Assignment_2_Task_6.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-02-25 11:57:18.734039: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "from sklearn.decomposition import PCA\n",
    "import matplotlib.pyplot as plt\n",
    "from itertools import combinations\n",
    "from matplotlib.cm import ScalarMappable\n",
    "from PIL import Image as imge\n",
    "from keras.models import Sequential, Model, load_model\n",
    "from keras import callbacks\n",
    "from keras.layers import Dropout, Dense, Flatten, Conv2D, MaxPooling2D, BatchNormalization, Input\n",
    "from keras.optimizers import Adam\n",
    "import datetime\n",
    "import os\n",
    "import keras\n",
    "import keras.regularizers as l2\n",
    "import pickle\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from tensorflow.keras.models import Model\n",
    "from IPython.display import Image, display\n",
    "from sklearn.model_selection import train_test_split\n",
    "from keras.utils import plot_model, to_categorical\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "import plotly.graph_objects as go\n",
    "import seaborn as sns\n",
    "import sklearn.metrics\n",
    "import keras.datasets.fashion_mnist as fashion_mnist"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Task 6:\n",
    "## Task 6.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''Code for loading the dataset''' \n",
    "def load_data():\n",
    "    #train_X: (60000, 28, 28)\n",
    "    #train_y: (60000,)\n",
    "    #test_X: (10000, 28, 28)\n",
    "    #test_y: (10000,)\n",
    "    (train_X, train_y_1), (test_X, test_y_1) = fashion_mnist.load_data()\n",
    "    n_class_1 = 10\n",
    "    #map to new label\n",
    "    train_y_2 = list (0 if y in [5, 7, 9] else 1 if y in [3, 6, 8] else 2 for y in train_y_1)\n",
    "    test_y_2  = list (0 if y in [5, 7, 9] else 1 if y in [3, 6, 8] else 2 for y in test_y_1)\n",
    "    n_class_2 = 3\n",
    "    #train_X: (60000, 28, 28, 1)\n",
    "    #test_X: (10000, 28, 28, 1)\n",
    "    #train_y: (60000, n_class = 10)\n",
    "    #test_y: (10000, n_class = 3)\n",
    "    train_X = np.expand_dims (train_X, axis=3)\n",
    "    test_X = np.expand_dims (test_X, axis=3)\n",
    "    train_y_1 = to_categorical (train_y_1, n_class_1)\n",
    "    test_y_1= to_categorical (test_y_1, n_class_1)\n",
    "    train_y_2=to_categorical (train_y_2, n_class_2)\n",
    "    test_y_2 = to_categorical (test_y_2, n_class_2)\n",
    "    return train_X, train_y_1, train_y_2, test_X, test_y_1, test_y_2\n",
    "x_train, y_train_1, y_train_2, x_test, y_test_1, y_test_2= load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',\n",
    "               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']\n",
    "groups_names = ['Shoes', 'Gendered', 'Uni_Sex']\n",
    "\n",
    "# rescaling data to [0, 1] \n",
    "x_train = (x_train)/255\n",
    "x_test = (x_test)/255"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we construct the first CNN classfier for item classification "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_3\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " Conv2D_1 (Conv2D)           (None, 26, 26, 32)        320       \n",
      "                                                                 \n",
      " max_pooling2d_6 (MaxPooling  (None, 13, 13, 32)       0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " Conv2D_2 (Conv2D)           (None, 11, 11, 64)        18496     \n",
      "                                                                 \n",
      " max_pooling2d_7 (MaxPooling  (None, 5, 5, 64)         0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " Conv2D_3 (Conv2D)           (None, 3, 3, 128)         73856     \n",
      "                                                                 \n",
      " flatten_3 (Flatten)         (None, 1152)              0         \n",
      "                                                                 \n",
      " FCN_1 (Dense)               (None, 3136)              3615808   \n",
      "                                                                 \n",
      " FCN_2 (Dense)               (None, 1024)              3212288   \n",
      "                                                                 \n",
      " FCN_3 (Dense)               (None, 100)               102500    \n",
      "                                                                 \n",
      " classification_head (Dense)  (None, 10)               1010      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 7,024,278\n",
      "Trainable params: 7,024,278\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# building the task 1, 10-class classification \n",
    "classification_cnn = Sequential()\n",
    "classification_cnn.add(Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1), name = 'Conv2D_1'))\n",
    "classification_cnn.add(MaxPooling2D((2, 2), strides = 2))\n",
    "\n",
    "classification_cnn.add(Conv2D(64, (3, 3),  activation='relu', name = 'Conv2D_2'))\n",
    "classification_cnn.add(MaxPooling2D((2, 2), strides = 2))\n",
    "\n",
    "classification_cnn.add(Conv2D(128, (3, 3), activation='relu', name = 'Conv2D_3'))\n",
    "classification_cnn.add(Flatten())\n",
    "\n",
    "classification_cnn.add(Dense(3136, activation='relu', name='FCN_1'))\n",
    "classification_cnn.add(Dense(1024, activation='relu', name = 'FCN_2'))\n",
    "classification_cnn.add(Dense(100, activation='relu', name = 'FCN_3'))\n",
    "classification_cnn.add(Dense(10, activation='softmax', name = 'classification_head'))\n",
    "\n",
    "classification_cnn.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "4800/4800 [==============================] - 379s 79ms/step - loss: 0.5062 - accuracy: 0.8132 - val_loss: 0.3842 - val_accuracy: 0.8608\n",
      "Epoch 2/5\n",
      "4800/4800 [==============================] - 357s 74ms/step - loss: 0.3465 - accuracy: 0.8749 - val_loss: 0.3474 - val_accuracy: 0.8726\n",
      "Epoch 3/5\n",
      "4800/4800 [==============================] - 382s 80ms/step - loss: 0.3067 - accuracy: 0.8894 - val_loss: 0.3057 - val_accuracy: 0.8913\n",
      "Epoch 4/5\n",
      "4800/4800 [==============================] - 363s 76ms/step - loss: 0.2804 - accuracy: 0.8981 - val_loss: 0.3023 - val_accuracy: 0.8876\n",
      "Epoch 5/5\n",
      "4800/4800 [==============================] - 396s 83ms/step - loss: 0.2573 - accuracy: 0.9070 - val_loss: 0.3120 - val_accuracy: 0.8892\n"
     ]
    }
   ],
   "source": [
    "# compile model and fit it to data\n",
    "classification_cnn.compile(loss= 'categorical_crossentropy',\n",
    "                           optimizer= Adam(learning_rate=0.001),\n",
    "                           metrics= ['accuracy'])\n",
    "\n",
    "batch_size = 10\n",
    "classification_cnn_history = classification_cnn.fit(\n",
    "    x_train, y_train_1, batch_size = batch_size,\n",
    "    epochs=5, validation_split = 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Task 1 accuracy on the test set: 88.40\n"
     ]
    }
   ],
   "source": [
    "_ , accuracy_class_cnn = classification_cnn.evaluate(x_test, y_test_1, verbose = 0)\n",
    "print('Task 1 accuracy on the test set: %.2f' % (accuracy_class_cnn*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1008x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "preds = classification_cnn.predict(x_test, verbose = 0)\n",
    "\n",
    "plt.figure(figsize=(14, 9))\n",
    "sns.heatmap(sklearn.metrics.confusion_matrix(np.argmax(preds, axis=1), np.argmax(y_test_1, axis=1)), \n",
    "            cmap='icefire', annot=True, fmt=\"\", yticklabels=class_names, xticklabels=class_names, linewidths=0.1)\n",
    "\n",
    "plt.title('Confusion Matrix for Task 1; Single Task CNN\\naccuracy= %.2f' %(accuracy_class_cnn*100), fontsize=15);\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Keras weights file (<HDF5 file \"variables.h5\" (mode r+)>) saving:\n",
