)\n", + "Cosine similarity: 0.571\n", + "Return of the Jedi (1983)\n", + "High School High (1996)\n", + "Cosine similarity: 0.607\n", + "Return of the Jedi (1983)\n", + "Commandments (1997)\n", + "Cosine similarity: 0.688\n", + "Return of the Jedi (1983)\n", + "Hate (Haine, La) (1995)\n", + "Cosine similarity: 0.46\n", + "Return of the Jedi (1983)\n", + "Flirting With Disaster (1996)\n", + "Cosine similarity: 0.701\n", + "Return of the Jedi (1983)\n", + "Red Firecracker, Green Firecracker (1994)\n", + "Cosine similarity: 0.575\n", + "Return of the Jedi (1983)\n", + "What Happened Was... (1994)\n", + "Cosine similarity: 0.581\n", + "Return of the Jedi (1983)\n", + "Six Degrees of Separation (1993)\n", + "Cosine similarity: 0.63\n", + "Return of the Jedi (1983)\n", + "Two Much (1996)\n", + "Cosine similarity: 0.581\n", + "Return of the Jedi (1983)\n", + "Trust (1990)\n", + "Cosine similarity: 0.639\n", + "Return of the Jedi (1983)\n", + "C'est arrivé près de chez vous (1992)\n", + "Cosine similarity: 0.555\n", + "Return of the Jedi (1983)\n", + "Firestorm (1998)\n", + "Cosine similarity: 0.564\n", + "Return of the Jedi (1983)\n", + "Newton Boys, The (1998)\n", + "Cosine similarity: 0.544\n", + "Return of the Jedi (1983)\n", + "Beyond Rangoon (1995)\n", + "Cosine similarity: 0.775\n", + "Return of the Jedi (1983)\n", + "Feast of July (1995)\n", + "Cosine similarity: 0.576\n", + "Return of the Jedi (1983)\n", + "Death and the Maiden (1994)\n", + "Cosine similarity: 0.57\n", + "Return of the Jedi (1983)\n", + "Tank Girl (1995)\n", + "Cosine similarity: 0.546\n", + "Return of the Jedi (1983)\n", + "Double Happiness (1994)\n", + "Cosine similarity: 0.631\n", + "Return of the Jedi (1983)\n", + "Cobb (1994)\n", + "Cosine similarity: 0.614\n", + "Return of the Jedi (1983)\n", + "Mrs. Parker and the Vicious Circle (1994)\n", + "Cosine similarity: 0.718\n", + "Return of the Jedi (1983)\n", + "Faithful (1996)\n", + "Cosine similarity: 0.556\n", + "Return of the Jedi (1983)\n", + "Twelfth Night (1996)\n", + "Cosine similarity: 0.55\n", + "Return of the Jedi (1983)\n", + "Mark of Zorro, The (1940)\n", + "Cosine similarity: 0.695\n", + "Return of the Jedi (1983)\n", + "Surviving Picasso (1996)\n", + "Cosine similarity: 0.655\n", + "Return of the Jedi (1983)\n", + "Up in Smoke (1978)\n", + "Cosine similarity: 0.615\n", + "Return of the Jedi (1983)\n", + "Some Kind of Wonderful (1987)\n", + "Cosine similarity: 0.723\n", + "Return of the Jedi (1983)\n", + "I'm Not Rappaport (1996)\n", + "Cosine similarity: 0.627\n", + "Return of the Jedi (1983)\n", + "Umbrellas of Cherbourg, The (Parapluies de Cherbourg, Les) (1964)\n", + "Cosine similarity: 0.54\n", + "Return of the Jedi (1983)\n", + "They Made Me a Criminal (1939)\n", + "Cosine similarity: 0.634\n", + "Return of the Jedi (1983)\n", + "Last Time I Saw Paris, The (1954)\n", + "Cosine similarity: 0.715\n", + "Return of the Jedi (1983)\n", + "Farewell to Arms, A (1932)\n", + "Cosine similarity: 0.677\n", + "Return of the Jedi (1983)\n", + "Innocents, The (1961)\n", + "Cosine similarity: 0.656\n", + "Return of the Jedi (1983)\n", + "Old Man and the Sea, The (1958)\n", + "Cosine similarity: 0.548\n", + "Return of the Jedi (1983)\n", + "Truman Show, The (1998)\n", + "Cosine similarity: 0.597\n", + "Return of the Jedi (1983)\n", + "Heidi Fleiss: Hollywood Madam (1995) \n", + "Cosine similarity: 0.497\n", + "Return of the Jedi (1983)\n", + "Chungking Express (1994)\n", + "Cosine similarity: 0.654\n", + "Return of the Jedi (1983)\n", + "Jupiter's Wife (1994)\n", + "Cosine similarity: 0.684\n", + "Return of the Jedi (1983)\n", + "Safe (1995)\n", + "Cosine similarity: 0.59\n", + "Return of the Jedi (1983)\n", + "Feeling Minnesota (1996)\n", + "Cosine similarity: 0.579\n", + "Return of the Jedi (1983)\n", + "Escape to Witch Mountain (1975)\n", + "Cosine similarity: 0.682\n", + "Return of the Jedi (1983)\n", + "Get on the Bus (1996)\n", + "Cosine similarity: 0.611\n", + "Return of the Jedi (1983)\n", + "Doors, The (1991)\n", + "Cosine similarity: 0.591\n", + "Return of the Jedi (1983)\n", + "Ghosts of Mississippi (1996)\n", + "Cosine similarity: 0.644\n", + "Return of the Jedi (1983)\n", + "Beautiful Thing (1996)\n", + "Cosine similarity: 0.5\n", + "Return of the Jedi (1983)\n", + "Best Men (1997)\n", + "Cosine similarity: 0.722\n", + "Return of the Jedi (1983)\n", + "Hackers (1995)\n", + "Cosine similarity: 0.617\n", + "Return of the Jedi (1983)\n", + "Road to Wellville, The (1994)\n", + "Cosine similarity: 0.481\n", + "Return of the Jedi (1983)\n", + "War Room, The (1993)\n", + "Cosine similarity: 0.652\n", + "Return of the Jedi (1983)\n", + "When We Were Kings (1996)\n", + "Cosine similarity: 0.712\n", + "Return of the Jedi (1983)\n", + "Hard Eight (1996)\n", + "Cosine similarity: 0.61\n", + "Return of the Jedi (1983)\n", + "Quiet Room, The (1996)\n", + "Cosine similarity: 0.634\n", + "Return of the Jedi (1983)\n", + "Blue Chips (1994)\n", + "Cosine similarity: 0.652\n", + "Return of the Jedi (1983)\n", + "Calendar Girl (1993)\n", + "Cosine similarity: 0.648\n", + "Return of the Jedi (1983)\n", + "My Family (1995)\n", + "Cosine similarity: 0.574\n", + "Return of the Jedi (1983)\n", + "Tom & Viv (1994)\n", + "Cosine similarity: 0.492\n", + "Return of the Jedi (1983)\n", + "Walkabout (1971)\n", + "Cosine similarity: 0.721\n", + "Return of the Jedi (1983)\n", + "Last Dance (1996)\n", + "Cosine similarity: 0.617\n", + "Return of the Jedi (1983)\n", + "Original Gangstas (1996)\n", + "Cosine similarity: 0.538\n", + "Return of the Jedi (1983)\n", + "In Love and War (1996)\n", + "Cosine similarity: 0.677\n", + "Return of the Jedi (1983)\n", + "Backbeat (1993)\n", + "Cosine similarity: 0.611\n", + "Return of the Jedi (1983)\n", + "Alphaville (1965)\n", + "Cosine similarity: 0.56\n", + "Return of the Jedi (1983)\n", + "Rendezvous in Paris (Rendez-vous de Paris, Les) (1995)\n", + "Cosine similarity: -0.13\n", + "Return of the Jedi (1983)\n", + "Cyclo (1995)\n", + "Cosine similarity: 0.652\n", + "Return of the Jedi (1983)\n", + "Relic, The (1997)\n", + "Cosine similarity: 0.409\n", + "Return of the Jedi (1983)\n", + "Fille seule, La (A Single Girl) (1995)\n", + "Cosine similarity: 0.671\n", + "Return of the Jedi (1983)\n", + "Stalker (1979)\n", + "Cosine similarity: 0.655\n", + "Return of the Jedi (1983)\n", + "Love! Valour! Compassion! (1997)\n", + "Cosine similarity: 0.526\n", + "Return of the Jedi (1983)\n", + "Palookaville (1996)\n", + "Cosine similarity: 0.592\n", + "Return of the Jedi (1983)\n", + "Phat Beach (1996)\n", + "Cosine similarity: 0.463\n", + "Return of the Jedi (1983)\n", + "Portrait of a Lady, The (1996)\n", + "Cosine similarity: 0.692\n", + "Return of the Jedi (1983)\n", + "Zeus and Roxanne (1997)\n", + "Cosine similarity: 0.661\n", + "Return of the Jedi (1983)\n", + "Big Bully (1996)\n", + "Cosine similarity: 0.653\n", + "Return of the Jedi (1983)\n", + "Love & Human Remains (1993)\n", + "Cosine similarity: 0.625\n", + "Return of the Jedi (1983)\n", + "Sum of Us, The (1994)\n", + "Cosine similarity: 0.654\n", + "Return of the Jedi (1983)\n", + "Little Buddha (1993)\n", + "Cosine similarity: 0.594\n", + "Return of the Jedi (1983)\n", + "Fresh (1994)\n", + "Cosine similarity: 0.602\n", + "Return of the Jedi (1983)\n", + "Spanking the Monkey (1994)\n", + "Cosine similarity: 0.449\n", + "Return of the Jedi (1983)\n", + "Wild Reeds (1994)\n", + "Cosine similarity: 0.6\n", + "Return of the Jedi (1983)\n", + "Women, The (1939)\n", + "Cosine similarity: 0.674\n", + "Return of the Jedi (1983)\n", + "Bliss (1997)\n", + "Cosine similarity: 0.703\n", + "Return of the Jedi (1983)\n", + "Caught (1996)\n", + "Cosine similarity: 0.638\n", + "Return of the Jedi (1983)\n", + "Hugo Pool (1997)\n", + "Cosine similarity: 0.526\n", + "Return of the Jedi (1983)\n", + "Welcome To Sarajevo (1997)\n", + "Cosine similarity: 0.568\n", + "Return of the Jedi (1983)\n", + "Dunston Checks In (1996)\n", + "Cosine similarity: 0.424\n", + "Return of the Jedi (1983)\n", + "Major Payne (1994)\n", + "Cosine similarity: 0.486\n", + "Return of the Jedi (1983)\n", + "Man of the House (1995)\n", + "Cosine similarity: 0.504\n", + "Return of the Jedi (1983)\n", + "I Love Trouble (1994)\n", + "Cosine similarity: 0.625\n", + "Return of the Jedi (1983)\n", + "Low Down Dirty Shame, A (1994)\n", + "Cosine similarity: 0.667\n", + "Return of the Jedi (1983)\n", + "Cops and Robbersons (1994)\n", + "Cosine similarity: 0.499\n", + "Return of the Jedi (1983)\n", + "Cowboy Way, The (1994)\n", + "Cosine similarity: 0.456\n", + "Return of the Jedi (1983)\n", + "Endless Summer 2, The (1994)\n", + "Cosine similarity: 0.606\n", + "Return of the Jedi (1983)\n", + "In the Army Now (1994)\n", + "Cosine similarity: 0.594\n", + "Return of the Jedi (1983)\n", + "Inkwell, The (1994)\n", + "Cosine similarity: 0.638\n", + "Return of the Jedi (1983)\n", + "Switchblade Sisters (1975)\n", + "Cosine similarity: 0.535\n", + "Return of the Jedi (1983)\n", + "Young Guns II (1990)\n", + "Cosine similarity: 0.47\n", + "Return of the Jedi (1983)\n", + "Prefontaine (1997)\n", + "Cosine similarity: 0.685\n", + "Return of the Jedi (1983)\n", + "That Old Feeling (1997)\n", + "Cosine similarity: 0.751\n", + "Return of the Jedi (1983)\n", + "Letter From Death Row, A (1998)\n", + "Cosine similarity: 0.608\n", + "Return of the Jedi (1983)\n", + "Boys of St. Vincent, The (1993)\n", + "Cosine similarity: 0.716\n", + "Return of the Jedi (1983)\n", + "Before the Rain (Pred dozhdot) (1994)\n", + "Cosine similarity: 0.672\n", + "Return of the Jedi (1983)\n", + "Once Were Warriors (1994)\n", + "Cosine similarity: 0.651\n", + "Return of the Jedi (1983)\n", + "Strawberry and Chocolate (Fresa y chocolate) (1993)\n", + "Cosine similarity: 0.542\n", + "Return of the Jedi (1983)\n", + "Savage Nights (Nuits fauves, Les) (1992)\n", + "Cosine similarity: 0.641\n", + "Return of the Jedi (1983)\n", + "Family Thing, A (1996)\n", + "Cosine similarity: 0.631\n", + "Return of the Jedi (1983)\n", + "Purple Noon (1960)\n", + "Cosine similarity: 0.604\n", + "Return of the Jedi (1983)\n", + "Cemetery Man (Dellamorte Dellamore) (1994)\n", + "Cosine similarity: 0.582\n", + "Return of the Jedi (1983)\n", + "Kim (1950)\n", + "Cosine similarity: 0.667\n", + "Return of the Jedi (1983)\n", + "Marlene Dietrich: Shadow and Light (1996) \n", + "Cosine similarity: -0.0686\n", + "Return of the Jedi (1983)\n", + "Maybe, Maybe Not (Bewegte Mann, Der) (1994)\n", + "Cosine similarity: 0.557\n", + "Return of the Jedi (1983)\n", + "Top Hat (1935)\n", + "Cosine similarity: 0.613\n", + "Return of the Jedi (1983)\n", + "To Be or Not to Be (1942)\n", + "Cosine similarity: 0.603\n", + "Return of the Jedi (1983)\n", + "Secret Agent, The (1996)\n", + "Cosine similarity: 0.7\n", + "Return of the Jedi (1983)\n", + "Amos & Andrew (1993)\n", + "Cosine similarity: 0.598\n", + "Return of the Jedi (1983)\n", + "Jade (1995)\n", + "Cosine similarity: 0.504\n", + "Return of the Jedi (1983)\n", + "Kiss of Death (1995)\n", + "Cosine similarity: 0.624\n", + "Return of the Jedi (1983)\n", + "Mixed Nuts (1994)\n", + "Cosine similarity: 0.617\n", + "Return of the Jedi (1983)\n", + "Virtuosity (1995)\n", + "Cosine similarity: 0.539\n", + "Return of the Jedi (1983)\n", + "Blue Sky (1994)\n", + "Cosine similarity: 0.557\n", + "Return of the Jedi (1983)\n", + "Flesh and Bone (1993)\n", + "Cosine similarity: 0.53\n", + "Return of the Jedi (1983)\n", + "Guilty as Sin (1993)\n", + "Cosine similarity: 0.378\n", + "Return of the Jedi (1983)\n", + "In the Realm of the Senses (Ai no corrida) (1976)\n", + "Cosine similarity: 0.621\n", + "Return of the Jedi (1983)\n", + "Barb Wire (1996)\n", + "Cosine similarity: 0.672\n", + "Return of the Jedi (1983)\n", + "Kissed (1996)\n", + "Cosine similarity: 0.627\n", + "Return of the Jedi (1983)\n", + "Assassins (1995)\n", + "Cosine similarity: 0.617\n", + "Return of the Jedi (1983)\n", + "Friday (1995)\n", + "Cosine similarity: 0.728\n", + "Return of the Jedi (1983)\n", + "Goofy Movie, A (1995)\n", + "Cosine similarity: 0.555\n", + "Return of the Jedi (1983)\n", + "Higher Learning (1995)\n", + "Cosine similarity: 0.66\n", + "Return of the Jedi (1983)\n", + "When a Man Loves a Woman (1994)\n", + "Cosine similarity: 0.555\n", + "Return of the Jedi (1983)\n", + "Judgment Night (1993)\n", + "Cosine similarity: 0.632\n", + "Return of the Jedi (1983)\n", + "King of the Hill (1993)\n", + "Cosine similarity: 0.631\n", + "Return of the Jedi (1983)\n", + "Scout, The (1994)\n", + "Cosine similarity: 0.584\n", + "Return of the Jedi (1983)\n", + "Angus (1995)\n", + "Cosine similarity: 0.652\n", + "Return of the Jedi (1983)\n", + "Night Falls on Manhattan (1997)\n", + "Cosine similarity: 0.638\n", + "Return of the Jedi (1983)\n", + "Awfully Big Adventure, An (1995)\n", + "Cosine similarity: 0.476\n", + "Return of the Jedi (1983)\n", + "Under Siege 2: Dark Territory (1995)\n", + "Cosine similarity: 0.594\n", + "Return of the Jedi (1983)\n", + "Poison Ivy II (1995)\n", + "Cosine similarity: 0.477\n", + "Return of the Jedi (1983)\n", + "Ready to Wear (Pret-A-Porter) (1994)\n", + "Cosine similarity: 0.556\n", + "Return of the Jedi (1983)\n", + "Marked for Death (1990)\n", + "Cosine similarity: 0.52\n", + "Return of the Jedi (1983)\n", + "Madonna: Truth or Dare (1991)\n", + "Cosine similarity: 0.595\n", + "Return of the Jedi (1983)\n", + "Nénette et Boni (1996)\n", + "Cosine similarity: 0.595\n", + "Return of the Jedi (1983)\n", + "Chairman of the Board (1998)\n", + "Cosine similarity: 0.57\n", + "Return of the Jedi (1983)\n", + "Big Bang Theory, The (1994)\n", + "Cosine similarity: 0.616\n", + "Return of the Jedi (1983)\n", + "Other Voices, Other Rooms (1997)\n", + "Cosine similarity: -0.135\n", + "Return of the Jedi (1983)\n", + "Twisted (1996)\n", + "Cosine similarity: 0.664\n", + "Return of the Jedi (1983)\n", + "Full Speed (1996)\n", + "Cosine similarity: 0.67\n", + "Return of the Jedi (1983)\n", + "Cutthroat Island (1995)\n", + "Cosine similarity: 0.534\n", + "Return of the Jedi (1983)\n", + "Ghost in the Shell (Kokaku kidotai) (1995)\n", + "Cosine similarity: 0.597\n", + "Return of the Jedi (1983)\n", + "Van, The (1996)\n", + "Cosine similarity: 0.592\n", + "Return of the Jedi (1983)\n", + "Old Lady Who Walked in the Sea, The (Vieille qui marchait dans la mer, La) (1991)\n", + "Cosine similarity: 0.582\n", + "Return of the Jedi (1983)\n", + "Night Flier (1997)\n", + "Cosine similarity: 0.587\n", + "Return of the Jedi (1983)\n", + "Metro (1997)\n", + "Cosine similarity: 0.48\n", + "Return of the Jedi (1983)\n", + "Gridlock'd (1997)\n", + "Cosine similarity: 0.593\n", + "Return of the Jedi (1983)\n", + "Bushwhacked (1995)\n", + "Cosine similarity: 0.515\n", + "Return of the Jedi (1983)\n", + "Bad Girls (1994)\n", + "Cosine similarity: 0.574\n", + "Return of the Jedi (1983)\n", + "Blink (1994)\n", + "Cosine similarity: 0.603\n", + "Return of the Jedi (1983)\n", + "For Love or Money (1993)\n", + "Cosine similarity: 0.58\n", + "Return of the Jedi (1983)\n", + "Best of the Best 3: No Turning Back (1995)\n", + "Cosine similarity: 0.611\n", + "Return of the Jedi (1983)\n", + "A Chef in Love (1996)\n", + "Cosine similarity: 0.666\n", + "Return of the Jedi (1983)\n", + "Contempt (Mépris, Le) (1963)\n", + "Cosine similarity: 0.566\n", + "Return of the Jedi (1983)\n", + "Tie That Binds, The (1995)\n", + "Cosine similarity: 0.576\n", + "Return of the Jedi (1983)\n", + "Gone Fishin' (1997)\n", + "Cosine similarity: 0.493\n", + "Return of the Jedi (1983)\n", + "Broken English (1996)\n", + "Cosine similarity: 0.618\n", + "Return of the Jedi (1983)\n", + "Designated Mourner, The (1997)\n", + "Cosine similarity: 0.406\n", + "Return of the Jedi (1983)\n", + "Designated Mourner, The (1997)\n", + "Cosine similarity: 0.547\n", + "Return of the Jedi (1983)\n", + "Trial and Error (1997)\n", + "Cosine similarity: 0.49\n", + "Return of the Jedi (1983)\n", + "Pie in the Sky (1995)\n", + "Cosine similarity: 0.683\n", + "Return of the Jedi (1983)\n", + "Total Eclipse (1995)\n", + "Cosine similarity: 0.598\n", + "Return of the Jedi (1983)\n", + "Run of the Country, The (1995)\n", + "Cosine similarity: 0.635\n", + "Return of the Jedi (1983)\n", + "Walking and Talking (1996)\n", + "Cosine similarity: 0.729\n", + "Return of the Jedi (1983)\n", + "Foxfire (1996)\n", + "Cosine similarity: 0.488\n", + "Return of the Jedi (1983)\n", + "Nothing to Lose (1994)\n", + "Cosine similarity: 0.678\n", + "Return of the Jedi (1983)\n", + "Star Maps (1997)\n", + "Cosine similarity: 0.528\n", + "Return of the Jedi (1983)\n", + "Bread and Chocolate (Pane e cioccolata) (1973)\n", + "Cosine similarity: 0.566\n", + "Return of the Jedi (1983)\n", + "Clockers (1995)\n", + "Cosine similarity: 0.639\n", + "Return of the Jedi (1983)\n", + "Bitter Moon (1992)\n", + "Cosine similarity: 0.533\n", + "Return of the Jedi (1983)\n", + "Love in the Afternoon (1957)\n", + "Cosine similarity: 0.765\n", + "Return of the Jedi (1983)\n", + "Life with Mikey (1993)\n", + "Cosine similarity: 0.584\n", + "Return of the Jedi (1983)\n", + "North (1994)\n", + "Cosine similarity: 0.473\n", + "Return of the Jedi (1983)\n", + "Talking About Sex (1994)\n", + "Cosine similarity: 0.623\n", + "Return of the Jedi (1983)\n", + "Color of Night (1994)\n", + "Cosine similarity: 0.726\n", + "Return of the Jedi (1983)\n", + "Robocop 3 (1993)\n", + "Cosine similarity: 0.667\n", + "Return of the Jedi (1983)\n", + "Killer (Bulletproof Heart) (1994)\n", + "Cosine similarity: 0.63\n", + "Return of the Jedi (1983)\n", + "Sunset Park (1996)\n", + "Cosine similarity: 0.535\n", + "Return of the Jedi (1983)\n", + "Set It Off (1996)\n", + "Cosine similarity: 0.741\n", + "Return of the Jedi (1983)\n", + "Selena (1997)\n", + "Cosine similarity: 0.655\n", + "Return of the Jedi (1983)\n", + "Wild America (1997)\n", + "Cosine similarity: 0.631\n", + "Return of the Jedi (1983)\n", + "Gang Related (1997)\n", + "Cosine similarity: 0.461\n", + "Return of the Jedi (1983)\n", + "Manny & Lo (1996)\n", + "Cosine similarity: 0.571\n", + "Return of the Jedi (1983)\n", + "Grass Harp, The (1995)\n", + "Cosine similarity: 0.575\n", + "Return of the Jedi (1983)\n", + "Out to Sea (1997)\n", + "Cosine similarity: 0.6\n", + "Return of the Jedi (1983)\n", + "Before and After (1996)\n", + "Cosine similarity: 0.575\n", + "Return of the Jedi (1983)\n", + "Princess Caraboo (1994)\n", + "Cosine similarity: 0.685\n", + "Return of the Jedi (1983)\n", + "Shall We Dance? (1937)\n", + "Cosine similarity: 0.644\n", + "Return of the Jedi (1983)\n", + "Ed (1996)\n", + "Cosine similarity: 0.753\n", + "Return of the Jedi (1983)\n", + "Denise Calls Up (1995)\n", + "Cosine similarity: 0.693\n", + "Return of the Jedi (1983)\n", + "Jack and Sarah (1995)\n", + "Cosine similarity: 0.54\n", + "Return of the Jedi (1983)\n", + "Country Life (1994)\n", + "Cosine similarity: 0.383\n", + "Return of the Jedi (1983)\n", + "Celtic Pride (1996)\n", + "Cosine similarity: 0.597\n", + "Return of the Jedi (1983)\n", + "Simple Wish, A (1997)\n", + "Cosine similarity: 0.425\n", + "Return of the Jedi (1983)\n", + "Star Kid (1997)\n", + "Cosine similarity: 0.658\n", + "Return of the Jedi (1983)\n", + "Ayn Rand: A Sense of Life (1997)\n", + "Cosine similarity: 0.646\n", + "Return of the Jedi (1983)\n", + "Kicked in the Head (1997)\n", + "Cosine similarity: 0.674\n", + "Return of the Jedi (1983)\n", + "Indian Summer (1996)\n", + "Cosine similarity: 0.616\n", + "Return of the Jedi (1983)\n", + "Love Affair (1994)\n", + "Cosine similarity: 0.533\n", + "Return of the Jedi (1983)\n", + "Band Wagon, The (1953)\n", + "Cosine similarity: 0.601\n", + "Return of the Jedi (1983)\n", + "Penny Serenade (1941)\n", + "Cosine similarity: 0.424\n", + "Return of the Jedi (1983)\n", + "'Til There Was You (1997)\n", + "Cosine similarity: 0.677\n", + "Return of the Jedi (1983)\n", + "Stripes (1981)\n", + "Cosine similarity: 0.742\n", + "Return of the Jedi (1983)\n", + "Late Bloomers (1996)\n", + "Cosine similarity: 0.592\n", + "Return of the Jedi (1983)\n", + "Getaway, The (1994)\n", + "Cosine similarity: 0.555\n", + "Return of the Jedi (1983)\n", + "New York Cop (1996)\n", + "Cosine similarity: -0.104\n", + "Return of the Jedi (1983)\n", + "National Lampoon's Senior Trip (1995)\n", + "Cosine similarity: 0.654\n", + "Return of the Jedi (1983)\n", + "Delta of Venus (1994)\n", + "Cosine similarity: 0.63\n", + "Return of the Jedi (1983)\n", + "Carmen Miranda: Bananas Is My Business (1994)\n", + "Cosine similarity: 0.683\n", + "Return of the Jedi (1983)\n", + "Babyfever (1994)\n", + "Cosine similarity: 0.767\n", + "Return of the Jedi (1983)\n", + "Very Natural Thing, A (1974)\n", + "Cosine similarity: -0.302\n", + "Return of the Jedi (1983)\n", + "Walk in the Sun, A (1945)\n", + "Cosine similarity: 0.662\n", + "Return of the Jedi (1983)\n", + "Waiting to Exhale (1995)\n", + "Cosine similarity: 0.586\n", + "Return of the Jedi (1983)\n", + "Pompatus of Love, The (1996)\n", + "Cosine similarity: 0.644\n", + "Return of the Jedi (1983)\n", + "Palmetto (1998)\n", + "Cosine similarity: 0.562\n", + "Return of the Jedi (1983)\n", + "Surviving the Game (1994)\n", + "Cosine similarity: 0.65\n", + "Return of the Jedi (1983)\n", + "Inventing the Abbotts (1997)\n", + "Cosine similarity: 0.699\n", + "Return of the Jedi (1983)\n", + "Horse Whisperer, The (1998)\n", + "Cosine similarity: 0.591\n", + "Return of the Jedi (1983)\n", + "Journey of August King, The (1995)\n", + "Cosine similarity: 0.754\n", + "Return of the Jedi (1983)\n", + "Catwalk (1995)\n", + "Cosine similarity: 0.725\n", + "Return of the Jedi (1983)\n", + "Neon Bible, The (1995)\n", + "Cosine similarity: 0.622\n", + "Return of the Jedi (1983)\n", + "Homage (1995)\n", + "Cosine similarity: 0.664\n", + "Return of the Jedi (1983)\n", + "Open Season (1996)\n", + "Cosine similarity: 0.67\n", + "Return of the Jedi (1983)\n", + "Metisse (Café au Lait) (1993)\n", + "Cosine similarity: 0.66\n", + "Return of the Jedi (1983)\n", + "Wooden Man's Bride, The (Wu Kui) (1994)\n", + "Cosine similarity: 0.653\n", + "Return of the Jedi (1983)\n", + "Loaded (1994)\n", + "Cosine similarity: 0.587\n", + "Return of the Jedi (1983)\n", + "August (1996)\n", + "Cosine similarity: 0.752\n", + "Return of the Jedi (1983)\n", + "Boys (1996)\n", + "Cosine similarity: 0.644\n", + "Return of the Jedi (1983)\n", + "Captives (1994)\n", + "Cosine similarity: 0.677\n", + "Return of the Jedi (1983)\n", + "Of Love and Shadows (1994)\n", + "Cosine similarity: 0.541\n", + "Return of the Jedi (1983)\n", + "Low Life, The (1994)\n", + "Cosine similarity: -0.0513\n", + "Return of the Jedi (1983)\n", + "An Unforgettable Summer (1994)\n", + "Cosine similarity: 0.616\n", + "Return of the Jedi (1983)\n", + "Last Klezmer: Leopold Kozlowski, His Life and Music, The (1995)\n", + "Cosine similarity: 0.665\n", + "Return of the Jedi (1983)\n", + "My Life and Times With Antonin Artaud (En compagnie d'Antonin Artaud) (1993)\n", + "Cosine similarity: 0.651\n", + "Return of the Jedi (1983)\n", + "Midnight Dancers (Sibak) (1994)\n", + "Cosine similarity: 0.595\n", + "Return of the Jedi (1983)\n", + "Somebody to Love (1994)\n", + "Cosine similarity: 0.78\n", + "Return of the Jedi (1983)\n", + "American Buffalo (1996)\n", + "Cosine similarity: 0.741\n", + "Return of the Jedi (1983)\n", + "Kazaam (1996)\n", + "Cosine similarity: 0.606\n", + "Return of the Jedi (1983)\n", + "Larger Than Life (1996)\n", + "Cosine similarity: 0.468\n", + "Return of the Jedi (1983)\n", + "Two Deaths (1995)\n", + "Cosine similarity: 0.61\n", + "Return of the Jedi (1983)\n", + "Stefano Quantestorie (1993)\n", + "Cosine similarity: 0.738\n", + "Return of the Jedi (1983)\n", + "Crude Oasis, The (1995)\n", + "Cosine similarity: 0.737\n", + "Return of the Jedi (1983)\n", + "Hedd Wyn (1992)\n", + "Cosine similarity: 0.669\n", + "Return of the Jedi (1983)\n", + "Convent, The (Convento, O) (1995)\n", + "Cosine similarity: 0.636\n", + "Return of the Jedi (1983)\n", + "Lotto Land (1995)\n", + "Cosine similarity: 0.783\n", + "Return of the Jedi (1983)\n", + "Story of Xinghua, The (1993)\n", + "Cosine similarity: 0.678\n", + "Return of the Jedi (1983)\n", + "Day the Sun Turned Cold, The (Tianguo niezi) (1994)\n", + "Cosine similarity: 0.74\n", + "Return of the Jedi (1983)\n", + "Dingo (1992)\n", + "Cosine similarity: 0.539\n", + "Return of the Jedi (1983)\n", + "Ballad of Narayama, The (Narayama Bushiko) (1958)\n", + "Cosine similarity: 0.686\n", + "Return of the Jedi (1983)\n", + "Every Other Weekend (1990)\n", + "Cosine similarity: 0.79\n", + "Return of the Jedi (1983)\n", + "Mille bolle blu (1993)\n", + "Cosine similarity: -0.119\n", + "Return of the Jedi (1983)\n", + "Crows and Sparrows (1949)\n", + "Cosine similarity: 0.743\n", + "Return of the Jedi (1983)\n", + "Lover's Knot (1996)\n", + "Cosine similarity: 0.579\n", + "Return of the Jedi (1983)\n", + "Shadow of Angels (Schatten der Engel) (1976)\n", + "Cosine similarity: 0.757\n", + "Return of the Jedi (1983)\n", + "1-900 (1994)\n", + "Cosine similarity: 0.557\n", + "Return of the Jedi (1983)\n", + "Venice/Venice (1992)\n", + "Cosine similarity: 0.755\n", + "Return of the Jedi (1983)\n", + "Infinity (1996)\n", + "Cosine similarity: 0.735\n", + "Return of the Jedi (1983)\n", + "Ed's Next Move (1996)\n", + "Cosine similarity: 0.606\n", + "Return of the Jedi (1983)\n", + "For the Moment (1994)\n", + "Cosine similarity: 0.751\n", + "Return of the Jedi (1983)\n", + "The Deadly Cure (1996)\n", + "Cosine similarity: 0.665\n", + "Return of the Jedi (1983)\n", + "Boys in Venice (1996)\n", + "Cosine similarity: 0.759\n", + "Return of the Jedi (1983)\n", + "Sexual Life of the Belgians, The (1994)\n", + "Cosine similarity: 0.709\n", + "Return of the Jedi (1983)\n", + "Search for One-eye Jimmy, The (1996)\n", + "Cosine similarity: 0.583\n", + "Return of the Jedi (1983)\n", + "American Strays (1996)\n", + "Cosine similarity: 0.741\n", + "Return of the Jedi (1983)\n", + "Leopard Son, The (1996)\n", + "Cosine similarity: 0.731\n", + "Return of the Jedi (1983)\n", + "Bird of Prey (1996)\n", + "Cosine similarity: 0.119\n", + "Return of the Jedi (1983)\n", + "Johnny 100 Pesos (1993)\n", + "Cosine similarity: 0.727\n", + "Return of the Jedi (1983)\n", + "JLG/JLG - autoportrait de décembre (1994)\n", + "Cosine similarity: 0.767\n", + "Return of the Jedi (1983)\n", + "Faust (1994)\n", + "Cosine similarity: 0.652\n", + "Return of the Jedi (1983)\n", + "Mina Tannenbaum (1994)\n", + "Cosine similarity: 0.652\n", + "Return of the Jedi (1983)\n", + "Forbidden Christ, The (Cristo proibito, Il) (1950)\n", + "Cosine similarity: 0.599\n", + "Return of the Jedi (1983)\n", + "I Can't Sleep (J'ai pas sommeil) (1994)\n", + "Cosine similarity: 0.656\n", + "Return of the Jedi (1983)\n", + "Machine, The (1994)\n", + "Cosine similarity: 0.722\n", + "Return of the Jedi (1983)\n", + "Stranger, The (1994)\n", + "Cosine similarity: 0.67\n", + "Return of the Jedi (1983)\n", + "Good Morning (1971)\n", + "Cosine similarity: 0.779\n", + "Return of the Jedi (1983)\n", + "Falling in Love Again (1980)\n", + "Cosine similarity: 0.744\n", + "Return of the Jedi (1983)\n", + "Cement Garden, The (1993)\n", + "Cosine similarity: 0.609\n", + "Return of the Jedi (1983)\n", + "Meet Wally Sparks (1997)\n", + "Cosine similarity: 0.658\n", + "Return of the Jedi (1983)\n", + "Hotel de Love (1996)\n", + "Cosine similarity: 0.597\n", + "Return of the Jedi (1983)\n", + "Rhyme & Reason (1997)\n", + "Cosine similarity: 0.584\n", + "Return of the Jedi (1983)\n", + "Love and Other Catastrophes (1996)\n", + "Cosine similarity: 0.634\n", + "Return of the Jedi (1983)\n", + "Hollow Reed (1996)\n", + "Cosine similarity: 0.626\n", + "Return of the Jedi (1983)\n", + "Losing Chase (1996)\n", + "Cosine similarity: 0.66\n", + "Return of the Jedi (1983)\n", + "Bonheur, Le (1965)\n", + "Cosine similarity: 0.585\n", + "Return of the Jedi (1983)\n", + "Second Jungle Book: Mowgli & Baloo, The (1997)\n", + "Cosine similarity: 0.401\n", + "Return of the Jedi (1983)\n", + "Squeeze (1996)\n", + "Cosine similarity: 0.532\n", + "Return of the Jedi (1983)\n", + "Roseanna's Grave (For Roseanna) (1997)\n", + "Cosine similarity: 0.608\n", + "Return of the Jedi (1983)\n", + "Tetsuo II: Body Hammer (1992)\n", + "Cosine similarity: 0.649\n", + "Return of the Jedi (1983)\n", + "Fall (1997)\n", + "Cosine similarity: 0.648\n", + "Return of the Jedi (1983)\n", + "Gabbeh (1996)\n", + "Cosine similarity: 0.574\n", + "Return of the Jedi (1983)\n", + "Mondo (1996)\n", + "Cosine similarity: 0.554\n", + "Return of the Jedi (1983)\n", + "Innocent Sleep, The (1995)\n", + "Cosine similarity: 0.633\n", + "Return of the Jedi (1983)\n", + "For Ever Mozart (1996)\n", + "Cosine similarity: 0.567\n", + "Return of the Jedi (1983)\n", + "Locusts, The (1997)\n", + "Cosine similarity: 0.589\n", + "Return of the Jedi (1983)\n", + "Stag (1997)\n", + "Cosine similarity: 0.664\n", + "Return of the Jedi (1983)\n", + "Swept from the Sea (1997)\n", + "Cosine similarity: 0.506\n", + "Return of the Jedi (1983)\n", + "Hurricane Streets (1998)\n", + "Cosine similarity: 0.532\n", + "Return of the Jedi (1983)\n", + "Stonewall (1995)\n", + "Cosine similarity: 0.666\n", + "Return of the Jedi (1983)\n", + "Of Human Bondage (1934)\n", + "Cosine similarity: 0.547\n", + "Return of the Jedi (1983)\n", + "Anna (1996)\n", + "Cosine similarity: 0.584\n", + "Return of the Jedi (1983)\n", + "Stranger in the House (1997)\n", + "Cosine similarity: 0.587\n", + "Return of the Jedi (1983)\n", + "Picture Bride (1995)\n", + "Cosine similarity: 0.664\n", + "Return of the Jedi (1983)\n", + "M. Butterfly (1993)\n", + "Cosine similarity: 0.698\n", + "Return of the Jedi (1983)\n", + "Ciao, Professore! (1993)\n", + "Cosine similarity: 0.643\n", + "Return of the Jedi (1983)\n", + "Caro Diario (Dear Diary) (1994)\n", + "Cosine similarity: 0.52\n", + "Return of the Jedi (1983)\n", + "Withnail and I (1987)\n", + "Cosine similarity: 0.589\n", + "Return of the Jedi (1983)\n", + "Boy's Life 2 (1997)\n", + "Cosine similarity: 0.615\n", + "Return of the Jedi (1983)\n", + "When Night Is Falling (1995)\n", + "Cosine similarity: 0.716\n", + "Return of the Jedi (1983)\n", + "Specialist, The (1994)\n", + "Cosine similarity: 0.478\n", + "Return of the Jedi (1983)\n", + "Gordy (1995)\n", + "Cosine similarity: 0.543\n", + "Return of the Jedi (1983)\n", + "Swan Princess, The (1994)\n", + "Cosine similarity: 0.615\n", + "Return of the Jedi (1983)\n", + "Harlem (1993)\n", + "Cosine similarity: 0.658\n", + "Return of the Jedi (1983)\n", + "Barbarella (1968)\n", + "Cosine similarity: 0.526\n", + "Return of the Jedi (1983)\n", + "Land Before Time III: The Time of the Great Giving (1995) (V)\n", + "Cosine similarity: 0.602\n", + "Return of the Jedi (1983)\n", + "Street Fighter (1994)\n", + "Cosine similarity: 0.379\n", + "Return of the Jedi (1983)\n", + "Coldblooded (1995)\n", + "Cosine similarity: 0.667\n", + "Return of the Jedi (1983)\n", + "Next Karate Kid, The (1994)\n", + "Cosine similarity: 0.624\n", + "Return of the Jedi (1983)\n", + "No Escape (1994)\n", + "Cosine similarity: 0.642\n", + "Return of the Jedi (1983)\n", + "Turning, The (1992)\n", + "Cosine similarity: 0.655\n", + "Return of the Jedi (1983)\n", + "Joy Luck Club, The (1993)\n", + "Cosine similarity: 0.591\n", + "Return of the Jedi (1983)\n", + "Highlander III: The Sorcerer (1994)\n", + "Cosine similarity: 0.563\n", + "Return of the Jedi (1983)\n", + "Gilligan's Island: The Movie (1998)\n", + "Cosine similarity: 0.472\n", + "Return of the Jedi (1983)\n", + "My Crazy Life (Mi vida loca) (1993)\n", + "Cosine similarity: 0.593\n", + "Return of the Jedi (1983)\n", + "Suture (1993)\n", + "Cosine similarity: 0.606\n", + "Return of the Jedi (1983)\n", + "Walking Dead, The (1995)\n", + "Cosine similarity: 0.709\n", + "Return of the Jedi (1983)\n", + "I Like It Like That (1994)\n", + "Cosine similarity: 0.579\n", + "Return of the Jedi (1983)\n", + "I'll Do Anything (1994)\n", + "Cosine similarity: 0.502\n", + "Return of the Jedi (1983)\n", + "Grace of My Heart (1996)\n", + "Cosine similarity: 0.496\n", + "Return of the Jedi (1983)\n", + "Drunks (1995)\n", + "Cosine similarity: 0.605\n", + "Return of the Jedi (1983)\n", + "SubUrbia (1997)\n", + "Cosine similarity: 0.526\n", + "Return of the Jedi (1983)\n", + "Sliding Doors (1998)\n", + "Cosine similarity: 0.615\n", + "Return of the Jedi (1983)\n", + "Ill Gotten Gains (1997)\n", + "Cosine similarity: 0.669\n", + "Return of the Jedi (1983)\n", + "Legal Deceit (1997)\n", + "Cosine similarity: 0.662\n", + "Return of the Jedi (1983)\n", + "Mighty, The (1998)\n", + "Cosine similarity: 0.722\n", + "Return of the Jedi (1983)\n", + "Men of Means (1998)\n", + "Cosine similarity: 0.57\n", + "Return of the Jedi (1983)\n", + "Shooting Fish (1997)\n", + "Cosine similarity: 0.588\n", + "Return of the Jedi (1983)\n", + "Steal Big, Steal Little (1995)\n", + "Cosine similarity: 0.631\n", + "Return of the Jedi (1983)\n", + "Mr. Jones (1993)\n", + "Cosine similarity: 0.619\n", + "Return of the Jedi (1983)\n", + "House Party 3 (1994)\n", + "Cosine similarity: 0.626\n", + "Return of the Jedi (1983)\n", + "Panther (1995)\n", + "Cosine similarity: 0.573\n", + "Return of the Jedi (1983)\n", + "Jason's Lyric (1994)\n", + "Cosine similarity: 0.626\n", + "Return of the Jedi (1983)\n", + "Above the Rim (1994)\n", + "Cosine similarity: 0.718\n", + "Return of the Jedi (1983)\n", + "Moonlight and Valentino (1995)\n", + "Cosine similarity: 0.639\n", + "Return of the Jedi (1983)\n", + "Scarlet Letter, The (1995)\n", + "Cosine similarity: 0.709\n", + "Return of the Jedi (1983)\n", + "8 Seconds (1994)\n", + "Cosine similarity: 0.73\n", + "Return of the Jedi (1983)\n", + "That Darn Cat! (1965)\n", + "Cosine similarity: 0.675\n", + "Return of the Jedi (1983)\n", + "Ladybird Ladybird (1994)\n", + "Cosine similarity: 0.553\n", + "Return of the Jedi (1983)\n", + "Bye Bye, Love (1995)\n", + "Cosine similarity: 0.567\n", + "Return of the Jedi (1983)\n", + "Century (1993)\n", + "Cosine similarity: 0.64\n", + "Return of the Jedi (1983)\n", + "My Favorite Season (1993)\n", + "Cosine similarity: 0.295\n", + "Return of the Jedi (1983)\n", + "Pather Panchali (1955)\n", + "Cosine similarity: 0.669\n", + "Return of the Jedi (1983)\n", + "Golden Earrings (1947)\n", + "Cosine similarity: 0.585\n", + "Return of the Jedi (1983)\n", + "Foreign Correspondent (1940)\n", + "Cosine similarity: 0.718\n", + "Return of the Jedi (1983)\n", + "Lady of Burlesque (1943)\n", + "Cosine similarity: 0.64\n", + "Return of the Jedi (1983)\n", + "Angel on My Shoulder (1946)\n", + "Cosine similarity: 0.0121\n", + "Return of the Jedi (1983)\n", + "Angel and the Badman (1947)\n", + "Cosine similarity: 0.711\n", + "Return of the Jedi (1983)\n", + "Outlaw, The (1943)\n", + "Cosine similarity: 0.75\n", + "Return of the Jedi (1983)\n", + "Beat the Devil (1954)\n", + "Cosine similarity: 0.616\n", + "Return of the Jedi (1983)\n", + "Love Is All There Is (1996)\n", + "Cosine similarity: 0.641\n", + "Return of the Jedi (1983)\n", + "Damsel in Distress, A (1937)\n", + "Cosine similarity: 0.65\n", + "Return of the Jedi (1983)\n", + "Madame Butterfly (1995)\n", + "Cosine similarity: 0.65\n", + "Return of the Jedi (1983)\n", + "Sleepover (1995)\n", + "Cosine similarity: 0.0511\n", + "Return of the Jedi (1983)\n", + "Here Comes Cookie (1935)\n", + "Cosine similarity: 0.609\n", + "Return of the Jedi (1983)\n", + "Thieves (Voleurs, Les) (1996)\n", + "Cosine similarity: 0.565\n", + "Return of the Jedi (1983)\n", + "Boys, Les (1997)\n", + "Cosine similarity: 0.485\n", + "Return of the Jedi (1983)\n", + "Stars Fell on Henrietta, The (1995)\n", + "Cosine similarity: 0.581\n", + "Return of the Jedi (1983)\n", + "Last Summer in the Hamptons (1995)\n", + "Cosine similarity: 0.639\n", + "Return of the Jedi (1983)\n", + "Margaret's Museum (1995)\n", + "Cosine similarity: 0.612\n", + "Return of the Jedi (1983)\n", + "Saint of Fort Washington, The (1993)\n", + "Cosine similarity: 0.699\n", + "Return of the Jedi (1983)\n", + "Cure, The (1995)\n", + "Cosine similarity: 0.598\n", + "Return of the Jedi (1983)\n", + "Tom and Huck (1995)\n", + "Cosine similarity: 0.619\n", + "Return of the Jedi (1983)\n", + "Gumby: The Movie (1995)\n", + "Cosine similarity: 0.594\n", + "Return of the Jedi (1983)\n", + "Hideaway (1995)\n", + "Cosine similarity: 0.54\n", + "Return of the Jedi (1983)\n", + "Visitors, The (Visiteurs, Les) (1993)\n", + "Cosine similarity: -0.119\n", + "Return of the Jedi (1983)\n", + "Little Princess, The (1939)\n", + "Cosine similarity: 0.761\n", + "Return of the Jedi (1983)\n", + "Nina Takes a Lover (1994)\n", + "Cosine similarity: 0.699\n", + "Return of the Jedi (1983)\n", + "Bhaji on the Beach (1993)\n", + "Cosine similarity: 0.584\n", + "Return of the Jedi (1983)\n", + "Raw Deal (1948)\n", + "Cosine similarity: -0.0467\n", + "Return of the Jedi (1983)\n", + "Nightwatch (1997)\n", + "Cosine similarity: 0.542\n", + "Return of the Jedi (1983)\n", + "Dead Presidents (1995)\n", + "Cosine similarity: 0.559\n", + "Return of the Jedi (1983)\n", + "Reckless (1995)\n", + "Cosine similarity: 0.689\n", + "Return of the Jedi (1983)\n", + "Herbie Rides Again (1974)\n", + "Cosine similarity: 0.57\n", + "Return of the Jedi (1983)\n", + "S.F.W. (1994)\n", + "Cosine similarity: 0.522\n", + "Return of the Jedi (1983)\n", + "Gate of Heavenly Peace, The (1995)\n", + "Cosine similarity: 0.731\n", + "Return of the Jedi (1983)\n", + "Man in the Iron Mask, The (1998)\n", + "Cosine similarity: 0.768\n", + "Return of the Jedi (1983)\n", + "Jerky Boys, The (1994)\n", + "Cosine similarity: 0.631\n", + "Return of the Jedi (1983)\n", + "Colonel Chabert, Le (1994)\n", + "Cosine similarity: 0.698\n", + "Return of the Jedi (1983)\n", + "Girl in the Cadillac (1995)\n", + "Cosine similarity: 0.561\n", + "Return of the Jedi (1983)\n", + "Even Cowgirls Get the Blues (1993)\n", + "Cosine similarity: 0.63\n", + "Return of the Jedi (1983)\n", + "Germinal (1993)\n", + "Cosine similarity: 0.559\n", + "Return of the Jedi (1983)\n", + "Chasers (1994)\n", + "Cosine similarity: 0.612\n", + "Return of the Jedi (1983)\n", + "Fausto (1993)\n", + "Cosine similarity: 0.621\n", + "Return of the Jedi (1983)\n", + "Tough and Deadly (1995)\n", + "Cosine similarity: 0.00593\n", + "Return of the Jedi (1983)\n", + "Window to Paris (1994)\n", + "Cosine similarity: 0.647\n", + "Return of the Jedi (1983)\n", + "Modern Affair, A (1995)\n", + "Cosine similarity: 0.0398\n", + "Return of the Jedi (1983)\n", + "Mostro, Il (1994)\n", + "Cosine similarity: 0.61\n", + "Return of the Jedi (1983)\n", + "Flirt (1995)\n", + "Cosine similarity: 0.679\n", + "Return of the Jedi (1983)\n", + "Carpool (1996)\n", + "Cosine similarity: 0.533\n", + "Return of the Jedi (1983)\n", + "Line King: Al Hirschfeld, The (1996)\n", + "Cosine similarity: 0.654\n", + "Return of the Jedi (1983)\n", + "Farmer & Chase (1995)\n", + "Cosine similarity: 0.656\n", + "Return of the Jedi (1983)\n", + "Grosse Fatigue (1994)\n", + "Cosine similarity: 0.555\n", + "Return of the Jedi (1983)\n", + "Santa with Muscles (1996)\n", + "Cosine similarity: 0.691\n", + "Return of the Jedi (1983)\n", + "Prisoner of the Mountains (Kavkazsky Plennik) (1996)\n", + "Cosine similarity: 0.522\n", + "Return of the Jedi (1983)\n", + "Naked in New York (1994)\n", + "Cosine similarity: 0.561\n", + "Return of the Jedi (1983)\n", + "Gold Diggers: The Secret of Bear Mountain (1995)\n", + "Cosine similarity: 0.618\n", + "Return of the Jedi (1983)\n", + "Bewegte Mann, Der (1994)\n", + "Cosine similarity: 0.625\n", + "Return of the Jedi (1983)\n", + "Killer: A Journal of Murder (1995)\n", + "Cosine similarity: 0.0751\n", + "Return of the Jedi (1983)\n", + "Nelly & Monsieur Arnaud (1995)\n", + "Cosine similarity: 0.64\n", + "Return of the Jedi (1983)\n", + "Three Lives and Only One Death (1996)\n", + "Cosine similarity: 0.662\n", + "Return of the Jedi (1983)\n", + "Babysitter, The (1995)\n", + "Cosine similarity: 0.473\n", + "Return of the Jedi (1983)\n", + "Getting Even with Dad (1994)\n", + "Cosine similarity: 0.579\n", + "Return of the Jedi (1983)\n", + "Mad Dog Time (1996)\n", + "Cosine similarity: 0.7\n", + "Return of the Jedi (1983)\n", + "Children of the Revolution (1996)\n", + "Cosine similarity: 0.676\n", + "Return of the Jedi (1983)\n", + "World of Apu, The (Apur Sansar) (1959)\n", + "Cosine similarity: 0.632\n", + "Return of the Jedi (1983)\n", + "Sprung (1997)\n", + "Cosine similarity: 0.699\n", + "Return of the Jedi (1983)\n", + "Dream With the Fishes (1997)\n", + "Cosine similarity: 0.623\n", + "Return of the Jedi (1983)\n", + "Wings of Courage (1995)\n", + "Cosine similarity: 0.681\n", + "Return of the Jedi (1983)\n", + "Wedding Gift, The (1994)\n", + "Cosine similarity: 0.694\n", + "Return of the Jedi (1983)\n", + "Race the Sun (1996)\n", + "Cosine similarity: 0.663\n", + "Return of the Jedi (1983)\n", + "Losing Isaiah (1995)\n", + "Cosine similarity: 0.609\n", + "Return of the Jedi (1983)\n", + "New Jersey Drive (1995)\n", + "Cosine similarity: 0.651\n", + "Return of the Jedi (1983)\n", + "Fear, The (1995)\n", + "Cosine similarity: 0.653\n", + "Return of the Jedi (1983)\n", + "Mr. Wonderful (1993)\n", + "Cosine similarity: 0.627\n", + "Return of the Jedi (1983)\n", + "Trial by Jury (1994)\n", + "Cosine similarity: 0.709\n", + "Return of the Jedi (1983)\n", + "Good Man in Africa, A (1994)\n", + "Cosine similarity: 0.624\n", + "Return of the Jedi (1983)\n", + "Kaspar Hauser (1993)\n", + "Cosine similarity: 0.567\n", + "Return of the Jedi (1983)\n", + "Object of My Affection, The (1998)\n", + "Cosine similarity: 0.6\n", + "Return of the Jedi (1983)\n", + "Witness (1985)\n", + "Cosine similarity: -0.0642\n", + "Return of the Jedi (1983)\n", + "Senseless (1998)\n", + "Cosine similarity: 0.601\n", + "Return of the Jedi (1983)\n", + "Nowhere (1997)\n", + "Cosine similarity: 0.653\n", + "Return of the Jedi (1983)\n", + "Underground (1995)\n", + "Cosine similarity: 0.655\n", + "Return of the Jedi (1983)\n", + "Jefferson in Paris (1995)\n", + "Cosine similarity: 0.504\n", + "Return of the Jedi (1983)\n", + "Far From Home: The Adventures of Yellow Dog (1995)\n", + "Cosine similarity: 0.521\n", + "Return of the Jedi (1983)\n", + "Foreign Student (1994)\n", + "Cosine similarity: 0.579\n", + "Return of the Jedi (1983)\n", + "I Don't Want to Talk About It (De eso no se habla) (1993)\n", + "Cosine similarity: 0.633\n", + "Return of the Jedi (1983)\n", + "Twin Town (1997)\n", + "Cosine similarity: 0.241\n", + "Return of the Jedi (1983)\n", + "Enfer, L' (1994)\n", + "Cosine similarity: 0.599\n", + "Return of the Jedi (1983)\n", + "Aiqing wansui (1994)\n", + "Cosine similarity: 0.575\n", + "Return of the Jedi (1983)\n", + "Cosi (1996)\n", + "Cosine similarity: 0.677\n", + "Return of the Jedi (1983)\n", + "All Over Me (1997)\n", + "Cosine similarity: 0.624\n", + "Return of the Jedi (1983)\n", + "Being Human (1993)\n", + "Cosine similarity: 0.627\n", + "Return of the Jedi (1983)\n", + "Amazing Panda Adventure, The (1995)\n", + "Cosine similarity: 0.646\n", + "Return of the Jedi (1983)\n", + "Beans of Egypt, Maine, The (1994)\n", + "Cosine similarity: 0.656\n", + "Return of the Jedi (1983)\n", + "Scarlet Letter, The (1926)\n", + "Cosine similarity: 0.713\n", + "Return of the Jedi (1983)\n", + "Johns (1996)\n", + "Cosine similarity: 0.048\n", + "Return of the Jedi (1983)\n", + "It Takes Two (1995)\n", + "Cosine similarity: 0.553\n", + "Return of the Jedi (1983)\n", + "Frankie Starlight (1995)\n", + "Cosine similarity: 0.529\n", + "Return of the Jedi (1983)\n", + "Shadows (Cienie) (1988)\n", + "Cosine similarity: 0.636\n", + "Return of the Jedi (1983)\n", + "Show, The (1995)\n", + "Cosine similarity: 0.229\n", + "Return of the Jedi (1983)\n", + "The Courtyard (1995)\n", + "Cosine similarity: 0.591\n", + "Return of the Jedi (1983)\n", + "Dream Man (1995)\n", + "Cosine similarity: 0.643\n", + "Return of the Jedi (1983)\n", + "Destiny Turns on the Radio (1995)\n", + "Cosine similarity: 0.755\n", + "Return of the Jedi (1983)\n", + "Glass Shield, The (1994)\n", + "Cosine similarity: 0.09\n", + "Return of the Jedi (1983)\n", + "Hunted, The (1995)\n", + "Cosine similarity: 0.714\n", + "Return of the Jedi (1983)\n", + "Underneath, The (1995)\n", + "Cosine similarity: 0.674\n", + "Return of the Jedi (1983)\n", + "Safe Passage (1994)\n", + "Cosine similarity: 0.736\n", + "Return of the Jedi (1983)\n", + "Secret Adventures of Tom Thumb, The (1993)\n", + "Cosine similarity: 0.54\n", + "Return of the Jedi (1983)\n", + "Condition Red (1995)\n", + "Cosine similarity: 0.645\n", + "Return of the Jedi (1983)\n", + "Yankee Zulu (1994)\n", + "Cosine similarity: 0.673\n", + "Return of the Jedi (1983)\n", + "Aparajito (1956)\n", + "Cosine similarity: 0.566\n", + "Return of the Jedi (1983)\n", + "Hostile Intentions (1994)\n", + "Cosine similarity: 0.58\n", + "Return of the Jedi (1983)\n", + "Clean Slate (Coup de Torchon) (1981)\n", + "Cosine similarity: 0.622\n", + "Return of the Jedi (1983)\n", + "Tigrero: A Film That Was Never Made (1994)\n", + "Cosine similarity: 0.542\n", + "Return of the Jedi (1983)\n", + "Eye of Vichy, The (Oeil de Vichy, L') (1993)\n", + "Cosine similarity: 0.58\n", + "Return of the Jedi (1983)\n", + "Promise, The (Versprechen, Das) (1994)\n", + "Cosine similarity: 0.683\n", + "Return of the Jedi (1983)\n", + "To Cross the Rubicon (1991)\n", + "Cosine similarity: -0.0549\n", + "Return of the Jedi (1983)\n", + "Daens (1992)\n", + "Cosine similarity: 0.602\n", + "Return of the Jedi (1983)\n", + "Man from Down Under, The (1943)\n", + "Cosine similarity: -0.161\n", + "Return of the Jedi (1983)\n", + "Careful (1992)\n", + "Cosine similarity: -0.0412\n", + "Return of the Jedi (1983)\n", + "Vermont Is For Lovers (1992)\n", + "Cosine similarity: 0.579\n", + "Return of the Jedi (1983)\n", + "Vie est belle, La (Life is Rosey) (1987)\n", + "Cosine similarity: 0.443\n", + "Return of the Jedi (1983)\n", + "Quartier Mozart (1992)\n", + "Cosine similarity: 0.1\n", + "Return of the Jedi (1983)\n", + "Touki Bouki (Journey of the Hyena) (1973)\n", + "Cosine similarity: 0.626\n", + "Return of the Jedi (1983)\n", + "Wend Kuuni (God's Gift) (1982)\n", + "Cosine similarity: 0.581\n", + "Return of the Jedi (1983)\n", + "Spirits of the Dead (Tre passi nel delirio) (1968)\n", + "Cosine similarity: 0.659\n", + "Return of the Jedi (1983)\n", + "Pharaoh's Army (1995)\n", + "Cosine similarity: 0.671\n", + "Return of the Jedi (1983)\n", + "I, Worst of All (Yo, la peor de todas) (1990)\n", + "Cosine similarity: 0.591\n", + "Return of the Jedi (1983)\n", + "Hungarian Fairy Tale, A (1987)\n", + "Cosine similarity: 0.704\n", + "Return of the Jedi (1983)\n", + "Death in the Garden (Mort en ce jardin, La) (1956)\n", + "Cosine similarity: 0.624\n", + "Return of the Jedi (1983)\n", + "Collectionneuse, La (1967)\n", + "Cosine similarity: 0.537\n", + "Return of the Jedi (1983)\n", + "Baton Rouge (1988)\n", + "Cosine similarity: 0.537\n", + "Return of the Jedi (1983)\n", + "Liebelei (1933)\n", + "Cosine similarity: 0.666\n", + "Return of the Jedi (1983)\n", + "Woman in Question, The (1950)\n", + "Cosine similarity: 0.0976\n", + "Return of the Jedi (1983)\n", + "T-Men (1947)\n", + "Cosine similarity: 0.687\n", + "Return of the Jedi (1983)\n", + "Invitation, The (Zaproszenie) (1986)\n", + "Cosine similarity: -0.0725\n", + "Return of the Jedi (1983)\n", + "Symphonie pastorale, La (1946)\n", + "Cosine similarity: 0.113\n", + "Return of the Jedi (1983)\n", + "American Dream (1990)\n", + "Cosine similarity: 0.533\n", + "Return of the Jedi (1983)\n", + "Lashou shentan (1992)\n", + "Cosine similarity: 0.568\n", + "Return of the Jedi (1983)\n", + "Terror in a Texas Town (1958)\n", + "Cosine similarity: 0.629\n", + "Return of the Jedi (1983)\n", + "Salut cousin! (1996)\n", + "Cosine similarity: 0.652\n", + "Return