      "...layers\n",
      "......conv2d\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......conv2d_1\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......conv2d_2\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......dense\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......dense_1\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......dense_2\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......dense_3\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......flatten\n",
      ".........vars\n",
      "......max_pooling2d\n",
      ".........vars\n",
      "......max_pooling2d_1\n",
      ".........vars\n",
      "...metrics\n",
      "......mean\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......mean_metric_wrapper\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "...vars\n",
      "Keras model archive saving:\n",
      "File Name                                             Modified             Size\n",
      "config.json                                    2023-02-15 22:34:21         4765\n",
      "metadata.json                                  2023-02-15 22:34:21           64\n",
      "variables.h5                                   2023-02-15 22:34:21     28131800\n",
      "Keras weights file (<HDF5 file \"variables.h5\" (mode r+)>) saving:\n",
      "...layers\n",
      "......conv2d\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......conv2d_1\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......conv2d_2\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......dense\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......dense_1\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......dense_2\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......dense_3\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......flatten\n",
      ".........vars\n",
      "......max_pooling2d\n",
      ".........vars\n",
      "......max_pooling2d_1\n",
      ".........vars\n",
      "...metrics\n",
      "......mean\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......mean_metric_wrapper\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "...vars\n",
      "Keras model archive saving:\n",
      "File Name                                             Modified             Size\n",
      "config.json                                    2023-02-15 22:34:23         4765\n",
      "metadata.json                                  2023-02-15 22:34:23           64\n",
      "variables.h5                                   2023-02-15 22:34:23     28131800\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_learing_curve(history, title):\n",
    "    # plot the learning curves\n",
    "    fig = plt.figure()\n",
    "    fig.add_subplot(1,2,1)\n",
    "    plt.plot(history.history['loss'], label='train loss')\n",
    "    plt.plot(history.history['val_loss'], label='val loss')\n",
    "    plt.legend()\n",
    "    plt.grid(True)\n",
    "    plt.ylim([0,1.0])\n",
    "    plt.xlabel('epoch')\n",
    "    \n",
    "    \n",
    "    fig.add_subplot(1,2,2)\n",
    "    plt.plot(history.history['accuracy'], label='train accuracy')\n",
    "    plt.plot(history.history['val_accuracy'], label='val accuracy')\n",
    "    plt.legend()\n",
    "    plt.grid(True)\n",
    "    plt.ylim([0,1.0])\n",
    "    plt.xlabel('epoch')\n",
    "    plt.suptitle(title, fontsize=20)\n",
    "\n",
    "    \n",
    "# # # Calling `save('my_model')` creates a SavedModel folder `my_model`.\n",
    "# classification_cnn.save('6_1_classification_cnn.h5')  #  save model after training\n",
    "# np.save('6_1_classification_cnn_history.npy',classification_cnn_history)\n",
    "\n",
    "# with open('6_1_classification_cnn_history.pkl', 'wb') as f:\n",
    "#       pickle.dump(classification_cnn_history, f)\n",
    "\n",
    " # It can be used to reconstruct the model identically.\n",
    "classification_cnn = load_model('6_1_classification_cnn.h5')\n",
    "classification_cnn_history = np.load('6_1_classification_cnn_history.npy',allow_pickle='TRUE').item()\n",
    "        \n",
    "with open('6_1_classification_cnn_history.pkl', 'rb') as f:\n",
    "    classification_cnn_history = pickle.load(f)\n",
    "\n",
    "# plot the learning curves\n",
    "plot_learing_curve(classification_cnn_history, 'Task 1 Learning Curve')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "then create another model for group classification."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_6\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " Conv2D_1 (Conv2D)           (None, 26, 26, 32)        320       \n",
      "                                                                 \n",
      " max_pooling2d_12 (MaxPoolin  (None, 13, 13, 32)       0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " Conv2D_2 (Conv2D)           (None, 11, 11, 64)        18496     \n",
      "                                                                 \n",
      " max_pooling2d_13 (MaxPoolin  (None, 5, 5, 64)         0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " Conv2D_3 (Conv2D)           (None, 3, 3, 128)         73856     \n",
      "                                                                 \n",
      " flatten_6 (Flatten)         (None, 1152)              0         \n",
      "                                                                 \n",
      " FCN_1 (Dense)               (None, 3136)              3615808   \n",
      "                                                                 \n",
      " FCN_2 (Dense)               (None, 1024)              3212288   \n",
      "                                                                 \n",
      " FCN_3 (Dense)               (None, 100)               102500    \n",
      "                                                                 \n",
      " categorization_head (Dense)  (None, 3)                303       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 7,023,571\n",
      "Trainable params: 7,023,571\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# building the task 2, 3-group categorization \n",
    "categorization_cnn = Sequential()\n",
    "categorization_cnn.add(Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28,1), name = 'Conv2D_1'))\n",
    "categorization_cnn.add(MaxPooling2D((2, 2), strides = 2))\n",
    "\n",
    "categorization_cnn.add(Conv2D(64, (3, 3),  activation='relu', name = 'Conv2D_2'))\n",
    "categorization_cnn.add(MaxPooling2D((2, 2), strides = 2))\n",
    "\n",
    "categorization_cnn.add(Conv2D(128, (3, 3), activation='relu', name = 'Conv2D_3'))\n",
    "categorization_cnn.add(Flatten())\n",
    "\n",
    "categorization_cnn.add(Dense(3136, activation='relu', name='FCN_1'))\n",
    "categorization_cnn.add(Dense(1024, activation='relu', name = 'FCN_2'))\n",
    "categorization_cnn.add(Dense(100, activation='relu', name = 'FCN_3'))\n",
    "categorization_cnn.add(Dense(3, activation='softmax', name = 'categorization_head'))\n",
    "\n",
    "categorization_cnn.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "4800/4800 [==============================] - 542s 112ms/step - loss: 0.2602 - accuracy: 0.8829 - val_loss: 0.2094 - val_accuracy: 0.9034\n",
      "Epoch 2/5\n",
      "4800/4800 [==============================] - 480s 100ms/step - loss: 0.1926 - accuracy: 0.9158 - val_loss: 0.2020 - val_accuracy: 0.8988\n",
      "Epoch 3/5\n",
      "4800/4800 [==============================] - 455s 95ms/step - loss: 0.1745 - accuracy: 0.9247 - val_loss: 0.1820 - val_accuracy: 0.9199\n",
      "Epoch 4/5\n",
      "4800/4800 [==============================] - 462s 96ms/step - loss: 0.1587 - accuracy: 0.9320 - val_loss: 0.1694 - val_accuracy: 0.9247\n",
      "Epoch 5/5\n",