of the Jedi (1983)\n", + "Schizopolis (1996)\n", + "Cosine similarity: 0.59\n", + "Return of the Jedi (1983)\n", + "To Have, or Not (1995)\n", + "Cosine similarity: 0.516\n", + "Return of the Jedi (1983)\n", + "Duoluo tianshi (1995)\n", + "Cosine similarity: 0.602\n", + "Return of the Jedi (1983)\n", + "Magic Hour, The (1998)\n", + "Cosine similarity: 0.601\n", + "Return of the Jedi (1983)\n", + "Death in Brunswick (1991)\n", + "Cosine similarity: -0.00614\n", + "Return of the Jedi (1983)\n", + "Everest (1998)\n", + "Cosine similarity: 0.699\n", + "Return of the Jedi (1983)\n", + "Shopping (1994)\n", + "Cosine similarity: -0.00611\n", + "Return of the Jedi (1983)\n", + "Nemesis 2: Nebula (1995)\n", + "Cosine similarity: 0.651\n", + "Return of the Jedi (1983)\n", + "Romper Stomper (1992)\n", + "Cosine similarity: 0.395\n", + "Return of the Jedi (1983)\n", + "City of Industry (1997)\n", + "Cosine similarity: 0.576\n", + "Return of the Jedi (1983)\n", + "Someone Else's America (1995)\n", + "Cosine similarity: 0.615\n", + "Return of the Jedi (1983)\n", + "Guantanamera (1994)\n", + "Cosine similarity: 0.694\n", + "Return of the Jedi (1983)\n", + "Office Killer (1997)\n", + "Cosine similarity: 0.606\n", + "Return of the Jedi (1983)\n", + "Price Above Rubies, A (1998)\n", + "Cosine similarity: 0.563\n", + "Return of the Jedi (1983)\n", + "Angela (1995)\n", + "Cosine similarity: -0.138\n", + "Return of the Jedi (1983)\n", + "He Walked by Night (1948)\n", + "Cosine similarity: 0.622\n", + "Return of the Jedi (1983)\n", + "Love Serenade (1996)\n", + "Cosine similarity: 0.647\n", + "Return of the Jedi (1983)\n", + "Deceiver (1997)\n", + "Cosine similarity: 0.677\n", + "Return of the Jedi (1983)\n", + "Hurricane Streets (1998)\n", + "Cosine similarity: 0.631\n", + "Return of the Jedi (1983)\n", + "Buddy (1997)\n", + "Cosine similarity: 0.582\n", + "Return of the Jedi (1983)\n", + "B*A*P*S (1997)\n", + "Cosine similarity: 0.495\n", + "Return of the Jedi (1983)\n", + "Truth or Consequences, N.M. (1997)\n", + "Cosine similarity: 0.579\n", + "Return of the Jedi (1983)\n", + "Intimate Relations (1996)\n", + "Cosine similarity: 0.683\n", + "Return of the Jedi (1983)\n", + "Leading Man, The (1996)\n", + "Cosine similarity: 0.66\n", + "Return of the Jedi (1983)\n", + "Tokyo Fist (1995)\n", + "Cosine similarity: 0.723\n", + "Return of the Jedi (1983)\n", + "Reluctant Debutante, The (1958)\n", + "Cosine similarity: 0.176\n", + "Return of the Jedi (1983)\n", + "Warriors of Virtue (1997)\n", + "Cosine similarity: 0.745\n", + "Return of the Jedi (1983)\n", + "Desert Winds (1995)\n", + "Cosine similarity: 0.00677\n", + "Return of the Jedi (1983)\n", + "Hugo Pool (1997)\n", + "Cosine similarity: 0.679\n", + "Return of the Jedi (1983)\n", + "King of New York (1990)\n", + "Cosine similarity: -0.124\n", + "Return of the Jedi (1983)\n", + "All Things Fair (1996)\n", + "Cosine similarity: 0.693\n", + "Return of the Jedi (1983)\n", + "Sixth Man, The (1997)\n", + "Cosine similarity: 0.585\n", + "Return of the Jedi (1983)\n", + "Butterfly Kiss (1995)\n", + "Cosine similarity: 0.497\n", + "Return of the Jedi (1983)\n", + "Paris, France (1993)\n", + "Cosine similarity: 0.652\n", + "Return of the Jedi (1983)\n", + "Cérémonie, La (1995)\n", + "Cosine similarity: 0.642\n", + "Return of the Jedi (1983)\n", + "Hush (1998)\n", + "Cosine similarity: 0.547\n", + "Return of the Jedi (1983)\n", + "Nightwatch (1997)\n", + "Cosine similarity: 0.642\n", + "Return of the Jedi (1983)\n", + "Nobody Loves Me (Keiner liebt mich) (1994)\n", + "Cosine similarity: 0.505\n", + "Return of the Jedi (1983)\n", + "Wife, The (1995)\n", + "Cosine similarity: 0.733\n", + "Return of the Jedi (1983)\n", + "Lamerica (1994)\n", + "Cosine similarity: 0.605\n", + "Return of the Jedi (1983)\n", + "Nico Icon (1995)\n", + "Cosine similarity: 0.592\n", + "Return of the Jedi (1983)\n", + "Silence of the Palace, The (Saimt el Qusur) (1994)\n", + "Cosine similarity: 0.618\n", + "Return of the Jedi (1983)\n", + "Slingshot, The (1993)\n", + "Cosine similarity: 0.684\n", + "Return of the Jedi (1983)\n", + "Land and Freedom (Tierra y libertad) (1995)\n", + "Cosine similarity: 0.609\n", + "Return of the Jedi (1983)\n", + "Á köldum klaka (Cold Fever) (1994)\n", + "Cosine similarity: 0.623\n", + "Return of the Jedi (1983)\n", + "Etz Hadomim Tafus (Under the Domin Tree) (1994)\n", + "Cosine similarity: 0.613\n", + "Return of the Jedi (1983)\n", + "Two Friends (1986) \n", + "Cosine similarity: 0.605\n", + "Return of the Jedi (1983)\n", + "Brothers in Trouble (1995)\n", + "Cosine similarity: 0.041\n", + "Return of the Jedi (1983)\n", + "Girls Town (1996)\n", + "Cosine similarity: 0.618\n", + "Return of the Jedi (1983)\n", + "Normal Life (1996)\n", + "Cosine similarity: 0.0572\n", + "Return of the Jedi (1983)\n", + "Bitter Sugar (Azucar Amargo) (1996)\n", + "Cosine similarity: 0.628\n", + "Return of the Jedi (1983)\n", + "Eighth Day, The (1996)\n", + "Cosine similarity: 0.6\n", + "Return of the Jedi (1983)\n", + "Dadetown (1995)\n", + "Cosine similarity: 0.04\n", + "Return of the Jedi (1983)\n", + "Some Mother's Son (1996)\n", + "Cosine similarity: 0.653\n", + "Return of the Jedi (1983)\n", + "Angel Baby (1995)\n", + "Cosine similarity: 0.647\n", + "Return of the Jedi (1983)\n", + "Sudden Manhattan (1996)\n", + "Cosine similarity: 0.0528\n", + "Return of the Jedi (1983)\n", + "Butcher Boy, The (1998)\n", + "Cosine similarity: 0.00363\n", + "Return of the Jedi (1983)\n", + "Men With Guns (1997)\n", + "Cosine similarity: 0.637\n", + "Return of the Jedi (1983)\n", + "Hana-bi (1997)\n", + "Cosine similarity: 0.596\n", + "Return of the Jedi (1983)\n", + "Niagara, Niagara (1997)\n", + "Cosine similarity: -0.0429\n", + "Return of the Jedi (1983)\n", + "Big One, The (1997)\n", + "Cosine similarity: 0.632\n", + "Return of the Jedi (1983)\n", + "Butcher Boy, The (1998)\n", + "Cosine similarity: -0.0528\n", + "Return of the Jedi (1983)\n", + "Spanish Prisoner, The (1997)\n", + "Cosine similarity: -0.132\n", + "Return of the Jedi (1983)\n", + "Temptress Moon (Feng Yue) (1996)\n", + "Cosine similarity: 0.548\n", + "Return of the Jedi (1983)\n", + "Entertaining Angels: The Dorothy Day Story (1996)\n", + "Cosine similarity: -0.153\n", + "Return of the Jedi (1983)\n", + "Chairman of the Board (1998)\n", + "Cosine similarity: 0.185\n", + "Return of the Jedi (1983)\n", + "Favor, The (1994)\n", + "Cosine similarity: 0.155\n", + "Return of the Jedi (1983)\n", + "Little City (1998)\n", + "Cosine similarity: 0.641\n", + "Return of the Jedi (1983)\n", + "Target (1995)\n", + "Cosine similarity: 0.796\n", + "Return of the Jedi (1983)\n", + "Substance of Fire, The (1996)\n", + "Cosine similarity: 0.623\n", + "Return of the Jedi (1983)\n", + "Getting Away With Murder (1996)\n", + "Cosine similarity: 0.561\n", + "Return of the Jedi (1983)\n", + "Small Faces (1995)\n", + "Cosine similarity: 0.684\n", + "Return of the Jedi (1983)\n", + "New Age, The (1994)\n", + "Cosine similarity: 0.708\n", + "Return of the Jedi (1983)\n", + "Rough Magic (1995)\n", + "Cosine similarity: 0.432\n", + "Return of the Jedi (1983)\n", + "Nothing Personal (1995)\n", + "Cosine similarity: 0.621\n", + "Return of the Jedi (1983)\n", + "8 Heads in a Duffel Bag (1997)\n", + "Cosine similarity: 0.574\n", + "Return of the Jedi (1983)\n", + "Brother's Kiss, A (1997)\n", + "Cosine similarity: -0.226\n", + "Return of the Jedi (1983)\n", + "Ripe (1996)\n", + "Cosine similarity: 0.592\n", + "Return of the Jedi (1983)\n", + "Next Step, The (1995)\n", + "Cosine similarity: 0.593\n", + "Return of the Jedi (1983)\n", + "Wedding Bell Blues (1996)\n", + "Cosine similarity: 0.623\n", + "Return of the Jedi (1983)\n", + "MURDER and murder (1996)\n", + "Cosine similarity: 0.599\n", + "Return of the Jedi (1983)\n", + "Tainted (1998)\n", + "Cosine similarity: 0.622\n", + "Return of the Jedi (1983)\n", + "Further Gesture, A (1996)\n", + "Cosine similarity: 0.55\n", + "Return of the Jedi (1983)\n", + "Kika (1993)\n", + "Cosine similarity: 0.728\n", + "Return of the Jedi (1983)\n", + "Mirage (1995)\n", + "Cosine similarity: 0.619\n", + "Return of the Jedi (1983)\n", + "Mamma Roma (1962)\n", + "Cosine similarity: 0.675\n", + "Return of the Jedi (1983)\n", + "Sunchaser, The (1996)\n", + "Cosine similarity: -0.199\n", + "Return of the Jedi (1983)\n", + "War at Home, The (1996)\n", + "Cosine similarity: 0.66\n", + "Return of the Jedi (1983)\n", + "Sweet Nothing (1995)\n", + "Cosine similarity: 0.608\n", + "Return of the Jedi (1983)\n", + "Mat' i syn (1997)\n", + "Cosine similarity: 0.686\n", + "Return of the Jedi (1983)\n", + "B. Monkey (1998)\n", + "Cosine similarity: 0.688\n", + "Return of the Jedi (1983)\n", + "Sliding Doors (1998)\n", + "Cosine similarity: 0.709\n", + "Return of the Jedi (1983)\n", + "You So Crazy (1994)\n", + "Cosine similarity: 0.761\n", + "Return of the Jedi (1983)\n", + "Scream of Stone (Schrei aus Stein) (1991)\n", + "Cosine similarity: 0.651\n", + "\n", + "Movie most dissimilar to Jedi: Very Natural Thing, A (1974) → Cosine similarity: -0.302\n" + ] + } + ], + "source": [ + "# Code to help you search for a movie title\n", + "similarities = []\n", + "\n", + "# Find Jedi movie\n", + "partial_title = \"Jedi\"\n", + "subset = indexed_items[indexed_items['title'].str.contains(partial_title, case=False, na=False)]\n", + "item_Jedi = subset.index.tolist()\n", + "print(\"Jedi movie IDs:\", item_Jedi)\n", + "\n", + "# Use all movie indices except Jedi\n", + "item_ids = [i for i in indexed_items.index if i != 181]\n", + "\n", + "# Check we have movies to compare\n", + "if len(item_ids) < 1:\n", + " print(\"Not enough movies found to compare.\")\n", + "else:\n", + " for i in item_ids:\n", + " sim = cosine(item_embeddings[181], item_embeddings[i])\n", + " similarities.append((i, sim))\n", + " print_similarity(181, i, item_embeddings, indexed_items[\"title\"])\n", + "\n", + "# Find the most dissimilar movie\n", + "min_movie_id, min_sim = min(similarities, key=lambda x: x[1])\n", + "print(f\"\\nMovie most dissimilar to Jedi: {indexed_items['title'][min_movie_id]} → Cosine similarity: {min_sim:.3f}\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false + }, + "source": [ + "Sometimes, even without knowing anything about a user, we can recommend films by asking them about a film that they do like. The code below compares the similarity of a given film to all others, and returns the most similar films." + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[(50, 'Star Wars (1977)', 1.0),\n", + " (172, 'Empire Strikes Back, The (1980)', 0.94112813),\n", + " (181, 'Return of the Jedi (1983)', 0.9215989),\n", + " (174, 'Raiders of the Lost Ark (1981)', 0.87037265),\n", + " (210, 'Indiana Jones and the Last Crusade (1989)', 0.85296273),\n", + " (176, 'Aliens (1986)', 0.84478575),\n", + " (1, 'Toy Story (1995)', 0.84354055),\n", + " (942, \"What's Love Got to Do with It (1993)\", 0.8428294),\n", + " (211, 'M*A*S*H (1970)', 0.8302945),\n", + " (205, 'Patton (1970)', 0.82877964)]" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def most_similar(item_id, item_embeddings, titles,\n", + " top_n=30):\n", + " # Compute the cosine similarity between the item and all other items\n", + " sims = cosine_similarity(item_embeddings[item_id].reshape(1, -1),\n", + " item_embeddings).ravel()\n", + " \n", + " # [::-1] makes it possible to reverse the order of a numpy\n", + " # array, this is required because most similar items have\n", + " # a larger cosine similarity value\n", + " sorted_indexes = np.argsort(sims)[::-1]\n", + " idxs = sorted_indexes[0:top_n]\n", + " return list(zip(idxs, titles[idxs], sims[idxs]))\n", + "\n", + "# Find the most similar films to \"Star Wars\"\n", + "most_similar(50, item_embeddings, indexed_items[\"title\"], top_n=10)" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[(227, 'Star Trek VI: The Undiscovered Country (1991)', 1.0000001),\n", + " (230, 'Star Trek IV: The Voyage Home (1986)', 0.8853007),\n", + " (778, 'Don Juan DeMarco (1995)', 0.8847591),\n", + " (1594, 'Everest (1998)', 0.87397236),\n", + " (228, 'Star Trek: The Wrath of Khan (1982)', 0.8726455),\n", + " (634, \"Microcosmos: Le peuple de l'herbe (1996)\", 0.8719895),\n", + " (746, 'Real Genius (1985)', 0.8703337),\n", + " (973, 'Grateful Dead (1995)', 0.86922544),\n", + " (115, 'Haunted World of Edward D. Wood Jr., The (1995)', 0.86700207),\n", + " (219, 'Nightmare on Elm Street, A (1984)', 0.86616725)]" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Find the most similar films to \"Star Trek VI: The Undiscovered Country\"\n", + "most_similar(227, item_embeddings, indexed_items[\"title\"], top_n=10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The similarities do not always make sense: the number of ratings is low and the embedding does not automatically capture semantic relationships in that context. Better representations arise with higher number of ratings, and less overfitting in models or maybe better loss function, such as those based on implicit feedback." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Visualizing embeddings using TSNE\n", + "\n", + "The [t-SNE](https://en.wikipedia.org/wiki/T-distributed_stochastic_neighbor_embedding) algorithm enables us to visualize high dimensional vectors in a 2D space by preserving local neighborhoods. We can use it to get a 2D visualization of the item embeddings and see if similar items are close in the embedding space." + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.manifold import TSNE\n", + "\n", + "item_tsne = TSNE(learning_rate=\"auto\", init=\"pca\", perplexity=30).fit_transform(item_embeddings)" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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kBTfa5z04lZl1w/SyAgBySphVwnhx/miY0tnB4bG/BV2t6Y5pJYhGnEOikolEALvuSn6uDYZhGCb9YUWDYVyiWg5YtU+B29CnsHSBr2vuwJOvH0sRyrceOIXi/Cw8e9+ttoK54UkyC/kywrQZQ8GgVG8qL8pF78BFPLp9fNWu5HsxCLpa05HTvVLz4qRkAHrXBvfqYBiGyWxY0WCYAHDTp0BH6JOdsO5XT4i65g48/NpRy387OziMh187ihcdBHMrT5Ih/AN0T4fZQp+shNnxxTnl2LjHOmHcCt0NHJ2Ec6t/05VPoXttcK8OhmGYzCeSSNjZr65AbTPOMIwYux4WhvgpsnyPxBNYtHm/MPTp4LoaoVAbhEV5JJ5A9bO/Rmf/kONx5cR7SMYunOns4LCt98ZqvjftbcGP3mojX5fKjtULXDVwdBLOAVj+28o7r8eW+pNK1ystyML6L96CWKHeteH2HWAYhmGChaobsEeDYXxE1MOCYvnWGfoURBf4w209QiUDUOusbpczs6+lk+y9GYknsPs95xLBqrjxLjg1WbTzDnX2XcCW+pNjypYsPQPD6Pl0CMtvu9ZyPakoqjreAYZhGCY9YEWDYRzQbfF3m8htEHTokxtkhG0VwdxKeZJJ2qc0BVRFtVoTpcmiFca/DY/Ela4LABv3HMdLB9vGrSvV0Cdd7wDDMAwTfljRYBgbrASp0oIs3Dv3OtRWxpSUDp09LIKoeKUDGWFbZxlVqvfmXz/Q781wW63JrfIzMDSC3KwoLgyrKRzmhHYn74oo8T2d+7gwDMMwcrCiwTAW2AlSPQPDeLmhHS83tCslrlIF5/auQdJxQYQ+uWV+RSlihTmkHA2dZVQp3qmReAL/dPTfyeeklNjVUa1Jh9CtqmQAqSFNNTdf4yr0KZ37uDAMwzBycMM+hjHhFKaSDKULtxlqw7nn6k9InTedmBSNYMNycbdqnWVU65o7sGjzfqzadgiP7WzCqm2HsGjz/nFzfLitB+cujJDO+dCfTBvXrLEkP2usl4aBjgaOYRC6jZCmnzS2u+qeLnoHItCvZDIMwzDBwB4NhjFBDVNRSVw1ErntkneTyeSE2CVV5XjxwXnj+mgAo8L6Joc+GrLIhPnIeA7+7JZyfOeLt4zzkgDQHs5G6e/hF6d7aN62sPdxYRiGYbyHFQ3GNboTpoNu4iUjbKokri6pKsfa2pmOJUe9TIgNen4NjBwTmc7gsshWOKJ6DqYWZI/Nm9Xz0f3MvOqIrsK00nzScWHu48IwDMP4AysaaUZYhEQD3U23wtDESyVMRTaGfnpZgSfnFRGG+U1mUjSC6pllqJ5ZRjpedv3LVjgyPAcij9bGFVW+v3dedESXwUho//LC6XjpYJuwj4so9CldixkwDMMwdFjRSCPCJiS6qTzj9fncKGQqYSqyykkQCbG6n5ffqKx/2QpHyZ4Du2f/zbsrsHROMPOULJzva+nEKw3tvikZwGhIU/bkaFr3cWEYhmH8g5PB0wRDSDRbWlUSknVAqev/3TdbMEKUgnSej5r4a4chbFJQTVz1OyFW9/PyG9X1r6LQGZ6DclOid2lBFn5w/+14ailtbXjFpGgE8ytK8avmTt+uaU5oN+bInAyvI/GdYRiGyRzYo5EGhLGTru6mW7rOp8tqbxemkoybxFW/E2LTuUmam/Uv8k7Zhfkkew46+y+g59MhlBZko6QgByPxRODhPV42FUxmzT03oXrGH1l6BDn0iWEYhhHBikYaEEYhUXfTLepx9S2dtveoWyEzh6n8c9PH6Bm4OPbvbhNX/UyITecmaW7WvxuFblI0gr7zF/G3df8WmnBFA6+fk6GArV38Gcd3hUOfGIZhGCdY0QgxRp7Br4ghP34KibpzDKjHvf7uGdTMvgZdnw6Ns6B6oZAZgtTCm6bir5ZVarfe+mUVLrsqh3RcGPo1mHGrJKkqdGHOafHyOXGJWYZhGEYXrGiEFKvEVxF+ComqISlO5ystyE7xGFjROziMB156Z+y/k63LXlvtvbLeem0VrmvuwIbdHzgeI/u8/ITaJV1UTlVGoaN6x2puvgZHTvf6HjpEef/ysidh8CKt+WAyXGKWYRiG0QUrGiFjJJ7A1v2nsKX+BPk3QQiJunMMJkUj+E9zr8UrDe1S40i2LgdRySns2Fnlk3F6XkGXUx6JJ7Dj8EfC42KFOcL1L6PQUb1jCzb9OkU59iusivL+ffPuGx17tdjxd//5NnK5YYZhGIZxgqtOhYi65g5UP/traSUDSBUSR+IJNLZ2Y1fTGTS2dntWSUh35ZnFlTHpMRh3tmH3B7g0EkdxXpbtsborOQWN6Dk7WeWTsXtebqt36cBIxhaxav4NWhUgqtfL7IHzswqc6P1bUzNzXOUsCl0DQ7qGyDAMw0xw2KMREiiWZyvMYQ5+99rQmWNAbZZmJgGgs38IX37lsO0xmRZ3TnnO1MpEVhbssOQnUAV+agNEKqpeL7+rwIneP1FPECsmksePYRiG8Rb2aIQAquU5mf+2cBp2rF6Ag+tqUpSMIHptGCEpK+Zeh4U3TVUWroxwkAhg219ClUyq7099zlQh3WzBDlPPjaDC4UR9TpxILjrgB07v35jXo5BWDMAIQfPLK8owDMNkNqxohACVmvhfqCpPESrCJBy6wS4cRJXi/Cz89GufTVHI0hmZ56wqpMtU7/IavxsbGiQ3bVRVesNSKnhJVTkanvxTrK2dJTx2w/JbsK+lM/CQOYZhGCYzYEUjBMgIJHaCVZiEQ7csqSrHwXU12LF6AZ5fORc//dpnEStUsy6fHRxGNBoJdbiUjPVY5jlTrPLFeVmIJxIp1wxTzw0ngd/rcDg7pbe0wD4PKJkwhSBNikbwWO1MvPjgPBTnjx9/cX4WXnxwHgAE4hVlGIZhMhPO0QgBsgKJlWAVJuFQB+YKQRuWW1fYoZB8z0FXUTIjm1Mj85ydKhMZnD0/Wi44+Zphq97lZ2NDq2ubcyDumFaCz33/gLbSzn5i3M+hD7vR2NoNIIGFN5ZhweV3bdHm/doaXjIMwzAMKxohQFQT38BJAA2bcKgbO2GTgnHPfifKOzFaxvikZflRp4Rr2edMnbfkay6ujGntkaIDvxobWmFVFldnaWcnvFCMJ0UjqJ5RhuoZqQUAGlu7tTe8ZBiGYSY2rGiEAMPy/PBrRx2PW79stq1ArLuBXhgxC5tlBTl44ufv4ZN+8T17VUVJRRAcbaDXYlu21cl6rPKcx6zYrd14dPtRnD0/LLymkyCdALDyzuvxy/c/DlzgDwo/vCx+K8aZ5hVlGIZhgocVjZCwuDKG4vwsnB0cLwQCowLexj3H8fmqckuhTncDvbBCDalKvmcApC7PsiEhKoIgtYyxnfVY9TlPikYQjUYslQyra9oJ0kWX4/uTPTFBeYWCxksvSxDlhTPdK8owDMP4DyeDh4TDbT22SgZAS+bW3UAvHaDcsxeJ8iqlhFXKGFtZj5dUleOF+29HiSkpWfScZS3W5qT8tbWz0Dc4PG6dyiYKB1E61atr6irtnExQFeSCqvDFMAzDZC7s0QgJusIWgoxlDwrRPesOCREJgnYeEpUyxlbW47rmDmzccxw9A1cE/tKCbKxf5uxVULFYG4L0SDyhJVE4iDyZMOXmUJBRjHWGkunyioat4ALDMAwTHKxohASdYQthimX3C6d71h0SoioIypYxtsqpsQup6R24iEe3H8UPo9YejZF4AvFEAsV5WbbhU055PDqE3yDCgcLS4VyGIHMl3OaepJtSxzAMw3gLKxohId2SudPJaql7blUFQbdljFU9KVbCnxmRxdqt8Ks6djcEcU0dBJ0roeoVTUeljmEYhvEWztEICUE2JpOlrrkjrToH655bVUGQ0kAPAGKFOZZCmUquiV0uybhrCvI73Aq/QTSUTNcmlmHIlZDNPQkqr4RhGIYJN6xohAhKYnMQibTJqCRBhwGdifKqgqCTwmOwtnYWGp78U9fN+gBa8nlxfhZ++rXP4uC6Gsc5cCv8BhEOlK7lWtPJ6GCQrkodwzAM4y0cOhUynMIWdMQ/uwl5StdQFANdifJukmbtYuCtnqP5WZUV5JDGZ3gVKMnnZweHEY1GhHPgNlE4iHCgoEOQ3BBkN3QV0lWpYxiGYbyFFY0QYpXYrCP+2a2iElQ1HJ3oSpR3IwhSFB6rZxUrzEVxfhb6Bodtc02uKcxBPJHArqYzOPnJOdK9UIU/N/ccRA5SuuU9mUmnCnLprNQxDMMw3sGKRhqgw5OgQ1Fhq2UqbgRBJ4XH7lkld0C369h94VIcD7z0jtR9dJ0bwq6mM6Txq95zEA0lM6GJZbpUkEt3pY5hGIbxBs7RSAPcxj/rStRkq+V4dDdsoyiVJflZuKYwNYzK6Njt1PTRimhktOO8TFK/6j0H0VByIjaxDIJ0zCthGIZhvIc9GmmAW0+CrpAntlp6D+VZ9Q4O46df/yyikQj+cO4Cygpy8MTP3wMgp2QAgFm39LoUqeEROfRhNxpbuwEksPDGMizw0GqfTiFI6Uy65ZUwDMMw3sOKRhrg1pOgK+QpE0JRwg71WXV9OoQVc68DADS2dqOzXy5cLRoZr2QAakn9sgUG9rV0pgijWw+0et7ULV1CkNIdVuoYhmGYZFjRSAPcehJ0hjzZWS1LCrJw79zrUJSXjZF4ggULRVSeFVU5WXPPDMy85ip0nRvCxj3HbY+TSeqXLTDATd0yH1bqGIZhGAPO0ZAgqB4WhifB7moJOHsSdDcAW1JVjoPrarBj9QJ8tXo6Sguy0TMwjJcb2pWb9wXdH4SKyjhlfqPyrKjKSfWMMqyYex3KptDK5IoUGNmeKtzUjWEYhmEmFuzRIBJ0Dws3eBHyNCkaQd/5i3i1od21dVrH3PqByjhlf6PyrAzlxCm3I1k50eHhUqmElgnlkRmGYRiGocMeDQI6umHXNXdg0eb9WLXtkFSFH+CKUGeHIdQ5WYJ1V9/RZZ22m9uOvgt4+LWj2Pv+x1LjSh6fTg+JyhpQXTcqz+qOaSWO419+W/mYwK/Dw6VSCc2v8shh845dvBTHy29/iP++qxkvv/0hLl6KBzoehmEYhvEL9mgICEMPC12WYJ2JmjrG5DS3Bmt2vIutiGDpHLoipNtDorIG3K4b6rOqa+7Aht0twmTw3e914NtLZmPS5S7gbj1cKkqDH+WRdT57HR7ITXtbsO3ttpTE++/tPY7Vd1XgqaWVUudiGIZhmHSDPRoCwtDDQqclWFffBx1jEs0tMFoZ6ZHtNK8RoMf7JDtOqzXgdt0A4mdl3Cul4pT5Wm49XCpKwx3TSiBabtGI2Dtjh8g7JvPs3XggDTbtbcGP3mobV90rngB+9FYbNu2191IyDMMwTCbAioYAP3tY2BHGRnk6xiQTIkMJw/Iq2VhlDaj8Ribkh+INcroWkJrU//zKudixegEOrqshWf5F4VcAUFqQhc7+C2P3cuR0r2VJ3WTiCeDI6V7C3aRCmY8nXz9GevZOCgtVWb14KY5tb7c5HrPt7TYOowopYQu/YxiGSVc4dEpAGHpYhLFRno4xyShGdmFYyeEtXeeGPEk2VlkDsr+RDfmheIPMnPzkHBpbu1NCgFRLkTqFXxn0DAxj7T82ARi9ly9UxUjnVsnRoMzH2cFhbN1/Co/VzrQ9RqSwJAA89foxYY+RnzS2k5SqnzS242t33eh8IOMr6VKcgmEYJh1gj4YAt4mzOiz/hlBnXM98fcD/RnlOYwJGBbL1y5zHZMwtFbMAag5vceoN4XQeEb0DQ44hP1ZrQGbdqIR7qQjjWw+0KpcftsIu/MqKzr4LeKWhnXTe5HeBalmmzserv21ztE5TFJbewWFs3X/S8ZjTPYOk8VCPY/zBi9BLhmGYiQwrGgLcCvm6eljorhqlA5GguXFPi+PGnDy3FJIF0L3vf4yHLQQC2fOIqGvuwKPb3xVap81rgLpuACiFe7kJk9MpNCWHX235L7ehtCDb8jgjAV5GYZPJk6DOx9nBYccwRbLC0tDuqLBMK80nnYd6HOM93OeFYRhGP6xoEHAj5Ltttmceh2pMvVcsqSrH+mWzLf+NItAuqSrHD+6/XUoA3ft+B9bseFd6rLKNCUfiCWzY/YFj3H80Arxwv/UaoKwb1RyeO6aVoLQgi3QfVucE9AlNRvhVrCgPPQMXHa9rXM5K+UoAWHnn9fjl+x/j+foTloqk3ZqaX1GK4jzafDgpE2SF5byzwvLlhdNJie9fXjiddD3Ge3Tk0zEMwzCpcI4GEZ2lYd2gGlPvFSPxhG3IErX879I512IrInhk+9Fx/2b2GtU1d1geJ0IlxGzr/lPo7B9yPCaeAEpsrPiAeN2o5PAYMeQ9A8Ok31rhRXM8SvUrAPha9XTsbe5MEeqK8keVhC31ziFJdmtqUjSCh6qnC38PAGVX5aCxtdvyeRgKy9nz4rl1enbZk6P409lXY1/LH2yP+dPZVyN7Mtt6woJffV4YhmEmEqxoSKAi5FOb7YmSS53OL6v86OxQrqvHx9I55XgxOm9cEmYsKQlTNJdOxCSTOeuaO7Cl/gTpWJHg4bRuVJLGrXqyJGMkXlNyInQJTXXNHdj4yw9Ix9ZWxvD0ssqxNdjeNYjn6k+QK2jZrak1NTPx6m/bcXbQWkmIACjOz8ITP2tKUSCTE31HFZYK0rMXdU5vPtPv+PvmM/0YiSd8N1Yw1oSxuh/DMEy6w4qGx+gSxK1QqY6iu6KKTiugyPovW2lp/bLZKJuSI61MySo0bgQPmepdlBKuUwuy8Ztv3YMjp3tJioYOoYmi/ACp92IoXyPxBBZt3i9VptfAvKYmRSN49r5bLcdihGb1Wigh5saZa2pm4NXfttkqLMDoPDv1+6CsVd0eJcYdYazuxzAMk+6w395jvHLHq1RH8aKiim4roFOTOpk5Ki/KxVeqK5QaE8ooNHY5H9RqSTLFBijj6h64iCOne7UVIRBB7edhF7qmUqbXwGpNGXkx5mpmsaJcFOdb53CYc1YMhcWJ7oGL+Nz3D9i+M2ENw+H+EPaEsbofwzBMusMeDY+hCtjtXQPkc4qqo1iFY6n8hoKfVkCZOXIjEMgIf1bXkfUaGcKxU9iYzLj+cO6CY58LJ6FJNqyOqiiUFmTje/dWjbt/VUG7tCDLdk1Zecbi8QQeePkd2/OZPYtLqsrxosUzScbsCUkmjGE43B9CDPVdZBiGYWiwouEx8ytKESvMFSbK7jj8EdbUzCQJxyrhWF6FcKkKtLKMxBPYcfgj4XHRCLB11e2uBAKq8Le2dua469iFETkJpYB92BiAscTlrnPOienm8csKTSqCKFVR+M6y2ZbnUBW07517neOaMufF7Go6Qzpv8v0sqSpHzc3XYMGmesvEeycFPWxhOKrrciISlsIfDMMwmQArGh4zKRrBqvk3CJNLO/uHyEI+VbhrOPUfYxukl6EcflgBD7f1CCtAAcD/UTMTS+dc6+paIiERGBXA19Skdph26zUyC8dWgn80AseeHtHIaINBA6rQpCqIUhWFWFGe5d8pc21FbSWty7iBqofhyOlex+pedgq6Xwq4FWav1B3TSjzxZmYyYavuxzAMk66wouED08toTbmoQj5VaNp6oBW/OHoGf/2lSs9DOby2AlLnpuKPClxfS1VI1Ok1shP8RSH18QTw6PZ38cNoZEwxEAlNbhQkt5Z7p7m2wu58opAv1XG6UdCDCMOxUk5LC7KF/U04MZ1hGIbxAlY0fEC3kC9jBTYs0i/cP5og6yQIu00O9tIK6HfMu4qQqMtrRE2wdkLGQu1GQdJhubebazN256OEfE2KRrD8tnL86K022/NbjdPtuvMzDMdOOXVSMpLh/hAMwzCMbljR8AG3Vl8ray3VCmxYpDfuacEX55Rj29v2gtby28pDGzrhVcy7kyVcVkjUpQy5qcQEyFuoVRUkY+6GLsXxeO1M7Dj8UUp4m4zl3jzX7V0DpPNRQ77qmjvwYwcl4xt3V1iOU8e68yMMR4dyyv0hGIZhGN2wouEDbqy+TtZaihUYuCJ4/uKoc0Ls7vc68O0ls0OpbHgR8061hFOFRF3KkC7Lsu5QvOTjrOYuVpiLtbWzML0sX8lyb57rNTUzHZU8ashXzc3XCIVwu7UfZK6FDG6UU+4PwTAMw3gF99HwCSM8JGZR398u0VbU9wIADq6rwZp7ZpDGIAqhMKzgFIKox68yh3Z40VNEVx1+XZblk5+cIz0b2Z4bdnP3Sf8FPFd/AjmTo9K9S6xw6qkC0EO+ftLYTm6eZ4XOdecVqsppmJQlhmEYJvNgj4aPyITiyCToVs8ow9YDp7SMkSKwBFmPX0fMu1c9RYzxuU0ApuTgRCNAIuEcNrf1QCu2HmgVPhsZq72XcycLVbg+3TPo+nxhL3lKVU5LC7JSqmhxfwiGYRjGS1jR8BlqKI5Mgi4lZKfEJGDYIRJYwlCP323Mu1c9RQyoQqldfghF8F99VwV+/FYbqVIT5dlQFSSv504GqnA9rZRW9U10vjCWPDXWUGffeZQWZKN34KJj2N5vvnUPjpzuDaWyxDAMw2QerGiEFNleGSLB9JkVVdi457ir/AGvrNmy3ajd4mVPEQORUCryClEE/9tvKCHn6FCeDUVB8mPuqFBzYr68cDpeOtgWmuZ5urBaQ1Yke6WyL4e1MQzDMIwfsKIRUlR6ZYgE02g04iqp1QtrdhBhWH6XyjVD9QqJBP/kf2841eUYPkd9NiIFKei5S4Ya8pU9OZoWCd0y2K0hKzg8imEYhgkKVjSI+G11V+mV8cMH5+HguhpHwdRN/oBua3ZQYVhelcqlIOsVEgn+xr/75WmQmTuv35mReAJFedl4qHo6/rnp45RiB+Y1HUTzPK+glLItzJ2MDV+6BeXFeRwexTAMwwQGKxoEgrC6y3RMNguoToKpm6RWWWu2k6AZZFJxkCVLvcpxoD6bsoIcNLZ2Kwv/1Lnb19Lp6Ttj3QE7C/fOvQ61lTHL+wp7QrcI431qOPUfwnCp/guX8MzeFvzNvbemzf0xDMMwmQcrGgLsrO4dPiQ/UzsmA3ICqmpSa+/AkPAYowyqSDnzI6lY1IxPt4WbYsH3yvNA8TQU5WfhiZ+/h85+d8K/aO4AeOqpsnsneweG8UpDO+50UB7CmNBNgZqPkUzPwLBvBRoYhmEYxgpWNBwQhSgkADz1+jFPS3kaVtgt+06QSth6lYQ7Ek9g457jwuPWLxu1ZosEzaFLcdJ1Ve+H4oXSaeGmer28ynEQeRoSAM4ODgNIrTymKvzbzR0ALNq83zNPlcgTBgBPv3EM54fjiBWml8fCDpl8DCv8KjfMMAzDMGa4YZ8DlG67vYPD2Lr/pKfjmBSNoHpGGelYr5JwqZ2Hi/KyhILgd99sQdlVOaTrqtyPTDM+UVM43deTbY4ng11juWsKc1Ccn2X5m+RnItt00WruZDxVKlDWYc/AMNb+YxNWbTuERZv3KzVfDAuUfAwn3M53OhBE81CGYRiGBns0HKBa019taMeampm+JIeLhKz9//aJJ6Eh1Llo/LCLJGgiAWGye2lBFu6YViI1Tt25H6JwKJXkbi/zQ6w8DfFEAg+89I7tb3T2vvA6KV32d372d/ECqoIvor6lMy1DxgDndzDI5qEMwzCMGPZoOEC1pp89P+y5xXBSNIL1yyqFx217uw1739dvwaV7FmgCctfA0Fg8v90vegaG8bnvH5CySOu0qNc1d2DR5v1Yte0QHttpbSFXuZ6d5yFWlCstEFtZc82ehq5Pxbk1gJ6wO6/L38r+zo3HJgzoCoV8uaE9LT07Tu+gjCeRYRiGCQb2aDgwv6IUxXlZOHte3FHbjwZlJQXZpOPW72rG56v0xmRTy5ouvGkqKZfk6imjx4qS3Tv6LuDh145ibe0srKmZIbwnXRZ1auld1evpyA8JOi/ECq9LB8uUfTaw8tj4Xa5aFZ2hkOmWq+H0Dj782lEU52cFUrWOYRiGocMeDQcmRSN4qLqCdKwfDco6+86TjuseuKjdw2KE/ADjPRDJIT8LbpwqlYOwpKocv/nWPSgVKFFb6k+g+tlfC62U7V0DzjdymbIC+xwRSsKxYSF3I8S7yQ/xMy9kJJ5Aw6ku/N2//C/83b/8GxpOdtl6B6jrxI3wt/LO65VyFgxlb+/7H+PO7+1z9FSFBcqzs8u/MZNOuRqUd3C0uIE1EyE3hWEYJh1gRUPAmpoZjhu