      "4800/4800 [==============================] - 445s 93ms/step - loss: 0.1473 - accuracy: 0.9373 - val_loss: 0.1652 - val_accuracy: 0.9319\n"
     ]
    }
   ],
   "source": [
    "categorization_cnn.compile(loss= 'categorical_crossentropy',\n",
    "                           optimizer= Adam(learning_rate=0.001),\n",
    "                           metrics= ['accuracy'])\n",
    "batch_size = 10\n",
    "categorization_cnn_history = categorization_cnn.fit(\n",
    "    x_train, y_train_2, batch_size = batch_size,\n",
    "    epochs=5, validation_split=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Task 2 accuracy on the test set: 92.80\n"
     ]
    }
   ],
   "source": [
    "_,accuracy_categ_cnn = categorization_cnn.evaluate(x_test, y_test_2, verbose = 0)\n",
    "print('Task 2 accuracy on the test set: %.2f' % (accuracy_categ_cnn*100))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "preds = categorization_cnn.predict(x_test, verbose = 0)\n",
    "\n",
    "plt.figure(figsize=(8, 5))\n",
    "sns.heatmap(sklearn.metrics.confusion_matrix(np.argmax(preds, axis=1), np.argmax(y_test_2, axis=1)), \n",
    "            cmap='icefire', annot=True, fmt=\"\", yticklabels=groups_names, xticklabels=groups_names, linewidths=0.1)\n",
    "plt.xlabel('predication', fontsize = 14)\n",
    "plt.ylabel('true', fontsize = 14)\n",
    "plt.title('Confusion Matrix for Task 2; Single Task CNN,\\naccuracy= %.2f' %(accuracy_categ_cnn*100), fontsize=15);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Keras weights file (<HDF5 file \"variables.h5\" (mode r+)>) saving:\n",
      "...layers\n",
      "......conv2d\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......conv2d_1\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......conv2d_2\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......dense\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......dense_1\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......dense_2\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......dense_3\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......flatten\n",
      ".........vars\n",
      "......max_pooling2d\n",
      ".........vars\n",
      "......max_pooling2d_1\n",
      ".........vars\n",
      "...metrics\n",
      "......mean\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......mean_metric_wrapper\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "...vars\n",
      "Keras model archive saving:\n",
      "File Name                                             Modified             Size\n",
      "config.json                                    2023-02-18 12:09:12         4766\n",
      "metadata.json                                  2023-02-18 12:09:12           64\n",
      "variables.h5                                   2023-02-18 12:09:12     28127752\n"
     ]
    },
    {
     "data": {
      "image/png": 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XqvHXcaTaz5HqWg5X+ams9nO4ujbEcz+Hq6z7rTuqWLznyxAJOXTyrfGbiGKJpZYkqkjCR5JY98lU0Sm2mjRPFZ081WTFVJMaU0WKVJEcU4XPxAMXt/h9a0J3kKZ60u15Mk9ycnLD44KCApYuXcqKFStISkoiPz+fqqqqY14THx/f8Njj8TQ75BLO22+/zfLly3nzzTd59NFH2bBhAw888ACXXnopS5YsYdSoUSxdupT+/fu3av3KWfx1hspaw94yX6Pkeri61kq6xzy3k3S1n0o7OR99bifpKj/V/rqIY/DECElxHuJMNVmVO0nz1JAWW01WTBUpnmpS46pISbCSrZWQK0miigTjI9H4iK+rJM74iKs7Qpy/klh/JbG1R4j1HyGm5ggxddXN7AT7BiAxEJdCSULrOi2a0E9yqampTY5Jl5aWkpGRQVJSEps3b2blypXHvc20tDQyMjL46KOPOP/88/nrX//KBRdcQF1dHYWFhYwdO5bzzjuPhQsXUlFRwYEDBxg8eDCDBw9mxYoVbN68WRN6lNXVGY7U+Cn31VDhq6W8qpZyXy0Vvloqqmoo99nPq2qtOvbyRmU+KxGDIW7pu8RTQxw1xFNDvBx9HGc/T5AaUmPrSI310yfWT1KMn5TYWpJiakny+ElMriExtZYEqW20jjhTjZdaYuuqiDXVeOpq8NRVEeOvRvxVUFtl3dfVWlfsiYSdeIlLBm+SdZ+QAnFZ1uO4lKPlcSkQZz/2JttlQbf68th4EGFNQQH5rfi7aEI/yWVmZjJ69GgGDRrEhAkTuPTSSxstHz9+PLNnz2bIkCGcccYZjBo1qk22++KLL3Lbbbdx5MgRTjnlFF544QX8fj/XX389paWlGGP41a9+RXp6Og8//DDLli3D4/EwYMAAJkyY0CYxnKyMMZRU1bG1uKJREi5reHw0CR9TZifviqpaTAQjDslxHtLjhVPiD3GKp5i+McX08uyle8JeusbupnP1LhJqQx83CiuwRxvIEw+xCVZSbLglQGycfZ/euNwT16j+tsLd9DtjkJ2I7WQdlxSUuFMaJV6n0YSuWLhwYaPn+QFHY+Lj43nnnXcaLa/v0dePk3fp0oX164/+zvK9994bcjvTp09veJybmxuyt//xxx8fUzZr1qwm41dN21PqY21RCeuKSlhXVMq6olJKK2tg2YdhX5Po9ZCaEEtKQiyp8bGkJnjpmhJvPbfLrMdeUuI8pMdU0KV6F2m+XSQfKSLx8Pd4S79HSrZDaRHUBAyBxHghoy90y4H00WwvLifnB2cEJeNjE+7R8hDJ2hN33Al2R0EB/c7JP651RJsmdKVc5NDhatbtLGVdYQlri0pZV1TCvnLrmIcnRjgjK5VLBnfHU76XEUMHWEk73mvfx9IpwUtyvIdYT9CM5toqKCmEQ9vh0Dbrftd2KNkBh3ZAVVAvO7krZORA77NhyLXW4/pbag+IOTpja3tBATlj8k/ULjmpaEJXqj0YA5/PI2vPNlh/IKCXGW8/jju2V1r/2OMN2fs8XFXL+p1Wj3ut3fv+/uDRQeBTuiYz+tQuDMlOY0h2OgN7diLBayXSgoIC8nN7NY7vcDEc2GEn7aBb2U4gYIwlNgHS+1oJus+5jRN2eh+IT2njHagioQldqfbgr4F/3M2ZAJtb/nLjiafOE081sfhMLIf9sRyu9ZCAl1xiGRYbT3xCEsnZSaQmJ5PWKZW4uAQr8R6Jg63xsOPoB0ivoq/h3fcaJ+2aoCOCqT2spJ1zXuOEnZEDKVngkB9UVkdpQleqPXi8cM8mVn6ynFHDh0GtD2qrrXt/VcDjaupqqthfUsruA6XsKynjYEkZpRWHiTXWrI9Ubx3dEg2ZCdAr3tDJW0ecqbHXUw7lVVBSfew2ApwGsCPpaII+Jf/YXrY3sZ13kjpemtCVag8i0KknvsTu0PWMhmJjDIUHKxsOWq4tKmXDzlIOV3cBICU+lkG9OjF0cDpDstMZkp1GdkZiyy8bbIz1LcH+0Pjkk08YfeHljpypoVpPE7pS7ajEV8f7G/c2JO+viko4dKQGgLjYGAb06MQ1edkMyU5naO80TumSQkxMGyRdEXucPg6Amrg0TeYupAldtViPHj2oqKg4pjwlJSVkuYLSyhou/uNy9pT5gNV4YoTTuqVw0YDuDOmdxtDsdE7PSiUuVselVetpQleqHaQlehl3ZjfqSvdwdX4eA3umkRjX8outKdUU7Q6c5O6///5G10OfPn06Tz75JBUVFYwbN46zzjqLwYMH88Ybb0S8TmMM9913H4MGDWLw4MG8/PLLAOzevZsxY8aQm5vLoEGD+Oijj/D7/UybNq2h7h//+Mc2f49O8fsrB3NxjpfhOZ01masTQnvoTvLOA7Dnq5CLEv214GnFn6v7YJgwI+ziyZMnc/fdd3P77bcD8Morr/Duu++SkJDA4sWL6dSpE/v372fUqFFcfvnlER2Me+2111izZg1r165l//79jBgxgjFjxrBw4UIuvvhi/u3f/g2/38+RI0dYs2YNO3fubDjTtCW/gKSUakwT+klu2LBh7Nu3j127dlFcXExGRgZ9+vShpqaGhx56iOXLlxMTE8POnTvZu3cv3bt3b3adH3/8Mddddx0ej4esrCwuuOACVq1axYgRI/jZz35GTU0NEydOJDc3l1NOOYXvvvuOO++8k0svvZSLLrqoHd61Uu6kCd1JmuhJV57Ay+dec801LFq0iD179jT8StCCBQsoLi7m888/x+v1kpOTg8/ni2h9JsxVm8aMGcPy5ct5++23ueGGG7jvvvu48cYbWbt2Le+99x7PPvssr7zyCnPnzm2z96bUyUTH0BWTJ0/mpZdeYtGiRVxzzTWAddncbt264fV6WbZsGTt27Ih4fWPGjOHll1/G7/dTXFzM8uXLGTlyJDt27KBbt27ccsst/PznP+eLL75g//791NXVcfXVV/Poo4/yxRdfnKi3qZTraQ9dMXDgQMrLy+nVqxc9evQAYMqUKVx22WUMHz6c3NzcFl1//Morr2TFihUMHToUEeHxxx+ne/fuvPjii8ycOROv10tKSgrz589n586d3HTTTdTVWVfj+8Mf/nBC3qNSJwNN6AqAr75qfDC2S5curFixImTd3bt3hyyvn4MuIsycOZOZM2c2Wj516lSmTp16zOu0V65U29AhF6WUcglN6Eop5RKa0B0g3KwQ1Tzdd0odpQk9yhISEjhw4IAmplYwxnDgwAESEhKiHYpSjqAHRaMsOzuboqIiiouLm6zn8/kck7icEovP5yM9PZ3s7Oxoh6KUI2hCjzKv10u/fv2arVdQUMCwYcPaIaLmOSUWp8ShlFNENOQiIuNF5GsR+VZEHgixPE1E3hKRtSKyQURuavtQlWpb2q6V2zSb0EXEAzwLTAAGANeJyICgancAG40xQ4F84EkRiWvjWJVqM9qulRtF0kMfCXxrjPnOGFMNvARcEVTHAKliXYovBTgI1LZppEq1LW3XynUiSei9gMKA50V2WaA/AWcCu4CvgLuMMXVtEqFSJ4a2a+U6kRwUDXUB7OA5dhcDa4AfAj8A3heRj4wxZY1WJHIrcCtAVlYWBQUFITdYUVERdll7c0osTokDnBPLccbRZu0aOl7bdkoc4JxYnBIHHEcsxpgmb8A5wHsBzx8EHgyq8zZwfsDz/wVGNrXevLw8E86yZcvCLmtvTonFKXEY45xYmooDWG2i0K5NB2nbTonDGOfE4pQ4jGl9245kyGUVcJqI9LMPCE0G3gyq8z0wDkBEsoAzgO9a/vGiVLvRdq1cp9khF2NMrYj8AngP8ABzjTEbROQ2e/ls4FFgnoh8hfVV9n5jzP4TGLdSx0XbtXKjiE4sMsYsAZYElc0OeLwL0N8OUx2KtmvlNnotF6WUcglN6Eop5RKa0JVSyiU0oSullEtoQldKKZfQhK6UUi6hCV0ppVxCE7pSSrmEJnSllHIJTehKKeUSmtCVUsolNKErpZRLaEJXSimX0ISulFIuoQldKaVcQhO6Ukq5hCZ0pZRyCU3oSinlEprQlVLKJTShK6WUS2hCV0opl9CErpRSLqEJXSmlXEITulJKuYQmdKWUcglN6Eop5RKa0JVSyiU0oSullEtoQldKKZfQhK6UUi6hCV0ppVxCE7pSSrmEJnSllHIJTehKKeUSmtCVUsolIkroIjJeRL4WkW9F5IEwdfJFZI2IbBCRD9s2TKXanrZr5TaxzVUQEQ/wLHAhUASsEpE3jTEbA+qkA88B440x34tItxMUr1JtQtu1cqNIeugjgW+NMd8ZY6qBl4Argur8FHjNGPM9gDFmX9uGqVSb03atXKfZHjrQCygMeF4EnB1U53TAKyIFQCrwtDFmfvCKRORW4FaArKwsCgoKQm6woqIi7LL25pRYnBIHOCeW44yjzdo1dLy27ZQ4wDmxOCUOOI5YjDFN3oBJwPMBz28AZgXV+ROwEkgGugBbgNObWm9eXp4JZ9myZWGXtTenxOKUOIxxTixNxQGsNlFo16aDtG2nxGGMc2JxShzGtL5tR9JDLwJ6BzzPBnaFqLPfGHMYOCwiy4GhwDcRf7Io1b60XSvXiWQMfRVwmoj0E5E4YDLwZlCdN4DzRSRWRJKwvrpuattQlWpT2q6V6zTbQzfG1IrIL4D3AA8w1xizQURus5fPNsZsEpF3gXVAHdZX2fUnMnCljoe2a+VGkQy5YIxZAiwJKpsd9HwmMLPtQlPqxNJ2rdxGzxRVSimX0ISulFIuoQldKaVcQhO6Ukq5hCZ0pZRyCU3oSinlEprQlVLKJTShK6WUS2hCV0opl9CErpRSLqEJXSmlXEITulJKuYQmdKWUcglN6Eop5RKa0JVSyiU0oSullEtoQldKKZfQhK6UUi6hCV0ppVxCE7pSSrmEJnSllHIJTehKKeUSmtCVUsolNKErpZRLaEJXSimX0ISulFIuoQldKaVcQhO6Ukq5hCZ0pZRyCU3oSinlEprQlVLKJTShK6WUS2hCV0opl9CErpRSLhFRQheR8SLytYh8KyIPNFFvhIj4ReSatgtRqRND27Vym2YTuoh4gGeBCcAA4DoRGRCm3v8HvNfWQSrV1rRdKzeKpIc+EvjWGPOdMaYaeAm4IkS9O4FXgX1tGJ9SJ4q2a+U6kST0XkBhwPMiu6yBiPQCrgRmt11oSp1Q2q6V68RGUEdClJmg508B9xtj/CKhqtsrErkVuBUgKyuLgoKCkPUqKirCLmtvTonFKXGAc2I5zjjarF1Dx2vbTokDnBOLU+KA44jFGNPkDTgHeC/g+YPAg0F1tgHb7VsF1tfTiU2tNy8vz4SzbNmysMvam1NicUocxjgnlqbiAFabKLRr00HatlPiMMY5sTglDmNa37Yj6aGvAk4TkX7ATmAy8NOgD4V+9Y9FZB7wD2PM65F/rCjV7rRdK9dpNqEbY2pF5BdYR/k9wFxjzAYRuc1eruOLqsPRdq3cKJIeOsaYJcCSoLKQDd4YM+34w1LqxNN2rdxGzxRVSimX0ISulFIuoQldKaVcQhO6Ukq5hCZ0pZRyCU3oSinlEprQlVLKJTShK6WUS2hCV0opl9CErpRSLqEJXSmlXEITulJKuYQmdKWUcglN6Eop5RKa0JVSyiU0oSullEtoQldKKZfQhK6UUi6hCV0ppVxCE7pSSrmEJnSllHIJTehKKeUSmtCVUsolNKErpZRLOC6hv/PVbt7bXkOZrybaoSilVIfiuIS+7Ot9/H1zNaP+4wN+8/pXbNlbHu2QlFKqQ4iNdgDBHr9mKAO8+1lf3YVXVhfxt5XfM/rUTG48J4cfnZmFJ0aiHaJSSjmS4xI6QE6ah2n5Q3lwQn9eWlXIgpU7+Je/fk6v9ERuOKcv1w7vTUZyXLTDVEopR3HckEugzJR47hh7Kst/PZbZ159F786JzHhnM6P+8AH3L1rHxl1l0Q5RKaUcw5E99GCxnhjGD+rB+EE92LynjBc/3cHiL4t4eXUhI3M6c+O5fbl4YHe8Hkd/Piml1AnVIRJ6oP7dO/GHqwbzwPj+vLK6kPkrt/OLhV/SvVMCU87uw3Vn96FLSny0w1RKqXbX4RJ6vbQkL7eMOYWfndePgq/3Me/T7Tz5/jfM+t9v+fGQHkw9N4ehvdOjHaZSSrWbDpvQ63lihHFnZjHuzCy+3VfBX1dsZ9HnRbz25U5ye6cz9dy+XDK4B/GxnmiHqpRSJ5SrBp1P7ZbCI1cMYuVD45h+2QDKKmv41ctrGT1jGf/5z6/ZW+aLdohKKXXCdPgeeiipCV6mje7Hjefk8NG3+3nx0+3MWvYtzxVsZfyg7kw7N4e8vhmI6Jx2pZR7RJTQRWQ88DTgAZ43xswIWj4FuN9+WgH8qzFmbasi2ryE3t+/A59tgbjkgFuKde9Navw4JvyXjJgY4YLTu3LB6V3ZceAw81fs4JXVhfxj3W4G9uzE1HNzuHxoTxK8OhxzMmrXdq1UO2g2oYuIB3gWuBAoAlaJyJvGmI0B1bYBFxhjDonIBGAOcHarItrwGj/47n/guwjre+sTfkCiD/Eh0DcuhYc7J/PrCYms3lXFe1sqeP21T3nr7RTyB/fj0uGn0r1LF6u+Jw609+5q7d6ulWoHkfTQRwLfGmO+AxCRl4ArgIaGb4z5NKD+SiC71RFd9ReWp09izKg8qK6A6sP2rQKqjwQ8DiivOdL4eVU5lO9p/JraSgDigdH2jTjAAOvsW/37iYlF7A+Ks0iG4kGQ3se+5dj3vcGb2Oq3qaKufdu1Uu0gkoTeCygMeF5E072UnwPvhFogIrcCtwJkZWVRUFAQcgUVlTUUrFofVBoDpNi3LHuFWBk6kmnnxo/HX4XHX4nH72v0+HDlEbYUH6Hw0GFi/VV0j/NxenwNvRN8xPn2Efvd/5Hge4sYU9toldXedHwJ3ahMzMKX0K3RrSq+G3Wetrs8QUVFRdj91d6cEstxxtFm7Rpa0Lbdse/alFNicUoc0PpYIknoocYeTMiKImOxGv55oZYbY+ZgfW1l+PDhJj8/P+QGCwoKCLfsRDkH8NX4eXPtLl78dDsbdpWRmhBLdlIdA/r1pGdaHKckHKaPZz89zD461+whoaKQuJLv6VTyPRSthLqgS/6mdA/o2QfcMnIgLRtiIz8BKhr7JBynxHKccbRZuwZnt20nxwHOicUpcUDrY4kkoRcBvQOeZwO7giuJyBDgeWCCMeZAiyNxgASvh58M782kvGw+33GIl1cV8uXWXXy6dT97y3zUNfy7ZwKZpMQPpUdaAj3SE8nuFccPEg/TL3Y/2RTTxb+HTr7dxJYVws7VsPF1qGvcwye1R1Cy73v0cQsTvmqxk6Zdq5NHJAl9FXCaiPQDdgKTgZ8GVhCRPsBrwA3GmG/aPMp2JiIMz+nM8JzOFBQcIj8/n1p/HfvKq9hdWsmuEl/D/a6SSnaX+ti4q5T9FdVYu7SHfRtGRpKXHmmJ9Orj5fSkw5wWd5DeMfvJqttLevVuko7sJKbwM1j/Ghh/YBRHE35GX/qV+CF+AyR1gaRMSOps3Sd3sWb76EHcljrp2rVyv2YTujGmVkR+AbyHNb1rrjFmg4jcZi+fDfwWq9v6nD23u9YYM/zEhd3+Yj0x9ExPpGd6Inl9Q9fx1fjZW+ZrSPi7S62Ev6ukksISH/+3vZYyXxLQx75ZebhrSjy9MuMYkFzO6fGHyIndTw9TTGbtHlIrd+Hd8Sl9SnfC94vCBJcQkOTrE759Sw54HPhh4PGekP3UUWi7Vm4U0Tx0Y8wSYElQ2eyAxzcDN7dtaB1PgtdD38xk+mYmh61zuKr2mF5+ffJfcbCO10o8VNZkAKc1vCY2RkiPq6N/l1j6JfroneCjZ1wFWZ7DdJZy0kw5Kf4S4qsPIZUHoWQHHD4AVaXhg41PC0r2IW7JAR8A8WlNzvnviLRdK7dx5ZmiTpYcH8up3VI5tVtqyOXGGEora44m/FIfu0sq+fKb7ZiEdFaWVvFmURWllce+PjZG6JoaT7fUeLr1SqBHcgy9E6vIjjtMlvcwXWIqyKCc5NoSYioPwpED1q1sJ+z5Cg7vB39V6MDF05Doz/L5YVtXa75+bLzV2/fE24+bKotr/DiSZQ2P4zv0B0pNTQ1FRUWkpaWxadOmaIfjmDjAObGkpaWxbds2srOz8Xo75jdYTegOIyKkJ8WRnhTHgJ6dGsoLEvaQnz+q4bmvxk9xeRX7yn3sLatiX5mPfeVV1uNyH4UHj7B6u49DR+pn3sQC6UA6MdKbLinxZHVKsJJ/t3i6pSbQLTWOHol19Ig7TNeYw2RQhscXkPiPHIDD+6ndWwSINc//yAHwV0NtFfhrrA+E2mr7voowE0daJybWSuweL8TGM6rGwBmLoMfQttvGCVJUVERqaiqZmZl06tSp+RecYOXl5aSmhu5UtDenxFJWVkZ1dTVFRUX069cv2uG0iib0DirB66F35yR6d05qsl5VrZ/9FdXsLfOxr6yK4voPgHLrA2BXqY+1RSX2Ad3GRCAzuQvdUnvRrVM8WakJdOscz8Gq7xkxZABpSV4ykuJIT7TuUxNiiQn8zVdjoM5/NLkfk/gDy6obP26qzL4v2fk93eOjnxwj4fP5yMnJoaKiItqhqDBEhMzMTIqLi6MdSqtpQne5+FgPvdIT6ZXe9FmtNf469ldUsa+sykr+5VXWreGxjw27yjhQUUWdgQWb1hyzjhiBtEQv6UlxpCV6yUjy2t82vKQnxpGR7LWXJ1nLEuNIT/aSGh/bqgulbS4ooHvnjtOT0ovBOV9H/xtpQlcAeD0x9EhLpEda04m/1l/HOx98yIBhIyg5UkPJkWrrvvLo40NHqimtrKG4ooot+yooPVJDeVVt2HV6YoT0RC9pSd6G3n5g7z892b63y9ISvWQkx2FMGw7nuFxJSQkLFy7k9ttvb/FrL7nkEhYuXEh6enrbB6balCZ01SKxnhhS44QfdE1p0etq/HWUBiT9wMR/KKCspLKaPWU+Nu8pp+RINYer/WHX6RH4nx8c4qw+Gcf7tlyvpKSE5557LmRC9/v9eDzhrzi6ZMmSsMuiyRiDMYaYDnywvK1pQlftwuuJoUtKfIt/77Wq1k9pZQ2lR2o41OgbQTVrN22lZzPfKJTlgQceYOvWreTm5nLhhRcyduxYZs6cSY8ePVizZg0bN25k4sSJFBYW4vP5uOuuu7j11lsByMnJYfXq1VRUVDBhwgTOO+88Pv30U3r16sUbb7xBYmLjv8Fbb73FY489RnV1NZmZmSxYsICsrCwqKiq48847Wb16NSLC7373O66++mref/99HnvsMfx+P126dOGDDz5g+vTppKSkcO+99wIwaNAg/vGPfwAwYcIExo4dy4oVK3j99deZMWMGq1atorKykmuuuYZHHnkEgFWrVnHXXXdx+PBh4uPj+eCDD7jkkkuYNWsWubm5AIwePZo///nPDBkypJ3+EieWJnTlaPGxHrqleuiWmnDMsoK6QrqnHVvudI+8tYGNu8radJ0Denbid5cNDLt8xowZrF+/njVr1gBWr/uzzz5j/fr1DTM65s6dS+fOnamsrGTEiBFcffXVZGZmNlrPli1b+Pvf/85f/vIXfvKTn/Dqq69y/fXXN6pz3nnnsXLlSkSE559/nscff5wnn3ySRx99lLS0NL766isADh06RHFxMb/85S/56KOP6NevHwcPHmz2vX799de88MILPPfccwD8/ve/p3Pnzvj9fsaNG8e6devo378/1157LS+//DIjRoygrKyMxMREbr75ZubNm8dTTz3FN998Q1VVlWuSOWhCV+qkNXLkyEbT85555hkWL14MQGFhIVu2bDkmoffr16+hd5uXl8f27duPWW9RURHXXnstu3fvprq6umEbS5cu5aWXXmqol5GRwVtvvcW5557bUKdz587Nxt23b19GjTo6hfeVV15hzpw51NbWsnv3bjZu3IiI0KNHD0aMGAHQMFV00qRJPProo8ycOZO5c+cybdq0ZrfXkWhCV6qdNdWTbk/JyUfPaC4oKGDp0qWsWLGCpKQk8vPz8fmO/Q3e+PijQ2Yej4fKyspj6tx5553cc889XH755RQUFDB9+nTAGvMOnkUSqgwgNjaWurq6hueBsQTGvW3bNp544glWrVpFRkYG06ZNw+fzhV1vUlISF154IW+88QavvPIKq1evDrVrOiw9mqDUSSA1NZXy8vKwy0tLS8nIyCApKYnNmzezcuXKVm+rtLSUXr16AfDiiy82lF900UX86U9/anh+6NAhzjnnHD755BO2bdsG0DDkkpOTwxdffAHAF1980bA8WFlZGcnJyaSlpbF3717eece6ZH3//v3ZtWsXq1atAqyTl2prrZlWN998M7/85S8ZMWJERN8IOhJN6EqdBDIzMxk9ejSDBg3ivvvuO2b5+PHjqa2tZciQITz88MONhjRaavr06UyaNInzzz+fLl26NJT/5je/4dChQwwaNIihQ4eybNkyunbtytNPP81VV13F0KFDufbaawG4+uqrOXjwILm5ufz5z3/m9NNPD7mtoUOHMmzYMAYOHMjPfvYzRo8eDUBcXBwvv/wyd955J0OHDuXCCy9s6OXn5eXRqVMnbrrppla/R8eqn/rT3re8vDwTzrJly8Iua29OicUpcRjjnFiaigNYbRzUtjdu3GiMMaasrKz1b7gNOSUOY9o/lp07d5rTTjvN+P3+kHHU/62iqbVtW3voSqmTxvz58zn77LP5/e9/78r563pQVCl10rjxxhu58cYbox3GCeO+jyillDpJaUJXSimX0ISulFIuoQldKaVcQhO6UiqklJSWXVFTRZ8mdKWUI9Wf2akipwldqZPA/fff33B1QoD/+I//4Mknn6SiooJx48Zx1llnMXjwYN54441m1zVx4kTy8vIYOHAgc+bMaSh/9913Oeussxg6dCjjxo0DoKKigptuuonBgwczZMgQXn31VaBx7//1119vuEjWtGnTuOeeexg7diz3338/n332Geeeey7Dhg3j3HPP5euvvwasa7jfe++9DeudNWsWH3zwAVdeeWXDet9//32uuuqq1u+0DkjnoSvV3t55APZ81bbr7D4YJswIu3jy5MncfffdDT9wsXjxYv75z3+SkJDA4sWL6dSpE/v372fUqFFcfvnlTf4UW6jL7NbV1XHLLbewfPnyRpfBDXXJ3OZ88803LF26FI/HQ1lZGcuXLyc2NpalS5fy0EMP8eqrrzJnzhy2bdvGl19+SWxsLAcPHiQjI4M77riD4uJiunbtygsvvODO0/uboAldqZPAsGHD2LdvH7t27aK4uJj09HT69OlDTU0NDz30EMuXLycmJoadO3eyd+9eunfvHnZdoS6zW1xczJgxY465DG6oS+Y2Z9KkSQ2/oFRaWsrUqVPZsmULIkJNTU3Dem+77TZiY2Mbbe+GG27gb3/7GzfddBMrVqxg/vz5Ld1VHZomdKXaWxM96RPpmmuuYdGiRezZs4err74agAULFlBcXMznn3+O1+slJycn5GVz64W7zK4Jc7nacOWBZcHbC7w87sMPP8zYsWNZvHgx27dvJz8/v8n13nTTTVx22WUkJCQwadKkhoR/stAxdKVOEpMnT+all15i0aJFTJw4EbB6wN26dcPr9bJs2TJ27NjR5DrCXWb3nHPO4cMPPzzmMrihLpkLkJWVxaZNm6irq2v4ablw26u/FO+8efMayi+66CJmz57dcOC0fns9e/akZ8+ePPbYY6778YpIaEJX6iQxcOBAysvL6dWrV8OQypQpU1i9ejXDhw9nwYIF9O/fv8l1hLvMbteuXZkzZ84xl8ENdclcsH4S78c//jE//OEPycrKCru9X//61zz44IOMHj0av//oD4bffPPN9OnThyFDhjB06FAWLlzYsGzKlCn07t2bAQMGtG5HdWThLsN4om96+dyWcUocxjgnFr18bus5JQ5j2j6WO+64wzz//POtjqMjXz735BpgUkq5Wl5eHsnJyTz55JPRDiUqNKErpVzj888/j3YIUaVj6Eop5RKa0JVqJ9bwp3Kyjv430oSuVDtISEjgwIEDHT5huJkxhgMHDpCQkBDtUFpNx9CVagfZ2dkUFRVRUlLiiITh8/kcEQc4Jxafz0d6ejrZ2dnRDqXVIkroIjIeeBrwAM8bY2YELRd7+SXAEWCaMeaLNo5VqTbVnu3a6/XSr18/CgoKGDZs2HFGfvycEgc4JxanxHE8mh1yEREP8CwwARgAXCciwTP2JwCn2bdbgT+3cZxKtSlt18qNIhlDHwl8a4z5zhhTDbwEXBFU5wpgvj3vfSWQLiI92jhWpdqStmvlOpEk9F5AYcDzIruspXWUchJt18p1IhlDD3Vh5OBD9ZHUQURuxfrqClAhIl+H2WYXYH8EsbUHp8TilDjAObE0FUffZl7bZu0aOmTbdkoc4JxYnBIHtLJtR5LQi4DeAc+zgV2tqIMxZg4wJ7g8mIisNsYMjyC2E84psTglDnBOLMcZR5u1a+h4bdspcYBzYnFKHND6WCIZclkFnCYi/UQkDpgMvBlU503gRrGMAkqNMbtbGoxS7UjbtXKdZnvoxphaEfkF8B7W9K65xpgNInKbvXw2sARrate3WNO7Tq7ffVIdjrZr5UYRzUM3xizBatyBZbMDHhvgjjaMq9mvru3IKbE4JQ5wTizHFUcU2jW4ZN+1MafE4pQ4oJWxiJ6KrJRS7qDXclFKKZdwXEIXkfEi8rWIfCsiD0Qxjrkisk9E1kcrBjuO3iKyTEQ2icgGEbkrSnEkiMhnIrLWjuORaMQRFJNHRL4UkfA/SukQ2q6PicMR7dqOxVFt+3jataMSeoSnY7eXecD4KG07UC3w/4wxZwKjgDuitE+qgB8aY4YCucB4e+ZHNN0FbIpyDM3Sdh2SU9o1OK9tt7pdOyqhE9np2O3CGLMcOBiNbQfFsbv+glDGmHKsP3S7n61on/5eYT/12reoHYARkWzgUuD5aMXQAtquj43DEe3a3r5j2vbxtmunJXQ91boJIpIDDAP+L0rb94jIGmAf8L4xJipx2J4Cfg3URTGGSGm7bkK027Udg1Pa9lMcR7t2WkKP+FTrk42IpACvAncbY8qiEYMxxm+MycU6Y3KkiAyKRhwi8mNgnzGmo/yApLbrMJzQrsEZbbst2rXTEnrEp1qfTETEi9XoFxhjXot2PMaYEqCA6I3FjgYuF5HtWMMXPxSRv0Uplkhouw7Bae0aot62j7tdOy2hR3I69knF/pGF/wY2GWP+M4pxdBWRdPtxIvAjYHM0YjHGPGiMyTbG5GC1kf81xlwfjVgipO06iFPatR2LI9p2W7RrRyV0Y0wtUH869ibgFWPMhmjEIiJ/B1YAZ4hIkYj8PBpxYH1q34D1ab3Gvl0ShTh6AMtEZB1WgnrfGOP46YJOoO06JKe0a3BR29YzRZVSyiUc1UNXSinVeprQlVLKJTShK6WUS2hCV0opl9CErpRSLqEJ3UVEJL8jXHlQqZbQdh05TehKKeUSmtCjQESut6+/vEZE/su+MFCFiDwpIl+IyAci0tWumysiK0VknYgsFpEMu/xUEVlqX8P5CxH5gb36FBFZJCKbRWSBfUaeUiectuvo04TezkTkTOBaYLR9MSA/MAVIBr4wxpwFfAj8zn7JfOB+Y8wQ4KuA8gXAs/Y1nM8F6n+NfhhwN9Z1t0/BOiNPqRNK27UzRPQj0apNjQPygFV2JyMR65KddcDLdp2/Aa+JSBqQboz50C5/EfgfEUkFehljFgMYY3wA9vo+M8YU2c/XADnAxyf8XamTnbZrB9CE3v4EeNEY82CjQpGHg+o1dU2Gpr5uVgU89qN/Y9U+tF07gA65tL8PgGtEpBuAiHQWkb5Yf4tr7Do/BT42xpQCh0TkfLv8BuBD+7rRRSIy0V5HvIgkteebUCqItmsH0E+5dmaM2SgivwH+KSIxQA1wB3AYGCginwOlWOORAFOB2XbD/g64yS6/AfgvEfl3ex2T2vFtKNWItmtn0KstOoSIVBhjUqIdh1JtSdt1+9IhF6WUcgntoSullEtoD10ppVxCE7pSSrmEJnSllHIJTehKKeUSmtCVUsolNKErpZRL/P9WYo+INKdH4AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# # Calling `save('my_model')` creates a SavedModel folder `my_model`.\n",
    "# categorization_cnn.save('6_1_categorization_cnn.h5')  #  save model after training\n",
    "# np.save('6_1_categorization_cnn_history.npy',categorization_cnn_history)\n",
    "\n",
    "# # It can be used to reconstruct the model identically.\n",
    "categorization_cnn_model = load_model('6_1_categorization_cnn.h5')\n",
    "categorization_cnn_history = np.load('6_1_categorization_cnn_history.npy',allow_pickle='TRUE').item()\n",
    "\n",
    "# plot the learning curves\n",
    "plot_learing_curve(categorization_cnn_history, 'Task 2 Learning Curve')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 6.2:\n",
    "Build a Multi-Task Learning model for the two MNIST fashion tasks. Our MTL architecture will be comprised of a shared CNN backbone of three convolutional layers and a single shared dense layer with pooling between the first two pairs of convolutions. The output of the shared dense layer is passed to two series of task-specific dense layers, one for each of the two tasks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def MTL(x_train,y_train_1, y_train_2, x_test, y_test_1,y_test_2, gamma): \n",
    "    #build the shared part of the network \n",
    "    shared = Sequential()\n",
    "    shared.add(Conv2D(32, (3, 3), activation='relu', strides = 1, input_shape=(28, 28,1), name = 'Conv2D_1'))\n",
    "    shared.add(MaxPooling2D((2, 2), strides = 2))\n",
    "\n",
    "    shared.add(Conv2D(64, (3, 3),  activation='relu', strides = 1, name = 'Conv2D_2'))\n",
    "    shared.add(MaxPooling2D((2, 2), strides = 2))\n",
    "\n",
    "    shared.add(Conv2D(128, (3, 3), activation='relu', strides = 1, name = 'Conv2D_3'))\n",
    "    shared.add(Flatten())\n",
    "    shared.add(Dense(3136, activation='relu', name='FCN_1'))\n",
    "\n",
    "    # collect it and feed input to it\n",
    "    inputs = Input(shape=(28, 28,1))\n",
    "    x = shared(inputs)\n",
    "    \n",
    "    # the first head with 10 output\n",
    "    out_class = Dense(1024, activation='relu', name = 'FCN_2_1')(x)\n",
    "    out_class = Dense(100, activation='relu', name = 'FCN_3_1')(out_class)\n",
    "    out_class = Dense(10, activation='softmax', name = 'classification_head')(out_class)\n",
    "    \n",
    "    # the second head with 3 output\n",
    "    out_group = Dense(1024, activation='relu', name = 'FCN_2_2')(x)\n",
    "    out_group = Dense(100, activation='relu', name = 'FCN_3_2')(out_group)\n",
    "    out_group = Dense(3, activation='softmax', name = 'categorization_head')(out_group)\n",
    "    \n",
    "    # build the model \n",
    "    MTL_model = Model(inputs=inputs, outputs={\n",
    "        'out_class': out_class,\n",
    "        'out_group': out_group})\n",
    "    \n",
    "    #compile it with custom weight function\n",
    "    MTL_model.compile(loss= {\n",
    "        'out_class': 'categorical_crossentropy',\n",
    "        'out_group': 'categorical_crossentropy'},\n",
    "                      loss_weights={'out_class': gamma, \n",
    "                                    'out_group': 1 - gamma}, \n",
    "                      optimizer= 'adam',\n",
    "                      metrics=['accuracy'])\n",
    "    \n",
    "    #train the model with the data\n",
    "    batch_size = 128\n",
    "    history = MTL_model.fit(x_train, {\n",
    "            'out_class': y_train_1, \n",
    "            'out_group': y_train_2},\n",
    "        batch_size = batch_size,\n",
    "        epochs= 5, validation_split=0.2)\n",
    "    \n",
    "    \n",
    "    # evaluate against test data \n",
    "    _,_,_, classification_accuracy_MTL, categorization_accuracy_MTL = MTL_model.evaluate(x_test, {\n",
    "        'out_class': y_test_1, \n",
    "        'out_group': y_test_2})\n",
    "    \n",
    "    return MTL_model, history, classification_accuracy_MTL, categorization_accuracy_MTL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-02-17 15:36:23.426664: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "375/375 [==============================] - 178s 466ms/step - loss: 0.2702 - classification_head_loss: 2.3143 - categorization_head_loss: 0.2702 - classification_head_accuracy: 0.1288 - categorization_head_accuracy: 0.8757 - val_loss: 0.1911 - val_classification_head_loss: 2.3062 - val_categorization_head_loss: 0.1911 - val_classification_head_accuracy: 0.1212 - val_categorization_head_accuracy: 0.9162\n",
      "Epoch 2/5\n",
      "375/375 [==============================] - 186s 496ms/step - loss: 0.1788 - classification_head_loss: 2.3063 - categorization_head_loss: 0.1788 - classification_head_accuracy: 0.1611 - categorization_head_accuracy: 0.9215 - val_loss: 0.1732 - val_classification_head_loss: 2.3046 - val_categorization_head_loss: 0.1732 - val_classification_head_accuracy: 0.1650 - val_categorization_head_accuracy: 0.9239\n",
      "Epoch 3/5\n",
      "375/375 [==============================] - 176s 469ms/step - loss: 0.1580 - classification_head_loss: 2.3027 - categorization_head_loss: 0.1580 - classification_head_accuracy: 0.1751 - categorization_head_accuracy: 0.9305 - val_loss: 0.1730 - val_classification_head_loss: 2.3051 - val_categorization_head_loss: 0.1730 - val_classification_head_accuracy: 0.1888 - val_categorization_head_accuracy: 0.9224\n",
      "Epoch 4/5\n",
      "375/375 [==============================] - 156s 415ms/step - loss: 0.1425 - classification_head_loss: 2.3006 - categorization_head_loss: 0.1425 - classification_head_accuracy: 0.1861 - categorization_head_accuracy: 0.9386 - val_loss: 0.1614 - val_classification_head_loss: 2.2982 - val_categorization_head_loss: 0.1614 - val_classification_head_accuracy: 0.1603 - val_categorization_head_accuracy: 0.9314\n",
      "Epoch 5/5\n",
      "375/375 [==============================] - 159s 424ms/step - loss: 0.1291 - classification_head_loss: 2.2970 - categorization_head_loss: 0.1291 - classification_head_accuracy: 0.1486 - categorization_head_accuracy: 0.9446 - val_loss: 0.1758 - val_classification_head_loss: 2.2932 - val_categorization_head_loss: 0.1758 - val_classification_head_accuracy: 0.1051 - val_categorization_head_accuracy: 0.9286\n",
      "313/313 [==============================] - 10s 32ms/step - loss: 0.1794 - classification_head_loss: 2.2937 - categorization_head_loss: 0.1794 - classification_head_accuracy: 0.1020 - categorization_head_accuracy: 0.9247\n",
      "Keras weights file (<HDF5 file \"variables.h5\" (mode r+)>) saving:\n",
      "...layers\n",
      "......dense\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......dense_1\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......dense_2\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......dense_3\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......dense_4\n",
      ".........vars\n",
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      "............1\n",
      "......dense_5\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......input_layer\n",
      ".........vars\n",
      "......sequential\n",
      ".........layers\n",
      "............conv2d\n",
      "...............vars\n",
      "..................0\n",
      "..................1\n",
      "............conv2d_1\n",
      "...............vars\n",
      "..................0\n",
      "..................1\n",
      "............conv2d_2\n",
      "...............vars\n",
      "..................0\n",
      "..................1\n",
      "............dense\n",
      "...............vars\n",
      "..................0\n",
      "..................1\n",
      "............flatten\n",
      "...............vars\n",
      "............max_pooling2d\n",
      "...............vars\n",
      "............max_pooling2d_1\n",
      "...............vars\n",
      ".........vars\n",
      "...metrics\n",
      "......mean\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......mean_1\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......mean_2\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......mean_metric_wrapper\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......mean_metric_wrapper_1\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "...optimizer\n",
      "......vars\n",
      ".........0\n",
      ".........1\n",
      ".........10\n",
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      ".........5\n",
      ".........6\n",
      ".........7\n",
      ".........8\n",
      ".........9\n",
      "...vars\n",
      "Keras model archive saving:\n",
      "File Name                                             Modified             Size\n",
      "config.json                                    2023-02-17 15:50:59         6830\n",
      "metadata.json                                  2023-02-17 15:50:59           64\n",
      "variables.h5                                   2023-02-17 15:51:02    124143184\n",
      "Epoch 1/5\n",
      "375/375 [==============================] - 183s 476ms/step - loss: 0.3621 - classification_head_loss: 0.5781 - categorization_head_loss: 0.2695 - classification_head_accuracy: 0.7846 - categorization_head_accuracy: 0.8731 - val_loss: 0.2379 - val_classification_head_loss: 0.3710 - val_categorization_head_loss: 0.1808 - val_classification_head_accuracy: 0.8662 - val_categorization_head_accuracy: 0.9188\n",
      "Epoch 2/5\n",
      "375/375 [==============================] - 175s 467ms/step - loss: 0.2235 - classification_head_loss: 0.3395 - categorization_head_loss: 0.1737 - classification_head_accuracy: 0.8760 - categorization_head_accuracy: 0.9238 - val_loss: 0.2047 - val_classification_head_loss: 0.3121 - val_categorization_head_loss: 0.1586 - val_classification_head_accuracy: 0.8869 - val_categorization_head_accuracy: 0.9309\n",
      "Epoch 3/5\n",
      "375/375 [==============================] - 157s 420ms/step - loss: 0.1897 - classification_head_loss: 0.2858 - categorization_head_loss: 0.1486 - classification_head_accuracy: 0.8952 - categorization_head_accuracy: 0.9351 - val_loss: 0.2232 - val_classification_head_loss: 0.3272 - val_categorization_head_loss: 0.1786 - val_classification_head_accuracy: 0.8826 - val_categorization_head_accuracy: 0.9231\n",
      "Epoch 4/5\n",
      "375/375 [==============================] - 202s 539ms/step - loss: 0.1705 - classification_head_loss: 0.2575 - categorization_head_loss: 0.1332 - classification_head_accuracy: 0.9054 - categorization_head_accuracy: 0.9428 - val_loss: 0.1944 - val_classification_head_loss: 0.3119 - val_categorization_head_loss: 0.1440 - val_classification_head_accuracy: 0.8881 - val_categorization_head_accuracy: 0.9367\n",
      "Epoch 5/5\n",
      "375/375 [==============================] - 133s 354ms/step - loss: 0.1533 - classification_head_loss: 0.2309 - categorization_head_loss: 0.1201 - classification_head_accuracy: 0.9155 - categorization_head_accuracy: 0.9492 - val_loss: 0.1838 - val_classification_head_loss: 0.2687 - val_categorization_head_loss: 0.1474 - val_classification_head_accuracy: 0.9032 - val_categorization_head_accuracy: 0.9375\n",
      "313/313 [==============================] - 9s 28ms/step - loss: 0.1908 - classification_head_loss: 0.2791 - categorization_head_loss: 0.1529 - classification_head_accuracy: 0.9027 - categorization_head_accuracy: 0.9377\n",
      "Keras weights file (<HDF5 file \"variables.h5\" (mode r+)>) saving:\n",
      "...layers\n",
      "......dense\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......dense_1\n",
      ".........vars\n",
      "............0\n",
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      "......dense_2\n",
      ".........vars\n",
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      "............1\n",
      "......dense_4\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......dense_5\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......input_layer\n",
      ".........vars\n",
      "......sequential\n",
      ".........layers\n",
      "............conv2d\n",
      "...............vars\n",
      "..................0\n",
      "..................1\n",
      "............conv2d_1\n",
      "...............vars\n",
      "..................0\n",
      "..................1\n",
      "............conv2d_2\n",
      "...............vars\n",
      "..................0\n",
      "..................1\n",
      "............dense\n",
      "...............vars\n",
      "..................0\n",
      "..................1\n",
      "............flatten\n",
      "...............vars\n",
      "............max_pooling2d\n",
      "...............vars\n",
      "............max_pooling2d_1\n",
      "...............vars\n",
      ".........vars\n",
      "...metrics\n",
      "......mean\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......mean_1\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......mean_2\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......mean_metric_wrapper\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......mean_metric_wrapper_1\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "...optimizer\n",
      "......vars\n",
      ".........0\n",
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      ".........8\n",
      ".........9\n",
      "...vars\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Keras model archive saving:\n",
      "File Name                                             Modified             Size\n",
      "config.json                                    2023-02-17 16:05:43         6848\n",
      "metadata.json                                  2023-02-17 16:05:43           64\n",
      "variables.h5                                   2023-02-17 16:05:47    124143184\n",
      "Epoch 1/5\n",
      "375/375 [==============================] - 182s 470ms/step - loss: 0.4245 - classification_head_loss: 0.5690 - categorization_head_loss: 0.2800 - classification_head_accuracy: 0.7849 - categorization_head_accuracy: 0.8674 - val_loss: 0.3012 - val_classification_head_loss: 0.4074 - val_categorization_head_loss: 0.1950 - val_classification_head_accuracy: 0.8446 - val_categorization_head_accuracy: 0.9124\n",
      "Epoch 2/5\n",
      "375/375 [==============================] - 162s 432ms/step - loss: 0.2556 - classification_head_loss: 0.3355 - categorization_head_loss: 0.1758 - classification_head_accuracy: 0.8774 - categorization_head_accuracy: 0.9224 - val_loss: 0.2373 - val_classification_head_loss: 0.3182 - val_categorization_head_loss: 0.1564 - val_classification_head_accuracy: 0.8863 - val_categorization_head_accuracy: 0.9316\n",
      "Epoch 3/5\n",
      "375/375 [==============================] - 149s 399ms/step - loss: 0.2134 - classification_head_loss: 0.2773 - categorization_head_loss: 0.1496 - classification_head_accuracy: 0.8982 - categorization_head_accuracy: 0.9354 - val_loss: 0.2220 - val_classification_head_loss: 0.2915 - val_categorization_head_loss: 0.1524 - val_classification_head_accuracy: 0.8946 - val_categorization_head_accuracy: 0.9328\n",
      "Epoch 4/5\n",
      "375/375 [==============================] - 133s 354ms/step - loss: 0.1881 - classification_head_loss: 0.2432 - categorization_head_loss: 0.1329 - classification_head_accuracy: 0.9096 - categorization_head_accuracy: 0.9417 - val_loss: 0.2049 - val_classification_head_loss: 0.2692 - val_categorization_head_loss: 0.1406 - val_classification_head_accuracy: 0.9000 - val_categorization_head_accuracy: 0.9375\n",
      "Epoch 5/5\n",
      "375/375 [==============================] - 130s 345ms/step - loss: 0.1706 - classification_head_loss: 0.2214 - categorization_head_loss: 0.1198 - classification_head_accuracy: 0.9182 - categorization_head_accuracy: 0.9489 - val_loss: 0.2127 - val_classification_head_loss: 0.2712 - val_categorization_head_loss: 0.1542 - val_classification_head_accuracy: 0.9011 - val_categorization_head_accuracy: 0.9374\n",
      "313/313 [==============================] - 10s 32ms/step - loss: 0.2170 - classification_head_loss: 0.2755 - categorization_head_loss: 0.1585 - classification_head_accuracy: 0.9014 - categorization_head_accuracy: 0.9337\n",
      "Keras weights file (<HDF5 file \"variables.h5\" (mode r+)>) saving:\n",
      "...layers\n",
      "......dense\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......dense_1\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......dense_2\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......dense_3\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......dense_4\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......dense_5\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......input_layer\n",
      ".........vars\n",
      "......sequential\n",
      ".........layers\n",
      "............conv2d\n",
      "...............vars\n",
      "..................0\n",
      "..................1\n",
      "............conv2d_1\n",
      "...............vars\n",
      "..................0\n",
      "..................1\n",
      "............conv2d_2\n",
      "...............vars\n",
      "..................0\n",
      "..................1\n",
      "............dense\n",
      "...............vars\n",
      "..................0\n",
      "..................1\n",
      "............flatten\n",
      "...............vars\n",
      "............max_pooling2d\n",
      "...............vars\n",
      "............max_pooling2d_1\n",
      "...............vars\n",
      ".........vars\n",
      "...metrics\n",
      "......mean\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......mean_1\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......mean_2\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......mean_metric_wrapper\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......mean_metric_wrapper_1\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "...optimizer\n",
      "......vars\n",
      ".........0\n",
      ".........1\n",
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      ".........8\n",
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      "...vars\n",
      "Keras model archive saving:\n",
      "File Name                                             Modified             Size\n",
      "config.json                                    2023-02-17 16:18:53         6848\n",
      "metadata.json                                  2023-02-17 16:18:53           64\n",
      "variables.h5                                   2023-02-17 16:18:54    124143184\n",
      "Epoch 1/5\n",
      "375/375 [==============================] - 137s 356ms/step - loss: 0.4805 - classification_head_loss: 0.5644 - categorization_head_loss: 0.2845 - classification_head_accuracy: 0.7851 - categorization_head_accuracy: 0.8648 - val_loss: 0.3128 - val_classification_head_loss: 0.3647 - val_categorization_head_loss: 0.1916 - val_classification_head_accuracy: 0.8660 - val_categorization_head_accuracy: 0.9144\n",
      "Epoch 2/5\n",
      "375/375 [==============================] - 126s 336ms/step - loss: 0.2840 - classification_head_loss: 0.3288 - categorization_head_loss: 0.1793 - classification_head_accuracy: 0.8777 - categorization_head_accuracy: 0.9197 - val_loss: 0.2760 - val_classification_head_loss: 0.3224 - val_categorization_head_loss: 0.1677 - val_classification_head_accuracy: 0.8814 - val_categorization_head_accuracy: 0.9241\n",
      "Epoch 3/5\n",
      "375/375 [==============================] - 128s 340ms/step - loss: 0.2406 - classification_head_loss: 0.2783 - categorization_head_loss: 0.1524 - classification_head_accuracy: 0.8968 - categorization_head_accuracy: 0.9336 - val_loss: 0.2465 - val_classification_head_loss: 0.2851 - val_categorization_head_loss: 0.1566 - val_classification_head_accuracy: 0.8929 - val_categorization_head_accuracy: 0.9289\n",
      "Epoch 4/5\n",
      "375/375 [==============================] - 138s 369ms/step - loss: 0.2087 - classification_head_loss: 0.2409 - categorization_head_loss: 0.1335 - classification_head_accuracy: 0.9099 - categorization_head_accuracy: 0.9414 - val_loss: 0.2296 - val_classification_head_loss: 0.2655 - val_categorization_head_loss: 0.1459 - val_classification_head_accuracy: 0.9042 - val_categorization_head_accuracy: 0.9367\n",
      "Epoch 5/5\n",
      "375/375 [==============================] - 189s 502ms/step - loss: 0.1871 - classification_head_loss: 0.2149 - categorization_head_loss: 0.1223 - classification_head_accuracy: 0.9202 - categorization_head_accuracy: 0.9476 - val_loss: 0.2158 - val_classification_head_loss: 0.2510 - val_categorization_head_loss: 0.1335 - val_classification_head_accuracy: 0.9068 - val_categorization_head_accuracy: 0.9443\n",
      "313/313 [==============================] - 14s 45ms/step - loss: 0.2296 - classification_head_loss: 0.2666 - categorization_head_loss: 0.1432 - classification_head_accuracy: 0.9032 - categorization_head_accuracy: 0.9400\n",
      "Keras weights file (<HDF5 file \"variables.h5\" (mode r+)>) saving:\n",
      "...layers\n",
      "......dense\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......dense_1\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......dense_2\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......dense_3\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......dense_4\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......dense_5\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......input_layer\n",
      ".........vars\n",
      "......sequential\n",
      ".........layers\n",
      "............conv2d\n",
      "...............vars\n",
      "..................0\n",
      "..................1\n",
      "............conv2d_1\n",
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      "............conv2d_2\n",
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      "..................1\n",
      "............dense\n",
      "...............vars\n",
      "..................0\n",
      "..................1\n",
      "............flatten\n",
      "...............vars\n",
      "............max_pooling2d\n",
      "...............vars\n",
      "............max_pooling2d_1\n",
      "...............vars\n",
      ".........vars\n",
      "...metrics\n",
      "......mean\n",
      ".........vars\n",
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      "......mean_1\n",
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      "......mean_2\n",
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      "......mean_metric_wrapper\n",
      ".........vars\n",
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      "......mean_metric_wrapper_1\n",
      ".........vars\n",
      "............0\n",
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      "...optimizer\n",
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      "...vars\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Keras model archive saving:\n",
      "File Name                                             Modified             Size\n",
      "config.json                                    2023-02-17 16:31:59         6864\n",
      "metadata.json                                  2023-02-17 16:31:59           64\n",
      "variables.h5                                   2023-02-17 16:32:01    124143184\n",
      "Epoch 1/5\n",
      "375/375 [==============================] - 295s 763ms/step - loss: 0.5594 - classification_head_loss: 0.5594 - categorization_head_loss: 1.0882 - classification_head_accuracy: 0.7890 - categorization_head_accuracy: 0.3928 - val_loss: 0.3683 - val_classification_head_loss: 0.3683 - val_categorization_head_loss: 1.0875 - val_classification_head_accuracy: 0.8627 - val_categorization_head_accuracy: 0.4099\n",
      "Epoch 2/5\n",
      "375/375 [==============================] - 237s 631ms/step - loss: 0.3323 - classification_head_loss: 0.3323 - categorization_head_loss: 1.0886 - classification_head_accuracy: 0.8780 - categorization_head_accuracy: 0.4243 - val_loss: 0.3158 - val_classification_head_loss: 0.3158 - val_categorization_head_loss: 1.0929 - val_classification_head_accuracy: 0.8849 - val_categorization_head_accuracy: 0.3873\n",
      "Epoch 3/5\n",
      "375/375 [==============================] - 192s 512ms/step - loss: 0.2806 - classification_head_loss: 0.2806 - categorization_head_loss: 1.0925 - classification_head_accuracy: 0.8957 - categorization_head_accuracy: 0.3457 - val_loss: 0.2795 - val_classification_head_loss: 0.2795 - val_categorization_head_loss: 1.0918 - val_classification_head_accuracy: 0.8968 - val_categorization_head_accuracy: 0.3279\n",
      "Epoch 4/5\n",
      "375/375 [==============================] - 193s 516ms/step - loss: 0.2459 - classification_head_loss: 0.2459 - categorization_head_loss: 1.0915 - classification_head_accuracy: 0.9077 - categorization_head_accuracy: 0.3379 - val_loss: 0.2791 - val_classification_head_loss: 0.2791 - val_categorization_head_loss: 1.0898 - val_classification_head_accuracy: 0.9006 - val_categorization_head_accuracy: 0.3625\n",
      "Epoch 5/5\n",
      "375/375 [==============================] - 197s 523ms/step - loss: 0.2167 - classification_head_loss: 0.2167 - categorization_head_loss: 1.0930 - classification_head_accuracy: 0.9193 - categorization_head_accuracy: 0.3207 - val_loss: 0.2687 - val_classification_head_loss: 0.2687 - val_categorization_head_loss: 1.0951 - val_classification_head_accuracy: 0.9028 - val_categorization_head_accuracy: 0.2848\n",
      "313/313 [==============================] - 12s 38ms/step - loss: 0.2778 - classification_head_loss: 0.2778 - categorization_head_loss: 1.0949 - classification_head_accuracy: 0.8990 - categorization_head_accuracy: 0.2856\n",
      "Keras weights file (<HDF5 file \"variables.h5\" (mode r+)>) saving:\n",
      "...layers\n",
      "......dense\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......dense_1\n",
      ".........vars\n",
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      "......dense_2\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......dense_3\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......dense_4\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......dense_5\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......input_layer\n",
      ".........vars\n",
      "......sequential\n",
      ".........layers\n",
      "............conv2d\n",
      "...............vars\n",
      "..................0\n",
      "..................1\n",
      "............conv2d_1\n",
      "...............vars\n",
      "..................0\n",
      "..................1\n",
      "............conv2d_2\n",
      "...............vars\n",
      "..................0\n",
      "..................1\n",
      "............dense\n",
      "...............vars\n",
      "..................0\n",
      "..................1\n",
      "............flatten\n",
      "...............vars\n",
      "............max_pooling2d\n",
      "...............vars\n",
      "............max_pooling2d_1\n",
      "...............vars\n",
      ".........vars\n",
      "...metrics\n",
      "......mean\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......mean_1\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......mean_2\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......mean_metric_wrapper\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......mean_metric_wrapper_1\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "...optimizer\n",
      "......vars\n",
      ".........0\n",
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      "...vars\n",
      "Keras model archive saving:\n",
      "File Name                                             Modified             Size\n",
      "config.json                                    2023-02-17 16:51:21         6844\n",
      "metadata.json                                  2023-02-17 16:51:21           64\n",
      "variables.h5                                   2023-02-17 16:51:24    124143184\n"
     ]
    }
   ],
   "source": [
    "history_collection = []\n",
    "accuracies_mtl = []\n",
    "for gamma in [0, 0.3, 0.5 , 0.7, 1]:\n",
    "    \n",
    "    MTL_model, history, classification_accuracy_MTL, categorization_accuracy_MTL = MTL(x_train,y_train_1,\n",
    "                                                                                       y_train_2, x_test,\n",
    "                                                                                       y_test_1,y_test_2, gamma)\n",
    "    history_collection.append(history)\n",
    "    accuracies_mtl.append((classification_accuracy_MTL, categorization_accuracy_MTL))\n",
    "    MTL_model.save(f'6.2_mtl_cnn_{gamma}.h5')  #  save model after training\n",
    "    np.save(f'6_2__mtl_cnn_{gamma}_history.npy',history)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Keras weights file (<HDF5 file \"variables.h5\" (mode r+)>) saving:\n",
      "...layers\n",
      "......dense\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
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      ".........vars\n",
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      "......dense_3\n",
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      "......dense_4\n",
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      "............0\n",
      "............1\n",
      "......dense_5\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......input_layer\n",
      ".........vars\n",
      "......sequential\n",
      ".........layers\n",
      "............conv2d\n",
      "...............vars\n",
      "..................0\n",
      "..................1\n",
      "............conv2d_1\n",
      "...............vars\n",
      "..................0\n",
      "..................1\n",
      "............conv2d_2\n",
      "...............vars\n",
      "..................0\n",
      "..................1\n",
      "............dense\n",
      "...............vars\n",
      "..................0\n",
      "..................1\n",
      "............flatten\n",
      "...............