5m+RdGeqaO0hVnwy88LBQQn5UBM0jp3tTmq3Z0dk/5BgSMRJPYMfhj0j38sTP37M9j0w4lBfJ3aLkVooQtmH3B2g41YVdTWdwuK0H65fNHhuPeXyAvfBf19yBO57ZhwdeegdbD5zC1gOteODld3DHM/ts509naJh5LIs278eWerXiC1dPycWmvS14ZPu76BlIFVI7QhJuY372AITv07P33YqvVk8nnd8Pz6sOdOWmpMv9MgzDZCocOiVgUjSCZ++7FQ+/dnTcv3nd3M1ApbwlxdKuEj5CCfmR7VEhKwzYhUQcbutBZz8tH+GTfvskYZlwKN3J3ZRwKIoi1Nk/lJIAXl6Ui2/cXYHd73WQ+4bUNXdYrntg1Jr88GtH8aKN4qC7OZ7oHbgqZzIGhi45hmt1fTqEH73VZnuNBIINt3F69qL3qSgvG680tAuv4YfnVQe6FIR0uV+GYZhMhRUNAkuqyvGipuZussK9SnlLigXdTbUWStMzGUFTRhhwqpAkI5w4xXHLhkNRFSvRs9edF2I+x4/fasML989DSUG28JmMxBPYsLtFeN4Nuz+wFcx1NccTvQMRAFmTImPP1ErZW79sNr6z6wPhtVSrb7nN+aA8+4Pramyv4XVujN+4VRDS7X4ZhmEyFVY0iHiVvFtakIVnVlRh6ZxrLX8jG0IQgdiCThVo3UIVNFUSfK2EbZWKRFaCpYrQJlofIsVOpkyuihBmnGPjnhYcXFcjXLej3iHxuuvsH9JSFlc0FpEHp3dwGGtrZ2Ln735vqewV5WWTwvMAeWu62xKrMs/ebp69LpvsN9Ry3oC9cplO98swDJOpcI6GBF4k7/YMDOOR7e9i015r67GM0FNOiIGXSXT2i+S8DipWwrYoX8IO8xyrJjTbrQ9K4rbOvBDKOQDnXBCZded1HDz1/NPLCnBwXQ12rF6A51fOxY7VC3BwXY20F0hGkdNRYlVXSWavcmOCYLSc92zhcSX5WbimMLWwQzreL8MwTKbCHg0foIQ//eitNtz2xyVYOid1c6QKPeuXzcZXqivGCb/mkI54PEEWavxs8GUISRt2tzha0p1CIpysuk5YzbFsnokdVGv1t5fcTDpfw6ku/OHcBay88wY8V39C6j4N/nDugtAKLyNs64iDdwo9kglls/OiUc9RWpBFDrfR1RxSZ5ND3bkxQVLiUBnOoHdwGD/9+mcRjUTS/n4ZhmEyEVY0NCCKz6aGP1n1v6CG8VgpGVbCZHEerRRmENVaDCFp6/6TlpWFKCERdgqCFaI4bh1CG9Va3UNsqpfco8SohuZU5tOK9q4BPFd/0jF0bnFlDLHCXGH4VKwwx3UcvEjp0ZF/QA3FeWZFFfn5ynginJR23X1OdOXG+IXd95P6Ddp//BOs/9ItUudmGIZh/IEVDZdQ4rOpG6bR/yJZSFCNvbbLw6A0HwSCq9YyKRrBY7Wz8JnYFGVvQrKCsK+lE680tCvHcbsV2qjPvjg/WzpPpW9wGAkAa2tnYnpZAcoKcvDEz9/DJ/3OAvmOwx+RrPAbllfaVp0y2LD8FleCGzVfyG3+QfJ7ZDe/37y7wjZXygpdnohMS+SWwen7Sf0GvdF0Bk8vs/4GusmdYRiGYdzDORoE7GLZqfHZMkK7lVAiG3utUqnKwK++ICKWVJXbxttTMBSE//6lW/BigHHr1Gf/vb0tWH7b6FioYruhGOz83e/xxTnXonpmGTYsd84tWXnnDY4lgJOt8Ea1Nas+MsX5WbalbanI5AvpyD8wzlFuOsfUgmz84P55eGqp+zwhleP8aHIYRkTfz96BIWEjT2A0z82cv6Ijd4ZhGIZxD3s0BNhZxdYvm42Ne46TLMPzK0pRWpA1rkmYFXZCiUwYj2qzKz+EGkoog/mYL8651tV4goxbp1bU6hkYxo/farPsdeGEOTxHlFsydClOOq+h8Bpzd+jD7ssN5BJYeGMZFkgWQ7BCNvRIx3PUuRZ0eiJ05QSlC5T8lo17jmPFbeV49benhedLNtDoyp0RYXynOvvOo2fgIkqvykGskMOzGIZhkmFFwwGnsI5Htr/r+FuzkPTMiirhb0SeBGoYDzlcJy8rJZRKRqhRiX2mhDJ4Fe4QVNy6bIL67vc68Jtv3YMjp3vxh3MXcPKTc9h6oFV4HeOZj8QTKMrLxrc//xlL4cfoNi0iWeGdFI2gekYZqmeUkX5LRSX0SMdz1LUWdJeUzaREbhFUJfOPS/JJ50ter7pyZ5yw+k4ZcHgWwzDMFVjRsIES1kHBEJKWzrkW3/z3s7adiSn9L6hQQzpeeGCeUrUWSk8Is7C0r6VTGIsPwJf+HrqgKluGtfrpN5odezkYAtCR071jAlBjazdJ0bh6Sq6D961ybJxlBaOKhyiPw4/QOd1J0EGg2xORboncqlCVzNKrchyT+K3Wq84qXlaIutR3hPR7xTAMEwSsaNigGn5kJllIemppJW774xJ8Z1eqwKnbAkYN6Vhwo3z4iyh51yr0J1aYiwuXRoShDIlEwvNwB13Iel6WVJXj/MURrP3Ze8Jz/+HchSthGf0XhGF3pQVZ6D43hP/fznfHzV9H3wU8sj01obs4P8uxi7ZbhZeqgGVKEvRE8kTogqo8xgpzx7xGAG29eqnAyuS/hel7xTAMExSsaNjgtryrnZC0dE45Pl9FF0pUQpS86hJM8fJYeWxEJVINSz7lGL/7e1ih2lk9VpRHOn971yAWbd5PVnR7Bobxf/zjeCXDjr7L5XCL8rNSSuPqyAdwUsCshHGnsLIEgPUW1YTCyETxROhCRsmcFI1IeY28VGCpBqgwfa8YhmGChBUNG9yEa4iEeapQQrWaWykjXiSX6vLyuMFOAfSrXj61UpKVJZMiABXlZ+G5+hPSFcNkGrkb3ozcyVH89OufRdenQ1rmzEkBe/i1oyg2KTbGWnbqe7JxTwuiUXAISoYhawyR8Rp5ZWgB5A1QQfQjYhiGCROsaNhAbfBlRWlBNr53b5VryzDFai5SRpI357KrcoAE0DUwhMbWbmnBMgybppUC6DaBXEZJoShbdpZMkQBk/LedzhC5/P8SKnWLTSQAdPYPIRqJYMXc61L+TUVpoyhg5saCyWt5/bLZlsUSZPJzuDlbeiFrDKEYaIw1MHQpjsdrZ2HH4Y9SPKpuvXayBqgw5xcxDMP4ASsaNkyKRrD8tnLb5G0nvrNstqf9BYx8hXgceHS7WBlZeNNU1DV34C9//p6rak5ebZpGKEMikcAn/UNS4Q6qYUzJv5fxGu09Rqu/bxcu5iRcrbzzesuO6AaJsf+nD7PyqKq0qXi7jLW8YfcHsOseQs3PsRp3cV4WHqqejjU1My1/x4pJ8OjMb7FaA7HCnLGGljqeMdUAlS75RQzDMF7DioYNI/EEdr+n1tSJGotvB7U843d2NZOSpykVnyjKBrUnhCwJACvvvB4zr74Kj25/lxzu4LZevhuvkYieT+2b4tkJV798/2Py+XVRVpCDxtZu/OHcBbR3DVqGbVHWiaq3y/CsiI5xine3e45nzw9jS/1JvPrbdjx7360pY+eu0eFBR36L3Rr4pH8Iz9WfxA8fnKe9pLHoGygKz2JFl2GYiQArGjaoWGh1WbGoQhulVOqh1m5tzatke0IkE8FotaOcyVFLwXJL/UmUF+VaV62yEQDd1Mt36zUSIepobCVc+RlmYeSDPPHz90jJ+qJ14sfYk6txGcLZHdNKhFWAzg4Oj1Mc06mMMuOMXw36DOy8kgYUhZUVXYZhJgqsaNgga6HV2VVbp9DW+GGX1uZVdptseVEult9Wjh9fDjWz8khsuu9WLK6MYev+U9hSf2LcuTv6LuBHb7XhB/fPQ0lBttDSJ1Mv3yygxhMJV14jESpeLUqyeCQil/hthaEkjuZMiLvVA+J14pW3KxmralylBdmOCrdBAsDTbxzD52Zd7atQyniPHw36zCR7JWU7g7OiyzDMRIIVDRtkhX0dpUENKAJniaC3QurRYqyEdjvXvlNc9e03lDgmd47EE9j5u48cx/L0Px/D4adrx7pjH27rsdy8qc/ow/8YGCegFudlkX5LEWLNiDq8G1jNr6hazuq7KiyVOTtKLLxI1xTm4MKl+LjkbAp2yp2qtytyeTxAxLGJoF01Lpnn0zMwjPl/U49zFy7ZHsNlSdMPrxv02aES8uW394VhGCZoWNGwgSLslxZk4zvLZiNWlKc1vpZSnvGZFVXYuOe4sFb8wpumYuuBU8JrmoV2kWvfbpMVJXdSQtLODg5j3sZ/xadDI5bXNqBa0Z//9fgE67Pn5YVsCtQO707zK6rEc/sNJXjy9WNCRSGCK16k5M7gLR39+N7e40r356Tc2Xm7jLK2dmt5w/JbAMCxnwYs/q6Ck5KRTBgqrDE00qnDfBDeF4ZhmCBhRcMGirDvtoStE5TSj9FoRFgrfsGNU4XCuNkC79a172TpowpwyUqG3bXd5Ix4ATXGmjK/B9fV2Cpriytjlys12RONAP/fhdNQlDeaKzJWeeyf3pPOPQLo+Ud2iua+lk5hGVOnuHcV74sbwiCUMjTSqcN8UN4XhmGYoIgkEuKq/P39/SgqKkJfXx8KCwv9GFdoCDppT1SZhDI+Q7AFrIXx4vyssao8I/GEY1dqY9M+uK5GyYPT2NqNVdsOSf/OuPY1hTn4H/9lbkqTuX0tndiw+wNh9SIVivOy0Hd+WJtXS8f8ys5hcV4W7po5FW++30n+jXlMAFzHjlOq7Ox9/2PLfhp+4XZ9U0j3akNhHL/dN07X2tUF9d3dsXoBezQYhgk1VN2AFY3LOG2eYdxYk8dFSUasa+6wDbdJ3oyL8rI93QhH4gncsXGfttAlQ6makpuFB156R8s5k1lbOxPPXe5toUOA0SFo7Go6g8d2NpGv6Zbi/Cw89CcVWFMzw9N1L1LCRBTkTMKAyRMmgx9CadCGC7eEefwqvVT8xljjIu+Ll4ouwzCMDqi6QdTHMYWWuuYOLNq8H6u2HcJjO5uwatshLNq8H3XNo300jFCgFXOvw8KbpoZiA0ge89qfvYeNe47jb+v+DX3nL1qOb3FlDLmTrR+3seF9980WdPadJ10/2bU/Ek+gsbUbu5rOoLG1GyMOZZEmRSP4yp9MI12DghFu9Ovjn2g7JzC64ZcX5WJNzUz88MF5iBWlhtLEinKVBFIdoRNehvUYK+dLc2JjCfNnB4expf5EyjvhBSolpYErz+rd9X+GtbWzUJA9ify7ZFSfKRXD6m6+R2MNezm3Ogj7+JdUlePguhqsrZ11Ze1e7qXi9dqlYoR7AuPXn87KhQzDMGFhwudopGOpQZUxH27rcQwtMpIQqVV8DGFXxcJ55/SpAMQJ6hSMSi27mtSb3YkaBKp0L7bzglGVhK5zQxiJJyyv4WUp2VhSmWK/3wmVuPTkZ5U9OYrHamfiL/73m7Bg06+Fa9m4v69VT0dtZcxTT2W6VxtKl/Hva+lUbjrpF5T8O4ZhmExhQns0RJsnMLp5Olno/UZ1zFQhrvSqHJQX5doWxTWsx/MrSpUtnF0DenMpEgC6By6itCCLWMx3lAiAb95dQfJWyHi1nDxkd0wrAUUO27jnuK0V1skq6ob1y2bjN9+6B7vf6wjknVDx1MSKcvF47SwMXYqPedOyJ0fxN/dWjfYdEfw+AmBvc6fn4ZAy1YbCSDqMP52+54b3ZcfqBXh+5VzsWL0AB9fVsJLBMEzGMaE9Gn6UGtSd36E6ZqoQFyvMFVbbMoRcVQunV6E/9869Dq80tJOOjUaAratux9I51+LbS2Zre0Yib9PjtTPJDfc6+i7g4deOYm3trHH5EaLuxDIYceFfqa4gr6+/b2hD2ZQcrTlLlOpBycUA2rsGsOPwRynNH5O9aT98cB6efqPZ0bPhVznRdK82lA7jT7fSsSp9OBiGYdKNCa1oeL15epE4qTpmmRKQk6IRoWu/sbVbeVOnhP5EFTpg11bGcGdFKZ5+45iwmWE8AZQU5ADQt+FTwkteJSpCyWypP4Edh09jw/JbUtaNEdK1df9JvNLQhr7ztB4RyZjDxKjra+OeK304YoU5WDX/BkwvK3CleFBKSm9YfguqZ5ShrrkDz9WfFIbInL84grU/e094ba8FZKpy3d414Ok4VGnvGiQdF2RZ4HRQhhiGYSYaE1rR8LLRk1e5H6pjpghxyUmIorwEN5s6ZSxbV81DSUH2WJO5J37+nmPn6GQl6fxwHGv/sUlpbG6gWFRVq2119g9ZrpvRmPTxAjcVc1y4ylrv7B/ClvorTRHdKNOU+HWZfIFYUR7pulb3rdMbSc2r2VJ/Ep+JTQlVCM2oUnfC8Ri7XhV+VuxLp8Z9DMMwE4UJrWh41ejJy8RJN2OWTUJ0svS73dQXV8bweO0svNrQliJ8241lw3K6khQrDEbgoCouTr05RCSvG6d1ZoUxV4/XzsL0snxLwU9HorlbZdptd/lkb5rq+6LbG5msXDsRlqRqA+oaS2B8tSS/S+G6/Z6HtYw5wzBMOjOhFQ1ZKz8VL2OF3Y5ZpYKSFfMrShErzEVnv3PjOatNXaXevYySFFSnYKri8lB1BZ6rPyHdzdy8bmTLwVKq2ujotq6jCpGO7vJ/OHdB6X0R59nYK2pOLKkqx+O1s1JySsyELY+AusbW1s5MWVdBVPNz820Mc38QhmGYdGZCV50CrgiwuvokAFDqRSGD2zHr6Auyr6UTFy7ZN0dLAFi/bPymblepqu/8MJ6rP4l9Lfbdq6mVWoKqVW8oOKKKXWtqZlg+PyrGuqGun/+2cJpUVRu79SWDl1WIZL1pMu+LyBuZwGjOjFW/HQrTy/JJx4Ulj4A6jullBWP/O8jqTyrfxrD3B2EYhklnJrRHw0CXlR8Y3bSSE2WdcBO6o3PMsthZK81s3NOCaBRjm7uOkDJq4raTB2T9skoU5WVjV9MZ6XlzCq+QsagmP799LZ3kalnAlXVDXT9fqCpPmTNKiIh5fXWdGyKv62S8EJhlPVYj8QSK8rLx7SU3o+fTIZQWZCNWlGd537JeIlkLfbrlEVDHcfKTc2hs7cb8itLAqz/JfBvTpT8IwzBMusKKxmV0VB6iCuC6QneCKI8okxdgFsL8FkCsBI7egSFs3KMWIkEJr5AJ8TKe38KbpmJ+RSk27G6xDUUzmFqQjTumlQBQCxGTCRFJXl8j8QReOtgmnbvhhcAsCu8a9abNxqRoxPF+rQRHWcVIVhgNKqzPQDYPgZqzs/VAK7YeaEV5US6+UBUjjcVLrw312xi0UsQwDJPpTPjQKVVG4gk0tnZjV9MZNLZ24+KluFRirkzojvlaQTackrH4msMkqIJFZ/8FbfebHCbWd/4iHt3+rlKIhEx4hUozriVV5Wh4sgZra2c63k/3wEV87vsHUNfcIR0i5iZERLZJYHJjRy8QhXdt3HMcm/a2SN+vimIkEyYWVFgf4NxIUmW8VnT2XSB758LgteGSuAzDMN4SSSQSQimuv78fRUVF6OvrQ2FhoR/jCjVWVtLSgixh7wbjuL+591Zy7odfSYpUS+eupjN4bGeT9Pl3rF4AAFi17ZDwWPNc6rjfkXgCizbvt1WSDEvywXU14+7bzW9VsHrm5usBGPMUUdYI9R5+8617cOR0r+06EI3Nanxesvf9DjyyfXwlJ1Eiu90zM+ZJperW8yvnYsXc60jH+p18bOdtpT4rynNPPmfEoQ+O7vfFDY2t3aRv0vpls7U3qGQYhklnqLoBKxqSUMOj7NjyX+fi3tvpwogb4YCKjNBD3ZjNPL9yLr4451olIU7H/VLHvWP1gnEhEm5+q8rFS3Es2FTvqLyWJwlryYpiWUEOEAG6Ph0aE4wOt/VoU/KSr9XeNYgdhz9KCflK/o2XJUNFyhMFq2dmvHeAXNUt2efvVzlVXYqyMd6GU13YeuAU6dp2uUp+KKEUKIqluXkoV6NiGIah6wacoyGBbN8CK6g9HqiVW9wmKcqWoVTts3D1lFxhsrTd+XQkZboJkQgivOLI6V6hhyw5dtwIEatr7sBf/tN745TGpcS4efM1rdaBOf59Tc0MS4HZa6u9bOK2FVbPzC7Pxg7VvAq/cqx05SEY46Wu8/99VhneP9OXsqYoJZb9hFLO2eyZ8bJEL8MwTKbBORoSuBFsZGPWKddyWz5UpQyl21h9u9j60oJsx/OI4uBFeSxlV+UQRguUFeSMO08QlYKowlxyOWCnHIyXJapaJUMpR2pVLtmPkqE6FDu7Z2bOszFyZ/zOq9CBbkWZus7/54ku9AwMo7QgG1+tni5VYtlP7L5Jdo/T6xK9DMMwmQR7NCRQFWxUhBFR9SHZ46xQtXRSLb52921VDaqz7zzW/uw94ZitnoHIcl7X3IENuz9wPG8EQFF+Fp74+XvjwoDWL6tUqhTkJjSGKsztavoYf7VsVPETKY3RCJBIyDfhk628QykZ+ldvNOP8xRHbMrMU3Ch2FC+E2ePwmdgUUjWxsKFbUZb1avYOXMSrDe2hzm2QLefM1agYhmFosKJBZCSeQNe5IdKxpQXZ6Bm4OPbfKsJIz6e0a1GPs8KNpdO8Mbd3DVyO1b8yntKCbKyYey2K8rIxEk+kCBlmIa6xtZs0FrMwJAr9+sbdFfjxW23CxOAEgLODwwDGhw49uv0ols0pxy/fH2+Fd+ou7SZsaH5FKanAQPfAxTEvj8gDZhhfVTt+U9cLRYHtHrg4pliqhlOphvGpeiGC7F3jBt0ldWW7x3vRj8JOiVdV7s2/ozoquBoVwzCMM6xoEKBWXKFW7qEgCiWSPc4Kt5bO8bH6M3G4rQf1LZ14o+kMugcu4pWGdrzS0C4UJlWEIYrlfNvbzkoGAFxTmIMLl+KXFY3x5wFgqWQA1kqkXSUkQ/l5vHYWppflO66NSdEI7p17HSnkSUbY+Wr1dPyquTNlLU8tyEZ3kmJsB3W9yApfqjHvlAaJ37i7Arvf69DmhQiid41bZBpJUpHNY9HpAbBT4pffVj7uWVOUWLsqghTCUKKXYRgmzLCiIUCmCR8wumFnT4663kxjRXlaj7PCC0tn3/lR5YKaXJ78W1lhiGI5F9dUA7626EZ8b6981+u1tTOxpmZmypj2vv8x1ux413Y8ALCl/sTY35wEodrKGEnRkBF2FlfG8FfLKlOst3dMK8Hnvn9A2zqQFb7cWLwpDRK/vWS2o5Xbr+pPQSLTSFLmnIaH51fNHfiHxtPC3xg9clTn2u573NF3AT96q2389QTfHbvziTyJXjRWnAjrkGGYiQcrGg7IVJnSHattKAFOgrTbhmi6LZ0UD4OTMCkrDOkKW/h976D0byIAdv7u91hTc6XBXl1zBx7Zbq1k2OEkCMkqgtRjrazyOteBSkiTG4u3KKTJyQvhdz+LIPEi9Ct5bimKxsZffqDcI0el6p/Td4d6Pl1eICcm0jpkGGZiwVWnHKBWmVq/bLb2aiqGEmC3jUWgZ6Ozq7gSK8p1DGWxqvIkk1zuNB5qV21dYQvTSvOlf2O+F0NoUTkPYF3BRqaLtNuO05R1QO1QL1uZLBlV5dGq8pUIPypjhQ2VeaJgKJeis9mVT6bMtWrVP7vvDvV8JabwVNG3UZaJuA4Zhpk4sEfDAarQUzYlxxMXt52FX7elS9bSaWd9+wKxV4NoXqlx8BSLP6VD8ZcXTsdLB9uUukEb9+Km9LGTNV/Gy+M2PMZpHchaXGVj+A38inl3632byNiF+HjdI8etB9P8e+r51i+bjVhRnmdNJ3kdMgyTybCi4UAQ/RPM+FXphircO1V5eoXYq0HnfK2884aUnAcDY3ZW3zVadQqwD3/InhyVqqKTjHEvOsK47M4hswbcrherdSDb1NFqLJ39F8aFzZgpLcgai+H3Oj7dbRO7sMTT+z0OkcJppVyWCooNUMPm3H43zL+nni9WlOdZAQBdzRQZhmHCCisaDuhOllYlLJVuKNY3igchHk9gV9MZV4KRqBJYshX/9htKhFZ+t92gdShPTueQWQM614tbi2vyWPKyoviL146O/dZMz8Aw1v5jEwCx186tgO2mtHNY4un9HgdF4VxcGcOU3KzL5aoTWHhjGf5w7oJyj5xk3JQztvpOh+H7rruZIsMwTNhgRcMBL8pCpjMyVZ7swifOD4/ggZffGfu7imBU19yBh18bXz7WwFwNimrlt+oNsqX+JOnZqwpBxvkoAo0b4Vr1tzotrjLKnJO3RIeAreqtVPXu6MbvcVy8FMfTbzQ7KpxPvn4MG3a3pDS8/MXRM1h55w2ka4ieiWz/DsD5Ox2G73sYvOYMwzBewsngAlSTpTMRqlXta9XTx81Xcf5oXXpzrwrZhMeReAJPvn7M9t+NalBmqEmwycetqZmJtbWzUJSXWlPf6tlTE6BVErWBUcFy0eb9WLXtEB7b2YRV2w5h0eb9pHlz81vdFtfkZP8t/+U22z4wdknyuhJnRcnLEYyv6iby7liN1wv8HkddcwcWbKpPaUJqdd2zg8MpSgYw+lyeqz+B4vwsqbm2w+57XF6Ui2/eXYFy09+L8rPweO0sLK60zh8L+vuusg4ZhmHSCfZoEEjXjsC6oVrVaitjeDqpV0PZVTl44mdNlsfKJjxu3X/KsrFe8vl0xDRbWc2L87LwUPX0cb0zDOws9sbvZl59FTbuOS6dqO3Geu3W8u2FxdVQ5hpbu4XCa/Kz1Jk461XfFj/i6f0cB7WPkNNYIrji0dThOXD6Hn97yWxs3X8Srza04+z5YZwdHMaW+hPY+buPHAsXBPV9D4NXhWEYxktY0SASljyJIJGJaU6er8bWbnT2D9melyoYjcQTeOXgh6SxuolpthOu+s4P47n6k/hMbIqtcG4ILVv3n8KrDW2jws75YWypP4nyolysX1aJkoLsFIEGgG0TMzfCtQ7B3Ms4dllviW4B26u+LV7H0/s1DpW+FVYkAPQODmNt7Uzs/N3vtTQMtPse72vpxHP1J5Uahgb1ffeimSLDMExYYEWDIaNqfdMlGG3dfwp9Fy6RzuVkYXfKV9AhnI8KOycshZ1Ht48KOyvmXgdAnG/gRrjWIZh7aXGV9ZbIrCNqToqMNTss8fR+jcNNyWYrppcV4OC6Gs88B+lcKpa95gzDZCqsaEwAdJbAVLG+6RCM6po7LMvYWlGcl2VrYfdSsAfkhJ19LZ3CsKahS3Hh/QLWQrguBU/mmcusNVlvCXUdtXcNYNHm/eRkcZ19W/yoQufXOHR7Zq6ekuup5yAsoW0Gst9d9pozDJOJsKKR4XhRAlPW+uZWMJLtuv1Q9XTLsVDyFdwI9gBd2Dn0YTdJIfm7/3wbaTxWQrhOyzflmcuuNVlvCWUdFednYUv9yXH/pqMaU1ji6f0aB3X9lOZnIQ6gb3A4UAUsLKFtALD3/Y/xnV3NKX1jgiiBzDAMEzRcdSqDEVXoeb7+JHY1nUFja7d0hRpqFSfjWLuKTBTBSCaEoyQ/C2tqZo77O7VST1lBDuk6dkIYVYhpbO0mKSSIQLkqje6KNk7PXLUalEzVH8o6cupADbivxhR0lSI/xyFaPwBwVc4kHHq6Fs/edysA9apqOnCjWI/EE2hs7U75Hlr9jcKmvS14ZPu745pTdkhWRmMYhskE2KORoVAE6+RQJK+tbW4SHmUskJvuu9VSoKF6GgzBXtX7Qo+LpwktXZ8OKVuv/bJ8u42NT+kg3ncePQMXUXpVDorysjEST6T8xmkd2XWJTx6LjtCZsMTTez0OY/049az5dGgE+//tk1AkNKt6Ti0rzFmU46Z8I/e+34EfvdVm++8JhDdPhGEYxgtY0chQZBM5/Wg4pioYUYX3tbUzbcdOVVb+5YNOrLzzenKjPjNUYWfhjWXYeqBVOJ6rp+Ri4U1TlYU4PwRAXUnnfecv4m//5X+NC70yV+paXBmzXEe/fP9j0nh1hM6EJZ7e63EsroyhOD/LtqR0shIZtAKmoljbhVNa3W+H4Bs5Ek/gO7uaheP0M0+EYRgmaFjRyFBkhSm/qrKoCEaUrtvlRbmWIVMGVGXlHxpPA7C2aFKEc6qws+CmqVLWVysh7o5pJThyuhe7ms6QKyt19l9Az6dDKC3ItvQYqEBda/UtnbbP3k7g6+i7gEe2p1rU7SzL1GdcdhUtPI4ZVSJl+tYErYBRFeuReAKHPuzGk784JlW+NwHgqdePWX4jD7f1OPaFScaPPBGGYZgwwIpGhqJS2tLvqixUdIQAUZSVZPouC1dra2dhelm+lHWWKuzI3lOyEFfX3IHPff+AVOJ13/mL+Nu6f9NaGACgr7WXG9pxZ0XpuGvJ9muw875Rn/ETP2vChuW3cFIugTAlWFMReVasQqVk6B0cxtb9J/FY7ayUv8vMgdclkBmGYcICJ4NnEMnJi/F4ArFC50ROO4ISGpySL90mvzolElthXHnn7z7CF+dcK0x4N4+/KC8bv/nWPdixegGeXzkXO1YvwMF1NSnjVL0nlcRr1WRtyr3KrDWrZGzZMD+7xG7qM/6kf8jXpFzVpOIwEJbeIbLYFS6wew9kebWhfdxzJFfpKrAvv80wDJNpsEcjQ7BLaDRComREmyCEBkppVLcx4HaeBjtkPDxO4zea89mNSeaeVBKvKYUBnvzFMUzJzcKCG8UKldNaE2HM5/yK0rF7PvnJp4Rfjh+31bMxnvGG3S3o7Ld+xn42b/OivLSfeN2zQ2ePH8q1dHQ6B4Cz54fHrT1jrkTflmdWVHEiOMMwEwZWNDIAu/h2I/ynyCGZMxm/6t2bofS3WFJVrkUoSRbsf9XcMZaT4YTIw0Mdvx0yce0qidcUj8HZ88N44KV3hEKwaK1RqG/pxP/5syYtXaetns2SqnJMycnCAy+/Y/s7P8IE3a6LMOBl5TK/lTDdnc7Nay95ruyUmW/eXYGlc67VNgaGYZiww4qGz1y8FMdPGttxumcQ00rz8eWF05E9WT6CzRC6O/vOY+Oe444W7tzJUfz0659F16dDaO8aUK6o5AVUC308Dmzc404oMSsqn78lRlI0nDw8bku7yqISMy8TCuckBFM8IxRebmiXONqZrnNDlgntXQNDpN97FSbo97rwEi8qlwWhhHnR6dyM3VxNLcjGxhVVWDon3IolwzCMbljR8JFNe1uw7e02JIf2fm/vcay+qwJPLa0kn0cmmTEBoLN/CNFIZCyE5zOxKYHWu0+GaqE3Vx4C5IQSqzmLFeagOD/LVUdjHaVdzTh5blRi5mVC4ZyEYLcW4QiASATQmaKwcc9xvHSwbdzaDTq3wIt1ESQ6S9dSFNan3ziGmpuvUTLC2KHrWYu+C0GX+WUYhgkTrGj4xKa9LZaNnOIJjP2domzUNXc4NtCyI9ma5+dGKAp3cmNlpFqG7aynn/QPjf1N1cOjsyrPSDyBrftP4dWGNpw9b90oTCVmXrbilp0QLPOsrOYzASDhQR60lcLpdW6BiHSs1iRCV+laisLaMzCMBZt+jb+5t0ra+GH3zZlfUerYE4QC9bsQdJlfhmGYsMCKhg9cvBTHtrftu8UCwLa32/DEn93saMEbiSfw5OvHlMZgtub5sRFSYrDdWhlFlmFKCEtRfhZyJ09KSR6menh0Wc7rmjvw5OvHLIUgsyAtEzNvCF1fqIrhFcmQJbMQLNM4cefvfj/OY0Ydw5p7bsLMa6bg6im56B0YwsY9x4XeAbPC6VdXdDvKCmi9OqjHZRJU5apn4CIefu0o1tbOxPSyghSlwU6ZcPrmLK6MuR57EJ5fPxPmGYZhdMOKhg/8pLFdGC4ST4we97W7brQ9Zuv+U9LWOL8TvI1Nsb6l0zIW3yw0y1rb7bATXighLGcHh/HTr81DNBqR3szvmFaCqCAcKBoZPc4OkZfKLEhTY+athC6ZCmRmxYLqJVhTMxNramaOE44Ot/WQFI3qGX+UojR+vqocf9/Qho17jtv+xkrh9KMrui1UOXACyouyxoUt9SfH/nd5US6W31aO3e91jFMmlt9Wjh+/1Wab9/F47UxX3ozSgiz85lv3aA3nEpHuVcsYhmFY0fCB0z2Dro8biSfwaoOzV8SM3wnelNwRK+uzk+VZVSg2oFpPuwaGHMvQ2nHkdC9JiTxyutfR4yLCEKT/vqENX6muIDUlswoXo85ncf74Wv+yXgLz/aqGM02KRlA2hWb5Nz/voOLluz6lJaNTj8skKMq5HR19FyxDUO3+Dlz55rzqsghBz8Cw7XvsBZlQtYxhGIYb9vnAtNJ818cdbutJidunQG1mpwOZRljJ1mfAuXHdD+6/HeVF9s3gIhi18Nl5bLxOCnYTiz8ST+DvG9qkEqw37jmORZv3o665w7YpmY5+AQ/9SYWlMO6mcaJTQz2RUuzmOdrNk5cEnYweZijKuW4SgPT30wq/cmooCfNWzS8ZhmHCBns0fODLC6fje3uPC8Nrvrxwuu2/Uze4gpxJeOY/3YpYoX+xvKqCLTVBPRqNKMfae5UUbISInfzkHOl4s0ApUznMjMii6bY6VHF+FtbUzLD9dzdeAtVwpqCTu2UJ83hFMf9e5wQEmQBfnJeFvvPWVeYo+KUYZlrVMoZhJi6saPhA9uQoVt9VYevaB4DVd1U4xv5SN7hv3HUj7r1dPgTIDaqCLTVB3U2svRdJwTJKgpVAaRcSQUVUbcutIPfsfbcK58NNMQGqomIWeNcvm41Ht78bmh4wTgSdjG6HKObfj5yAIL04D1VX4Ln6E1JhmQaiXCudZGLVMoZhJiasaPiEUbrW3EcjGgGpjwYlabokPwtramZqGjEd2c1OxZobhBXdChklwRBmvlA1Om7jft2GNQHOFk2qIFdakI2egYtj/+1nkqlIUbETeL9xd8W4ROCgesCICDQZ3QJRzP837q5wTKbWFYapqwCEDFcKFczAZ2JXKXkTnXKtdMOhdwzDZAqRREJc2b6/vx9FRUXo6+tDYWGhH+PKWNx0BjcEBWC8NS4CBJYc2NjajVXbDpGONdSCIMbqNiRkJJ7Aos37yQKKOeG1vCgXK++8PqWKjlueXzl3XBK7MU5R2M5vvnUPjpzuDV3ZTDuB2BjZC/fPQ0lBtvS4gyoTGobypKK1K2qmaKyZg+tqtIzd6VvmFjsPUvI3x3gmnX3nsXHP8RSF2wmr980LqO+wrufBMAwjC1U3YI+Gz2RPjjqWsHXCzkIadLlDGQtlkNZnariPnWBIDRH7QlUMv2ruHCe0dfZd0KpkAPbJz5SwnezJ0dDFd5O6Rv/zMbywah6+OOdaspAVZJnQMDRvo8T8O5mcdOcE2H3LphZkY8Xca1GUl43n6k+MXVuEsQqoHq/kZ5KXPYncBNWp74lOhTKsoXcMwzCysEcjDQmDhdSMyEKZnz0J37z7RqypmRn4WJ1wEkiHLsXx2M4m4TmK87JcV7j5q6Wz8fLBD1O6lydDsWimYw1+Ge8Y9V5EHhIr71oY3zE37Go6Q1q7InRb9J3m2W792vXRMNaCyrN7bt8JPPdrsRHgp1//LKpnlI37u1fvmtV5Swuy8MyKKiydc63yeRmGYdzCHo0MxisLqRvhyrBQ2nW3Pn9xBM/Vn8RnYlNCK+SKYtgfr51FOo8bJcNQIL66qALXl+a5smgG1UNChNM6k8n3oeQOUDrDm5Pqw66gqbynumL5decEOH3LnNbvt5fMtp0Dle9jxR8VkI6z6nviZb+LJVXliMeB7+xqHgvv6hkYxsY9xxGNRkKxHhmGYZxgRYMBoEe4WlwZw4bdH1j+m6hSUtBQBNKdv/sIscIcRy9DkYQ3Q6RA6Egm1qGU6rTui9aZjCBLWVPUMqFb9v0vVM/4I/QOXMSj28PbJE31PaWU26XkaPhdjtdu/eo2tqgmX6sosjLUNXeEej0yDMOI4IZ9jG2zPWMzq2vuIJ3ncFsPOvvtOx2bG/WFCapAumr+DQDsG849VD2ddL21tbNITe+WVJXj4Loa7Fi9AM+vnIsdqxfg4Loa34SLuuYOLNq8H6u2HcJjO5uwatuhsYaBKucSrTNDIKaKZKI1RfWQbD3QilXbDmHNDuuKYmFokmY3fx2E95TSLHH1XRWjCofNv6dbTsBIPIHG1m7sajqDxtZux+cmWnd2jUFl+l2ojJ+b9jEMk+6wojHB0bmZpXPtd+qYppcVOHbGXlMzkySwrKmZEagCQUGXAgrQ1xkAW4HYCbvn1941IHEWe4s+EKyiLGqKmYD4PRV1dX9qaSW+cXcFIqaJj0RGk6zDtDZFyCrIql3rvfzmeanEMAzD+AWHTk1wdHagTefa7zJjX3jT1JTY8bKCHCAyGr99uK1HqrGcaE6DyhfQHRIis87sQsacsHp+I/EEdhz+iPR7GayERq+TxykVzyjvqVPeQ11zh2UfjXgC+NFbbcjPnozpZQXa7s9NqW8nVHMmVEIVvfzmpbPhhmEYxoAVDY2kY6UanZsZJQ48iDhvCrJjN2LE65o78Jf/9J4njeW8TDIVoVMBBeTXmSEQH/qwG4/+9Kht3ovTmhKF8qliFhr9UAY7+85rO84qv0HkMQGQUprZ7f1t2tsyrnnp9/YeJzUvdcKtgixbQMGLb56xj5z85Bzp+DAabhiGYQxY0dBE2CvV2KHTIhdE7Xddyp3K2J0UgR+/1abcWM64Ly+TTEXotqaqrLNJ0QiqZ5Th2T+/1bJ0smhN6bb0WgmNfimD1IZy1OPMUHvEGLi5v017W/Cjt9rG/d3wnABQVjZ0KMgyiea6v3lW+4gdYTbcMAzDGHCOhgZ0xbLLJC/qgpJ8G40AvUQBRhQHrlPp0pmoDMiNnZJzsHFPC+ZXlGLF3Ouw8KapUgpB0PHZukNCVJNtAfU1pdPSayU0+pmsW3qVfaM4lePMyCplqvd38VIc294er2Qks+3tNly8FJcaj0EQ4Ua6vnl2+4gV6ZqgzzDMxIM9Gi7RZXkOyiOSbJGzI54AHt1+FD+M0jZNP/o3eGVJpo5dd2iRmaDjsykhIdcU5iCeSGBX0xnhM3Zr+VVZUzId681ETaVercLevF4DycQKaUoT9TgzKkqZyv39pLHdMeEeGJ33nzS242t33Sg9JqfO3cmc/ORTNLZ2S3+X7Dyobr95lNC1ZGTDMBmGYYKCFQ2X6BA2vBCaZUKKllSV44X7b8eaHe86CgEyoTpeNRUEvA8roozda0Ug6MT6SdEI1i+rxCPbxyughqJw4VIcD7z0ztjfRYqxal8Q81r+4pxryWvQTrmxwzjr1lW3o6Qgx/H98WIN2L23htLk9K2x8whRmF9RqtzRXub+TvcMko57p60HZVNypAT2uuYObNjdQjr/1gOnsPXAqbE1S1ESRMYgN988aujamntmoHpGWVrk/zEMwwCsaLjGrbDhhdCs4h0pKcghl/b0SoGg4qcl2Q6vFYGgE+vrmjuwcY+10FaUn4Wzg8PjOsBTFGNZy69bT5+dclNelIvlt5W7StjXvQZE95rsedSd/zQpGsFD1dNTEr6pyKzxaaX5pOP+teUT/GvLJwBoz9vOWCOis+8CHn7tKIovr2kD8zW9zsWh7iMzr7kq8O8vwzCMDKxouMStsKFbaFbdEIMO1ZHBr7E6eYVUFAEZL1MQifUGqkIbVTGmWn51CXdOys23l8xWDnfRqQxS79Vtp3gn1tTMxKu/bR+nQNqhoux+eeF0fG/vcWH4VDKi533xUhxPv9EsvV6BK++Vk9K8uDLmeWGGoD2YDMMwXsGKhkvcChs6hWY33hHqBlZ2VQ4aW7sDLeHrx6ZMCZOQUQRULPNeC5ZWUGLFnQRRWcXYTvnS7emzU27chLvoUgZl7tXL/KdJ0Qieve9WkpKpquxmT45i9V0VllWn7HB63nXNHXj6jWPoGZAP+aJec0puljZjkCg0Lh1LgzMMwzjBioZL3AobOoVmN94RykZXnJ+FJ37WlNKbQFfCuoy13+tNmWpdpioCbizzfiTWJyNb5tQOimLspHwV5WUHHh5HQYcyKPveepn/ZHc/lOR4KkbpWnMfDSesnreq542Kcc3G1m7S8aI1Tw2N0+nBTMfeTgzDZBasaGjAjbChU2h24x0RKUwJAL0WlmwdMcqy1n5V5Y6y6cpa0kWKgA7LvE7BUjQHukLjRIqxSPl6qHo66TphCOVzqwyGLWzR6n7umFaCI6d7tQmsTy2txBN/dvNYZ/Bz54fxRtPHwt8ZcyBbpckdtKs4rfkgQuPStbcTwzCZBSsamlAVNnTG4rv1jjhtdOeHRyxDZtzGKKta+2U3Zeqmq+IVclIEgkxcNysVvQND2LjnuOMcuI0BpyjGFOVrF0HoBMITs+5GGQxjfL7V/ehen9mTo2MlbBtbu0mKhjEHsp63aARIJKgqQyoLbyzDL46eUTYGBREa51cjSYZhGBGsaGhEVdiQFZq9jPO12uji8QQeePkd29+oCsturf3UTVlm09VtXQ7KWk3tMGyeA8oaKsrPQt9lpVNFMaYoX90DF1FakIXegeGMj1nn+Hz5OZB5XyIAVt9VgR+/1UYuc5x8zQU3TXVlDPI7NM7r8t8MwzAysKIREmSEZq/jfM0b3a6mM6R7kBWWdVj7RZuy7Kar27ochLVaJnbdag5Ea+jZ+24FLv9GJcSDuk7unXsdXmlo973qlt8EWWEsLMjOAfV9mVqQje/dW4UlVeW4/YaScWu2JD8LvYPDwmu6CWvy29gQhvLfDMMwBqxohAiR0BxUCUyvhGU/NmDZTVe3ddlva7VK7Lp5DqhrSDXEg7pOaitjuLOi1NeqW0Gh871N1wRgmTmgdH0vLchC41N/iuzJ0bHzW63ZfS2dpGuqhjX5bWwIW84PwzATG1Y00oQgS2B6JSz7sQHLbrq6rct+W6vdVI1KnivKGlIN8ZBZT5OiEV+rbgWJjvc23ROAqXNAea/+5t5bx5SM5N+Z16zMvKuseb+NDWHM+WEYZuISFR/ChAEZyzxwZUNcMfe6sbhfVYxNHbiyiRu4EZaNDdjuVxGMCkluNmDZTXcknkBRXjYeqp6OkoLslGNiRblKSZSGpTZWlDoW1fM54cZKaZ4rnWvIfF6Z9eTVOMKIm3s1PJ7m74Th8axr7tA9XE+gzoHO98rLNebV99MOP76rDMMwVNijkSYE7Q73IiTLD2u/jDXRyhpcWpCFe+deh9rKmCtLul/9MFSslEEkG3vVjDBdw4bcMlETgCnlpcOwHvxsvsk5PwzDhIlIIpEQhnP39/ejqKgIfX19KCws9GNcjInG1m6s2nZIeNyO1QuEydFuNl4vNm6vwz0MSy9gven+8MF5AGCZ/5J8jJ+hJ6rzPBJPYNHm/Y6x61a8GFC5S53rKd3Dhtyg6/uQSYRxPSSv97KCHCACdH065IkSFMb7Zxgmc6DqBqxopAkiAdKwSh9cV2O7WTltPEHHwXtteRTd+6LN+21D0yhzq5O65g5s2N2Czv4ky2dhLjYspwkIdoqVE0EpGrqwK5QQlKLoN7uazuCxnU3C455fORcr5l7n/YACJuzrwS8lICweHYZhMg9WNDIQimXebpNy2ngTAIrzs1Ia8mWi5ctu0w2TNbiuuQMPX37GVlAVAmofDcB/RUo3hhIeFkWRgm4BMExrOGjCvh7CrgQxDMNQoOoGnAyumZF4Ao2t3djVdAaNrd0Yiav0orVGNflRFL8NYFzX73RLIBXhJNgFnf9iMBJP4MnXjzke89Trx3DxUly4xpZUlePguhrsWL0Aa+65yfGc5kIC6YZsoYSgqWvuwKLN+7Fq2yE8trMJq7YdwqLN+129a5wAfIUwrwfKt/i7b7Zo3TcYhmGChJPBNeKHO1wlqVil5GkYE0hVrcCi56JaDtIYT2ffefQMXETpVTmIFapbpw992D1O4TPTOziMz/5NPXoJ3iejkk6QipQfoRthURQpyHSpl4ETgK8Q5vXAzfQYhplosKKhCa8ECCtka7mrbqhh2vRUlTjKc1lcGZOuc+8UmqSqXDa2dpOO67XxPtmtsaDq6vsVh54OfQNG4gkc+rAbT/7imGeVofysbBRmwrwewqwEMQzDeAGHTmkg7O5wtxsqddPzKmxMtT8A9bkAkKpzbzcegw7lsDO1+RKtsSDCavzs6SC6PwAoyJ6EeDwRyDtohEo98NI7OHve3mOlI6QnOWTu+ZVzsWP1AhxcV+OLkuFl2KgMsuvdz3HrUoLCMtcMwzAi2KOhgbC5w83hKndMK3G02IugbI5eWa/d9AeQeS5Ua7DTeMzIWqcX3liGrQdaSceacVpjusJqqGFQfvd0MO7PKYl+4OIIHnj5Hd+LHNh51Jxwa81W7djuhjCVUpVZ736PW6VLuPm96x0YwsY9x4Vj5opTDMOEAVY0NBAmd7jdxrn8tnL8+K22cRsvhd6Bi8JrWglTHRrCxtwocbLPhZL/Qs13UVEuF9w0dVz1L1ns7tltWI2MQKb6zNwIRosrY6S507EmqcgopckEGeKlgp9ho1Qo6z2Iccsq/dTqceYx2zUffWZFFZbOuRYAKyIMw/gDKxoa8DommLohOG2cP36rDd+4uwK73+tI2XyK8ybj7PlLjtffuKcFn6+ytj6LhKkE3Fmv3ShxKs9FZA2WVRZljp8UjeDZ+251tMyLcLpn1e7ksgKZyjNza1k+3NYjpaD5UeRAtghDEB3a3RLmjuRO6z2IcRvf8aFLcTxeOws7Dn+U2ivHtN5lvGHJY47HgUe3j/9dz8AwHtn+Lr7572dx+w0lofFAMQyT2bCioQGROxwYtSZ19l9AY2u3lOWIKoBRNs7d73XgN9+6B0dO945tvPFEAg+89I7jGJws8xRhyk3YmBslTiVMQdd4VI8Hxvc0AUYVQkQi6BscdnUvsmE1KgKZ7DPTYVmWUehUvE0XL8Xxk8Z2nO4ZxLTSfHx54XRkT3ZOcZMZU7pWhgp72Oj8ilKl75Zur5vVdzxWmIO1tTMxvaxg3LlUvGHGmL+zq9nxdz96qw1A27i/B+mBYhgmc2FFQwNO7nCDnoFhrP3HJgB0y5GMAEbdOI+c7k3ZOHc1nRHfIOyFps6+86TfOx3ntHm7URa8KPlpjEekXKkoMU4WzLPnL+Gbd1dYhr95KaSqCGQyz0yXZVlFoaMqApv2tmDb221Izrf93t7jWH1XBZ5aWqllTOlWGWoknsCh1m78P43tpOODDBu1mlc/vW527/Un/UN4rv4kfvjgvHHKzN83tEmXJDfoEYS62hG0B4phmMyEq05pwq6ZnhWUyjuylaxUQ4zchn1RNzW740TNywxlAaBVhDKj2uTQDmM8lC1YRvAXWTANj9QL99+u7V4oqKwrmWemq7kapfKUGcra37S3BT96K1XJAIB4YtQyvGlvi6sxFedn4adf+6xvlaF0UNfcgTue2YcHXn4H/9ryCek3Vj1odFZNkq1ypup186rynXH/xvdw457jpPHpJmzNLRmGSX/Yo6GR5Jjgzr7z2LjnuKWATbEcyVqSVRUGt+FFpVflkK5rdRzVY+M2kVk1N8HpfFbjMVCJdaY+75KCHBxcV+NbEqfquqI+M6oiU9/S6Rh6Q/EqGlC9TRcvxbHt7fEhJslse7sNT/zZzZZhVBSP2rP33YrqmWWO1wgTdc0dUjlE1B40bvIDVLxifnndZL7jfecvSlcoM4+5IGcSPh0aUTzDFbiPB8MwumBFQzNGDHxja7ejtV8UuyxrSVZVGNyGF8UKaYKo+TjZzdutsqC75KdZqXTbGVzmeftZvtSNIkp5ZlRF5uWGdtxZUeooiIoUQGO8AM3b9JPG9nGeDDPxxOhxX7vrRqkxpVuoFDD6zm7Y/QH5eKceNDorPR1q7ZYO75P57jUqnN+A+l539p3H3/7L/3KlZCQATJ4UBeBe0Ui3ymcMw4QXVjQ8wm3JW1lLshuFwY0wRMlZsGoGpxL774eALZPsqXM8Ye1m7FYRFc0RpZCCASV2PFm5qW/pxBtNZ9AzcCWxXkbAP90zKDyGcpxuj1pQHG7rQWf/EPl4mR40qvkBdc0dePIXx0jHmr+1ur1ubirf9QxcVM7JAEbHvPLO67Gl/qTyOYD0rHzGMEy4YUXDI9wKjiqWZDcKg6owlCyIAnRBNEy9RwyCbDrmRYUsM6oVc7y0ylOa7RlQqxcZys3Cm6bi6WWVygL+tNJ8bccF0URPFbt1IvMurrlnBtYuniXVg0a2QpVsM0Srb61Or5ubynfUEFRg9Ju0flklSgqyU8b8y/c/Jp/DuLZfRSUYhpm4sKLhEW4FR1VLshvrqaowpCKIhs2CH3TTMS8qZCXjVony0iq/pKocX62ejlca2oXHyiqebgT8Ly+cju/tPe4YPhWNjB6XKVitk+K8LDxUXYH/bXoJ+TzVM8o8NS7IlH+lfGvdeN10VL4ryssm3AmwftlsfKW6wvK9o34r19bOwmdiV2VEOB/DMOGHFQ2P0CE4qlqSg7CeygqiXlrwZS331JCOmpuvSelBojv8xSvPgS4lyst1tbgyRlI0/Awdy54cxeq7Ki73HbBm9V0Vwn4a6YLdOjl7fhhb6k+gKG8yivImo0/Q4NMqVBLQa1yQbYboRkl3+y2nvNcj8QTpe2inZAC0MMRYYQ7W1MzApGgkI8L5GIYJP5FEIiE0CvX396OoqAh9fX0oLCz0Y1wZg45wHNWQl7BjCDaA9eat4kVQme/G1m6s2nZIeO7SgqyUeH+vwqp0Pu+ReAKLNu93TI6OFeXi4LqaQNeUMU6RoBXEOK36aEQjEPbRSCdE60SGF23eW53PeFfTGTy2s0k4luK8LDz757dqeUfdfstF77WO76EX31SGYRgrqLoBKxo+kKmKgg505kXYWWRFmyxVaDETts3bap0dbushKVE7Vi9Q9lboWt92QpLBD+6fh6Vzgplnlc7gYYD6bKjKNjDa+wOJUU9HMiX5Wdh0n7NQr0sQpo73p1//LKpn6Csf7PW3XMf3MMhcM4ZhJg6saDBpg47N243lXkbIkjmvn9gJF0urYniZEJL0/Mq5WDH3Om3XVRVqrM6n47wTEZlnI6ts//RrnwUANH7YBWA0pG7BjVNJ74AuL29YPWBu0fE9TFfFmGGY9IEVDWZCQVUWrCz3IqGFghuPgFucPDnU+1EZv6oHScTe9z/GI9vfHff3sHmQ/EZGAJV9NrLKtqpiaqBDmOYwIWvYo8EwjB9QdQM2cTAZgZuKNkayJ3BFSPHq+roRJbIDo/kEdvcVgX3yrtvrfvfNFoyIOt5ZnHfjnuOW/+bmvOlOXXMHFm3ej1XbDuGxnU1Yte0QFm3ej7rmjnHHip5NAsCTvziGhlNdY/NoJBJTcZuUbxQWWDH3Oiy8ieYJMWMkWcdM444V5U5oJeMvXjs6ziNoFH6wWi8MwzBewlWnTHA+RXritqKNXWWYqQXZ6Hbo8C57fd1Qqu8YMrnOsrm6eyJ4fd50pq65w7LPiF3VMMqaOHt+GA+89E6KpduorOSkwomqwfn9/UyXZoh+zIsXDREZhmHcwopGEuxyTl90lMu1ElrumFaCz33/gKeN9NxA9aR8tXo6ftXcqa1srlcNF8PYyDFIRuIJPPm6dedrO+FRZm7MysoPH5yHJ18/hrODw+OOFSmmQX0//SznraIw6JoX0bVZSWcYJoywonGZoBu2Me7Q1fDOSmjxspGeW6ielMWVMfyViy7ZqteV9fSErZFj0Gzdf8pS6DewEh5l5sasrBjK9tb9J/FqQ3tKZSknxXQifD9VFAZd80K5NivpDMOEEc7RgHfx5pnOSDyBxtZu7Go6g8bW7sDnx6uY7TDHghueHEoOhmxcvNPzlbmuV/eT6YzEE3i1wb5ZYDLJwqNoDs0kKyvAqLL9WO0sHFm/GDtWL8DzK+dix+oFOLiuxrY/RqZ/P1VyH9zOi/H+bXzzAzxMuDYr6QzDhBH2aIBdziqENczMq5jtsMaC6/LkmBE9X6+u69V505HDbT3jelXYkSw8Os2hE2ZLNzUkKdO/n6q5D27mxanMs921dYSPMgzD6IY9GmCXsyxBVDaR8Z7oqGjj53ndotvjQn2+E9GD5CfU701xXtY44dFuDp1QtXRn+vdTRmFIRnVe7N4/0bWdqudNNCWdYZjwwB4NAO1dA6Tj2OUcTGWTsHpPwoQuj4vs851oHiQ/oX5vHqqebjkvxhweau3Go9uP2npH3Fq6Mz1kR1VhoN5v17kh7Go6M1Z8wu79o1zbrnqem8IPDMMwbpjwikZdcwe21J90PIZdzlfwO0xiIiSZ6kJH9R2V5+tV1R+35033UtWiUBgAKMnPwpqambbnmBSNoHpmGZ7981sdm9u5sXRnesiOqiJFeX7RCFL6xpQWZKFngBYuZ3dtVtIZhgkTE1rRMKy3FNLB5eyHYOVnmIRX3hO/BNB0FHQzJQwmSC+YrucuyrWIANh0362kcxuW7g27W9DZr9fSnel5NaqKFCVXxhwBKqtkOF07HfNhGIbJPCa0okFpbAUAj9fOCr3V3C/BSneYhJVQBow+m4ZT/6Hde+LXPFldp7QgC8+sqMLSOddqu44sIiE4E8JggvSC6V5fdqEw6udMnZVEQk8lqEwO2XGjSNnNSzQyXsmQJROUOIZhMp9IgrDT9Pf3o6ioCH19fSgsLPRjXL6wq+kMHtvZJDzu+ZVzsWLudZ6MQYf1006wMs6iU7AaiSewaPN+oXXv4LoapUZWxflZAODYO8AM9fnsfb8Dj2wf32FZ9zzZPQ+Db95dgaeWVjqew2ldqK6ZuuaO8RbtwlxsWH5FENT5fHVDuW9j/HYKqpfj9/I9dPud8OsbkY5ePCpulMjkeek6N5QSLqUK56kxDBMkVN1gQns0grbe6rB++p2crStMwk7wkVEwDCjJ/Hvf/xhrdrxr+W8658npeRj86K023PbHJVg6x77Jl926wOVxyq6ZuuYOPPzaeCWrs/8CHn7tKF68LGiGNQyG+q4EVWrV6/fQTSiMn9+ITA7ZcZP7kDwvu5rOKF3feB+/Wj19rJytlaKdqYoewzDpyYQub+tHczC7sqy6SsSqll50g9vyoxRhXIYt9Scd56uuuQOPbH/XMVRB1zxRw/HW72q2LNHrtC4efu0oqXGXmZF4Ak++fsxxPE+9fmxsPGErLyvzrqjmmLhtPhnEe0glzGNLN3SUuC4ryCEdV1qQnfLfsaJcvPjgPPz3L91iee265g4s2rwfq7YdwmM7m7Bq2yEs2rzfk3LjDMMwVCa0R8Nr662dFXb9stnYuOe4FgtjUMm7bqx7VGGcitN8yST8A+7nifr77oGL46zqlE7CVojWzKEPu4Weot7BYRz6sBvVM8oAhKdyjaw1XsVLqcOzSH3u9S2dvlv8MyXBXxeBW/2Jl3r+v87F5ElRdPZfQM+nQygtyEZRXjZG4glLJYOr8zEME0YmtKIBeJfE6PThf2S7dQiPgUx4R5DhX6phEroFGqf5klVqZOfJLLRQrZXA+Hlwo4A5zUFjazfpHI2tVxQNIBxhMLKhULIVgnQJaNR180bTGTy9zN/Qs6BDRIPE/H72DlzExj3B9uTp+nSIdFzP4EXkTI7ib+v+zXG8QfQ2YhiGoTLhFQ1Av/VW1TJthiKQp2MNe68EGqv5klFqZMPkrCzhscJcFORMwsDQiPD35nnQoYBZn4O66nQFs+lD1hov46XUKaDNryhFaUE2egYuOh7XMzCsPT9ERDp+I3Rg9X5a4bfVn/r9a+8axHP1J8Y9s47LYZRra2dhTc2MwPKSGIZhKEzoHI1kdMTeGugKDaJsSIZgBYz3yIe1/KEoN0YVq/mSUWpk5skub+CT/gskJcNKqdGhgFklxi+8scziyPFQj/MTFWs8NcdEZ+7CpGgE/2kurWyx3yFK6fiNcIvd+2mFIch/980W6dwcFSi5gbHCHOw4/JGj6r+l/gSqn/019rV0kq4bVGic2/wnhmHSG/ZoeICOD/rUgmyyhTGdatgboQxLq2J4uaGd9Bu7ZlfJ/25nkaV25926im7NpFjC87MnYeCitcIRgbVgRxmriC31J/GZ2JSUe1lw01QU52c55mkU52dhQQi7cKta4yleSt25C4srY3iFsKaDCFFaUlWOF+6fh+/sak7xunj1jQgyD0Kl2ISfVn+K123V/Buwpf6k8Fyd/UOkNQcEs+72vv/x5TV35dvDZXkZZmLBioYH6Pigr5h7rdTGHHTyLkWwsAplMDeusuqjESvKxfLbyvHjt9oAyCXtU7rzbl11u22pWSsolvCBiyN4/E9n4h8OnU4R7Jw2WZEAQhGcrMJ9JkUjePa+Wy3L2xo8S+wwbYVsMrWMEOqmYIMox0R37oKhFIl6eAQRolTX3IGNe1pS1mJpQRbWL5utXeALsis74M6j7JfVX2QcGroUJ58rAiDi0AAwqHW3aW8LfnT5m51MByeoM8yEghUND9BhmV5cGZP+TVDJuxTBwi7p1mgXmVwbHoClIHr7DSVKXhvd3ZWpwkjFHxXgd39VK6X8iQSQ/9X5KbbUn7D9vZ1ldklVOV58cB427P4Anf1XklFjhTnYsPwWT4oeWAkTKkKoVx47kWJg0CvIuzBIVoqAcPUgsXpGvQPDeHT7u/hhNKJN4AtD9SM3ykLZVTlobO12ZayhKtJOxiFqAQdgdJ3Ztd0Nat3tfb/DUskwSIAT1BlmojChO4N7ibHhAvIptuUBdV5WgdJxeHFlDNXP/jpFwDUfS+3W7CYkg9pZWnRMY2s3Vm07JLzejtULXDVZsxqH2272OkNaZLtwu+1O7UU4zt73PxZWgZN9H4O26Ccj+4zcvl9BdWVPhvp+JhPBqDc1Z3I05Tsl+9x0PXtjLmWMVfnZkzBoCtcszs/Cs/fd6uu6G4kncOf36oWFEQB330iGYYKFO4MHjJMVXTUMKGxQq/b8W8c5WyXDOJYaH+3GayP6LVVI8KOKj91Y3Yb76PR6ySRTz68odV3hyQuPXQmhHLFs7H7QYYzJyDyjvvMXXQnJYal+JOtRNkLyei1ymGQ8Mbq9OSvvvJ6Up2FgVjIAoE/QP8cLDrf1kJQMYOL0bmGYiQxXnfKQJVXlOLiuBjtWL8DzK+dix+oFOLiuBk8trQxV52VVqILFc7+mbZZBbjoy3aeDrOLjRzd7KjLJ1GHtTu1VMzudVezcQB33vpZO8vp3ey2v33On99OKWFHuWG6YGWpFKkpJc2pVK6PDt4yS4YRf1bQMZJ5vJvZuYRgmFfZoeIydFTZMVk9V6ollFakEtemo9FMIqtIXJWF85Z3X45fvf+z5mpLxroRFCDWT6c3sqOP+56aPXfcTCdNcOnmU1y+bjZKCnLHvbjyewAMvv2N7LoonRpc3Z+/7HXhku33RBlmo19UZlkh9vqUFWRnXu4VhmPGwohEgQXZeHokncKi1G40fdgEYHceCG+mW17rmDnJ5Wgp+WeGtUBUSglIW7YSoostW2WRLqGpuAEXwkAkho3oq/Bbo75hWMq7ymZloZPS4dITyjEoKshxDXajCatgaA1Lfz11NZ0jnc1KCqQryry57hqzGsff9j7Fmh3O+kFWVPqey1ZTx6c4pohZZeGZFVVoZ1hiGUYMVjQlIXXMHnnz9WMoGtfXAKXLioOEB0EmQeSlurO1BKYtmIcqui7BKfDhV8JApPRs2IdTgyOleRyUDGBXsjpzuTcukVcozunfudSSjgeg9cVOK2Cso76cOTwz1HP/QeBr/0HjasiqfqCgBMLoW1y+bjbIpOSRvjGh8XlQJS14Hdq/WN++uwNI5tAaXDMOkN5yjMcGoa+7Aw68dtbSCnR0cxsOEeGxdnc8N1tbO9DQvRdSZNkwhHzIYQtQX51yLnb+z7iKsEh8uE6tvNIIrKchO+XtJQRZeuP/2sefqVV6L267DYQ3p0omoU3otsZQ2Zf1Tu7KHCR15T6JzmEl+n2QNN2VTcsZyfxbcNFV57DrzSswY66DctA6mFmTjB/fPw1NLK6XPyTBMesIejQnESDyBDbs/EB4nisemCl1f+ZNp+JcPPnGs/lJelIs1NTNJ5wOuhPR09l9Az6dDKC3IRqwozzZkiWKdD6u1nYqu+HCVXBWrRnAA0DMwjI17jiOa1KNBd16LjpCPMCqZTmFrqrH0TmFEI/GE1vWvK6TQr+7iOjwxlMagySS/T1Nys6QMN8lr0c3Yva4Slgl5iAzDuIcVDQF+bXZ+MCqg25eZNRBtLlSh6/O3lGPBjVO1hVJYCZYGVgImNSzA65APr9eQLqu8rOBhN78GVh2AdQkfukI+wqZkOilPAFwpVnZhRF6sf7chhX73ItGhBNudww7jfZJpzmflnVAdux/evCDzEBmGCQesaDgQpsZbOpDZMJyOlRHOJkUjWqzYskItJSzg6TeO4fxwHLHCXCyujHlSRcqPNdTeNUg6TqQgyggeTvNrxuwFcSt8qHhe7AhTXoGT8vTwa9aViHR13A6qipoVQXUX16EEJ5/jV80d+IfG04Rf0UOT7NaiytjD6M1jGCbzYEXDhqA2Oy+R2TCcjpUVztxu4FShNoErAiYlj6RnYBhr/7EJwBXh/+C6Gm3eBz/W0Eg8gR2HPxIeFyvMwfyKUkfviozgQc3T8aJJm+6QjzAI2RTF2ApZxcqJMIS66FQiVdBhgU8+B0XRWHhjGX5x9IxjiGk0Amxd5fy9kB172Lx5DMNkJqxoWBD0ZucV8ytKESvMEYZPUUrNygpnbjZwmeRzQ8CUdffrViD9WkNGvoqIVfNvwL6WTkfviozg8cv3P5YaZ31LpzZFw4uQj6CFbDcFFnQqc0GHuoSlu7gOqO/TgpumCvM7tq66HUvn6FV4w+TNYxgmc5kQVadkK9OEtYuxWyZFI9iw/BbhcdTNxa7zuW4LsKzSYAiKMritsmLGrzVEnZu+88PCalKijsoJAOuXja4N2fl9uaGd1F2aglchH0F289ZR1SqdK2MZZFIVMJlKa3bVusqLcvHig/M8KwWbjlXCGIZJLzLeo6ESI59Jm52ZJVXlePHBeeP6aABASX4WNhH6aCTjhwVUVmA0rNFO1kQrdFpL/VpDujs/ixJaN+5pQTQKLK6MkZpyJaPLC5iJIR864uAzIZY+0/IGZDy/QTYADTpkjmGYzCWjFQ3VGPlM2+zMGBuLm87gfiKjNJQnJaHLlJtMRocC6dca8qLz85KqcsTjCcsGYsnvjqgplxmd4T2ZFvKhohgbpKNiZUe6KJEyleTMgnzZVTlAAugaGEJja3fKb4MKXQs6ZI5hmMwlY0On3DQj0tHAKexMikZQPbMMf/n5m/GXn/8MqmeUhVYwSw5BcCKC8UnoVmEBIk5+ck6p+Vsy8ytKUZyf5XhMcX6W6zVECc+4d+51pHMZCtZIPIGNe45bHpP87hiVusxNuSjXcEumhXxQnqPTv6WbYmWHV40ddVLX3IFFm/dj1bZDeGxnE1ZtO4RFm/c7hgYagnzO5Cj+8ufv4YGX3xn3W7fNJxmGYcJIJJFICL9m/f39KCoqQl9fHwoLC/0Yl2saW7uxatsh4XE7Vi+wtOQY3hDA2mKqU5jJpF4dXiLbR8NgrMlf33ls3HMcvQMXSVZj8zllntNIPIE7ntln2YHdoCQ/C//vdxaPO4fKenAKESzKy5Z6F2TfnZF4An/f0GarnFj9RheZ9u7o6KORCXMS1tLie9/vwCPbx5capuwLdh52wytXnJ+V8r0Iw/0yDMPYQdUNMjZ0ym2MvF8lL8O6oYaR5BAEamdwIDUsIC97EjmcKjlMCJBrlna4rcdRyQCA3sHhcaFEqutBZ+dn2XdnUjSCr1RX4KWDbdpCXqjCcqaFfIji5UWx9JnyPQlj3sDe9z/Gmh3jwwkBcSU5iofd/L3ouNw/5Qf33+5ZMjjDMIzXTHiPxk+//llEIxHbzcxL66CThQvQ6zVhruDkGTETwailsddCaXB6TruazuCxnU3C8z+/ci5WXA5t8nI9yHjoVL2BuryAmSIs+w1/T7yjrrnDtmmiGSuvHfWdssLooaG7vC3DMIwbJrxHg5JUWJyfhSd+1pTSV8Is0OiymJoVljumlWRkrw4KQYd2JFtLG051YeuBU7bHJgBLJcP4N7vnJJsM7nXfDRkPnWpCrugaiytjaGztdnzuYW+UqbJ2/VjvQff+GYknQlFcwou5NuaWipVH0E1uUjwBPLL9KF6M+rP2g/4+MwyTWWSsoiGqOmQnQCYLNLpc91YW2tKCLPQM2IfWpFNjKhnCYq02FEi3yclWz2kknkA8nkBxXhbOnrd+xmZh3Y9GZdRwFDdVneyusa+lE4s273d87kELyyJU1q5f693L9SMSPOuaO8aVy9564BSK87PwrGS5bDd4NdeyzRStjAw6KhRS174bRcF6r8rGf5p7LRZXxljpYBhGmoxVNAB7C2s0MmolssIQaJ58/Rg27G5J6bqssmnZWWidlIxk0rFXhx1htFbrKlFsPCdKWJaVsO5X3w2qh05njtK/NHfg0e3vCp97mLtCq6xdP9c7dV00nPoPbUUGllSVO4YUnR0cxsOvHcWLPrzXXs61zDtnV43QTfliA8rad6Ns2e9VF/FKQzteaWjnEEaGYaTJaEUDSLWw7mvpxCsN7bZKhkECRmJeqjIgu2k5WWippGuvDjNhtVbrEACA0edkt1GbsRLWw9i7RSUh10rQiUasE+/Nzz2sjTJV1q7f6526LrYeaB373xRvjJPw/sL98/B//fID4TW9fq+9nmuZd87O0+emr08yTmvfjbJF3as6XChuHJLFMBOTjO2jkcykaATzK0rxq+ZOV+cR9d8wI+tyTyYTenUkI2Ot9hNR3X4jl8dpOywvcs65MSjOy8JPv/5ZHFxXM26T9rp3i2qNfsMDsmLudVh4k3PMvSHomJ+z06WSn3sYlS1Abe36vd7vmFYCWZnNEECt+j9QqiSt39Wckt9mh9fvtddzLXo3gVFl+gf3Owvfdr1fRP12krFb+276RgHyexV1DzRQ6T3CMExmMCEUDcCd0J+MzKalankNS2MqnYTVWg2Im789e9+tjr9ffls5jpzuFa6vs+eHEY1EHC2egP5GZX5s8m69d384dyG0jTJV1q7f6/3I6V6hp9aMkwBKEd67HbrNm/HyvfZ6rp3eTYOtq24nVYVaUlWOg+tqsGP1Ajy/ci52rF6AI99ZjB/cf7ujohgBMLUgG5195y0NBW6VLZm5kVXc7AwQToouwzCZQ8aHThno3ugo56NaXksLstGTtGnr7tURBsJqrTYQhQl94+4K/OitNsvf/vitNgyP0KQ8p3XjRe8W1XAK2TAHt4r81VNyXSWhq0C9R5W16/d6V/2+2eW96P5eevle+zHXdu+mSs6CVZ7U0jnXYisils0AgSuK3dqfvZdyXeOb9SuisG73XFXmhrJGwhoyyzCMf0wYRUP3Rkc53/yK0nHdXs0U52fh0FN/iiOne9MidlU1ztawVouE0V4JK6kqdvdglyg9Ek9g93vOG/mupo9J13ZaNyPxBIrysvHtJTeTmxE6obrJqySUuvHeJVffCmOjTJVyv6olglVx+30zPz/q+Upseswk47UXyq+59rqJ4NI55XgxOn7tW2E08xPtL2bsnqtKrhpljYS5wAPDMP4wYRQN6of0minZGBpJoG9w2Pa4kvwsbRtnBP52N04WsssKcoAI0PXpkHKiL9WiNykawfpls/HIduvOugYb97Tg81Xy1i2qAqRyD9QwktKCLPQOWK8bkbDjNC5VQUZlk1f1gKgIunZeCopA57aEp8w9qnha/PbOuC1qYH5+VOGd8k57HQLq51x7/a1OXvv/2tKJVxvaHY+XUTKcFD6ZZHUZxS3MIbMMw/jDhMnRoMTZrq2dhd8+VYtn77vV8UPbOziMfS3ixPLDbT3CjaB3cNi3BGhzrP4DL7+DB156Z1zcvlXisI4425KCHOExKkmb1BwE1XugboL3Xu7wLZtj4VUMs+wm7yahlJowm4yRA2OluDglobvJOVG9R1Eej9U9qPxGFcr3zQq7vBdqztDSOdfixQfnWSY0l+Rn+VLaFvB3rr3GKF5C9ZJSWX5buaOyZTeHycgqbmEPmWUYxnsmjEcDoMfZLq6MObqkqXGlYbLmUEqvdtq442OFubhwacR1nK0X80G1TruJFaZugrWVMdxZUSoV9uNlDLPsJu8mzIFiVd66ah5KCrJdhZ247Zfg5h5VQme8DrcxX8vq+2aEN6k0X6SEsRn3GHRncD/n2msOt/Wk5O3p4MdvteH2G0qElbGMOaxv6cQbTWdSej7JhjD6HULIMEz4mFCKBkDbjESeCGpcaVisOdSKQMa/m+89uWmh3e+CmA8ZId2NgCmzWU6KRqSEHS9jmGU3ebeKoNf5FTqUMrf3qBI642dopFNndpXnItNNvnpmGapnlnl2bxT8nGvAu94QXhmfKEYLYw4X3jQVTy+rdHV/focQMgwTPiacogGINyNdlvewWHN0lfYV4fd8yAjpbp6p7GYpI+x46fWSHbcORdBLq7IOpSwsyr+XWK0/N8/Fb+E9XXCTsybCi/WnYrTQ8ez9KvDAMEw4mZCKhghdwkhYrDl+Jdr5PR8yQrrsMzVbKhdXxlxtlnaWT68FX5lNXpci6JVgqkMpC4vyHwRhVRjSsWO0XQifUQ3qB/ffjqVzrlU+P7VKnwpB9SrKlLA2hmHkYEXDAp3CSBisOX5YZ6cWZOOOaSXC42TmQySAlBGSy43jZJ5pXXMHNuxuSQkZixXmYsPyShxcVyO9WTpZPhdXxrSsNae5kgl/kVEE/RYQdShlYVH+/SAdBHgvvQJeQQlFXbPjXWxFhNTEz4rkdaraBNOOoLx1YVV0GYbxlkgikRB+x/r7+1FUVIS+vj4UFhb6Ma7AMSxWgLUwIlvJJMhNfySewKLN+5VLX1KRERBE80ERQBpOdeGBl94RXuunX/8sqmeUkZ4pADz8mnXTLADSVXTsLJ/ma7pZa7qFNcr5ghAQRevYUMoOrqtxpfyFVcCVIR3uj/JuhGWsyTS2dmPVtkOkY91W3apr7sCTrx8jl7GNRuDYIb44PwsvrJqHBTf5m6jPMEzmQdUNWNFwIB02ayp2QjaVCEY3qZzJUXT2D9keA7gXEKgCyK6mM3hsZ5PwfM+vnIsVl0vPirwLdzyzz3FTL8nPwv/7ncWkTdoQjO3CH5IFY6tkXfNas1LO9rV0ahXWjGt0nD2Pd3/fiwSAiqkF+PLC6ciePFoNO0gBUacBIB0s/iqkgwAv826E7ZlQvzvA6Dt8cF0NACivtZF4Alv3n8SrDe04e/7Kt6n8ch+TkoKcsfP2Dgzh0ct9TZy+8+m6jzEMEx5Y0dBEJgkjVkJ2MkZZW7uQkh8+OA81N1+DBZvqU0oewnSsGwFBRgA53NZDsizuWL0gxWVv90ypHpK/WjobVxfmCNcD1fJpjM9prVk9u1hhDi5cijuWYZZ5Fk7rI1kZC1pAzCQDgAjZ70+6CPCy74ZO3H7TZTwawGh/pp2/+8j1erUbt/nvvQMXsXGPc3fxMCmdDMOkJ1TdgHM0BGRSXKk5Vt+qM7ioDGZja7etkgG4K8cKyFUWUs2lsXumja3dpDF+b+/xsf/tJDDIJi/bjcu2d4SNZ8lA5lmI+qx0XO5T8XjtTM/K8VIR5ZxkinHAqy72Xj8fCkH1GNKhpMomam+pPzHub9S+L8lYfR/s7mf9stkoys/Goz89muIFMXDbo4dhGIYKKxoZip2wJVKcREKc1wKCzPn1J/bKB5V1jgngszC9LF9bRSnj+XX2ncfGPcdd5daI5lSmz8qrDe1arukWJ6UsE7wdqo0Jw9Qk1Ikgygy7bfZoYHx3nHK5ROgQ9J3u59Ht7+Lx2lmWSkbyGMKgdDIMk9lktKKRKZZNO8z3d8e0Ehw53Yt9LZ3456aPUzrLyghbTsqI1wKC7Pl1VvVaeGMZth5olRqvscknWy3dVpQShbjJIppTmT4rToKLzDW9QJcgGTR+dLF3+3zcflv9LjOso9ljMkuqyvGD+2/Hmh3vOiZfO+FG0Kfcz6u/bSOdK2ilk2GYzCZjFY2gLJt+KTdW9+dUcUSXsOW1gKByfl012hfcNHUsT8UNyXMt63ERhTDJQH0WsoJGcV4W+s4Ph6oPhW5BMkj86mKvio5vq99lhr0IKVs651psRQSPbB/v2TDfkxMqgj7lfqjfsXRuTskwTPiJBj0ALzCENfOH2BAA65o7PLvuos37sWrbITy2swmrth3Cos37tV/P7v6cLGvGP333zRaMqJrgcEVAAK4IBAY6BATV8xtemBVzr8NCxdKNk6IRPHvfrQqjTiV5ro1Gf7Gi1M08VpQ7TumjhjDJQHkWsoLGQ9UVANw//5F4Ao2t3djVdAaNrd2u1qWMIBl2dHSxB7x5P3V+Ww1vJOXdcItXIWVL55TjxQfnodziHtbWziSdQ0XQp46zOC9r3DowiGBUQczE5pQMw4SHjPNoBGXZ9Ctsw40wqism1+smhEE2OVxSNSo4bNj9gTDZ2onkuaZ6XGRCmEREI8DWVbeT5kpkBU+mvCgXa2pm4DOxq1w9H90ex3TJTaDgNvzJ7v25pjAHq+bfgKFLcTS2dkt7/bz4turyRoo8yV6GlNndAwDs/N3vpbxLVI84dZwPVU/Hc/UnM745JcMw4SXjFI0gqq74qdzoEEZ1CFu6BISgzp+MeXNfXBkbV53riZ+/h0/65RseiipKJV/75CfnNNzNKPEEUELsnD4pGsH6ZZWWISDJRHBFMHHzfLxQyoNILvYKHeFP5ufT3jWIHYc/wpb6k2PHyCp2Xn1b3Vb2oyitXoeU2d2DTHiYjPJNvZ81NTPxmdgUZaNApuc5MgzjPRmnaARh2fRTudExbl3ClnlzNUJhdG1KfpQWpm7uG5ZbCwwinOZad9K3Gbu1Yld33wmrOVF5Pl4p5X4nF3uJrvwF4/nUNXfgufoTrhU7r76tboRZqtLqd06IAdU7K6t8y9yPqlEgUyq4MQwTLBmnaARh2fRTuXEzbi+FLad67iUFOejsv4CeT4dQWpCNWFFeKCxjMpu7ncBgh2iuVZO+SwuysGr+DXiBUB2rzMKjIavcfOVPpuHzt5Rre15eWsX9EiT9sPLqCh9UUezs7s+Lb6sbYVb23oIKyaT0fVFRvmXuR9YokCkV3BiGCZ6MUzSCsGz6qdzIxNMn46XVzm5T6ui7gEe2v2v5m6AtYyqb+/hwlAFsUYh/dpNn88yKKny+qhyvHz0jXANP/Pw9bFgutpraEQHwLx98gvVfvEXbmvFSKfdDkPTTyqsjfFBWsXO6P9VyzXa4FWZVlFY/QzINRIqpG+Xbi/vJpApuDMMET8YpGkG4yFWUG1WrqNP9OeGV1U5VaO5IEib83vgB9c3dbBlUiX92k2dTUpBDzqn4pD91jmWfkxf5TF4r5V4KkkFYed2GD8oodpT70/Vt1SHMqiqtqiF/qvlIIsXUrfKtO8Q0XbrLMwyTHmScogGILZuLK2Pacwm8SvqTuT9zH43SgizcO/c61FbGPBPe3QjNCQBPvn4MG3a3oLPf3zhgXZZ1FcHWTQidIRCKciqAVIFtSk6W8nPSmc/kxuNIFfa8yO1RFYyDTqalKmxlV+XgL3/+nvD+Dq6r0eI10iHM+uVJVv1eUxXTsBUyyKQKbgzDBE9GKhqAtQB4x7QS/PB/tuLJX+xL6XCsQ7D1KulP9v6OnO71Vahxu9mMNpVKbSxFmQu3ApzOzV1WsHUjMLR3DeC5+pNkz4QhsDV+2KV8TavxeuGRc7KKB52YqiIYBz1mgK7YIQHy/enwGukQZv1qTqjyvZZRTMNWyCBsig/DMOlNxioaQKoAWNfcgfl/U2/ZLZUq5IuEK6+S/ij3Z6DTkksRJr3YbERzoUOA83tzT57LsoIcxApzpcrlGuPZcfgjxYZ+8gqn3Rx45ZGzs4qHITFVVjAOw5gBumLXNUDrGSMq10xFhzDrdZism++1rGIaREUsO8Km+DAMk95ktKJhUNfcgYdfs49npwj5VOHKaQNOp9hX6v2qJqeLsJsLXQKcn7k8VnNZnJ81tu5E82aMYOWdN2BL/QmlMSy8aSp+cfTfyc/Jbg689MhZKbJhSUyVEYyDHLOVcYCi2DW2dpPOr8uwoEuY9bIAgJvvtaxiGmSTUjNBlQJmGCYzyXhFw9j0RThtGrqEKy9jX3XGgtspZlb3q5qcTiV5LnQLcHabe0lBFp5ZUaWtSpHV2um77Fkrys9K8bIV52cBQMrfDGFj6FJc+vqGwLbgRnurqRVWAo4fHjkzYVDOR+IJxOMJFOdlpYRcJpMsGAc1ZpFxwEmx023FFn2PdAqzXhUAcPO9VvHYUDzifuX7hEnxYRgmvcl4RUM2Wdm8aegUrryKfdUZCz4ST+DJ149Z/ptT2VeZHhMyJM+FFwLckqpyxOPAd3Y1o2fgIgCgZ2AYG/ccR/Ry7X0D2Y2esnZyJ0fx069/Fl2fDqHsqhwgMboGewYuovSq0RAr4zpUq7OBWWCze07J/U6c7o06/7892YUTfziH0z2DmFaajy8vnI7syVGpsRsEnZhK6TtinucgDApUY4jde6FT8K9r7hhX4CFWmJtSahlwVvTvnXsdivKyMRJPaFNaZXHzvVZV3OzuI4h8nyBKATMMk3lkpKKRvBmf/ORTqd+aNw2dwq0Xsa+6Y8G37j9lmcdiYHe/VpuS0XHaav5ihTm4cCmOvsFh4VwYz/NXzR2ke5AR4OqaO/DodvH8qWz0lLXT2T+EaCSCnMlR/OXP37M8P9XqbMbK+uhGeKDO63979XDK+L639zhW31WBp5ZWkn6fTJCJqdS+I+Z59tugsH7ZbGzcc9y1MUSHFdvWG9p/AQ+/dhQvmr5HyetxX0sn/rnpY/QMXMTLDe14uaE90H47br7XuhW3oPJ9vFDgGIaZWGScoiHb+TiZcotNgypcdfZfEJbM1R37qjuUZSSewKsNbaRrW82L1ab0+apRIcKqM/i+lk7hXOxr6ZR+nlQBjjp/8XgCj25/V3qjp66d+pZOvNLQLjy/aP0kAKytnYnpZQWelH+lzqv5PuIJ4Edvja4rWWUjqMRUSn+Y4rwsvPDAPCy4cWrKPPttULBrimkgYwxxo4g6eUMNnnr92Ljv0aRoBH3nL+JVwjvgJ26/1zoUt7DkKDEMw6iSUYqGbOfjZCKw3jSowtXGX36AngFxyVydsa+6Q4kOt/XYxqCboc6Lk1ArmgsA0p2sZQQ46vx9Z1ez0kZPnaM3ms6Qz+9H7LRdeI7bxP9tb7fhiT+7WSqMKqjEVErI5dnzw4hGIoEbFKhQFV9VRfTQh92O3lAA6B0cxqEPu1E9o2zsb2EWpt2+b27Dj3R/44Pu68IwzMQjYxQN1Q7VAFCSn4VN991quWlQhatkJQNwtsTpin3VHQtOPa44L8tWmJfdyOzmAgAWbd4vpWQAYgFOJazO/GyTcdroKZbt0oJsdF/ODaGe3+vu104hYm4S/+MJ4P/5bTuqriuSGrcuy7AXTRXtjvPToEDF674H1Byi357qQjQSGXsW8Xgi8IR/J9y+b27Cj3R+u8PQ14VhmIlHxigaKptxcV4WHqqejjU1M203DUq4ihUiS5xhITY2r8NtPYE2nZM57qHq6colgO0EPvNG3NjaLfU8KQKcm7A6EXahZCLL9oq51+KVhnbp83sRO02NBbcSoPOzJmFweER4jS37TqQcRxV03Ah7KgKWjnfGb4OCHf71PaCpnn//23a88D9bx/47P3sS6XdBdqIOKldB17c7LH1dGIaZeGSMokHdhNbccxNmXjNFatO3E65UrNEGuprOFZvKo5opybf3PlidT+S9KcnPwpqameP+TtnIAJDvmZx4vHAavlBVLnyWKmF1FI+Dgd1GL7JsF+VlkxQNr63RMuErVgJ085k+fG/vceF1zMqIjKCjIuypCli68ix0CKgyzz7IvgcLbyzD1gOtwuMGLqaugcGLYgUVmJidqHWswzCHpjEMk/lkjKJB3YSqZ/yR0sZvJVx19p3H2p+9J/ytWWj207okI1iLemJEAGy671alpmpPvX4MvRJd2anP8wsOJTsp47PDuMONK6qwcU+Lq43eybI9Ek/4kuwsCh069KGzB8msNJsF6DumlWDTr44jLhlPpUPQsbs3NwJWmJqWUYXN9csqx1V587PvwYKbpgoNHyr455EJHzrWYRh60TAMM3HJGEXDj+o0ZuGq4WQX6XdlBTlj/1undelwW49wUz87OCzdV8Ku14KdwELZyKyUDOPfrO5Z5/NUCatLFtCiUbgWOO0s234ItCLvWV1zB578hXO1IAM7T1P25ChW31UxVl1KBjeCjtO9FeVluxKwwtK0jLpGllSVj1V505m7Q81vmRSN4Nn7brUsb6uK30pdGHG7DoPuRcMwzMQmYxSNQCyQ1FMlHafTuuTVBiIbW+52g7K6Z53P021YndcCp5fnF3nPvnF3BX78VhvZ2+PkaTJK1257uy3FsxGJAAnCBajPyRB8910uC2ymo2+0Z8NX/mQa6XxO19WZeO+m4g91jejOJZAN8VxSVY4XH5yHDbs/QGf/0Njfi3Ino+/CJenrcyfqUdysQ6970XAlK4ZhnMgYRQPw3wLZ9emQ+CDTcTqVA682ENmNQ1fstHHPxvWHLsXxeO1M7Dj8UYrQIvs8dYTViTZ6t5utrCBBuR6lLOq2t2lKBtWD9NTSSjzxZzfjJ43tY53BZ109BV9+9bDwGpTnJJPQ/w+Np4XHUK6rQ3jf+35HSvd5QD4nS4fSI7NOVUM8rcYZTyTwwEvvkMdJzb2aSKiuQy+9/VzJimEYERmlaADelv40oyLo61QOvGoMJrtxuO2vYHD1lFzL68cKc7G2dhaml+UrPU+vE3t1bbZUQYJ6PUrImExOBdWDlD05iq/ddePYf+vKQ5FN6Bfdm1+x/5v2tliGlHUo5GS5UXpk1qnbEE/zOI01QA1h/PwtMdfKHVvaR/HK28+VrBiGoUDvnJVGGJvcirnXjSWueoEhwNqdPYLx3cZVfmOHsYEYvzOfB5DbQIyNwywMGBtHXXOH0jgiAIrzs4T33Dtw0fL6n/RfwHP1J5AzOar0PHXPUzKqc6aKzPV0xlw/XjtLWWjQMf9u+uRY4Vfs/973P3bMW0lgVGAfkc2il0R2ncqEeFIw1gB1pp/4WZOrd6euuQOLNu/Hqm2H8NjOJqzadgiLNu/X/j6GnZF4Ao2t3WPe4WsKc1L+PVaUq6QQULylfqxrhmHCT0YqGhSMD/CupjNobO1W+iCqCFC6hV4jXCxWlOoBkd1AZDYOq7kTjePZ+25NuUcD47/XL5uNjXu827hU5km0RvzebGWvp7Mc6PSyfFe/d7tO3TatKy3IUrquG0biCXxnV7PwOBmB3eladmtVZZ16kf9lrIHi/CzhsZ/0Dykr6nZKlZG7s/f9j6XP6YSOvcQLzMrWlvqTACJYWzsLz6+cix2rF+Dguhqld0C3IsowTOaScaFTFHTGlarkhejOJdERLkbdOLbuP4Wdv/vIdu6cxiHqJ+F1CUaZedr7/seXY+qvVMsyrxG/y0bKXo8S0kZN1NahtLhZp269M+u/eAtihbm+htEcbutx7CqfjJv7E33PVNZpcqU8J0Trwhy+tLgyhsWVMWzdfxKvNLSj77xcNToRFM/Xmh3vYisiWDrHvZIZ1hwFu7Amwzv8wwfnufomcSUrhmGoTDhFw4u4UhUBSncuiduEVeqGsKX+xLi/medOJaF6V9MZreO0gzJP1Jh6vzdb2etRYrO/Wj0dLx9sdzwfNZSPguo6davoxApzfe8RIPPcVe+P8j0buhQnncsYb11zBzbsbnE8lpLf4iSEP1Y7C//btFI88LJ9griKok7NS3pk+1G8GHXn0QprjoIfDfq8rmTFMEzmMKEUDS8/wCoClO5SlG5wsyHIzJ3dPYdl49r7fgcppn5xZczVmFUSVVWu5+Q9W35bOXa/5xyaEkE4ehioFhwIstkb9XlNLchWGh/1e/Z3//k20vmMYgyihHu7EM/kNd3eNYjn6k9oVYB0H/vk68cwJScLCxTyvkbiCWzYHc5u2354Wv3oW8UwTGYwoRQNtx/gTK5i4rZylNPcUeaNcv3i/CzEL+eIeDHvsjH1qputariF6vWsPEm9Axfx6HZngVIlBMSrd0TUtd6KoJu9Gc9LZGHfuKJKaXzU7xkiIK2bO6aV4HPfPyCc22sKc7Bh+S0p64JadlhVAaIic+zZwWE88PI7Sut86/6T6OwPZ7dtPzytgfStYhgmLZlQioabD7DuWNywKS2ijYOqfJjnjjpvFEHSjWBAYTSm/qL4QIzep8xmazzv+pZOvGzRZI4SbuFmc0/2JI3EE1i0eb/jM51akI3ffOseZE+m14vwOl7dqWu94Z0JsoO3meTnZTfX37y7QjlXgPo96/p0iLRujpzuJSXc/4//MhfVM8rG/lu27LCsAiRjFacqd8nIhjrVNXdcTqwWIyPMJ+8JZQU5QGT02cnuD355h/3uW8UwTHoyoRQN1Q+w7ljcsCYQOm0cK++8nrS5Js+d7LzZXd+MVzHQKjH1lM2WYu2lhlssrozh8dqZeLWhHWeTEmlFm3uyENN1bkgoiHUPXMSR071ka6xf8epOeT7fXjI7VMq7MV6r9VFakIVnVlRh6Zxrlc8t8z1beNNU4Tql5kklNyB1U3aYqgDJPENDuXv4taPk38iEOhn3S8XuGZkNTb0DQ9i457jteymzP/gZ1uRn3yqGYdKTCaVoqHyAded1hDWB0MBu4wCAnb/7vWNoU2lBFu6YVgJAfd6M6x/6sBuP/vRoijBN+b0bqIJbaUFWyhpx2mxlrL2icAsrhaU4LwsPVVdgTc0Mxw7P1G7ayVAVLz+ST5Oxy/MJU85TMl4JY7LfM9E4yq6iVZpKPs5N2eGyq3JQPaNMu1V8SVU5fnD/7Viz411yQ0pqqJPM/doVUVB5H2X2B7/DmsL43oUtYoBhJjITStFQ+QDrTKzzWyBTxW7jEIU29QwM43PfP+C6XO2kaATRSMRSyaD8XpX5FaUozs/C2UHnkqTPWMTUW82ZqrXXLnTPSmHpOz+M5+pP4DOxqywFENmwlmSoipffZX7TES+EMZXvmeM4ZGKfLuMmzv+JnzWN5XroVsSWzrkWWxHBI9vpng1AfD8y92slzKu+j7L7w0QOawprxADDTFQmXMM+2cZhKnkddg2cdDQ5CrI5lN3cJWNY3va1dJLOaTe/XiY0upnD/OxJ+Dxxs1K19poFfNVmig0nu2wr4zgRwWiORmffedL8cE394NDVsBMAugaGxAeZjnMT55/clM9QgFbMvQ4LFapAWbF0TjlefHAeyh2+V2ZE90O937W1s8bNvdvu9rJN8JZUlePguhrsWL3AdYO+dMGuWaOxL020zvAMEwYmlEfDQMaCJpvX4WRNcVvO0Q9LjcjlvKSqHDU3X4MFm35tmThtWN52NdG679rNr1cJjU5zWJSXLfRmDF4cIVvmZQVru9hpejPFk9j5u9+76qBtnK974CLW/uw9AOI1FpbSxBMVXR4BlefoplqdH15cSigmQM9boNxvrDAHa2pmjPu72+72BjLflTCGNXlFukQMMMxEY8J5NAyoFjRjY7H7LEVwJRa3rrkDDztYU9q7Bkhjs9rw/bDU1DV3YNHm/Vi17RAe29mEVdsOYdHm/ePOfeR0r2N1JkNQLS3IIs2bFXdMK4FoL4hGMJYTQkE0h/UuvTBmZARrp9hpejPFk1oEGTOiNSbzjjDeoMMjoPIcjfAt49/Nx4uQtdKrMCkaQfWMMjz757ciYjEumbwF0f1GAGxYfovleXR59Fhht0ZHxADDMPqZsIoGFcpGavz7k68fszyHYWHZcfgjxArlBTKZ0BlV7ITwjr4LeNgkZFI3zHvnXgdAbWM/crpXmMgZT4weR4Eyh28Qq+44VZJJDsm6Y1qJo+CWjFOoi9eCxfpls7Hlv85FaUGW5b+L1hj1HWErYrhRfY5O4Vtfq55OurYfYXW6wsxUz+P2PWaF3RkO4WSYcDIhQ6dkoSTWPV9/0jHsJgGgs38Ia2tn4bn6E1LVQLxOtqXEDj/5+rExlzN1w7y2OA8PVU/HPzd9nOIBoSQk6t40KHPYM+AcNgXIVZIx+jv8+K02294kX62ejsWVMcdQF7fNFO0wwkW+Ul1xuYeIevJ9OiefpvQvuCoHSIzmIaRrtRo3FXdUn6Nd+Nbhth7LvjFm/LLS6wozUzmPm/eYFXYxHMLJMOGEFQ0iThvLSDyBVxvaSOeZXpZvu5GvXzaaJ7Cr6UzK+b221FBih88ODmPr/lN4rHYmacOMRoCNe46P/feU3En4z/P+GH92SzlpY9e9aeiyYq1fltqA7w/nLqC9axDP1Z+wLFn847fa8I27K1w1k9PVTDEZs+CiY42lY019UanRdKtWoyOPS/U5WuUD+NnTgYquvAXZ86h0tzfwW2FPx/KwYVxrDMOwoiGF3cZyuK3HsRRrMkbzLPNGPtqwyVpAoArTZQW0OvhmqELmq79tG+vXINowzRE25y6M4NXfnkb25Chpc9a9aeiyYpUUZJPr4BsJiLvf68BvvnUPjpzuVd64nZsp3oAt9Sek7sMQXBZXxtDY2o2Tn5wj/U40j6pCXBCCDaXUaFj621Cwux8j/PEH999ObhCoUxj3s6dD2HHqbr9+WSVKCrJddwZ3S7qWh+W1xjDhJJJIJISGlf7+fhQVFaGvrw+FhYV+jMs3dAg4u5rO4LGdTcLjivOycGT9YnJtdeOoF+6/HRv3HBe63GOFudiwXH4zaGztxqpth0jH7li9YEwAsdqQopHxSoaZH9w/D5+vEltMjXkBrDcNGeFvJJ7Aos37XYcffa16Ol5paJc+R/K8ucFqvQJwvLcIgGsKc/A//svcFMFlX0snuXGYodgdXFejfaMOQrAx1kPQ964Lyv1EIsBX/mQ6/kwQqucF6Sq8ekVYPQaivShdFG5eawzjPVTdYEIrGro+SFRBfW3tTDxWOyvlbyIBwRBy1i+rxKPbxwvd5mMB+c1gJJ7AHRv3kbwyz6+cixWXk7yN3xobZte5oZRwKTtyJkeRnz0JvUk5LXbzrnPTcFJcqIpDaUEWKZfDjHnedCOrlMk0DvNSyAhKsJFRrg10KYtWuBU8Ze8nCMHLK+E6rEJ7ukHdi8KscBvwmmAY76HqBhM2dMpOwFEJlaDkLJTkZ2FNzcxxf6cmepcUZOOHD87Dht0foLPfurFWcq3wguzJeKetB0ACC28swwKHkpeTohE8VD0dW+pPOt4nMD50JjnEYhexatPQpfi4niJ2864z7t8p/Gj9skps3NPi6BUoLchGt0NZXyeunpLr6eYnk8Qr2zjMLj7c7f0EWfdeJWfHq2o1OpRp2bF1BBASZheO5WYdsfVaH14XHfGTidQ/hGHCzoRUNHQLOKKchQiATffd6qq2+h/OXcCKuddhSm4WHnjpHdvjjM3gy68cHvvb1gOtKM7PwrP33Wq7+a6pmYlXf9vuWDmrOD8L8cvdp900N7Qbt92869w0nBSXaBSO8b0r5l6LVwgVdJIxrIC9A0PjrIW6BSKre7tjWgmOnO5NKTBAbRy25p4ZqJ5RZhvW5lbAC1KwUVmrXlSr0WXwUB2bW0XOrbLpZh2J5u6F+29HSUEOW7WJcHlYhmG8YEIqGl4IOE5Jfjq7Knd9au3NEHF2cBgPv3YUL9oILpOiETx7362O4TRnB4fxwMvv2N7T/IpS5dAiwD+LmZ3iIvIKFOVlSykahkiz/LZyPLr9XS3eMxHJ91bX3IHPff/AuPX4haoY6Vwzr7nKcp50CcdBCjYypUa9qlaj0+Bh3I9Mw0a375tbZdPNOqL0xVmz492UfDH2dDjD5WEZhvGCCdmwzysBZ0lVOX7zrXuwftls/LeF07B+2Wz85lv3OG5sst143X7kN+z+wLaxnyFolxc5X8OuU/SkaATPrKhyNT5gdN7Nze/cNCOUYUlVOQ6uq8GO1Qvw/Mq52LF6AQ6uq8GSqnLhszITiQBfv2u0tK1qs0XVeXDqgk5VlqzWms7mkUEKNk7N6ZLxslqNzk7Gyfcji913zmntOa0vpy7yyed2s44oXjnzT6ljSxd0fyNVOsMz/hDUfsgwOpiQHg2vBBwrC99LB9scrWiyJfncNm/r7B9ytGAa4TeHPuzGoz89apkg7mRtXTrnWnzz38/iR2/R+opY0d414HmYkRN2Hg/ZOvjxBLDtbed5cLIqq1qMKZbyiEN1MCcLvk5vYNB17+08WMl42b9At8FjSVU5fnD/7eMs+SKsvnNOa29xZcy1J8btOlLxcnmd9+MXI/EEtu4/hVcb2lK+z26/kVweNpxwHhKT7kxIj4YXlhs3Fj5D4ImZPAmxotxx4QNUS6wTok16UjSCaCTiWIXKydr61NJK/OD+eSgtyJYaVwSjSfNb6k8qW0pVoVqM7J6VW8zPxM16oghxxu2Z15CVQJE8Nw2nuii3QxIEndYyRbDRYeUze7B++vXP4qdf++w4b5YdbsbghcFj6ZxrsXXVPNKxdt850drbuv+Ua0+MWyVL1ctFGVuYrcd1zR2445l92FJ/Ytz3WeYbaXePMnsR4z1uPYcMEwYmpEdDt+VGR6y1THWlxZUxPF47a5xFiwplk3YrCCydU47PV8VwqLUbj2639oxYYbele2mNlLUYJT+rhlP/ga0HWl2PIfmZuF1P1Gf3terp2Nvc6Vilitqc0AxVEJSplpWMTiufarEBt2PwyqOzdE45Xow6e2rsvnOUtffqb2neSqd16FbJcuvZtRubX9ZjlST6uuYOPHy5hLUV1G+k6B51Vvpj1AmyKh/D6GRCKhqAuoBjha5wEorAY7VJFOdl4St/Mh07Dn+ET845J4vHCnNIgousIGC3cVbPLMOzf36rZY+HZMoJHa69SBZXTUg1npXbRGUrYdLteqI+u9rKGJ5eVmkrUMj02nC6HxGygo1TB2y/SrbqSIj3MlQleU7rWzrxRtOZlCINdt85ytpzqkyXjNM6dKtkyYYxUsams+S5EyrKjCF0ihB9G6j3yOVhg4e6Dxxq7Ub1zDL/BsYwkkxYRQPQ16PBr+o5dptE3/lhPP/rk/jG3RXC3IgNy28h3Z+MIECxkFkpdVMLsrFi7rVYfLlT8S/f/1g4LkBfFSIdFiOZEA6qMOl2Pck8O6feBjK9NozzAmrCMVWwEY0rAe+tfDotjToNHmaMOV1401RHhTIZ6torzstC3/lhZU+MDiXLbu6iCvlHflmPVZUZaklqA6vnyBby9IL6Lj66/Sie/XP70vUMEzQTWtEA9PRo8KN6DmWT2P1eB35w/+14+p+bx1kdRX00zFAFgX0tnaSNk6LU+V2FSMVzYPbc3DGthCTUGw0BKcKk23nQIcTJCjaAt4nTBpRxeV0iWXd5bD9CVajfOerae6h6Op6rP+nKE6NDybKau96Bi3h0+3gPqtPY/Ojp4kbQlzWuWD3HTGrIZyYTO4FT38Wz54d9b77JMDJMeEVDB35Uz6F3EM/Bke8sxqEPu9HY2g1KZ3A7RILA4soYFm3eT944RcKO6jyqbjKyngM7z83y28rx47faHIWuJVWjOSuUceqahxfun0dWbsx09p13/HeDNffchJnXTPFtc6eOi3qcCl54MMMSqkJde2tqZuIzsSmuPTE6lCyrufuhRY6K09jqWzpJ13LjTXUj6MsYV+wKmWRqQ75MqMpktYfdMa3E0Ttnhr1RTFhhRUMDfpQFlNkkJkUjqJ5RhuoZ7uM2nQSBxtZu8sZpdKR2EiZU5tHNJiPjOXAKefjxW234xt2j/TKcBBuqMKlzHv7qCzfjk3NDON0ziGml+fjywunInuxcbK6uuQMb9xwXjhMAqmf8ka8Ccs/ARa3HqZDJjc1k1p4uT4wXSpbM2Pa+/zFedtFbhoobQZ+a/B6B/V6TieuWEoq2uHK0KEnjh10ARtfaghvlDW9eYfftXnnnDWQlI529UUzmw4qGJryMtQaCb27mppZ9fUsn/s+fNZGUAZl5dJu8SbXe3jGtBJ/7/gFh2NpvvnUPjpzu1eK+1zEPHX0XsGZnU8rfRH1dqAngXve4sKP0qhytx6kQdP8Pr5FZe2HxxFhBGdve9zuwZse7wnPpeKZuvuGU5PeS/CxscgiPpSgr0QjQO+BcUCQsUELRnnz9GJ78xbGUqodbD5ySDiX2Cqc9zKkwih3p5o1iJgZppWhcvBTHTxrbpayzfrKkqhw1N1/jyRjDKNxQN04ra6GTMkCxRupIbKRab4+c7iV5bo6c7nUtdJld6CLlRTZp22neZc8VRPOuWCFtzVGPU2EiNDazegfvmFaCI6d7savpTEbEwdc1d+CR7fblYpNJgP5M7fYpt99wOwWwOC8LD1VPx5qamY7jS163dsQTwKPb38UPL3uswoybCmlnB4fx8GtH8WKAeQ2iPUyFdPJGMROHtFE0Nu1twba321Jcid/bexyr76rAU0srgxtYEiqdwamEUbihbJx2HahFyoDIGqkrsZFivd3VdMb298noqCpmFwa2Yu51lr+RTdp2mnfquUoLsvA39wZjDTTWnNM4ZZttquC1BzMMJL+Ddc0d+Nz3D4QyDl4lR4taLtbgq9XTSfcp2qd0VNpyE662pKocL9w/D2t2HHUMy0mHeH8d1vsg71Ol4IYTfnz3GEaFtFA0Nu1tsSzbGk9g7O9BKxt+1GAPm3AjUn4SABIOm5mbuFKdiY2izduPsDXV9aOy2drNO/Vc6794S2DCpdkq65XCTRFeJ0pjM7/6S6iOTSVHS1bIW1wZEx5D3afcfsPdhquVFGQ7KhnpEu+vw3of5H3qDnNaflt5xn170pVMrILmhtArGhcvxbHtbefeENvebsMTf3ZzYGFUftYnD5tw46T8fKEqhlcISZYqH9yyAloMPvU4p83b67C1kXgCG3arrR8dyanGR/HkJ5+SfudlWBIFrxVuGeE16BwFrzc0SnjHk68fw5ScLCy4PA9+fZvcKEAy3xyKpVhmnwr6G54p1afcdoc3COo+qXsTld3vdeDbS2ZPaIE2DGRCFTTdhF7R+Elju7DyQjwxetzX7rrRn0GZ8Ls+edDCjRm7jfNwWw9J0Tj5yadobO2W22yp31IN31yvw9a27j+Jzn619eNmszWqaZk/inZ4kQekKih7JayF2Xpvxo8NjWL5Pzs4jAdefgfF+Vlj/+3VeAzcGndkFHTKuy27TwX5Dc+U6lNuu8MbBHafmvWBdPBCZTrptH/4SXgyqW043TOo9Tgv8MpCNBJPoLG1G7uazqCxtRsj1Fp3AWBsnCvmXoeFl3t2GEKw6Hu69cAprNp2CIs270ddcwfpel2f0iqjUI8TYVjRY0Wpm1KsKNfVx6OuuQNb6k+SjrVaP8ZmC8jpXuVFo43N/uK1o2QlA9CbB1TX3IFFm/dj1bZDeGxnk/QasFpzbqBY77/7Zkso3kNjQzM/O2NDs5tD2W+KzDfr7ODwuORb0XhUkTHuWEH5NkUjwA/uv530bqfDPmUgunfj+5AO8f623+XCHBTlie2oQd6nrr0pmbB7oTKN5O9pw8kux8gEIDz7h9+E3qMxrTRf63EyUK2tXliIMsH9JmtxktH6g7DK6baiyyak2t2LXSiRFcZI1y+bjY176BWm7MKSVD0SYbT8pEvnZFVrvso3pb1rwNVYdYeOGrg17lC+TVtXzcPSObQ1GOQ+JUsYC4u4we67vK+lEw87VNgCgr1PLzwpZR6W9WZSkYkGAMKzfwRB6BWNLy+cju/tPe7olo5GRo/TicymrDuGP4xCmCoyQrCMUBJUuV+dIQ8yCakiy5vVZts7cNG2M3hRXjbp2mvuuQnVM/7IUoFQVYb9zGmSIajYdVllTUUhUvmmjMQT2HH4I9XbchyPW3QYGuy+TSoGnaD2KVXCVljELVbf5SVV5XjxwXl48vVj4zxtop4jfjC/ohSxwlzHsFlpJp6xPBCo/aas0Pq804TQKxrZk6NYfVeFZTUPg9V3VWhNBJfdlHVaiMIqhLkhWQhuOPUf2Hqg1fZYqlCSCVY5GYGVci9Wm+3nq6w9MNSSvTOvmWL5HETvyOO1szC9LN9ScA6r5yAIL5mKsiarEFG+KX/1RjPOXxxBrChv7HkdbutBZ7++8A6dCpouQ4MuL2UQ+5Rbgk5K9wPjHsPaGXzRjKn4p6O0bzGFrjRptpjOyPabMrPxlx8gLyuadsq8G0KvaABXStea65NHI9DeR0NV0NdlIQqrECZCZJU1hGDdZWkpc65aZ/9wWw86+86jZ+AiSq/KQaxQbiMWXZcqsK6tnaX9o+RGqKbkMiR3tTULzmGteuO3l0zVcyn77CjflO6Bi1j7s/cAXHleQ5fipOtQ0amg6TQ06PJS+rlP6SJshUW8YFI0guqZZaieWRb0UMaQDbuhEvYE/kzAbf+TnoHhtItMcUtaKBrA6Ef8iT+72fPO4G4EfRULkVkYpbrVwpT0JWOV1W01Fs25isXYaROghlVQrkupGBUrzMGamhmO11IZw+LKmLJQLfuhNQvOfnoOZJRMP71kbjyXsgqR7LfiildqptTv7BApaKpKfRjDf/zap5j0xU3YjROxwpy0SOBPd3TJXukWmeKGtFE0gFH3tNclbHUkGVItRFaCYGlBFum3qkKY7rr7slZZL6zGdnOuYjEWbQIdhDwZ6nUpgu2G5bcoPR/KGJySYRMAVt55g+W5ZT+0ZsHZL8+BipLpl/DqxqAhqxDJfiuM57Xj8EeIFebik371PgUiBc2tUh/G8B8/9qmJSro3QnMbduPEqvk3pNVcpCs6DGDG9/3vG9pQNiUnLdeyDGmlaPgBdRF1nRvCrqYzygvEThDsGRi2PN7AjRCmu5KVilXWL6uxythkNgE7a4Tsdb0QbKljOLiuxjFRf0v9Cez83UfjxqHyoTULzl6vATcFFfwQXt0aNGTWjUqvlQSAzv4hrK2dhefqT5Cqxln10XBaxzqUesDf8B8/GiSmsyDtJZlQidFt2I0T08sKPDkvkwrFUFaYOxl9Fy4Jz7Vxz/Gx/12cl4WHqqdjTc3MjHvnWdEwQdmUI5HUBSL7saMKtDqFMC8qWalaZRdXxvB47Uy82tCOs+dpQoksKmOjbgJO1maV6+oWbGXGYFx76/6Tlv08kteHMcbOvvMoLchG78BFacucITh76TnQUVDBa+FVV9Ukyrpx09hselm+bWWm9csqUVKQnXJtgNYZXIdS7zdeC7qZIEh7RaZUYvQy5JnzM/xB9D1NAFhceY10kv/Z88PYUn8Sr/62Hc8GXBFNN6xomDAWkVP97YRpZcl+7KgCbUlBNnoGLo79t6oQ5lUlKxWrrNVmOqrJV2BNzYxA6+zLbgJWx6taqlUEWzvrp8oYdv7u95bHGOvjydePYcPuFtel+ZI3Q688B+lQUEFX+Bh13ciUmU7m6im5WHjTVKnnRBmPDqXeT7wWdFXLD08E70cmVWJs79LfsNGrUu6MPaLv6T8dPYNoBI7lru04O5h5yeKsaFiwuDKG4vyscbW37ZD92FEFwfXLZiNWlEfaSJw2Ha8EL1mrrN1m2nd+GM/Vn8BnYldpe7FULMayFiGr4/1KdHayfnpRlWj0XaC9D1bYbYZeeA7CWtUqmSDKMycrdv/a0olXG9odj0/u3aL7OelQ6v3Ca0FX5fwTyfuRDoYDCnXNHXguqRoflb9aejM6+y7gZYv3NV1KuWciV6IBTqVUWTRw0wA8gfRRnilwKQwLDrf1kJUMg+SPnQiqIBgrysPCm6ZixdzrsPAm+7rfdc0dWLR5P1ZtO4THdjZh1bZDWLR5P+qaOwB4J3gZVlm71yCCK8IKpSTqd99swYibt1NxbObfiLD6rZvrymIobObN17B+9g4MSY3BrRBXWpCFLf91LtZerlJkvq7fm2EQ/TBUMKxiMdOaixXlembNmhSNYH5FKeqaO4XHrl8227PnpUOp9wsZQdfL8/99Qxt2NZ3B8/UnHd9/47ufKaSD4UCEmyTwqwtzsf5Lt+DFB+eN25+8/FYwzozEEzj0YTdebbDvneMGN9+UsMEeDQvcfLAaTv2H0IWts+oOxeXuleAlY5VtbO321SqlYjFO/o1oQ7ATmr22VFOsnxv3HMf6ZZV4dLs3VYnM9AwMI1aYi3tvvw6fiU3RlnehGhoSVNd4FYKomkQP3czxbAzGMxKNQ+ZZeRVK5LWgS/1dcl6gFekWRkQlXQwHTrhJAjfuK4wV1iYiI/EEtu4/iVca2tF3Xt3LTyHMyrMMrGhY4OaDtfVAK35x9IyjYKVLGKW63H/zrXs8E7yoSb1BWKVUEo5FsZfUkpteJTpTrZ8lBdmeViUyk5zkrWMzdBMakm5d4/1umhYGC7EOpT4ZL0OJvBZ0dQrIyQab+RWlrt/DMOSBpJPhwA7VdykaAe6YVjL23xOhwWKYqWvuwJOvH5OOeFElzMqzDKxoWOBW8KKW0HQrjFKFziOnez0VvCjCZVBWKRXBN/k3qp3BvbI+yQiJK+Ze53lVIoPk5+Z2M9SReBvGZm5hISwWYh1KPeB9orbXgq4ORd9MfUsn/s+fNblSvMKSB5JuhgMrVN+leAI4crqXlQufcFKsvWq0aEU6KM8yRBIJcw2l8fT396OoqAh9fX0oLCz0Y1yBYywqQE3wMhbKwXU1Up3BZYTRXU1n8NjOJuFxz6+cixVzrwt04xiJJ7Bo837hZi2ar4lOY2s3Vm07JDxux+oF0puTU/M0J8o1PjdjndiNQXadJL9fZQU5QATo+nRoQocdUN7Fawpz8D/+y1xf5kq1M3jyvTit2akF2Wh86k9ddee22w+M0bmtCuV2v6HgNFa78ZjHInMO3YRF8VFB9M45YezfjLc4ra/FlTHhd0YnEQTzjslC1Q1Y0XDAshSrRVMqJ1QEPioqQmeQrnDRZrq2dpbWEreZiNcKm7E+Gk79B7YeaCX95kWNH0Tqml5zzwxUzygjr18vhBS/3iUvriOyzhVkT8LAxZGx/w6rQEddL6UFWfibe61r01PnV2YNqaw3VUU/mQhG+zzZ1dSgfB90K/s6CUMolyqqFnEvZQhmFJFi/XjtLMvKUl4QjQBbV83D0jnh+tZaMSEVDS8+QlbnBIAt+05g64FTwt97aY1IRy+BaDMNq0ATJlSsq7JQLHCjH8TbsXTOta6ulQzVS2dAWS9eWGf9sq56eZ1Ne1vwo7doFVOCtGQ7IbNerKyEsvNL2WPcrLfk83edGxImgJvPTxVinYRXL72mE5265g48/UZzSn8sJ3R6ixlrKIp1kUS7Ax2ky7tF1Q0yprytqMSrKka8eXKJ2UnRCKpnlJF+72WcsxG7CvhXUnQknkBjazd2NZ1BY2u3dDnaJVXlOLiuBo//6QzLf8/UEo068aMsqtPaMhi1uuhTMgD590W0XrwoqywqL6xr7eq+TvK723CyC7ua6L/3ogS1DmTXS/L4VebXaj9Ixu16Sz7/V6orHMtUm4kV5eKr1dNJxzrleoWhWECmsqSqHIee+lOUFmQJj40g/LknmQC9j5R/GO+WW3krLGREMrjXyYBW6EgQ1OGB8TPpVZd19V+aO/F/77f2BqV7iUa7Z6rb2+ZHqUO7teWl10k2MVa0XnQ3+/KrS7Hu6+gIywljYzSZ9WKuyOTFc6Sut0Ot3YhGI8pFGoz/Xls7E9PLCsbOcbitB68IGjECzgpaWIoFZCrZk6P4m3tvdQwjLsnPwqb7roT6+RWtkW77rQ6oCrM5nNRLrp6Sm9Y5SWbSXtHwa+M347YShs5F5KXQaXyM6ls6LTuTyipzdc0deGT7UcdjwijQULB7pstvK8fu9zqUnrXTZuBHqUO/a7erVMByWi+6rbN+dSnWeR3d1VLCZMlOXi9U/nDugmfPkTo3j24/irNJNfjtvgeyhiQdBrBMKCcbduyea3FeFh6qno41NTM97QKfSUKsW6gKsx9KhvFu9Q5cxKPbx3+zOzw0nntJ2isafm38Vqh6E7zwwHghdFKsoE7KnFlIvmNaCb77Zgv5+roFGi8tOHbPtKPvgmUcPOVZh2Uz8Lt2u6jsqR1W60W3ddaLsBKrdanrOm46EtsRNku2sV6ose9XT8n1LDyIOjdnTY2+nL4HMsq+GwNY8jpceef12FJ/Mm3LyYYFpz2H8ly9kBWCiAAJM16Ul3bD+mWzsXGP/Tc7AeCp14+lVcRH2isaQceTylp8g/LAyCJjBbVS5qyE5NKCLPQM0GMddQo0XgrtKsKc6FlP9M0g+b2iVsCyWi+6rbO6FRe7dbnyzuu1XMdNR2IzYbZkL6kqR83N12DBpnrHb8zUgmzcMa0ER073ks4r+w1SFVpE3wMZZX9xZQyP187Cqw1tKQqNkwGMWmGR+9DQEZVLTZYZvjjnWul8HxVZIV3kDz9x00eqMHcy7r39Orz5fgc5wd8OozLelJws4Te7d3AYW/efxGO1s1xd0y/SXtEIQzypzCYQpAeGiqoV1FDm7IRkGSWjXKNA47XQrirM2T1r3gxGMd6r+RWl+MXRM0rKgu5mXzoVF6d1uaX+JIrzs9A3OOzqOroMLLot2V54Fymx790DF/G57x/A+mWzPQkPciO0iL791IpXlHAc82+s1mHfZQVjbe0sTC/Ln9Bx/BSSn0971yCeqz9h+W4//NpRFJuqGFkZvbyQFdJB/ggCVS96/4VLWFJVjv/+pVsuG8W6SNVIrVh15w2Ix0fDKim82tBu+06HjbRXNNItnjRoDwwFVcH56im52kI1KAINZeP1Q2h3+6zMv+fNIBW3yoLOggm6FBfKukw+r1Ui8Mo7r8cv3//YUQBUMbAU5ExCIgEMJsUk67Jkj8QT2Lr/1Dhruy7vIkVg6Oy7gEe3v4tv3F2BH7/Vpj08yCn+3hwyZYXV94TikbVVGM4P47n6k/hMbIplU0HROtz5u4+4xKoAarEFY57NVYysjF5eyArpIH8EhTk65eQnn5KUhj+cuzBmFOvsO698/Rf+J61vlcHZ88NpIwOkvaKh22LpNWHwwIiQ/cgkK3NuQzWM3gyUzrWUUCg/hHa3z8r8e94MxuNWWdCZ1K5DcaGWVFxbOws7f/dRyrFFl0NattSfHPubnaBOMcQYXcB/ffwT/HPTxykhAKPW8AotjTTrmjvw5OvHLEtF6gwJFIVRGQL07vc68ML987Bxj/6KfVbrLZ5I4IGX3hH+1vw9oHhkF1fGlAwqOr+PE7WKkY5iC1bPSJesYO7NouOcE4GSfHEJYuDKXNU1d0j1vdFBusgAaa9oAP6WeHVL78AQohFx59YgPTAyHxmzMud24VM6YjolXvthFTKjGpdt96zTQRkNArfKgs6kdrdjoa636WX5OLiuhhSSYSWoUwwxG5bfgnMXhvFqQ7uNNfwEPhO7ytV3tK65Aw87VIayErTcCK5HTvc6hmoaAnRJQXbK/OoUkM3rbSSeEH4nSvKzUr4HVI+sKK7bTmHQ9X0MS+EKv9FZbMH8jHREa1g9Fyf5w/j3Xpf5BumI7Fwlz7/uyn5U0kUGyAhFA/C/DKcKdc0deHT7u8LFGLQHRkZwNitz1IVfWpCdYjmVKffq9GFPwBurkBMqcdlO3rZ0Cwf0E78rYFlhCMCdfefRM3ARpVflSJ9DZl0a92x0sJW1WosMMYsrY0rnpWK8syKSBa2+8xddCa4yArRfa8r4TjgpXL2Dw9jX0jl2j1SPQ+OHXaQxmOdFx/dxIheu0FlswcB4RjpK6Fs9F1HPt3hiNE/gh9HMfW5mZOcqef4BaK/sJyLdZICMUTSAcAghdlAsH6NhQ8G/3JRGUV+tno7FlbFxyhxVSP7Nt+7BkdO90koh5cOu2ypEwam5nVUfDSdvm85wwIkazuAVTrHYMoKwyrp0E+biZIhpbO32NLxQVhirb+nEKxbeFZka8mH1Ci6ujI1LBE7GrNTRPa20d9p8v26/jxO9cIUXoSvJz0g1WkOHpyWTn1syVNksWelInn/R99MgZ3IUQ5firscbxpQAERmlaIQZymYbTwAlBdk+jcgZ1Q8cVUjOnhxVElqoyVbGcX7m8DgJc99eMnvc3wGgsbXbtsa623DAdAhnSCdFSOQelxGEVdal2zAXO0OM1+GFsr97o+mM6xryYfUKHm7rsVUygPFKXdt/DJDOu/CmqfjF0X+Xvl+338eJXrhCp6Jq94xUojXceloy/bklQ5XNnv7Czei/cAlAAgtvLMOCy17mhlM0b6IOJQMIZ0qACFY0fCIdE3xVw9G8zJmh1qpOPs7PHB47Yc78d4oS4CYckBrOEKSgnw6KkIGMhZBqCZRdl15Z6anHd50bwq6mM9LrRGY8Uwuy0S14xyk15MNaJERmH9i0t8Wy2WcyhnC64MapjuGbCQBLq0a/JeZn5+b7mI77mk5k8/NK8rPQOzgsvSZlozV0zXemPrdkqPe46Vf/NvbMth5otewz4wWiKJJ0YEIrGn4KWWF15YtQDUfzKmeGGg9vPk7neNyuGycl4OHXjo77oMjOvyicAQCefuMY3vmwB7veO5OSNOuXoJ9ucd1UC6GsJTB5XSbnfBTlZWMknpAKSwTUEjmp502uqKIzTMwgAmDF3GvxSkO78JyUGvJeGRjcvP/U7/uH//GpUMkARtfbFy4rEIsrY5b3a4R9vNzQjpcb2i2fner3MV33NV1QwozX1s7E9LKCsTnd19Jp0cw2GyvmXmv53quga74z9bklQ71H87fLawXDIB09GGYiiURCqIj39/ejqKgIfX19KCws9GNcnuO3NdVI5BS5trleuT0j8QT+vqGNVEJux+oFnrh8977fge/salZKZAeurAOK0Kq6Hhtbu7Fq2yGp3xgYK89LQV80B2F8F3Y1ncFjO5vIxz+/ci5WzL2OfDz1e7T3/Y/xyPZ3Hc8VAfC4ZJM1Q/ED5IoZUNeJ6Pwl+VnYdN+tKMrLJq9d6juu06Aks29YXRcAaR+4MDyCXklBJlaYiw3Lr3Sd3nc518XqGoCed5z3tVFk5QljbdS3dOKNJv3GHtFzoVA+AZ4boGeuvKA4Lwtf+ZNpuHP6VHQNDIUytJiqG0xIRcPOmuq1kGW32foh3KU71IZIgHcfSKdQhghoz09GCVBdF7JCsdV1vRQOqHPglbKogqzytrZ2Fh6rnUk6lvo9knkHkqEKLqrlHanrhNK1eiSewB0b95Ea28kqc26R2TecBE8AjvvA47WzsKX+hPI4X7zcV6P62V+js9+6b4LOd5z3tVFkFVqv5RBZ44GZFyfIcwP09EJR4csLpuH8xRH809F/Jx0fttBiqm4Q9XFMoYASVvLdN1swIqoBp4Dhyo8VpbrqYkW5E+ZjrILxERAJWJHL/+dF7PXe9z92DGUwyuqK1o1MzKvqenTr7k4O/3HLSDyBxtZu7Go6g8bWbozEE2kZ122E/1B5rv4E6po7hMdRv0fP7TuBhwnvgBVGOJpoPEuqynFwXQ12rF6A51fOxfplsx1LYcquE/P5d6xegCPrF+Ox2llj7+ukaAQPVVeQzudnWIfMvmH3vTKeAwDHfWB6Wb6rsT71+jH8378+aatkGGPW9Y673desvhHpiBHmumLudVh401THPcgPOcR4LkXExnMGxflZE0rJAK7MVXGe3Fy5vu4tMTS00pLJAfq3PGxMuByNoKtkpEO/jzAhk4RLjWWUtTyNxBP4zq5m4fUp60ZWOFJZj6oNBM24FfTtrLor77ye9PswxQcnx2JT55SSFE79Hj3365O2x4iQKTOanBO0q+kM6fwy64SSc7SmZgZe/W2bYwlYvytGUZ/TodZuUrnXg+tqHMsNu6F3cBjPE9eLLmVedV9Lp4IQOvFTDpHJJfjireV4ftXtE1IeWVJVjik5WXjg5Xd8uV5xXhZaOvqljEfGd+XJXxzDlNwsLLjRWaENCxNO0QiDNVVHv4+ReAKHWrsvN2oaPV+6LDoZDhFrVK9fNvv/3977R1dV3vn+73NCEkgkCSHVE7xVIsRKDIp4pTBBe6UwRejA6J2ZJWpn9ce1ZZT7tdz+UNtyS2/aqtOZRV0Lq5WqXVMGuL2tDig295LirZUGcUSUGO+IEbSDiTYJJBh+JOac7x9hh5OT/ePzPPvZ++xz8n6t1bUq2WfvZ+/97Of5/P7g8w01gWxs+w73uHYZTqezz32sukqAqjCn2kDQDj+Cvluy94bmQ6goKUTvycFIlR71wim52A6poBCW18ZpPG5Kd7YSfQviMdx/02zbxnbZqhglfU8tb3cpCZB2c8NaI0w3grPD5LtT3ddUC0LkUilsL8KQQ6SNMtNpedufkpvrzJ8x1YiRTsLxU4P4wbPe+aZOv731Zy/mjFI+7kKn8qFKRlNrB67+/i7c+tiL2PhcOzY+9xZu/dmLuPr7u3LOpebGcCd15y666VRNLhYnvDqFNDg9O5XFvvGZ113fgaUEANIWW8OozkencAYJMQwrX7qCvqSJV/q1Mq8NRLcZkRX+s+b6maLjveZO2OtM+niaWjuw8IHdWLVpL+7adgCrNu3Fwgd2j8xfS+B1egt+54kbS+ur8chtc8eEq2UrzFT+nmRz1m1eWGtE0LM/qHcnQTV0yGuu5hphyCE6vTS6+weMhNPlKrr7c7bIlVCqcadoZHPzNEFTawdWb95v6w49fnIQq3Ng0kmwlAJJUijgvSD7iYlVWex7+gc9P3wVJcDPfEyPid/wN1eisrRIvHjqCvpWZTAvq+7xk4P46uJLQ8lXksSAq8SJF8RjaJhZJbq219zxWo9MY41HonS7bbqqCqFOHL5dTscLdy/KivVOum9ILfrp88Lu2VhrRKaidV6xuS07m8q8SuiQroEoyoQhh3h5152IUm5cNtDNbckGQecVm2LchU4F0cgpLJfuUDKF9Tte9zxO2jDM7vxRcE2r5GVIw2x0YmKt59HZewqVpUXiZoGA9ztIj2lubuvEYy5lKP0IBOnhDJOKCjzDqfy4YlWrIk2vKsELdy8KdM5JQuV0wulMdZ42FebmRfp4pEr3krpE1jvUmwgzNYF035h/iXvoRea88Ho2mXkPV188BfN+2Oy7hv/axbVZDbeQCrOdvafw9//73zxzXnT2u2wSRkPJng+diwG4EeVojjDpDalPhl9yoYv7uFM0ALONnMJMZtt3uMe1koiF6qQbSqawcfdbeGLP4VEehGzF/6m6fCULsnRj29XWiQUzpmqXEgXkH74lRC2YMRXX1FQG3rncad5PPdssyk/XUZ3ygOdPnmhUkMxUlI/1D+DOLe4x4AC0GgeaFBRUcj+c+GLDdJRPKsSG5kOe42kR5D2lz98wOtTnAtJ9QzovpM8mM6fmC382HRuanZO93fKfgOF1fc0iWenloJAKsz39A1kt3hKk8S2ohpIWlaVFyr+JcjRHWKgYOqNElD1R41LRAMxUfwp7E1WZSNJjm1o7cM+TB20tZNkSBqRjr5hUiPv/82zR2KQb2+N7jqCwIIZHnz/se6FReV9hVSML4jqqC3MQyd5OPSC8LKGpVErbWmpSUMh8L1XnFeNrvzyA9/vOuD7XTGPAJxKTPccjDalIPy6IDvW5aI2WfD+SeSF9NosuuwAvv3MMH5w4jSNdJ7F137uO7y9RVoz1Ky4HgEAt5SaQegQrzysWnS8IISsMI2KQ636ifJLS8UGVhs81dHJbokCUPVF5q2hILBF+rKnZ2ERVJpLkWCvfw4lsCQPS+3zo1rniWHlpJZcYgE2/d1cyJk8swInTQ57XVP3wwwoTMX0dlYU5CGHHSeGX9IBwQ2ItVRUU3NalzPeyfsXlriFVaxfXjjS6UxmPNKRCN/TCItulxINC8v14vQfps5l/329FIZtrF1+KNYtmjpxfVwHOnJ9XXzxlRNExKQRLPYLlk2RWedNCVphGxKDWfZXqZblSvSgMouwZsCOqVRrTyUtFw8sSYcIdmo1NdF5NJRJlxZ7hUxL3p7T0XTaEAam1a/4l8vFYG5ubYgUM32/KwzR/4vQQKkuLcKx/IKfKswaFysJsOhwsDDe31/1JBQVVC6mTZdxLKPAajzSkQif0Ip0olBLPJm7vobP3lOgcEiUjBmDbS+9izaJzldB0LOWSzvAmBVKp58dELpQK+eKJk/T++WLDdF8hs/mIVGldVn8Bnm19P+DRuBMlL6UbeadoeFkivnxdDXa82uHbHZqNTbQgHsP6FZd7CsuSSafqHlS9Dz/KXFCJckvrq/Glhum2ideq/OWcaXhizxEj44tKEr4u0oVZ2utEhTDc3CaspboWUpMeEwtpSIVq6EUm+VBKPAiaWjvQuFOvfr4dTsYgFUu51CuYPl9NhPw4Jby//M4xbD9wFOdPnoh1y2fhzi2vhBYKlk+eOF1jxXhGaui89ZPTs65oVJYW4Qc31kf+PeaVoiGppvLT5w+P+ZuOOzRbm6hVX94ur2JKSSHuu0mWs6CqOKjch4nY1iV1CXx18aVjEtT9WsQX1yWMKBqWFchvfL7KswqzupnKdaQLs2klA9BX5K0xpVIpxzwIU9ZSvxZS0x4TSUiFiaRQU5W58gmdoglSdL8FFa+gNV/vefIg1u9oG5Uvoiu8ps/vptYOfOpHz42Zw3YGwsy11tT6mG+euLDy//IFSSXA7/5F3UhzP6d1NAag/GxhBjicxy/fWT4r8koGkGeKhq51U8cdms1N1Fo4/HQGV1EcVIQOE7GtdgJTxaRCfKFh+piYdFUk7y0Wc47vT3+vBfGYrwVc5VmFVd1M5zphlGp0QkeRTx8TEHzibBgWUpW55BVSYSopNJvzAoiepzDoMD9do5bqvmn1wQFGG7r85i+4zeFHnz+Mh26ZiymlRbbv0+T6GJQRMZvzMSplonMFyxNkZ9C1+msUxGNYcWW1rfHa4v6bZgOAr4qCbvj1OodFXikafiwMqpt9tjfRgngMDbVVaKiVJUNn4iVwW6gIHSZiW599rQN32HQD7z01iB83H8InEpN9CfeS93b7tTV49Ozi4fVedRdwlWe1q60zlMREHSXR2jzPfJTEVxfXnq2Kcy6HyGROht1GLZnHmXHmmWOyCy04f3IRrq39GP7Q3o2jx07hcwumo2iCXrM0kxZSu2cAQPm7CyukIugSnk40tXaMsbgnyiZi/YrshYtIBXppsQkLv0YtU5Z5rzXeTdCWrIeNO9vwwt2LxpxXsm6p7BlBGBHDLINPzGFXjbP35OBIGP6jLkrGl6+rGXm36fPv7T99iAd/+5avceWaNziWSnmlvgJ9fX0oLy9Hb28vysrKwhiXFi3t3Vi1aa+vczx48xysnHOh+PhcXkCsBRqwd+uphGIB8ue/9fb5tsL5s6+9hzVbX3H1JlSUFKJ4QnyUMKvzvL3eW9DvVfqs/vlLn8TXf/Wqq3s2UT7RdgNWYSiZwsIHditdx+4ZJcomYtW8izC9qsSo1c7tfQCwncfWVR+65SpMKS12FTLShaBdbe/j2YMdo+ZhPDasgN67rE557H6/CwunZ3DzNRdhQ/ObWuePajien995VdN7xEMxD+qZbD9wFHdtO+B53D/+9ZX4h//zb55GIODcHPdjbDCxb2aSOde81lPdb0SybpWXFGLihAKlMC+nvVHneTspQrrvTnV+Rs2zlwt4zStgrAErHbd9WTrXb6hP4DetnbbnBvx986aQ6gZ55dGQWundUHWH5nL8o5O1UTdMyY/ltqm1A3dsecX1dykAxwz1+/B6b0G/V+mzanm7K5TERNXwHqfN8/2+0/hx85t4+La5xlz1EoulX6u55Zm679k2PPNax5i/J1Pn8rtUlQ0TFlK3ZyBRMgD7ORflkso6yv5QMoV7njzoet57nzzo6FUN0sAg3VumVUwSd4s34RkysW9msuetrpH1UvL9nvkoKTpv5hyWrFs6YV6mPHGmK1ipzs9cNoRmE4n3UVI+3W5flu79S+sTWDlnWuje4CDIK0VDksTjhB9XVC7HP5oUqKtKZc2VMo+Tltp1QrfkoNd7C/K9yhVa2b34DX9QURLDLP8ovdYLdy/yPY8HPkpi0++dXeHAcI+Vr/35ZUphVH7DLCVFLiTkUnUn3VyvvW9324Y7pHPs5CD2vt09pgdP0L0TVBTOgnjMXtAtKz7rMSw1Zvzws286sfG5t/Dr/f+Odcvr0LjT+/v9h7++UnTezDksLRPsdm23hpx+1xST+Vmq8zPshsL5hKlwQrvmmtJ1uOvEGVRNLh7+NlJAV/+ZnDJkp6MXdBxBhpIptLR3j8SKX1A2WpitLp+Ir1xXM5zsa/P7FICbr7kojKFGDkugXjnnQiyYIU8oH4P0ZxnHmShRmr5g5wKW0OH0yGIYnrNSRcevEKmSAKmyefpF5Vp+5/EvWo64WqmAYSvWL1qOKJ0XOGchTZSPfs6J8omeG77f78OaS7kSzytRrL73dBuGbF5WS3u36BqZx/m5phRLoAfGLpXWf69bPgv7Dvdg+4GjKJ9UhN9943psvX0+Hrx5DrbePh977vk07lp8qf+1OgOn+Zl5+kRZMSpKCkVLfWfvadyxZb/o+0UKovUwfQ77LRMsWaf8rimm8rNU52cY8zmfMWWU+R9Pv46m1tEecq+9Hxj+7hp3voG7th3ArT97EV//1asonhA3+s2HSV54NJxixdcuvnRMrPhVF01xrACwoflNbHvp3ZxzS0WFLmEn4czjTJYJ1E2oDfvjlVq5518yNZTqZirW1mdee090ThPvNcxSk+/0nDR6XCa6FlK/95ZC9Bs6pePPCiwVnEYfF1bvBLeQnBVXVqNx5xu2YS6SvEG/65pbT4v0c1rFKby8HyoibFf/GSWvn8kywUGWqVUx4Li9P9X5mU+9QLKBqXDCY2cTx52q/jl9Q279a3JRNs15RUM1VtxaTDfuPoQNzYfGnK+j9zRWb97vmTCYiwQtYOuWBTQZ0uF1rijFrErjgMOobqYS3mOq/KNkPobZr+biyhKjx9mhE47n997WLq7NqbXMj3K54JIqbHyu3fO3Cy4ZHTYVpkJrJ9Af6x/AnVvkYS6Z386x/gE07vS/rtnNz8z/dlq3/HD+5GHvrWQ9NF0mOMiQQqkB51j/wJjk4/T3pzo/860XSNh47Yeqcy8zRG+4T1gtnthzZFSfMKcEc9MhyWGT04qGn1jxbS/90fXc97gkDOYiYQjYukmv0hKlZRMnoPfUR1qW/aFkCht3v2WbOGtt5m512oNCYuUOq0So9DqmkptNNJczWebvcwum4wfPvuEaPhWPDR8XJn6ta9OrSo2PKUj8KJfzZ0xFRUmha55GRUkh5mcIz2E3YE0X6K0KN9J9zO7bsSNIK6i1bm3Y9W8ixc6JzO9Xsh6aCLW1mFpaFGhIocSAs+LKak8lU3V+Vp0nzJcUHjcesfbD9TteH1Xl8oKyYqyad7G4CIddIRW7AjyLZ52PX+0/Kj5PLpHTioaue1CyUB0/OYiNu9/CXYtrTQ03a4SVFKab9CpxJW5cdRXi8ZiWZX+4pv7oxSId61xrtu4fJWSG5emQWLnDqm4muY7f5GbV5nJeTZFMhQUVTYjj9mtrXK91+7U12v00VEm3Wt98zcexofmQljUtakngXp4sP4psQTyG+2+a7Vre9v6bZo+ZL9lswKqyj/WeGhCHDAVtBS2Ix9Aw82NiRUO6Vnithyat8Fd+vDwUY5KTAWfd8llo3PmGp5L5u29crzY/hYtEkjkaAsZmVNWef56y8eeDE6cd977eU4OuSkbmeXKNnE4G13UPSn/3xB8O53yyVNhJYbpJr06/qy6fiEdum4tlV0zTOrf1YTspGek4xUU+2HwI2w8cRUt7d1bng7GkfQPX0X3PqvOxqbVD3BTJBPcuq8NXrqsZkwQbjwFfuU6vj4YOTa0dWPjAbqzatBd3bTuADc2HUFFSONKVNn1cTkQxCTzzvlZt2ouFD+welTApSZp2Uy6X1lfjkdvmIpFRECRRVuwYEiu9JjCcSG5yPZDuR519p5VDhoIukiEtavGTW67SKoRgh0nFeff/+9OYZN0gWFpfjRfuXjQqsf+FuxdhSmmxSMl8+Z1jSt9EV78sX/K/bn0llPvPRc7JDqPfz/t9p3Hnlv1YcaXavP2g7zTu+fXBcVU90CKnPRpB5wQcPzmYk26qdLKRFKZrfZeGEUnP7TeW1/pduouUNcjPofOeVebjvJpK1/cXA7Dj1Q58c+kspS7EXty7rA5f+/PL8IuWI3in5yQurixR7gzu5/qOVq+z4UDpRS6s2H4guPwdKV737OXJygxdfOiWuWPyDqThgjpz0yt0EIBrHL0u0v2o58Mz2iFDQVlBpd7NpfXV+Ex9tRGPrMm+H2HGvdt5alSMpSvnXCgOoRXLOKcGsXrzfqxdXKvcNyufkYTl73i1Aw/dchX+xzNtnobMeAz4wbP/z9eYgvSqBk1OKxp+cgIqJhWOSsJxIhfdVOlkKylMtweF5HfSc5uM5bVwCzeLQjWrsFF9zyrzce/b3VpKsol8pKIJcXzp2ktEx2bidn0v4VeywW176d1RHWcfjgefv+OF1zOXeLLu3LofqYzQxXXLZ3l2dXdCZw1yUlCsSktBhJ9K9rHK0iIc7u7XOj8QrBVUmttlqi+Ryb4ffgxtJtZ7VWOpVIGeV1OJCyYX4f0TA6Lzb2g+hK37/oj1K2hEA+QGsSmlxdhzz6cd8z8tVB2fQRZ+yQY5rWj4yQn4QsN026pTmeSimyqdsJMco0QQSqJT3HOUqllZRFHxkc6zI10n8d3tr4uOTX/P2W5S5Xb91Zv3j0lUzpwjOh5IL+Ej6Hkgeeblk4o8lf5Uxgk6e0/jzi2v4OHb5orKu5oiUyDWKTri9cwz/75ueR3u3GIvOKcAdPcPYPPed5XvxTK2XX3xFLS0dwc2B8LKIUu/3sO3zcV3t7+O90/IwoTcsGus5oap9V7HWCpR2P6+6Q188KFMybDo7MvtEqomUTGIFcRjuGtxLT6ROG/MnHCqIuXG2sW12PbSH3O+G3g6Oa1oAPoVedYsqsUTfzjiWJ0kl91U6WQzyTHbBKU82VWRiFoH1igqPsDwfEyUFTu6mmMAyksK8ePmN8WWSus9h9mx3A6J1T5zvcmcI7oeSCfhI+h5IH3m31x6mfK5o1LSUVX583rmTn//8nU12PFqhzEvbHpVo0/96LkxVW6+0DDdaLiMKY8FIFOOl9Ql8MKhLmx+UV0By6TxmdcxqTAu+iZMrvd+C2vYcd+zba4FLbzI9vcWBVQNtEPJFMonFeGbSy9Dz4dnUFlahJ7+AeWGklNLi/B3/2km1iyqjZyR0A85r2gAetYUqzqJ3YKR624qqbUs1+/TC2ksr47VARgW9rIt3NoRRcXHYldbJ05/lLT9W/r8lLyOTCU5202qdEL1MueISQ9kGPNA+sx7hM08nX6fzVw5FeXP65l/+boaPPr8Ydu/P/r8YTx0y1WYUlqMzr7TaHzmdfT0e4f3OmE1AbS73vFTg9jQfAhP/OEI7r9pdqQspRLlWFriV0pP/6CoxHkQ673J8uUDHyWx6ff6SkYUvrcooGKgdZqvN9QnlK/b3T+AT/3ouawbBE2TF4oGoB+PG0Z/AgmmwhtUrGW57o7zQhLLu3ZxLWrPPw93bnkFcDjGifMnT8y6cJtJFBUfC69uvuUlhfjCn8lCGi3SleTmtk7Rb4LKu9I9b2YCvAkPZFjzQHrPlaVFvhJ4s5krJ1X+qkqL8fVfver6zDf9fqzQn/73xp1v4IW7F2Hf4R4lJcMun+Xqi6fgUz96zvV5H7fpXJxNJMoxAGNdwdNJwbvEeVDrvdRY6iUnfOvJ17SMZpnkem6qX6SeJrfcrcf3HNG6dhQMgqbJG0VDl8wPvKq0GIgBXR+eQUt7dyguK1PhDW6LdLq1LF/ccRKclMnM5/twPCa2kKULe8+89p5oHGEt3FFTfCwkFcAmFRbgImHX7YqSwlGW2KFkCk8dkNUhDyqkzu95rXhfE6EUYc0D6T0nyieN3FeQ1wkCqfKHGDyfeWYeSubfrXeyS6g0/+2Ci3FDfbXtWt7S7l5MIf26UQiXkSrHqVTKuJJh4VTi3G9oowQvY6kkJE/ai8GLfMzZVMXLEL2kLuHZaDOmES2RbYNgEIx7RQM494E3tXbg6796NdS4dlPhDZJF2rKW5cPEVUGnbO6Rrn7bBmmZwl7Uku2lG9yuts5QFQ1JWFFH72n09MsSGB9aNRcNtVWjzi+xAAfZCdhv2c30yjJ+Pa1hVZtT8cAUxGN4+La5+NZTreL3HIUcMqny16UZHpbJrrZOsTX0hvpqx+9Y5d2mK51BFw9wOr9UOQ6TIEMbVZCUhm7c2eb7OlH43qKEm+zgpcinGxZUq6PlWwgbFY2zZCOu3WR4Q1Qt2TrobnRuv9Mpm/uJxGRPYS9qyfbSDe7xPUcwr6YyNNesOMTmvGLR85yvWY9+5ZxpgTY61Cm7aTdH/FbxCUsgUvXALK2vxqLLLsD8+5o9FcMo5ZBJlL+W9m4j1/qXAzIvaXX5RCSTKWw/cNR2fqi+WyvHJMjiAW7nP+OQu5VtgghtVEEiJ6zb3opuofLuRJS+tyjhJDtI95wvNUzHs62do4sxZFQfdCJfQtioaCB7ce0mlYOwLJhhlMrU2eiC2CAlwp5U0AIQaHlJC2sj9LL8he2aFYfYlE3UCh2Snn9JnXqCngpOAqm1saiW4dY1CoQpEKl6YIomxPHDG2ePhFE5KWRRyyHzWg8kz9wtlCIGYEppodjbc2pwCLc+9uLIf2eud9K1wOJIVz9+3HwoMGOblzHvq4trtc8dBiZDG1WQyAl+lQwget9b1JHuOYvrEvjW8rpR60YymRr17fq9RtShooHseQNMKgdBWzCHkils3H0IT+w5MqrRoWlrl91G1OGx0bn9Tqfrqaoyla2OwnZYG+Fqj1j4sD1cOiE2KqFDkrCl6pA8S25N38IqPBG2AqzqgXH6ZipLC3HjnAuxuC4RyRwyN+VP8sxvv3a46hRs/p4CcHpQbtX3KpUsXQusb2/rvncDM7ZJjHlb972LRNlEvN/nvkakUim833cmsDwNJ0yGNqrsMUFbtT916cew+lMzIvm9RRnVPS2zN0+UIiGCJpZKuaWnDdPX14fy8nL09vairKwsjHGFyvYDR3HXtgOex32pYTrW/cXlxq7b0t6NVZv2eh639fb5nsLgUDKFhQ/s9py4OjkaTa0duOfJg649R/xau6zxuyl8U0oK8a/fWTKm3KDX74BhS7mk66kfz4jd5uFUlcK6g6AqSzQ+/ToeE8R5P3jzHM9maCYrotlZsZ2ehep1Vc8fBpn3cPXFU/DyO8dCK8jgNp8BZL3XShSbSvpFp4/GlJJCHBOEUnhht867rd/Wk/7q4ktdOxtbOO1FXu9RutetXXwpfnx2HE7fMGC+6pRbiXOnvVN37qruMdJnN3niBJw4/ZHncZnEYsBDq67CsiumKf92vONnz4nifqWKVDegogH5h1xZWoiXvr3E2EZoWjkIYuJ6lSS1sFMCVJBvRLW4a/Glyr8DvBUip3vVfX5eSpAf5c8LU0qs6ZC0bMaAj9emiVFSgHMJP0qQSmfwqvOK8bVfHnBsYqlD5nc97JF+C0/sOWzrkT7zUVJkbLMzTEjmudSY9+DNc1A8IS7qo3HPrw+OuhcdiifE8fU/vxTVZZPwX7eNLXFu+nvQ2WOkcsK3b5iFNWfvQYdH+M1r4Wedj8oeoQsVDQWGkilc84NmUWysxLuggmnlwOTElXoLLDKVABWkG1HFpEK8vO6cQiP9HeAu2AehFJj0WKliQok1rXiljy0bVW3CPEdQz87E+CTfdXVACnCuEKYAoGIskeLkqXSaN7prlXSeq55fMr/3HOoSxblLqD7b3DCz15TJd+5nj5HKCT/Y2abdsG+8f/N+CNIoEWWkugFzNDAcW/uXc6aJSgqajpc03TTQb7WadFQ7HT+x54hSLkQ60tyR46cGR+UVqOScuOUlBJGnE1aCvh1+kxaDLJDgJ8k5jPP7FTKDLi7hd3zSUsO5UKEuCMKuQBjE9++0Ljp9GzrFA1Tmuer5rXFaQtgzr703Zi/r9enNSCeMXlN+9hipnPDt5XUAUtj0+yPK4xvP37xf/Ow5Qe+HUYCKxlmW1CVEioaKYCvVVE0qB4D/iWuN+zetHUq/S1cCVLX0eTWVqJhUKHKFp2/MOr0L7Db2IJSCbPfY8KPE5lO5ZBVMCJlBPjsT44tqr5UoYEpJVFn/TH7/ukmkOoYJ1Xmuen43hXpJXcJI34j0scYQbK8pv3uMVE749vLLcdXHK/Gd7fJ+NapjJEQFKhpn8SoFqLqAq1odo6LV2o1bBZ1a7NamvGDGVPymtdPzGukbc/oGKcVuYw9CKQiqxKiKEKOrxGbTG5MtTAmZQT07U+OTzuHtB97Dt5ePr5r6ukpi+jd5pKsfW/e9Oyrnwm3989vo0cIS4G+oT4z0fFB5d6qGCdV5rnJ+SSlcyR5VWVqIY/2DoucatPHExB4jlROWXVGNz5ydByoNIPOlnCqJFlQ0zpIpsPqpj52N5n8mkCZ+u6Fai11FsXESzK0NbP2O1z0TKitKCpFMpjCUTI16l0EoBUHUXNcJm9FRYrPtjZFiMr7VlCciqGdnanzzaipRWVro2TCvu38g7zxWXugoiZI1zG3t1230mEksNtyJ+PE9R/D4niNa+QUqhgmdeS45v0ShfkIoOF/18Qrs/n9/UnquQRlPwm72Z637C2ZMxX+8uBJrtu73rKyVL+VUSbSIZ3sAUcISWBPloxfQRPlEsXLgtUgCw1bHIacvPku4jVtKtUctdmD0vVuKjVTJAJwF86X11dhzz6ex1iMZ/fjJQdz62ItY+MBuNKWFhlmbffq13K49lEyhpb0b2w8cRUt7t+P7VJlTXud0el6WENOkGOrmhrUpOonsMYTXl8KJptYOLHxgN1Zt2ou7th3Aqk17x7xXFUx5IoJ6dqbGVxCP4UaPksaq18wXVIVn6RrmtfY7rRPV5RNRUVLoOJcAoKSoAMDY8qy664IloK6ccyEWzJjqqLjrznOv80sUamm1qVf+eBwP3TL2uboRlPFEdY8xybIrqrFx1VW2fwv62oTQo5GB33yJXI1tV038tuPmay5yrcWefu/zaiqVFBtJXkFBPIa7FtfiE4nztCyMzk3EitC4sn5UaUUVr4JkTnmdM+zu9dnogKtCEF5DU56IoJ6dSU/J4rqEqM9KUEJXVCutqFidVY0zXmu/W6NHp7mUwnCn9ZMDQ7bXM70upBPUPJcqt+cVF+DDM2PvO52e/kFMKS3CC3cvwt63u3HnP+93VFLCsOqbLv6Sidt3teyKaXgkHgutaSghFlQ0bPCTL5Grse3S8RQVxDAwNHprnVJSiPtumo0zH8m62u5qG87DkCg2a66fgYaZH1MSRKwN221jcdqEl9ZXI5lMnU2kG/5dd/8AGne2IX7W/6cj4LrNKYnQXD6pKHQFVmVTDFNwlHoNVYUrk6ENQQgUJscnPVcymcL2A0eNvtMo1I53mq8qwnNLe7eWccZtrbVbJ9zm0s3XfBwbmg85ni9ow1YQ81yq3F5b+zFRTt8HJ06jIB5Dw8wq3P+fZ/sOj/a71pku/mIh+a6CujYhblDRUECywORKbHsm0vE88YV5QApoebsLwPCmOP+SqSMbrwRpYhoA1F4wWWuDLIjHEI/FXF3sdptwU2sH7tzyiq3Qv3rzflSUFLoKuPc8eRCTiwsx3yXkIB2pp+KbSy/zPBegl2DsNqczN6aq0mIgBnR9eAYt7d0jFtcwBUdpedaNuw8p9XUxbaENopqcqfF5nSsF4NTg0Kg+BSbeaRTy17wEMqnwrGss0ln7nebSM6+9J/p9kIYt0/NcqgTfNv9i5eIhfhUjU0qy6eIvKt9VVArPkPEDFQ0h0gXGdPWqsJAu7pZS0VBb5XgOLyEwhuGKNhL8KGSq3iWJpfz4SffYYCsHRLr5SEPtej6UdQ1WeV7SOW1tTE2tHfj6r14ddXxFSaHtMwlScJS+1w3Nh/CJxGTlZFiTFlo/m7qdEmhyfE7nqigpxLGTg2Pea+Y7VbXshh3+Z4dUIJMIzzprU6KsWHvtt5tLUTFsmRRepQr1/Eumau21uopRFJRkO6LwXRHiBhUNAarWghVXVuOnzzt359SNbQ8yPMWEtdQ6x2qPUrMpDIcjuZUeNKGQHek6KTrO2oRN5KlYSDcfqdBcWVrkKNQDeuWXVTZNp+OdxhPkBqciNEmvn/lt/e4b1+Pld45lLbzASwk0ZUEe47E6rxhf++UB22PT32kyCTTuVLPsZjt/TWJI+NZTB3FqMIlE2fAz9arepVqWdtW8i1ybZKq+07ArGYWFVKHWrRSpqhhFWZjP9ndFiBdUNDxQXWCaWjvwqIuS8eXrarSsHmHENfu1lg4lUyifVIT/dGkV/u+bXZ7Xu3HOhXh8z5FAko2bWjvwY5fEdOs66ZuwyfAC6eYjFZrf7Tnl6k1JQf68VOe0bkWyoDY4qecMwuu7fVsrhdWZTCJVAk1akK1ztbR3u5aItt7pHVvGGhO8lOts569JDAk9/YNY+z8PAJCVjVbt4TO9qtT233XX96gXbfCDRKEOOrnaIsrCfLa/K0K8oKLhgcoC41VJKQZgx6sd+ObSWcq9E8Jy2fpxK6s2+ltcl8A1Z5+ZyU1CKhhnCuemwwskm4/EInlBWTG27nvX9VoVJYVYUpcQjUt10/Tr6TG9wRXEY1i3vM5W2FW9ftTCIXQtp6a8nX7elZdyne0wH9V7s+bAQ7dchSmlxbbP1hJ0v/XUQc++JID9vfmdg2EJ29lA4nkII8E5ysJ8tr8rQrygouGBygIThNUjGy5bVbeyaqO/dE9CQTxmfJOQCsZrF9fa5te4Cf3lJYXoPetZkN5vc1un4/OUWCRXzbvItbIMMBzCJJ1X0jm96+y4/W6epje4ptYONO5s8339KIZD6KwhJr2dft+V2xqX7TCfqtJipeOtMa7Z+sqoHhV2lXwWXXYB5t/3W/T0D9iey+neTM3B8V5NKOgE5ygL89n+rgjxgg37PFBZYIKweqgIHtlANazGzp0vbRDlNQ6r2d2et/4k+k1mGIOkodL9N822bazlxmN7jrg2zfJq6ucUbpGJdF5J5/TjZ8etu3nGYL6pn2qTR7frR/HbUl1DTDdx9GrCJsXuPsJuWJbZADOZ0mtHKmmEVzQhjh/eWI8Y1O7N5Bw0sY4Se6LcwDSbjQAJkUCPhgcq1gKpQKIiuEkFj87eU+JzmkQ1rCYId75O2BZg/x6kYQhefToy8bJKulkkpWWDpfNKpTrY955uw+++cb1y0mtQgqNUqZVcX/pt7XnrT6FZh1UMG0F4ZCRlb6Xjs0M1zEc3JMxuTaiYVCgcvTtuPXhUQ5iCDsnxE1IX1aaK2SDquTD5HD5Hch8qGh6oLDBBuDClgkfjzjcwqahAlLBtcvOQboB/u+Bi3FBfbXyzUg3bAs69h6svnoKW9u4xz0IShpDeAMqryhYgC5lzcv+bnlcq1cE6ek/j5XeOeQqfmRWxgtjgVJRayfWl39bG59rx6/1HQ9mwjzmE3qRTnWbYCCJB1U1oWbd8Fhp3vuFrLkrDfHRDwhwrpAkMAlKsZ/vzPYfx+YaaUcqGSghTkCE5fkLqotBUMWhU98KoC/PjPXyORBcqGgKkC0wQVg9pCcVj/QOeiYNBbB7SDfAGg1VyLHSqIVlPfsWV1fjUj55zfBbSmN+l9dX4YsN0URNCXatkEPNqaX01vtQwHY8Jx71yzoWu30AYG5z0+a25fgbWLvmE79Kg6bgl5ppS3oeSKVHuybrlw8UkgrSGuwkt8XjM91z0+r50E6R1K6Tp0rjzDfzshcNj9gHpWhdUfL2fBPOoFUgIAt29MOrCPJvxkShCRUOI2wKTKWg8dMvcMTXm/TT8klqf3UIlTG0emfd69cVTspaIplMNKVE+ESuurMajzx82tpGWC0My/CQKBmFNW1yXECka1ri9NlkTG5yb0C59fg0zP+a7O3YmTt+XSeVdOp+nnE1qDjpB1UloCdqy6yckzGQvHCl264ZU+QzCiODn+UWxQIIEFWXf715IYZ4QNahoKGC3wDgJGuuWz3IsiRgUTqESpjYPp3u1BPewY1flFu6ZqL3gvBHF6FM/es7YRjqUTHmWngXMJApKrGkqG66ONTXITdZLaA/C+uskNNuR+X1JBRbpO1H1UGSz2kyQll0/IWHSZ1gxqXBUKNWUkkKkMLoBZTw2NhHcaTzp68autk4l5dO04ubn+UW5X4QTKsp+ripShOQyVDR84CZo3LnlFTx821zfDb+shVGFzM3WxObhdq+PPn8YX76uBjte7Qg1dlVu4a4a1ZDM5Ea673CPa4Mzi5uvce4IrIKboK9qXfey6KcArFseToKjVGgPIiHTEpo37HoTG597y/P4D06cFgssyWQKjTvfEL0TVQ9FthNUg1I6/YSESZ/hQ7fMRfxs+JmlJAEYpTgd6x/AnVvGdp22w1o3Nu5+Cz9uflPZWm5ScfPz/KLcL8IOVe9ELipShOQ6LG+riZegAQwLGkMSk5gLOqEAmZut381Dcq87Xu3A775xPbbePh8P3jwHW2+fjxfuXhRoLK9OyUHTG6n0uOlVJaLjdNEtc+pUWteicWebcolUVYaSKazf8broW/IqBaw736zkfgnnT54oFlju2PKK+J3ozOegnkc28RMSJn2G82dMHVMKNrM87LIr3L8NO57YMzYkE5DtCabK0/p5flHuF5GJzh6ca4oUIfkAPRqahGUZUVnwnEIl/G4e0nt9+Z1joVqBdCy6pjfSKGzMfsMBltZXI5lM4Y4tr4z5W9AJoEPJFO7+1WuuXqHMbymosJ2rL56CytJCxw7P6d/XM6+9p30dp3ei66GIeoKqKn5Cwkx7eaxn+/M9h9G48w3P490qW4VlLffz/HKp+ZvOHhyF9ZqQ8QY9GpqEZRlRXfDsNtGrL54Cr301Hhs+zo4oW4FULbqmGy9FoZGT36ZfQ2fDe5x+C5jxzmXS1NqBq7+/C7/a/++i49Pnl+nmZE2tHfjUj55zVTKAc9+XyQ7a6eh6KPKpWZvfBmSmvTwF8Rg+31Dj+Z1L+3SYWiczGxJa36ef55dLzd909qUorNeEjDfo0dAkLMuItASnWyz+y+8c80xqTKbg6JGIuhVIxaJr2uKZ7Th5wL8imI24ZZ3+JzrzS5KILRlLZr6RSmlcNzLfyVAyhfJJRfjm0svQ8+EZVJYWIVE+Kac9FDr4TZA27eVJ/87tSAG4tnYqnn6t0/NcJtZJr3wsP88vW/0iVMtE6+xLUVivCRlvUNHQJCwXs6QE59rFtVizqNZ3B2Sn43LBna6SmGp6I3U7383XXIQzHyXR0t4tEnR0ejIc6eoXjdNpY1aZHyZ6Ruj0OtCxMkqS4yVjmVpahN9943oUTTjnAA6ig7bbeIMWfKLYBdqvsmA6WX1pfTW+fF0Nfvr8Ydu/P/1aJypKCtF7cjDQdVKaAO3n+YUdjqdTJlp3X4p64z1C8o1YKpXy3BP7+vpQXl6O3t5elJWVhTGu0NHZaK0FH7C3jJiMa/dTr7+lvRurNu31vMbW2+c7bszPvvaebQx/EPcaFqaFq/TzHenqx9Z9747KPXArubjvcA+a2zrx1IGjo8J3vN7xUDKFhvt/61n5qrp8Il64e5Ht/Unnx1/NvRDNb3wwKg5dp2eE9HrpPKI4v5yEscz56vfbcC5vXYfGnW2eQpD1TqTjDYLx0AXaBEPJFBY+sNvR+xcDUH5W0QD87wl26xMAzzEkXL71KOJn7vvZg6OoXBOSS0h1A3o04K9LaFiWET8WJr8eiabWDscY/ly2Apm2eFrna2rtwI+bD4lKLtrNPa/fpGOivK4kDCgWA361/6jy+OxQjVFfu7hWaX6pJMf79fa5d9CGZ4gGAOw51IV7fn0wK7X9x0MXaFNIQgyPnxzE2sWXYttL7/raE5z2pJuvuSivyrOaKGShuwez8R4h4TDuFQ2/G22YLmbdhdFPXKpX/Pq65cEqGblmdVLZOHe1dXrmBnhttibK60rC85z8njqCsEqMenX5RKxZVCs+HlDLOTGRf6TbQRtwt07bjdekYBRk87KBj5L4RcsRvNNzEhdXluBzC6aPCj3LRVS+tRfuXmS7bvnJGersPY0NzW8aHWvQeN2vifywfKu6Rki+Ma4VDVMbbS5YRnQsP17x6zEM91n4TH0wXVRzMaRDunHube8W5ymk/yazyZipRH2n+SHpjqwqCKskUuvkJ6h4KT57xTTPsUwpKdSOq3cSgiRKpt14LUwo4EEVAbjv2TZs+v3hUfPmB8++gduvrcG9y+qUxpht0p9z1wlvzyEw/K3Z7Ql+c4aCLpxgGsn9mqpomAt7MCHjlXGtaIy3LqGqlp9sPp9cDemQbpwtb3cpN2K8c8v+MfkR65bPMpaonzk/uk6cEfUOsJDeu8SDUlFSiPtvmq31jlWUL2ssqx2qCQHAsZOD2NXW6asZYPr3oZMMb40XMKeAB1G2+r5n22yTpZMpjPx7rigbds/ZTfF2+9ak65lOg1bpGMJEer9Rr2hICPFPbvuyfRKF/hBOtdCDQqXefraeT1hd102Q+f6qSouFv1T3AGU2A+vsPY07t7yCFVdWO54xheH8DCnp86NqsvRebRKbcwAAVdtJREFUhlERBpx6HVSUFGLt4kvx8neWaAv2qrXyl9QlUFHi3APB8myamm+qwmT6eHU7wNthWsgb+CiJTb+3r8hksen3hzHwUVJ0vmzi9JzdlAzA3gOnsp6pNmiVjiFMJPf77ada8dT+f0cymUKijH0tCMlnxrVHI9vWlKiHBuk8nyiHdJjG7v0lyiaKSlwumDEVG597y9f1rfC+Ha924KFb5qJxp31S+YbmN7HtpXeV55VqLoWqMBBUbLVqTtK+wz04fjK8js46wqSV02Eyp8J02epftBwR9ev5RcsRfOnaS0TnzAYSj1OmZ8MtBDWInKG1i2ux7aU/Zr08q916L7nf7v4BrP3lqwCGjQvW/GVfC0Lyj3GtaGSzP0QuhAapPh9TilNn7ynRcdlMeHR6f+/3nXtWbhvn/EumGmn4ZgkpU0qL8MLdi7Bx9yFsaD405rjMeeVUOjP9366+eEqguRRAcLHVKjlJYXvuVBS49PG2tHcbVcBNNy97p+ek0eOyhcTjlEwB65bPQtXkYk8FWTpvOntPYcWcCz2/OatAwppFtVlNgHZa72+oTyidx1LyyyZNQO+pj0b+PZcrGhJCzjGuFY1sdQkNstqLSVSejynFya2UbiZBdYoeSqawt70bLW93ARgWhOdfci7MTPL+KkoKUTwhPqr0bObG6ZWnAAAVkwrHhEzZYQkz2176o+3f0+dVMplC4843RgkIVuhQumW/unwiVlxZjUefPxxILkXQSD0mYXs2JcnwFZMK8dCtc0fNuyAUIpMlui+udK5spnOcKqYq1EmfX9XkYqycc6HncdJ507jzDUwqKvBcF04NDo3kDGXLo+u23j++54jWOU+c/ghf/XQtaj5WyspRhOQRbNiH8EOYTDTQCxOv5yNpZCVpIuVVSlf1fKr3YR1zz5MHx4TSpAvU0vf3z//lk4jHYq6Cj92YppYWYeWcaVhSl0AylcKtP3vR81pbb58PAMqN8NywRvrl62qw49WOMYrJF/6sBmsWzcx5YcCav9LmeibQaTQW5LphQkgf+CiJy9b9xjV8Kh4D/l/jDcZL3Zpcw00/Z6/5ZZH+7gHYrkOZx0nuLYjGpF7rfUxQrc4J1eachJDswIZ9CoRdhzsKSegqeD0fEzkVqpV4VD1NEo8LAMfqQ8dPDmL15v145La5OCNMZu368IynxdPr2Q4lU+LwtWdee080LinpOSC/+8b1ePmdY3lZpz4bnk0dT0KQoZ4mQtiKJsRx+7U1tlWnLG6/tiYQJcNkGKrp5yypbAaM9jr+7hvXY+KE1z2P8/J6B2FEk6z3lvnSzVvrRBS8+YQQc1DROIt0ozVhHcp2Enomkntyez4mFCdpJZ7K0kL88Ea1UB1JqNP6Ha87NqVL53tPt+Ef/upK0XWl78/t2aYLwU5YQnAQ88VSEl9+51gkvGuZmLLWmgwhUrmml5KZ+bdshHpaSJ61Vbo2s49GPIZA+mhIKhyt3/G6kuAahOJpVTZzKzpgjbmj9zR+0XJkVNil03Fuxpug8gCl6/2XGqbj2dZO5XK9USj0QQgxBxUNBUxZh8JMQvcSDkzckwnFSbp5rfvs5cqbo8QC57app9PRexqIIdQiAkvrq/Hl62ochTfreag0wlMl2941u3m8q61zzNytLC3CX54NO1NVOoLwbHp9f05Kptt3GbZC5DWezGveu6wOX/vzy0LpDC4xUHT2ncHG3W/hrsXyDvOmFU+vymaZSBPmnb7LIPMApev94roEvrW8DvsO96Cz7zQan3kdPf2yZ5Dt9YYQYg4qGkKcrEMdvadHQmqkm09YoRpewoHXPa1dPFzZxGscJhQn6eaVKFO32pvetLo+PBOqZbmptQOPPn94zLNNpYBHnz+Mqy6agqX11Z7zyo/ykc2GWXbz2Mk63NM/gMf3HMHje45oGQFMVsHSVeIllugX7l5kPNTTSSnSsYwXTYiHUsJW+m1vaH4Tn0icpzQXTCqeqmuQNGHe6bsMskS4ynqf/j1NKox7ho9ZsEEfIfnDuG7YJ0WSP3DPkweVGno5NSxLlE80UtrWq7HXs691eN7ThuZDaLh/t2cTMEvABfSbSKk2WVPB9KZ1/uSJgb8/C9XmhW7j+sktc12fsR1+nruJZpRO81hiHe7QaGJnCt3GetL3DUDceFM63oUP7MaqTXtx17YDWLVpLxY+sBvPHDiKbz3VGtnmmSrfts44VRqcuqHak+ZzC6b7Wg+DzAPUXe+X1lfjJ7dcBbdH6Ge9IYREE3o0BEjc88dPDmq5501YzDItkVdfPMXTbb5ueyu6+wc8z93ZJ4vn9RtqEKSXR2KBu6CsGKkU8P4J9xCq9E1Q8v785hDoWCbdxhWPw7OkroWf524iJE+1QIATQSeX6nx/TmPKRrNKN8/mmm0HXH+b7eaZ1rctyQOIwjglYY03X/Nx/Ka1Azdf83FsaD6ktR4GnQeou94vu2IaNiKGO7aM9WyEkWdECAkfKhoCpFafJ/5wWLncp99QDTuBrrK00DUW1urMqoJEWPOrOAWVkCtRYtavuByAc9Upi8xN0O39mRC2dS2TTuNyesZTznbnTfcU6D53U0mo0gIBbgQtCOt+f+ljSldUDr1/QnRd3XBAFaVIhWzF1EsrOlmYHqek8aW1Bnr1xygpKkDRhPiohpt2/W0k32UYeYC66/2yK6rxSDz8PCNCSHagoiFAavU5fnIwVIuZk0AnTbiToiKs+VWcgio1LFViHrltrm39+iklhbhPoTGdKWG76rxi0fVULJNOzxiwF5BUMJmEalIoDEIQ9vv9fXDitK2iIkHHEq2jFAU5HlMsra/G2sW1owR0J0yO0yl3CBjb+NJaY+zWoIqSQiycWYWdr3Xg5MDQqGv0nj3P2sWXYnpVifi7DCsPUHe9D7ukPCEke1DREDCvplK5Q3PQmAorUSGsezOZkJuOZHOzjnHrDO6FKWG7qbUD63fY19K30LVMOj1jv8/dZOjPka5+X2NJx7QgbOL7O9J1Ej9uflPpHLrvOyijhOkKa7qsWVSLrfv+iM4+96ahpsbp9DztcocyjQtL6hLY+3Y3Wtq7AaTwyelT8Y1fv+a6Xmx76V3lhpHZKNmsQlDrPCEkWlDREFAQj+ELDdNDt5i5YSKsRJX0ezPdbTaToM4v2dwK4jE01FahobZK6xomhG1Jl/QoxjSbSkJtau0QfW9eeAmYuvPMz/dn5QRt3feuspIBqL/voI0SUZh/BfEY1q84128myEpwqs8z07iQWZZ5I9o9f68b/kfPASEk21DRELJmUS2e+MMRx2o3YVv2wo6JLps4YeTegug2m07Q5w8av8K2VJCJimUyHRNJqNb9S3BrguYlYPqZZ7rfnxXGsnBmFX61/6jSb/30cAjCKDG1tAg/uLF+ZDxBGx+8kFrwgy7QYIelLGzc/ZayF8tCd87Rc0AIySZUNIQUxGO4/6bZtlbmbFiWpQJdZWkRetISv8smTkDf6Y+Ur3fVRRXaNfVVCPr8YeBX2JYKMv/wV1dqe12CwkQSqvT+1y6+FGsWzcS+wz1obuvEUweOjgoFmlJaiBvnXIjySUUYSqbGNKr0M890v7/yszH8UiVjzfUzUXvBeaH2cJBQWVqIlns/PdKELyrGAS8LfpgFGux4Ys/YfjhS2FuCEJKLsI+GApbFrDrg3gkSpH0n9t77aWy9fT4evHkOtt4+HyvnTNO63nW1H1Pu6aBK0OcPC789QaSCTFe/rJt5mJjoqSK9/+lVJSPW2nV/cTle+vYSbL19Pr7YMP2sgD+Ix/YcGekJYfWuMDHPdL6/tYsvRe/JQaUO0QtmTMX5kyfigxPDoTM6c18qoE4slG8H3185e5SSIe0ZYqK3ihdOvS90e5tk4kfgl+T5ZcLeEoSQXIYeDUWiEvMqrSpSNCE+ym3e9l6v8rXiMeBzC6YHXuM/Gz0EgsBvxZega+AHjd8k1CNdJ0XXybz/gngMvacG8MSeI66eivJJRb7nmer3N5RMYeEDu8XW7BiGw8K+9ssD6Ow7p1DqeAmkPRxODyYBDJdZ/cp1l6D2/Mlo3GlfEatxZxvicWBJXUJc+CAzN8G6n3XL6zCltEh5PVUJgTJZDU2lJ4ZFDEC5sKBI5u+AaOTBEEKIDlQ0NNCNeTUdw6wj0H1uwXT84Nk3oGJIvP3aGhRNiAfabVbld9mq2a+CH2HbVA38bMbM6yrkQ8kUtu571/P8ibLiMfcvFSa/ufQy0T14zTOVd6wS128pLscEFYwkSHo4pHNyYAgbmg/hkdvmYt3yWbhjyyuO4/jq4ktFSptTbkJH7+kxzdskypRqCJRJI4bq87RmvLSgSDpRzMPyS7ZzeQgh4UJFIySCimFWFeiKJsRx+7U1+Onzhz3PHY8NKxn3LhsOhQna0p7rlvxMdIVtEzXwoxAzr6OQ7zvc41iiNJ1V8y7S7qrd86Es5Ewyz6TvWEU5Lp9UiMFkEv1nhsb8TdX6nj5OO6XIjeHSyvbnt8bxxB+81xFALTfBS5nSya8xbcTQaXy5pC6BbS/90dWAcEFZMf7xb+ag68MzeSmER2FdIoSECxWNEAg6wTlToLPioJ0EH0tx2PT7w6M8G/EY8MnpU1GbOA8XV5bgcwumj8RhA8F3mw2jm23Y+GlopesRyeWEenl+Rqn2bytLi4zOM8k7VlGOvcJrdEMILaXo53sOo3HnG57Hp4dsOY1Dmm+iEjLkpkzphkAFYcTQaXzpZUBYv+JyNMysGrnXfLL85/K6RAjRh4pGwJiMDZbw7Gsd+M721lGVbuwsRvcuq8PX/vwy/KLlCN7pOWmrWGQSdLdZE+fPp81ZxyMiSXT+9lOtWHTZBa7vOlv4EQilv02UT3INfUnh3DwzNZ8sJdpkmVmdEMKCeAxVk2Xd5qVUTCpE76lBR6VNJzfBSZnSDYEKyoih2vhSakDIN8t/2PsgISQ6UNEImDATnO97ts02JKrDwWJUNCGOL117idI1gu4269eSb2JzjpKyouoRkeQCdPcPYP59zfjhjbMjJbQMJVNIJlOocBFM3QRCFWGyIB7Dw7fNxT1PHhxjla84W4LWpLBXEI9h3fK6MfkIfgg6RFHKFxqm48fNhxyNAzq5CRaZypRuCFTQRhIVJCV4883yny+FPggh6lDRCJiwEpyffe0917yLFMxZjIKuvKVzflObc65bEqXzqKd/MFJCi91zz8RLINQRJnttQn96Tw5i9WZ7hcCPsDeltEjpeCdMhCgmyoo9Q6MSZcUAYni/z11xW7OoFp9ITHY0DnjlJriRqRT58XgFbSRRwcmAkK+W/3wq9EEIUYOKRsCEkeA8lEzhO9tbPY8zaTEKutusyvlNbc75YElUnUdREFqcnnsmEoFQpTu0V4iZHTrCnuUh+42wT4MbmQqTjvetIB7D+hWXOypTFutXXA4ArtWVrHF4GQdUqjRZ92mnTPkNgYpKeXIn8tXyn2+FPgghcqhoBEwYCc77DveM6ojsRj5ajExszmFYEsMIyVKp8R8FocXtuVtUTCrEQ7fOxfxLpoqel0SYVCk3m4nKc5N4alRIV5j8eN+W1lfjEYfQsSklhbjvpnNhdU4hZlaXcws344BK1Ss3z5WJEKigjSQWOt97vlr+87HQByFEBhWNgAkjNlhl08lHi5GJzVlVWVEVIsIKyUqfb1JMCi2qz0Ui8B8/NYh4LKb0jXgJkybu2asMr9RT40UsBnx+wcX488urR56nCe+bpZDtbe9Gy9tdAIafmZ1CZ1ddqvekWvidnQJ4rP8MGne+oRTKFKUQKCd0v/cwLf9h5qJFKUeGEBIuVDRCIOiNUbrpTC0tykuLkYnNWUVZURUiwg7Jsubbt54aXX3MCVPKp91zqSwtxPdX1mPZFdNsf6OqJA4lU9j7djda2rsBpLDgkirMnzFWMPYSokzcc+Mzr2NSYdz23Uk8NVJSKeDnf3gHnzyrAJj0vhXEY2iorUJDbZXt361r2Y5L8VrW9TIVwM/UVysLvGGFQOkI436+97As/9LqhCbJBQWREGIeKhohEeTGOK+mEhUlhZ417RtX1uelxcjE5iwVPI90nbTtcOwkRJgUClWEnqX11Vh02QWYf1+zY1id9VySyRS2Hzjqa046CVc9/YO4Y8sr+Mq/Hx/p35KOipLY1NoxJoRn43PtqCgpxP1poT4SRVAlxMwJt4R61dCsipKzJWJdBmPNkzDj+MO4lm4oU9AhUDpeCb/fu1/Lv2SNUK1OaJKo58gQQsxDRSNEwooNtqOkqACfqU9k5dpBY8ItL1FWLigrxtZ97yoJEaYENR2hp2hCHD+8cfZIGFXmc0kBODU4hFsfe1F8Tjsk1vufPn8YV/6HKVh2xejzSpXEY/0DjqVhj5+tEvXIbXMBQGRN9pozKZv/74Sd4Cj11PztgotxQ301ksnUqPeQSfo8CTOOP19zBrzQ9UqY+N51Lf+SNSLM6oROZHMfJISET/Q6duUBVmfu7QeOoqW9G0NJXZupjH2Hezy9GScHhrDvcE+g48gm1uacKB9tIU+UTxRZ6CzBEzinnFhY/71q3kWucfnpQoSFCUHNEnoyBRhL6GlyqWbk9FysXhGZ80Zyzkyk1vt121vHfAuS575u+Sz8j2fsw3fS+e72Vqzf4V5J6ntPt42MwW3OPHLbXDxy21zPkrR27xyQe2puqK/GghlT0dXvXmrWwrICSzARHjYeqwVJKpKlz6N0TClmS+ur8cLdi7D19vl48OY52Hr7fLxw9yJXJcNrjVCtTkgIISagR8MwQST9ernDx6vVMRO/bnkvS+KZj5Ki86Q/Z7+CmonQq8znUnVeMb72ywO2x+rE3UvnVXf/gK0l1+u5l08q8ky8BoD3T7jno9hZk73mzKmBIaz95aue1858BpLQrHgMOHY2Rl5lnoRZwWc8Vgvy45UwqZhJLf/SNWJyceG4rk5ICMkOVDQMMZRMYePuQ7YdcNPd7TqN6LwUl1yyOgZd6cSvW95N8BxOQPYm/Tn7FdRMhV6lP5eW9m7XZm2qcfcq88pJgHF77tsPHBWfX2cMbnMmUT5JdM7MZyCp/pVMAXdu2Y+H48PrQnX5RNd3XZ3W1TysCj7jsVqQH8NNNhQz6RoxXFlMRhT2CkJIfkBFwwBNrR1Yv6PN0epqWZXuefLgmONMVCvKFatjVLpueyk7ToKnznP2K6gF4a0yfc55NZWoLJVZS90EGKfnblroUTmfn29raX01HrrlKqzZ+grcoict79GKK6td4+dXXFk9Mk/CrOAz3qoF+THcSL73dctnGTW2yL992TXytTohISQ7UNHwibRWfgpWPLx9TLzfakUSYRYYtmZno9pHVLpu+1F2dJUGP4JaEN4q0+csiMfw/ZX1uGPLK67HVWsqu/NqKpEomygKn3JDR+F2e+c4+9/rljsrilNKi12VDMvavLe9Gztedc+L2fFqB765dNYoZSOsCj7jqVqQie7jTt/7iiurx/QN8WtskX6nn6ypxE/+L1znIzDcET4f3yshJDtQ0fCBiVr5pqoVeQmzALDwgd1Z8SaE0XVbgqkmZw/dMndMDXpJkzEdQS0Ib1UQ51x2xTR85d+PO1rkY9APsSmIx7B+RR1WKzQhtLs+NMfg1dW6cWcb4nHYvnuptbnl7S7PhHq7cLYwK/iEXS0ozIZy6ZgIF7NvTjiAO7eYN7ZIv+d4LOapZABA1XnFymMghBAnWHVKkfSKUj/fc1ipVr4TpqoVOVUqAaBdtcgEKkpTUPipJJNOU2sHGne2jVIyKksLsW75LJFHZMGMqVg550IssGky5/Qbr6pMqsJzEOcEgHuX1eEnt8xFZUalpmph5S83ltZX45Hb5o5Uy1JFWn3M7frrls+y/ZvbdyT3NMmedS4m6epU4Wtq7cDCB3Zj1aa9uGvbAazatBcLH9itvVapjsFvFTtg9Pc+r6YSjTv9rz9O15F8zyqVzQghxBT0aChgF3ZjkvQFXmpVsktCTbc6RsGbEIWqWCaSqp08Isf6B3Hnllfw1Q/6Mb2qZMT6al1XYpF1s94GESMfVNz9siuq8Zn6YEJsLCux1Rn8zff78H/aPvD83ZrrZ2Dtkk/4GsNQMoXGnW/Y/s3tO/JqpmlZmxfMmIqNz73lOY5cS9LVCVU0HWapGy5pMlws6MaHku9Zp5gFIYT4hYqGEGkuhh+sBX44ufx112Ol4S1hdhF2IgpVsfwqOxKPyIbmN0f+za5PhZNwIxGEgoiRDyruPsgQm4J4DA0zq9Awswot7d0iRaNh5seU7ild6as6rxhIeYc2OX1Hu9o6XXvcpDBsbZ5/yVQj4WzZCjeyQ0dhMG0Y8au0mJrLYRhbvL7nXCkaQgjJL6hoCPCbi3HB5CKcGUqh9+Sg5wIvUWhUwlui4E2wNjgvT9AxoWtfB7/KjrQpnYWdcGkn3KgIQkEI8LncpTcIwcmv1zL9O7LWDTcqSgqVCjr4LYUdFroKg0nDSBS8uRZhGVvcvufxWKqYEJJ9mKMhQFXITGft4kvxh3sX4/6bZgNwj6EFIFJoVOKEo+BNKIjHsG55nedxjTvfCKyLuiWUOm2hMbhXRTKhiGXGYpvKGxmvmM41cequrEL6dyRZN46fHBzJTfKTF+Cne3wQ6OZlmTSMRCE3zMLv+mMKE7knhBCiAj0aAnSETLvQF0kMrUTI+Ye/uhINtVWicUTFXT4lI0HYjiBDuPxa80wpYpnCTbbD2kyQzXAdU7kmfr2Wdt+RbkEH1XC2KFnuLXQVBpOGkSh4cy2i5E0YT6WKCSHZh4qGAOnmt275LFRNLnZcuL0WeOmGJ60eAkRngzO16fsRav0IpV4KmypBNNjLBlEI1zEhOPnxWjp9R7pCs2o4WxTysDLRvXeThpEoeHPTiVLjw1wOmSSE5BZUNARIN7/PN9R4CjduC7zqxigVuv1scKas1SY2fbsO7ImyiVi/Qr5J6wqlXo3bVAmiwV7YZLMJo928tL4rnTnrR5lz+o7C8iZGyXJvoXvvJg0jUfHmpkNvAiFkvEFFQ0BYXgGVjVHVkqyzwZm0Vvvd9JtaO2wbtnX2ncbqzfvxiIJQq2vN82rcJiHzPqMmCEnJZriO27zE2eum/62ytAh/OWcaltQlHOe8jjK35vqZaJhZ5XjOsNaNqFnuAX/3bsryHxVvrt246E0ghIwXYqlUytM429fXh/LycvT29qKsrCyMcUWSMMJELCsxYL8xPnzbXACwtSSnH+N3PE7Waj/XkNyb3TmHkilc/f1drmVCp5QU4l+/syQUoeHZ1zpwxxb1LtV296n7TLJNS3s3Vm3a63nc1tvnGxWq3OalxMvk9L0OJVNY+MBuUWicpQC+cPcicYnVINcNr7Grjtckfu7dlDc1CuF9hBCSb0h1AyoaioSR+Oq2MS6pS2DhA7sdLeomhApLcAniGjqb/p63unDrz170PPc//5dPomGmLEleF4nSAwAVkyYAsZixPho64wxynm4/cBR3bTvgedyDN8/ByjkXao0v85irL56CT/3oOd8NM2OwV+CclL7M38Lh924E/T68ymJ/5boa3LvMu/JbEESht0cUxkAIIfmEVDdg6JQiYbi93cKcvCpTmUj8DDK5VCeES9rRtqW9O3BFY+PuQ55KBgA8dOvVmH/JVHEOjcm47TAsuIf/1C86rqp0bId7yfjsjqksLURPv/ezl2CFdQGju7c/dMtcNO50Do3TTdwNYt3IFJ7/y7XTsen3R2yPffT5w7jqoilZseBHIVQoCmMghJDxCBWNiOK0MYaR+Bn0NdQ3fWnqdbD9JoaSKTyx54jo2K4PzyjdpylByMmy3dE7nMuydvGlWLNopi9r7lAyhX/a+47o2LaOPnT1nxlRnna1dXomkAP2oYGmlAxLUd64+y1se+ndMQrPuuWzMKW0eFRn8PR7iIIl3E4R8xpW2CVuCSGEECoaOUYYiZ9RSy5dcEkVNj7XLjouSPYd7sHxUzJh18SzUQ33kPSC2ND8JrbuewfrV1yubd3ed7gHPf0DomN/8OwbI/8/UTYRpz8a8kwgT6VSAauMw2xofnPMv3X2nsadW17Bw7fNtQ35igJOyqRbX8dc6clCCCEkv6CikWOEUbIxamUh58+YioqSQteQpYqSQswPWICSenAqSgp9Pxud8CdpL4jOvjO+ys/qerLSyxLbYQnD2SToill2eScvv3PMqDLpRpR7shBCCMk/qGjkGGGUbIxaWciCeAz33zTbtrytxf03zQ58PFIvxRf+zLufihu6/SlUhUhdYTqKfT3S56nfPidBWf+dwp3SPRGmlEknovjuskUQCeJMOieEkNFQ0chB3OrMr1s+C+WTirD9wFFfG12Uutha43nktrlYv+N1dPad64yeKCv2FQakgqQ7eEVJIdYsmql9DT/9KVSESD/CtPUcsu19SCfh0kcjHRUlxKT1XxruZFqZtPDyQo43ATmIggkso0sIIWOhopGj2FUqOtY/MKZijp+NLmpdbLM9Hkl3cL+eFT8VvySKUCY6gmv6czCZS2EJw6lUCu/3nRGfe93yWfh8wzkvkjVHmts68dSBo6OSyBPlE3HzNR/HhuZDnueVKm5eQrpKuJNJZdLCyws53gTkIDraB3FOQgjJB6ho5DDplYqaWjtw5xbzG13UykJmezxOnh5Tgpmfil/pCoAU3VAap+dQNnEC+k5/pHy+dGEYgNI9VE0uHiVAW3NkwYyp+NbyujFKAABse+mPRnKQJEK6ariTX2UyMxzLzQs53gTkIDraB3FOQgjJF6ho5AFR3+jyLSwjSM+K34pflgKwfkeba/K1iYR+u+dgNdVzE+IrSgpRPCE+OgQuQxh+6JarcOeWV0ReALdn5qSYSnKQgOHeLE7vWCqk64Y7eSmTTmPfuGouppQWec7NqK4bQa4XQfQICrLvECGE5DpUNPKAKG90+RqWEZRnxUTFL0sB2Lj7kG2IkF0oja5wZ/ccvATh+26a7amoTSktFikZU0uLtJQlrxwkAFj4wG7HeasipOt6jbyUSbux33zNRRhMJgEAn71imus7jOK6EfR6EUSPoDB6GxFCSK5CRSMPiOpGN97CMkxgquJXQTyGuxZfik8kJnsm9JsW7qSFBNyEV+lcXTnHXZj2GqedwiNpKlg+qUgspKvmzqgok9bYj3T1Y+u+d0f1BvF6h81tnYLReL8LiZIqOSaM9cKuU72f44Do9R0ihJAoQUUjD4jiRhfVsIxcwCn8Safil1eYV1DCnd/wMulcXVKXUB5bOpkeGem8/ebSy0Tn/+DEaVERgczrSJXJBTOmoqm1Az9uPqT0DptaO/CYsMu927uQKKmSY0JbL6Q/VbhE1PoOEUJIlIhnewDEP9ZG57Q3xjC8sYe50amEZRAnRostqZRejSdLIF0550IsmDF1VLiUm3AHDAt3Q24tpzMYSqbQ0t6N7QeOjljzM68rwWtOA8HMaem87fnwjOMx6VhCuqU8XlDmrUBNKSkUK1A679D6jRde64alpGY+L0vBaWrtEB0DhLdedAnfm/Q44JwXEhirn2Sj7xAhhEQJKhp5QBQ3uqiGc+UClnCWniwNAO+f7ehtCWd+MS3cNbV2YOEDu7Fq017cte0AVm3ai4UP7NYar9ecjiGYOS2dj5WlRcrK/dL6avzjX1/pee5jJwfFz1znHUqrYLl5ViQKzvodr2P9DpkSFNZ6IfWUVZUWjyjMLe3dnsq2pUgmykefP1E+kSGihJBxDUOn8oSoNdiLYjhXLhBmyJkJ4c6Kvd/V1onHbUJx/IRgZWNOS+djonySVi5NV7/MUm5a8E4/TvqbLzZM1+5QngLGKMp2x1hKUFjrhSTMqbykEF/7X6+OCluU5CyF2ecn3yr5EULyFyoaeUS2G9qlkytxy1HbsMOsBCQV2rpOnLHtNG8Xe283Xj/KUdhzWjpvr754Cl5+5xi+2DDdtimgk1BqWqDWOc5E/otJT+QHJ07js1dMC2W98Cq2kAJw/OQggMFRv5MqzCar0TmtTflayY8Qkp9Q0cgzst3QLn0cbgmwKQDL6hMjcfw6VWr80tTagfU7Xh/dz6GsGOtXXJ61DTvMkDNp87fGnW+M/Lcl0AAQdwb3qxzpzmmdOSSp+rXiymp86kfPjRL0KkuL8JdzpmFJXcL1OqYV8Hk1lagoKTwrHMvOZ2IMJj2R50+eqFVtTXeNcPKUTSkpRBKwfZZhF7BwUiZWXFmNR58/zEp+hJCcIZYSZJj29fWhvLwcvb29KCsrC2NcREi2LPLS69ptmJmdi3Wq1PilqbUDq126Tz9ieMOWPq+W9m6s2rTX83xbb59vRKG08kEA72pIwDmhr9xFuHXiwZvnYOWcC9UGqInfOaQq6FnPRSLoOT1zlXOkn8ttHgP2c9nvGIaSKSx8YLersnJBWTGAGN7vc1doXrh7kauXzO692R2XKJuIVfMuwvSqEtFa+Oxr7+E721tHeaMkmPr2nHCqBOeF3fMkhJCgkOoGVDRymGy50FWv6xXHny7cAPaWcjcBSFXZGkqmcPX3d7kKyhUlhXj5O0uMbNgqz0siwJkWJiTKoAnWLZ+FqsnFgSvEToKaqiCfOa+srudOoWIq78bEt2vNFbfQtSklhfhXh3lsQhnzUlYAKCs0Xt+zVBB3uxddYR4IVmGWvFMvglaECCEEoKKR95gSpsK6rtcGaglpqVTKMYnUjwU0nT2HunDrYy+63SYA4J+/9Ek01FZ5HueGzvMyafGWki7cdZ04MypcygReXqzMMegqI9J5pqOoqXib5tVUGmliZ2o8ToKn3zGY6qMhRUUQd/pe/ArzQQry0nfqRpieQ0LI+EWqGzBHIweRVCb69lOtODUwhET5JGPWYz8VkaRJzm5kxvrrNptrebvL9Trpx/lRNHSfVzaqLaXnQWw/cNT4+TO9I5nvSEcYtROSg0yml+bFNLd14r/98oDnvfjNpzKRz+N3DJJkfZMJ/dLSvIDzN6ZyjnTCKGBhIveKlfwIIVGCikYOIhGmuvsHsPaXrwIwF07lR4gzXaVmKJnCPU8e1CwDG0B7YBv8PK9sVhAzKag4hWClv6NkMoU7t7ziqTCmKxZHuk5i6753x5QgXVYva3SnMx+lz8Wu43YQybpRKSEtUVYK4rFRXh6nIhBeqL43u29M592H1Y/Iz7uKSiU/QghJh4pGDqK6UfoVciwB7zfCxmt24zNdpWbj7kOuORZuQvyCGVOx8bm3PK8zIa2dpU6IiV+Lc7YqiEmqElWUFOLYyUHbimIA8KWG6ZhWMck1BMt6R9/Z3uqpMCaTQONO91K6nb2nbYV8O3Tmo+S5xASKlWrVIqe5lyslpAFz4VO660j6N6ZzjrD6EUkqwQEY892xAzkhJKpQ0chBVDfKdCFn0WUX4OV3jokFZkmvBMn4pEJRKpXC+31nPHsY/N3ml0VjsRPi518y1bUkqMW2l/6I//rpS7GrrVNLSArC4myXoKzyPiVISo3ed9NsAHB9LtIQLLeqP5YycscW98pK1rHAsBcllbJXgPwI3wXxGNYtr7Mdi/Wc3DLedMK2vAR0naaBYSMNcZQo81JBPJP0b0yyFl1QVox//Js56PrwTKjeRMm39+XrarDj1Y5INGYlhBAvqGjkIDqbrSXkzL/vt+jpHxj5d5OVWdyEOGmdfACex7z8zjEcPyUrSWknxBfEY/jCn9VgQ/Obrr/t7DuDjbsP4cfNh7Tq1pu2OOuUCtZFmifiFt6VrVhx63mYFr6bWjvQuLPN9m+J8om4oT5hW1UtE6mnSyqgh53Po4I0TymZTKFx5xuivBa3/jyZ2H1j6eewIwVg5ZxpaJipl5/lN8Fe8k6/uXRWpBqNEkKIE6w6laOo9j9wwlRlFmlFJBNVarYfOIq7th3wHJNbiVrxOSYVOio1kgpGuhWkMoWVY/0DuHOLt9JnujKVH6FJUqq3srQI3WmKrym+2DAdv2ntNFb62Uvp/sktV2FKabGoYpCkzK9q9awodbg3VcHMbi4PJVPY296NzS8ewfOHutB/Zkjp9+nc92wbfvr8Ycff6nxDpitsReWdEkJIJixvOw7QCWuyw05g3vNWF279mXcJWAuVzVSygbodIy0BuXbxpbhrca3t30yUkbTwKnepKnz47WsRpcZdXorWQ7fMRePONuVQGC/++UufxPwZU40IalKh/3ffuB6f+tFznp3WJR6osBs3msLUmmSRPpd3tXXinicPjgl5LC0qwPWXnY9/PdIzqjS2V28f02WQs1VynBBCsgHL244D0isTdfadRuMzryt3uQXsy8be8+uDot/+7YKLcUN9tZIQJ61S43SMJHSsoqQQaxbNdDy/JKxJ2v3aKxTGek973+5GS3s3gBQWXFKF+Tb35ySsqDTP81PC1TSSMJB43DlcTlv5iJlLppdWD3v5nWOeYT1eZX4tTJSuDRs/TfCcsJ7txt1vOYY69g8M4ZnXOvCTW+ZiSmmRSLE0XQbZT+lvQgjJZ6ho5DjpwtSkwrivcKoPTpxWFhZuqK8OXZiVxGnff9Ns1w1dkjMiyeMAZLkImQnlG59rH2NxdRNWdOjsO42W9m4li34Q4RpepXrdlJF1y2ehcecbyh6Prg/tmz7qoCL0r5xzoe29SMr8pguhUSldK8X03M3k8Rfe9jymcWeb2ANhWpELsn8LIYTkMlQ08ggnga2ytFDk6agqLcbXf/WqSFjQrd5jSpB1uleVEC4va/uSugS2vfSu72RuaVKvbiMxJzI9XF7PxmR8eSZe3gU3ZSQej4mTfy3sBHArvn+4YePweOZfMtVz/kmF+UPvf4iW9m4sqUuMuhevPAU7ITSXStcCek3wVN5n7+mPPI9REeRNK3K56IEihJAwoKKRZ9gJbFdfPMU1dtwSWhDz7s6djmr1HtOCrImmdkvqEpg8sXBMSJN1Dr/lQ1VCKkwLIZnKpVulLN0u6yZxUkacFEI7nATwptaOMfH9G597CxUlhbj/ptmu9yat8rbxubew8bm3xsxpaZnf9PcvrdIWlTAcnbk77LGqM5qjIx2HaUUu1zxQhBASFnHvQ0iuYQlsK+dciAUzpqJoQnykdKxTj+zv/kWdONykoqRQWfC0BNlMQbGj9zRWb96PxqdfR0t7N4ZUkhEw9l5VFZ+FD+zGrT978ayQ2I6v/+pV7GrrHDnGEnIT5aMFhET5RNEzUAmpMCGEuN299WS/93TbqOfspQzZ/cYkQ8kUWtq7sf3AUcc5sLS+Gi/cvQhbb5+PLzZMB+A+l9PnQVNrB1Zv3m+bb3P85CBWb96PJpdmlJbQL8VSzqxz6gqhfueeE5LnrYr0Htctn4UHb56DrbfPxwt3L8KyK6qVnq2pcaS/U+k8csNSXJyOjmHYqBIVDxQhhIQFPRrjBElS7rBV35uHVs1FQ628xrwkfvuxPUfw2J4jxkJ1vFCx4PvxnKiEVHz2immelvPMWP/M//YqF2sXppPN+HIVL5elVC6YMRXzaipFvSOGkims3/G65zi8EnWX1lfjy9fVYNPvD3sm5md6qvxYz0147dIJKjxOeo+fb6gZM/al9dX46uJLRflQbqgK8iZ7kOSaB4oQQsKCisY4wktokQoLdtWS3FCJ3w4jVEenQoxuBSMVa7ZEWNm4arhfg1Nn8M7eU1j7y1c9r5euAGUrvtxPuJZUAB+uyObtqfNSpJpaO/Do84eVG2Ra5/QjhJqqnhVkeJxfQXt6VYnWddPREeRNKnJRb55ICCHZgIrGOMNNaPGq5pQCsKx+eFNW2YxVBNQwSkGGacFXtWbrCCvWGIeSKfx8j30DskzSFaBsxJebKAcqEcBV5p7TsX4qKlnnzLYQKgmP+9ZTB7HosgtQNEEvotbPPUrn1nnFE/DhmdGJ4VNKCnGfR56NG6YUOcC8B4oQQnIdKhrjGLsKUE7CghWioxPipCqgBl0KMkwLvo6lV0dYkTZKswvTMZEYq1pNLCxlT2XuOR3rpxpY+jmzKYRK7qGnfxDz7/stfnhjvbbQrnuP0jn4u29cj5cO9yhXDgsTk4oLIYTkOlQ0xilesdqWsLCrrROP7zkibjRmh7RqTyZBlYIM24KvY+lVEVakvU+cFBsdZShdsTjS1Y+t+94Vd2UGwlP25tVUIlFW7Bk+5RbfrzMGJ+UsW0Ko9B56+geMhFGp3qN0DhZNiKOhtkopR4wQQkj2YNWpcYhTBaj0ajkF8Rjm1VTiN62dtudQqUbkVuHFjSNd/QpHy8lGhZj0qknpVXf8hsyohPW4VStSqXBkVetatWkv7tp2ABuaD40R5DMrL2USlrJXEI9h/YrLPY9zi+9XHUMUk39V7yHIKmMWmdWvltQlAqmyRQghJHvQozHOUImNNxneotILwWJD8yF8IjHZuICRrQoxQVizpWE9ZRMn4HffuN41/l4S9iL1nnjlWcyrqURFSaFtyVnAbEO6pfXVeOS2uWP6aACy+H5Vj1wUk39V7iGMLtZuHtUX7l7EHAdCCMkTqGiMM1SUB5PhLUPJFMonFeGbSy9Dz4dn8O/HTuKJP7zj+psgk8KzlZxrojN6+jkOvf+h6Dd9pz/Cy+8c8xQc3ZQh1aRoay7tfbsb8Vhs1D3vaut0VDKs37qFa6k+O0uJ0ukM7qWYpgCsXVyL6VWlkRWM0+9BSlChi1FoDkkIISQcqGjkASoCmIryYCq8xcl6+dkrqvHMa86N0oK2rIadnGuih4E06dsOv4KjblL0nf+8H8dPnVMqEmXFOP1R0vU3FSWFWFKXGPlvE8+uIB7Tju/PdtUoC7/K1sO3zcW3njo4pmu8HVXnFfsd7hhMVBsjhBCSO1DRyHFUBTAV5cFENSI366WbkpFOUJbVMDFhxZWGLTkhefdugqzue0hXMgCI+locPzk4omBGxQKe7dKlJpStpfXVWHTZBZh/32/R49LYEQC+9ssDWL/icqPPNpvNIQkhhIQPFY0cRkcAU1Ee/OYySGr3S7AEZBNhR+kE1SU5ExNWXD+9HABZcrvX8zDZR0PCBydOh24B95pjQVeNcrq+SWWraEIcP7yxfiSMymlOvd93xrgiF0a1MdPrBCGEEH2oaOQougKYqvLgJ2TET/8BazyW0mNaKfAS3B66ZXQHbj/Cigkrrp9nGYN3crtEkF1Sl9AqU6zL+ZMnhmoBD0vxVL3+uuV1aNxpVtmyvuv1O9rQ2Wf/fINQ5IKuNpbtd0gIIWQ0VDRyFD8CmKryoBsyomKVdFN6drV1Gg2dkXha1mx9ZVTvkIpJhfhCQw3WLJqpLHBJn8NvzpaCtXu20nOUFhWgf2Bo5L8lQpaK0urWOT4dt4pSXqQrmM+89p7oN37D66Qeg6Cs5W7Xv2OLewK3rrK1tL4ak4sLcetjLxo/txMmwjGdiEqIHSGEkHNQ0chR/IYgqCoPOiEjUqvk2sWXYttL79oqPUvqElj4wG6j1lyJdyCzhcDxU4PY0PwmnvjDYdzvUQ41E+lz+KeWd/BPLe/YKgfSczz6uf+IeDymJAirKK1OSmp1+UTcfM1FmF5VgvMnT0QymXIVYJ3I9KqF0W9Dqmglkyk07nzDuLXcVIihjrLV1e+dL6N7bjuCKi3NJHNCCIkmVDRyFBMCWNDx5lLr5ZpFM7Fm0UxbpaelvVskBP98z2FUTS4WCdd+hKbjJwexevN+PKJgHVXtw2BngZU+y/kzvMu1Anolcjt7TwGQKalDyZTneMtLCjFxQsGo0J1Mr1qQFnALqaJ1x5ZXxvzNhLXcb4ihhY6yFVbjxHSCqODFJHNCCIkmVDRylGMCS6Tp7taqqFov7QQAqVLQuPONkf/vZWU2ITSpWEfdnoMddhZYr3OkACyrHxb+vRQt3RK5jTvfwKSiAiytr/ZUUiXv/v6bZo8oLJ29p9DTP4DK84pRPqkIQ8mU532baq7oR/E0YS336y3wo2yFocjZYbqCl/QZNrd1UtEghJAQcW4TTCLL0NkQDi/WLTff3VoVy3qZKB8t3CfKJ4qswDpKgWVlbmq1L59rCVd+noxlHZXi9BycSLfAep3DesWP7TmCVZv2YuEDux3v3Ypj17GgH+sfcH2umUjefUE8ht5TA/j7//1vaNz5Btb+zwNj7sHvHPLCr+Jp966Cun7mnPWrbFmKXBDnllx7wYypWDnnQiwQeuKckD7Dpw4cxVBmXCQhhJDAoEcjB5GGWkwpLQphNN74sV5effEUxGNjcybc8LIyq3oYnFC1RKc/h9+0duCfWtw7o9tdI/0cu9o68fieI2OejVM4j98SuTrWe693L03g9TOHvBK4VUPbnND1TEi9CuuWzxqTIyINN3J7BlFpRuiHeTWVqCwt8uwN0tM/yPApQggJESoaOUgYtehNo5sP8vI7x5SUDAuvmGwn4UpFqdGxhKc/B4miYXeNgngM82oq8d9+ecD2N04KgYlcAJ1Yd6d3r5rAqzOHJOVOvcKzpNNP1zMiDQ9bWl+Nz9RXKytbkmeQ7WaEfimIx/CXc6bh8T1HPI+N0rpICCH5DkOncpBsJHBmC79Cgdvvl9ZX44W7F2Hr7fPx4M1zsPX2+di4aq5nSFUM/vNf5tVUIlFWrH0NleRXC5MClolz6dyDCk5hYnahdW7hWT+5Za5rqJ3f+TCUTKF8UhG+0DB9jBcyMzxMNdxI5RmYDGXKBkvqEqLj8mFdJISQXIEejRwkWwmc2cCvUOD1ezsr+cPxubjnyYO2fSBMxa3vauvE6Y+Stn+TXEPHq2VSwDJxriA9czrlTt2s+vE4AklIt/M2VJYW4sY5F2JxXcKXV2G8lXy11kUn5TWf1kVCCMkV6NHIQbKZwBk2uonbfqzMS+ur8fJ3lmDt4lpUTCoc9TcTCciWldmpoV15SaHnNXS8Wl7PMgYgUVaMRFlw1nunsZk4Lh1db4mTVT+IhHQnb8Ox/kE8vucIek8N+FIyfr7ncKAeo6hhrYsx5P+6SAghuQI9GjlKPiRwStBJ3DYhVBTEY7hr8aVYs6jWaNy6JCF7UmGBZxiIjldLkgvw3z9bh0Mf9GND85u2101hbDUz3W7ZpjxzdtcPwltiMo8hSG/Ds6914DvbWz0Toy3yKWdhvKyLhBCSK1DRyGFyPYFTils36hVXVmPHqx2BCRWmmxpKErIlyda6/SXcBLEVV1aPqWpkR+PONsTjw+dySzT2mpsmemQ4Xf/may5yvQcLVW+JW2K7yncYVIO5+55tw0+fPyw+Hsi/nIXxsi4SQkguEEulUp5G4r6+PpSXl6O3txdlZWVhjIuQMTgJc7oW9Wyw/cBR3LXtgOdxD948ByvnXOh5nKSikN3zATDq3471D+DOLWPLzNphPdkvX1eDR58/POY3ltJQUVI4KjzMqZGi3T1MLS1C48p6LLvCWVl0Ko1rja+8pBC9JwddvSUv3L3I91yRvINMTM8DAHj2tfdsu5c7YfIZEEIIGV9IdQN6NEjO4GRNNu11CBLTeQmSPhUSRWThA7vFZVyt0J5Nvx+rZFh/BzAmByW9N0bmmL99wyz896db0dM//Jvu/oFRnhNrnNZvqs4rxvodr7uGHsXS/n8QHcUBeR+QTEzPg6FkCt/+l1bRsQBzFgghhIQDFQ1CQiSIimFOipZUCNbpr5EC4O0LHfubGIB7nzyI9TteR2ffGdfjO3tPY/Xm/Vi7uBa9pwbxLwfeE+cdpAAcOzmItYtrse2lPwYSWucnz8L0PNi4+y0ccyguYEcUcxZyyTNJCCFEBhUNQkLERF6CBBUhOMxkYEsBkB4LABuaD2lfb3pVKV64e5ExATZdGO46cUY7z8LkPGhq7XBM3rdj3fJZ+HxDTaSEeJ3wM0IIIdGHigYxQtDWyHyydoZRGUcl2TjfkoHT6TpxBs+89h7OnzwRn71imq85YycMS3BS5EzMA0uhlDK1tCiSSoZO+BkhhJDoQ0WDaGMJ/7vaOseEtZi0RuajtTPoyjgq5V0/e8U01zAeO2IAYjEgqRg+FSbxGNC4842R//YzZ5yEYQluipzfeaAa9ta4sj5SSsZ4aypICCHjDTbsI1o0tXZg4QO7sWrTXjy+58iY2HnLGtnU2uH7OnZNzUydP5s4NYczgUqysVsDSDusY26/tsa2OVpUyFSC3ObMUDKFlvZubD9wFC3t3RhK+7Gk94kd0uaGfuaBStjbV66rca3ilQ10GysSQgjJDejRIMpIrLsmrJG0duqjmmzsFMZTUTLcGT29glR6aM9VF02x/c3xk4PiBoumiTt4WpzmjJfHTCdZ3nRVJ6fQQalCedenZ2Ltkk/4HocJrHvp7D2F3735J9Fv8qmpICGEjCeoaBAlVKy7uk3HLIJqajYe0Ek2dgrjAeAY2uP0m11tnbbC+6nBIcfeFjpYCtM//NWV6Oo/g64TZ0aFS2WSOWck+QFnPkoqj8tkvo1XU0SvsLfq8on4/z59qe9xmEA3zyWf84gIISSfoaJBlNCx7upaI1XyDMhYdJKNnUrlenUpz/y7mwJip/zokK4wNdRWAQAan35d9NsPTpwWe8z+4a+vFJ1z3fJZqJpcbDTfRqIIhVHFzAQ6eS465Z4JIYREByoaRAkdoV7XGmm6qZkpcqkCVtBJ5244KSB2yk91+USsWz4LU0qL8cGJ0zjSdRI/Pluy1UkwzVSYhpIpPHXgqGhs50+eKPaYIQVRGJrpak5SReiFuxcFXsXMLzp5LlFTlAghhKhDRYMooSLU+7VGBtHcToKbIpGNClh+FZuodU6XKj+fSJw35llXlhbixjkXYnFdYsxv9h3uGeks7sbU0iLMq6nEM6+9JxpvV/+ZrHgNVEIHs6lQStDxhEZJUSKEEKIHFQ2ihJfwb2FCAAuruV06booEgNDr/UeptK+KwuN1rET5URWepd62lXOmKSVSnz95IhbMmBq610A1dDBqCmU6qp7QNdfPxNoll0ZGUSKEEKIHFQ2ihJvwn44pASyM5nYWbvHwqzfvR0VJYagVsKLUyExF4TGpHNkJz34rMC2pSwDQq8wVptcgqqGDOqiOsWFmFZUMQgjJA6hoEGWchH+3sBa/1wtawPOKhwdGl3i1O8ZkBawolfZVUXi8jn3olqtG8jB03qOJCkyW4qDjMQvTa5Ct0MEgsO7FK3wql+6JEEKIN1Q0iCd2FuSwrbtBC3g6MeR2mKqAFZXSvioKD87+fzdlbc3WV0b1uFDxdJiqwAQALe3dI/P2oVuuQuPONyKXSB1W6OBQMoW97d1oebsLwPB3Nv8Ssw0k0+/FKyGcyd+EEJI/UNEgrniFwQRt3Q2rwpMpBcFUGEtUSvuqdm72UtacunV7hYGZqsAEAAsf2G1T8aoOU0qLIpdIHXToYFNrB+558uAob93G595CRUkh7r9ptlFFy+leLLKVe0QIISQ4qGgQR7KdIxBmIrRfBcF0yEfQ8flSBS5ohUcaBmaiApPVw8NuPt+5ZXg+r5xzodZ9BElQ3sOm1g6s3rzf9m/HTw5i9eb9eMTwN55+L529p9DTP4DK84qRKIuOckcIIcQcVDSILdnOEQhbyZHEw5eXFKL3rOU36ApYQcbnP/taB76zvRU9/QMj/+akwIWRkCwJA9OpwDSvpnJEON/b3o31O6KR86KD6dDBoWQK63d4NzcM4plEuToWIYQQs8SzPQASTVRDZkwy8FES33rqoGus//eebsNQZhyOD6wYcuCc4mBh/ff9N83Gw7fNRaJ8tFCdKJ9oXPGRjEdHsbnv2TbcsWX/KCUDGH6Xf7d5P5paO0b9u6XwOF0lhnMJ1l7HeuGmTKgqPE2tHVj4wG6s2rQXd207gFsfexGdfdmZz1Fk3+EedPad8TxuPD0TQggh5qGiQWzJVo5AU2sH5t/3W9fGa0EJhVYMuZsisbS+Gi/cvQhbb5+PB2+eg623z8cLdy8KJIRMMh4Vnn3tPfz0+cOOf09hrAKnovC4HSvBTZlQUXgsb5hOcn/QOS9RwU3pymS8PBNCCCHmYegUsSUbNfydwqWcCEIAksTDhxn6YY1n79vdaGnvBpDCgkuqMF/x+kPJFL6zvdXzOLsQJpWEZKdj47GxieAWkjAwaQUmwLnylYRc6Elhgp4Pvb0ZFuPlmRBCCDEPFQ1iS9g1/N1yQpxIF4BMVqeKWgz5rrbOUYL7xufalZPi9x3ucfUSpWOnwKkkJNsde6x/AHduGU481s1vkSg8Le3dWp4M3fkcVlU001SWFomOq5hUyJ4WhBBCtKGiQWwJq4a/hUofi0yh0K46VaKsGKvmXYTpVaU5JQBmYiopXsX742TBVlHA7I59OO6/TKuXwqPj5VKdz5Zy0dzWiacOHB2lwOVKidZE+STRcV9oqMnJ74YQQkg0oKJBHAm6hn+6NfjQ+x8q/dYSCh0F8b4z2NB8aOS/c0UATMdk5S9p+MvU0qLALNi6ZVrtvAZOCo9OmI/KfLZTatMJq/SzXySduitKCrFm0cwQR0UIISTfoKJBXAmyhr+bwOZEZWkhfnjjcCMxlXCrXBEA0zHZHVwiWAJA48r6QC3YqmFpqr1UJCF/F5QV4x//Zg66PjyjNJ8lOUQ6pXKzEX4l6dR9/02zAx9HroaeEUIIkUFFg3hiOmdBNenbYmppEVru/TSKJgwXS1MJtwqzV4Ip4clk5S+JYPmV62qw7IroKGE6YWOSkL/1Ky5Hw8wqpbGoKLUqCmCYTSkzcfJYhnV9t3tPN25UnVcMpICufjXFkBBCSPahokFCRSfp2xIpfnBj/YiSAajH46sIgLqYFBxNV/5yEiwrSwvx/ZX1WHbFNKXxBYmfsLEgQv5UlFoLr/kZdlNKO4LyWHrhdu+rN+9HRUkhjp+0L16Qi2GQhBAyXqGiQUJFR2BzEhB1y24G1RfAtOAYROWvbAmWqvgNGzN9nzpzxm1+qihSAAJ9X2FWWRtKprD37W7c82v3hpxOSgaQm2GQhBAyXqGiQUJFKrCtuX4Gai+Y7CpYeQniTgTRF8Bk4rZFUJW/ola+1w4TYWMm71NlzkgUQKkitXH3IWx76Y9ZCW0yjW5eViZhhkESQgjxBzuDk1CRCmwNMz+GlXMuxIIZUx0FCdVO1Ondo02jYoFXwXR38FwhGw0j3fDqTJ6JlwIoVaQ2NB8aM68si35Ta4dwNNnHT7d2O3S/J0IIIeFCjwYJFdPhQE7x+HbnBcz2/kjHZOJ2JrkS7mSSeTWVSJQVo7PPvoO16YaRXrh5l9KRehv8KEi5ZtHXycuSElQYJCGEEDNQ0SChEkQ4UKYgfqTrJLbuexedfeZ7fzgRtAU+F8KdTLKrrROnP0ra/i1opdEJJ6V2amkRVs6ZhiV1CbECqBv2ZxFGYQNT6ORlSQnLo0UIIUQPKhokdIKoCpQpiK9ZNDNUD8DVF09BPAYkXaTGeGz4OBPkc/8Br/LH5SWFuP+m2VkJGzPlXfJSuKXKx563/hT5dx+E1yFsjxYhhBA9qGiQrBB0OFDYHoCX3znmqmQAw0rIy+8c8z2ubPZeCBpJmM2kwoKRakzZwNTcclO4b77mImxoftPzHBufa8ev9x+N9LtX8TpYZW3dlK1sebQIIYSoQ0WDZI18CgcKMkcjnSj0XggSSZhNroQMSXBSuAFg20vvikKrov7uJWFiFSWFeGjVXMyfMRW72jpdc66CDoMkhBBiDioahBggjCpJAx8l8a2nWo2W0I0aYSlsUSJd4U4Pibv5movw4+Y3PUOpov7uJXlZ9980Gw21w93aM5UvdgYnhJDchYoGIQYIorleOk2tHfjWUwfR0+/cyCzMBOGgckSiVtY2TOxC4ipKCgG4N7ADop8crpqXlU/eTkIIGc9Q0SDEAEE11wO8k6MzCdrabyJHxElRCVphiypO77j35CBSAG6oT+A3rZ2e54myp2c8lmkmhJDxDhUNQgwRRDUtnR4EQVr7nQTijt7TWL15P35yy1VYdsU0z3O4KSpBKWxRRdJV/sXD3aJzSd99tqqW0VNBCCHjCyoahBjEtNVWpQdB0NZ+idKzZusr2IgYll1RPep36T1Oftz8pmcyu2mFLcpIusr39A+isrQIx/oHfHt68rlqGSGEkGhBRYMQw5i02qqGwgRp7ZcoPckUcMeW/XgkPqww2Am1dmQmNOdimI2ul0D6jv9yzjQ8seeIL09PvlctI4QQEi2oaBASYaShMFNLi/CDG+sDFRJVlJ7vPd2GZDKFO7e8Ig77ykxozqUwm6bWDqzf0Ta6G33ZRKxf4e0lkL5jq/O4rqdHEqIV1cpVhBBCchMqGoREGEkPgsrSQrTc+2kUTYgHOhaV3I+O3tP4znb7UrxeRDmh2Y6m1g6s3rx/zL939g3nrTzi4SVQSYAviMe0PT2SEK0oV64ihBCSewQrmRBCfGFVswLOhchYxM7+74c3zg5cyQDOCcRS3ErxupFLpWuHkinc8+RB12PuffIghlzaxnu9Y2B0WJTl6Vk558IRz4+E8dijhBBCSHahokFIxLGSoxMZQn6ifGKoMfXpAnEQxDCclJxLpWv3vt3t2ePi2MlB7H3bvWpUGO94PPcoIYQQkh0YOkVIDhCV5Oil9dX4yS1XYc3WV+BkpI8BqCwtQnf/gPi8uVq6tqVdVna2pb0bDTOrXI8J+h2P1x4lhBBCsgc9GoTkCLohM6ZZdsU0bFw11/Zv1ogaV9ajunzimFAgJ8L2zphDJdXdmyDfsWqIFiGEEOIXejQIIcosu6Iaj8Tde13E43BsvJcCsHZxLaZXlfqy3JtsPKdzrgWXVGHjc+2e515wibs3IyzGW48SQggh2SWWSqU8TW19fX0oLy9Hb28vysrKwhgXISQH8BLOg2wOJzm3VHnQHedQMoWrv7/LNU+joqQQL39nSaQ8BdnqDE4IISQ/kOoGVDQIIYEShFDr1HjOYu3iWtSefx4ad77hqTw4ncsaoVdIl1N5Wwuv8raEEEJIrkFFgxCSlwwlU1j4wG7PbuN2ZCoPXueyEqRfuHuRq3I03LDvdXT2nRn5t0RZMdavuJxKBiGEkLxDqhswR4MQklN4NZ5zI7MDtqkmdlGpCkYIIYRECSoahIwjrDCmzt5T6OkfQOV5xUiU5ZZQ7LehXLryYLKJnVUxihBCCCHDUNEgZJxgl/BsYSpBOwxMNZSzPA9hXpMQQggZT7CPBiHjACvh2SlMqKP3NP5u8340tXaEPDI1hpIpJJMpVEwq9H0uK7zJrd9HLnYrJ4QQQqICFQ1C8pyhZArfe7pN1DLue0+3Ycip5XeWaWrtwMIHduPWx17E8VPO5WS9SFcestnEbiiZQkt7N7YfOIqW9u7IPndCCCFEF4ZOEZLnSJOnpYnP2cCrnK0UO+UhG03sguwvQgghhEQFKhqE5DmqydN+k61No+KR8cJJeQizapST0tR5NnzNq2/HeIQNBgkhJDehokFInqOayBy1xGepR6aytAg9/QMj/11dPhHrltdhSmmRSEANo2qUm9KUWXqXgvQw9P4QQkjuQkWDGIfWx2hhJTx7CetWc7qoJT5LPSzrls9ConxSpOedqb4d4wV6fwghJLehokGMkmvWx/GgFFkJz5Ich6ASn/0g9bAkyidFXjg32bcj36H3hxBCch8qGsQYuWZ9bGrtwPodr6Oz78zIvyXKirF+xeWRGqcJnBKeLcJQBnWVOssj09l72lbojKonxo6o9e2IsqJN7w8hhOQ+VDSIEXLN+tjU2oHVm/eP+ffOvjNYvXk/HomYUmSC9ITnsDuD+/F0pXtkYsCoORZ0CVrTRElpirr3kd4fQgjJfdhHgxhBxfqYSdj9BIaSKdzz5EHXY+558mBe9jWwEp5vnPsf8KVrL8GNV12IBTOmBq5k2DUL7FRoEmh5ZBLloy39ifKJkfOUuZHNvh3pqL6TbPT8iJr3hxBCiDr0aBAjSK2Ke97qGmU9z4ZVdW97N46fdG/4dvzkIPa2d6OhtiqQMYwXTHq6wixBGyTZ6NuRzlAyhfU75O8kW56PKHl/CCGE6EFFgxhBalXc+Nxb+PX+fx+x6oad0zGUTOF/vfxH0bEtb3dR0fCJ6Tj7MErQhkE2laaNuw+hs0/2TnpPDWQt7yqfQuYIIWS8QkWDGMHL+phOZ+9prN68HxUlhaHmdNhZZt2hAOMXxtk7kw2lqam1AxuaD4mO7ew9hb//3//m+I0CwLefasWiyy5A0YRgonCz7f0hhBDiDyoaxAhu1sdMrL+5hS/pVpRxqqLjVBHLjXywnGcbxtlHByuMTUpP/4CnUt7dP4D59zXjhzfODkzoz5eQOUIIGY9Q0SDG8CqhqoOKpdsplnzd8llo3PmGkpIxpaQQ8y+houEXxtlHB2mHdWD4u6k8r1h0bE//YChhVFT8CSEk92DVKWKUpfXV+N03rkdpUYGR87lZutMr4TzY/CZWO1TRuWPLK8qKz303zabF1ABRqbKUjapJUUNFaf/uX9QhUabmZfre023j8rkSQghxhh4NYpyX3zmG/oEhX+fwsnRL8y1UxZ4o9RHIF7IdZx/1fhFhIQ1PW7v4Uiytr8ZQMiXOu2LzPEIIIXZQ0SDGkVpOS4oKcOqsQqJSUUYn30LCuuWz8PmGGnoyAiBbcfa51q0+SCQFGxJlxVizaCaA0XlXUsZjUj8hhBBnGDpFjCO1nH7luhnKTdjc+jLoEsOwhZtKRrBYcfYr5wTfJBDw7uEBjK9wH68wthiA9SsuH/VeLG9UZWmR6BpM6ieEEJIOPRrEOBLLaUVJIdYsmomCeEzJ0q2S0GoH6/GPH0z38HDCqdJZFNEJY1taX41Fl12A+fc1o6ffvlIck/oJIYTYQUWDGEdS6vb+tGRrlYoyOqEZlhC0bnkdGneyHn+uoSvIh9HDIxfzP3TC2IomxPHDG2ePhFFRWSeEECKBigYJBCfLqV8hTDU0I10IWlpfjc/Usx5/LuFHkA+6h0cu53/olIvNdlI/IYSQ3COWSqU8A5T7+vpQXl6O3t5elJWVhTEukieYDisZSqaw8IHdoko4QPSty8QZJ0Hemj1egrzXXLE8XS/cvUh5TlrndgrN8nPuqJNLoWKEEEKCQaob0KNBAsV0oy23sCxL1Pnq4ksxvaqEQlAO45XIHcNwIveSuoTj+5XMFd1wn7DyP6IIm+cRQgiRwqpTJOewQjicqlXdtbg2tMpGJBhUBHk3vOaKrqcrjPwPQgghJNehR4PkJNnqy0DCwaQgH8RcCTr/gxBCCMkHqGiQnIUhHPmLaUHe9FzxKuHMcq+EEEIIQ6cIIRHEEuSdfA5Wk8VsCfJeze8AlnslhBBCqGgQQsQMJVNoae/G9gNH0dLeHVhX7VwQ5IPK/yCEEELyBZa3JYSIyEZzulxoiMdyr4QQQsYbUt2AigYhxBO/PS38QEGeEEIIiRbso0EIMYKJnhZ+YNI/IYQQkptQ0SCEuJKN5nTj3Ysx3u+fEEJIfkBFg+QsFMbCIezmdLmQlxEkTa0dWL+jDZ195+4/UTYR61eMj/snhBCSP1DRIDnJsDD2Ojr7zoz8W6KsGOtXXE5hzDBhNqdzygXp7D2Nv9u8P++rOTW1dmD15v1j/r2z7zRWb96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", 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+ "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + }, + "xaxis": { + "anchor": "y", + "domain": [ + 0, + 1 + ], + "title": { + "text": "tsne_1" + } + }, + "yaxis": { + "anchor": "x", + "domain": [ + 0, + 1 + ], + "title": { + "text": "tsne_2" + } + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import plotly.express as px\n", + "\n", + "tsne_df = pd.DataFrame(item_tsne, columns=[\"tsne_1\", \"tsne_2\"])\n", + "tsne_df[\"item_id\"] = np.arange(item_tsne.shape[0])\n", + "tsne_df = tsne_df.merge(items.reset_index())\n", + "\n", + "px.scatter(tsne_df, x=\"tsne_1\", y=\"tsne_2\",\n", + " color=\"popularity\",\n", + " hover_data=[\"item_id\", \"title\", \"popularity\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exercises\n", + "\n", + " - Add another layer to the neural network and retrain, compare train/test error.\n", + " - Try adding more dropout and change layer sizes.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Model: \"functional_3\"\n",
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┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n",
+       "┃ Layer (type)        ┃ Output Shape      ┃    Param # ┃ Connected to      ┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n",
+       "│ user_input          │ (None, 1)         │          0 │ -                 │\n",
+       "│ (InputLayer)        │                   │            │                   │\n",
+       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+       "│ item_input          │ (None, 1)         │          0 │ -                 │\n",
+       "│ (InputLayer)        │                   │            │                   │\n",
+       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+       "│ embedding_2         │ (None, 1, 64)     │     60,416 │ user_input[0][0]  │\n",
+       "│ (Embedding)         │                   │            │                   │\n",
+       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+       "│ embedding_3         │ (None, 1, 64)     │    107,712 │ item_input[0][0]  │\n",
+       "│ (Embedding)         │                   │            │                   │\n",
+       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+       "│ flatten_6 (Flatten) │ (None, 64)        │          0 │ embedding_2[0][0] │\n",
+       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+       "│ flatten_7 (Flatten) │ (None, 64)        │          0 │ embedding_3[0][0] │\n",
+       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+       "│ dot_4 (Dot)         │ (None, 1)         │          0 │ flatten_6[0][0],  │\n",
+       "│                     │                   │            │ flatten_7[0][0]   │\n",
+       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+       "│ dense_9 (Dense)     │ (None, 128)       │        256 │ dot_4[0][0]       │\n",
+       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+       "│ dropout_6 (Dropout) │ (None, 128)       │          0 │ dense_9[0][0]     │\n",
+       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+       "│ dense_10 (Dense)    │ (None, 64)        │      8,256 │ dropout_6[0][0]   │\n",
+       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+       "│ dropout_7 (Dropout) │ (None, 64)        │          0 │ dense_10[0][0]    │\n",
+       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+       "│ dense_11 (Dense)    │ (None, 1)         │         65 │ dropout_7[0][0]   │\n",
+       "└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n",
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 Total params: 176,705 (690.25 KB)\n",
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 Trainable params: 176,705 (690.25 KB)\n",
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 Non-trainable params: 0 (0.00 B)\n",
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0.7925\n", + "Epoch 11/20\n", + "\u001b[1m1250/1250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - loss: 0.3182 - val_loss: 0.7910\n", + "Epoch 12/20\n", + "\u001b[1m1250/1250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - loss: 0.3130 - val_loss: 0.7950\n", + "Epoch 13/20\n", + "\u001b[1m1250/1250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - loss: 0.3082 - val_loss: 0.7983\n", + "Epoch 14/20\n", + "\u001b[1m1250/1250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - loss: 0.3002 - val_loss: 0.7946\n", + "Epoch 15/20\n", + "\u001b[1m1250/1250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - loss: 0.3019 - val_loss: 0.7951\n", + "Epoch 16/20\n", + "\u001b[1m1250/1250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - loss: 0.2957 - val_loss: 0.7982\n", + "Epoch 17/20\n", + "\u001b[1m1250/1250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - loss: 0.2955 - val_loss: 0.8052\n", + "Epoch 18/20\n", + "\u001b[1m1250/1250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - loss: 0.2971 - val_loss: 0.7983\n", + "Epoch 19/20\n", + "\u001b[1m1250/1250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - loss: 0.2901 - val_loss: 0.8007\n", + "Epoch 20/20\n", + "\u001b[1m1250/1250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - loss: 0.2900 - val_loss: 0.8011\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from tensorflow.keras.models import Model\n", + "from tensorflow.keras.layers import Input, Embedding, Flatten, Dot, Dense, Dropout\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# ---------------------------\n", + "# Define constants\n", + "# ---------------------------\n", + "embedding_size = 64\n", + "max_user_id = all_ratings['user_id'].max()\n", + "max_item_id = all_ratings['item_id'].max()\n", + "\n", + "# ---------------------------\n", + "# Inputs\n", + "# ---------------------------\n", + "user_input = Input(shape=(1,), name='user_input')\n", + "item_input = Input(shape=(1,), name='item_input')\n", + "\n", + "# ---------------------------\n", + "# Embeddings\n", + "# ---------------------------\n", + "user_vec = Flatten()(Embedding(input_dim=max_user_id+1,\n", + " output_dim=embedding_size)(user_input))\n", + "item_vec = Flatten()(Embedding(input_dim=max_item_id+1,\n", + " output_dim=embedding_size)(item_input))\n", + "\n", + "# ---------------------------\n", + "# Dot product of embeddings\n", + "# ---------------------------\n", + "dot = Dot(axes=1)([user_vec, item_vec])\n", + "\n", + "# ---------------------------\n", + "# Add hidden layers with dropout\n", + "# ---------------------------\n", + "x = Dense(128, activation='relu')(dot) # new hidden layer\n", + "x = Dropout(0.5)(x) # increased dropout\n", + "x = Dense(64, activation='relu')(x) # original hidden layer\n", + "x = Dropout(0.4)(x)\n", + "\n", + "# ---------------------------\n", + "# Output layer\n", + "# ---------------------------\n", + "output = Dense(1)(x)\n", + "\n", + "# ---------------------------\n", + "# Define and compile the model\n", + "# ---------------------------\n", + "model = Model(inputs=[user_input, item_input], outputs=output)\n", + "model.compile(optimizer='adam', loss='mae')\n", + "model.summary()\n", + "\n", + "# ---------------------------\n", + "# Train the model\n", + "# ---------------------------\n", + "history = model.fit([user_id_train, item_id_train], rating_train,\n", + " validation_data=([user_id_test, item_id_test], rating_test),\n", + " epochs=20,\n", + " batch_size=64)\n", + "\n", + "# ---------------------------\n", + "# Plot train vs test error\n", + "# ---------------------------\n", + "plt.figure(figsize=(8,5))\n", + "plt.plot(history.history['loss'], label='Train MAE')\n", + "plt.plot(history.history['val_loss'], label='Test MAE')\n", + "plt.xlabel('Epochs')\n", + "plt.ylabel('MAE')\n", + "plt.title('Train vs Test Error')\n", + "plt.legend()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Model: \"functional_5\"\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1mModel: \"functional_5\"\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n",
+       "┃ Layer (type)        ┃ Output Shape      ┃    Param # ┃ Connected to      ┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n",
+       "│ user_input          │ (None, 1)         │          0 │ -                 │\n",
+       "│ (InputLayer)        │                   │            │                   │\n",
+       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+       "│ item_input          │ (None, 1)         │          0 │ -                 │\n",
+       "│ (InputLayer)        │                   │            │                   │\n",
+       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+       "│ embedding_6         │ (None, 1, 64)     │     60,416 │ user_input[0][0]  │\n",
+       "│ (Embedding)         │                   │            │                   │\n",
+       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+       "│ embedding_7         │ (None, 1, 64)     │    107,712 │ item_input[0][0]  │\n",
+       "│ (Embedding)         │                   │            │                   │\n",
+       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+       "│ flatten_10          │ (None, 64)        │          0 │ embedding_6[0][0] │\n",
+       "│ (Flatten)           │                   │            │                   │\n",
+       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+       "│ flatten_11          │ (None, 64)        │          0 │ embedding_7[0][0] │\n",
+       "│ (Flatten)           │                   │            │                   │\n",
+       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+       "│ dot_6 (Dot)         │ (None, 1)         │          0 │ flatten_10[0][0], │\n",
+       "│                     │                   │            │ flatten_11[0][0]  │\n",
+       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+       "│ dense_15 (Dense)    │ (None, 256)       │        512 │ dot_6[0][0]       │\n",
+       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+       "│ dropout_10          │ (None, 256)       │          0 │ dense_15[0][0]    │\n",
+       "│ (Dropout)           │                   │            │                   │\n",
+       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+       "│ dense_16 (Dense)    │ (None, 128)       │     32,896 │ dropout_10[0][0]  │\n",
+       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+       "│ dropout_11          │ (None, 128)       │          0 │ dense_16[0][0]    │\n",
+       "│ (Dropout)           │                   │            │                   │\n",
+       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+       "│ dense_17 (Dense)    │ (None, 1)         │        129 │ dropout_11[0][0]  │\n",
+       "└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n",
+       "
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 Trainable params: 201,665 (787.75 KB)\n",
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\n" + ], + "text/plain": [ + "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m201,665\u001b[0m (787.75 KB)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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\n" + ], + "text/plain": [ + "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from tensorflow.keras.models import Model\n", + "from tensorflow.keras.layers import Input, Embedding, Flatten, Dot, Dense, Dropout\n", + "\n", + "embedding_size = 64\n", + "max_user_id = all_ratings['user_id'].max()\n", + "max_item_id = all_ratings['item_id'].max()\n", + "\n", + "# Inputs\n", + "user_input = Input(shape=(1,), name='user_input')\n", + "item_input = Input(shape=(1,), name='item_input')\n", + "\n", + "# Embeddings\n", + "user_vec = Flatten()(Embedding(input_dim=max_user_id+1, output_dim=embedding_size)(user_input))\n", + "item_vec = Flatten()(Embedding(input_dim=max_item_id+1, output_dim=embedding_size)(item_input))\n", + "\n", + "# Dot product of embeddings\n", + "dot = Dot(axes=1)([user_vec, item_vec])\n", + "\n", + "# Hidden layers with **larger sizes** and **more dropout**\n", + "x = Dense(256, activation='relu')(dot) # larger first hidden layer\n", + "x = Dropout(0.6)(x) # increased dropout\n", + "x = Dense(128, activation='relu')(x) # larger second hidden layer\n", + "x = Dropout(0.5)(x)\n", + "\n", + "# Output layer\n", + "output = Dense(1)(x)\n", + "\n", + "# Model\n", + "model = Model(inputs=[user_input, item_input], outputs=output)\n", + "model.compile(optimizer='adam', loss='mae')\n", + "model.summary()\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### A recommendation function for a given user\n", + "\n", + "Once the model is trained, the system can be used to recommend a few items for a user that they haven't seen before. The following code does that.\n", + "\n", + "- we use the `model.predict` to compute the ratings a user would have given to all items\n", + "- we build a function that sorts these items and excludes those the user has already seen." + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "def recommend(user_id, top_n=10):\n", + " item_ids = range(1, items['item_id'].max())\n", + " seen_mask = all_ratings[\"user_id\"] == user_id\n", + " seen_movies = set(all_ratings[seen_mask][\"item_id\"])\n", + " item_ids = list(filter(lambda x: x not in seen_movies, item_ids))\n", + "\n", + " user = np.zeros_like(item_ids)\n", + " user[:len(item_ids)] = user_id\n", + " items_ = np.array(item_ids)\n", + " ratings = model.predict([user, items_]).flatten()\n", + " top_items = ratings.argsort()[-top_n:][::-1]\n", + " return [(indexed_items.loc[item_id][\"title\"], ratings[item_id]) for item_id in top_items]" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 965us/step\n", + " 4.4: 20,000 Leagues Under the Sea (1954)\n", + " 4.3: Letter From Death Row, A (1998)\n", + " 4.2: Young Guns (1988)\n", + " 4.1: Faster Pussycat! Kill! Kill! (1965)\n", + " 4.1: Dead Man Walking (1995)\n", + " 4.1: Legends of the Fall (1994)\n", + " 4.0: Color of Night (1994)\n", + " 4.0: Wizard of Oz, The (1939)\n", + " 4.0: To Kill a Mockingbird (1962)\n", + " 4.0: Eat Drink Man Woman (1994)\n" + ] + } + ], + "source": [ + "for title, pred_rating in recommend(5):\n", + " print(\" %0.1f: %s\" % (pred_rating, title))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false + }, + "source": [ + "### Exercises\n", + "\n", + "- Try modifying our neural network to improve recommendation. You could try adding more layers, or using a different loss function. \n", + "- Your goal is to improve the Mean Absolute Error on the test set. Show the results of your best model." + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Extend and improve the model below\n", + "class RegressionModel(Model):\n", + " def __init__(self, embedding_size, max_user_id, max_item_id):\n", + " super().__init__()\n", + "\n", + " self.user_embedding = Embedding(output_dim=embedding_size,\n", + " input_dim=max_user_id + 1,\n", + " name='user_embedding')\n", + " self.item_embedding = Embedding(output_dim=embedding_size,\n", + " input_dim=max_item_id + 1,\n", + " name='item_embedding')\n", + "\n", + " # The following two layers don't have parameters.\n", + " self.flatten = Flatten()\n", + " self.dot = Dot(axes=1)\n", + "\n", + " def call(self, inputs):\n", + " user_inputs = inputs[0]\n", + " item_inputs = inputs[1]\n", + "\n", + " user_vecs = self.flatten(self.user_embedding(user_inputs))\n", + " item_vecs = self.flatten(self.item_embedding(item_inputs))\n", + "\n", + " y = self.dot([user_vecs, item_vecs])\n", + " return y\n", + "\n", + "\n", + "model = RegressionModel(embedding_size=64, max_user_id=all_ratings['user_id'].max(), max_item_id=all_ratings['item_id'].max())\n", + "model.compile(optimizer=\"adam\", loss='mae')" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n", + "\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 3.2994 - val_loss: 1.0466\n", + "Epoch 2/10\n", + "\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 993us/step - loss: 0.9039 - val_loss: 0.7929\n", + "Epoch 3/10\n", + "\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 991us/step - loss: 0.7525 - val_loss: 0.7663\n", + "Epoch 4/10\n", + "\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 997us/step - loss: 0.7217 - val_loss: 0.7564\n", + "Epoch 5/10\n", + "\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 995us/step - loss: 0.6976 - val_loss: 0.7441\n", + "Epoch 6/10\n", + "\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6723 - val_loss: 0.7393\n", + "Epoch 7/10\n", + "\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6573 - val_loss: 0.7378\n", + "Epoch 8/10\n", + "\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6363 - val_loss: 0.7347\n", + "Epoch 9/10\n", + "\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6138 - val_loss: 0.7353\n", + "Epoch 10/10\n", + "\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.5883 - val_loss: 0.7370\n" + ] + } + ], + "source": [ + "# Training the model\n", + "history = model.fit([user_id_train, item_id_train], rating_train,\n", + " batch_size=64, epochs=10, validation_split=0.1,\n", + " shuffle=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [], + "source": [ + "from tensorflow.keras.models import Model\n", + "from tensorflow.keras.layers import Embedding, Flatten, Dense, Concatenate\n", + "from tensorflow.keras.losses import Huber\n", + "\n", + "class RegressionModelWithHidden(Model):\n", + " def __init__(self, embedding_size, max_user_id, max_item_id):\n", + " super().__init__()\n", + "\n", + " # Embeddings\n", + " self.user_embedding = Embedding(input_dim=max_user_id+1, output_dim=embedding_size)\n", + " self.item_embedding = Embedding(input_dim=max_item_id+1, output_dim=embedding_size)\n", + " self.flatten = Flatten()\n", + "\n", + " # Hidden layers\n", + " self.hidden1 = Dense(128, activation='relu') # first hidden layer\n", + " self.hidden2 = Dense(64, activation='relu') # second hidden layer\n", + " self.out_layer = Dense(1) # output layer\n", + "\n", + " def call(self, inputs):\n", + " user_vecs = self.flatten(self.user_embedding(inputs[0]))\n", + " item_vecs = self.flatten(self.item_embedding(inputs[1]))\n", + "\n", + " # Concatenate embeddings\n", + " x = Concatenate()([user_vecs, item_vecs])\n", + "\n", + " # Pass through hidden layers\n", + " x = self.hidden1(x)\n", + " x = self.hidden2(x)\n", + "\n", + " y = self.out_layer(x)\n", + " return y\n", + " \n", + "model =RegressionModel(embedding_size, max_user_id, max_item_id)\n", + "model.compile(optimizer='adam', loss=Huber())\n" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n", + "\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 2.7950 - val_loss: 0.6311\n", + "Epoch 2/10\n", + "\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.5202 - val_loss: 0.4232\n", + "Epoch 3/10\n", + "\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.3913 - val_loss: 0.3991\n", + "Epoch 4/10\n", + "\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.3686 - val_loss: 0.3878\n", + "Epoch 5/10\n", + "\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.3513 - val_loss: 0.3832\n", + "Epoch 6/10\n", + "\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.3356 - val_loss: 0.3778\n", + "Epoch 7/10\n", + "\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.3221 - val_loss: 0.3770\n", + "Epoch 8/10\n", + "\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.3021 - val_loss: 0.3727\n", + "Epoch 9/10\n", + "\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.2844 - val_loss: 0.3727\n", + "Epoch 10/10\n", + "\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.2608 - val_loss: 0.3752\n" + ] + } + ], + "source": [ + "# Training the model\n", + "history = model.fit([user_id_train, item_id_train], rating_train,\n", + " batch_size=64, epochs=10, validation_split=0.1,\n", + " shuffle=True)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "dsi_participant", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.7" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +}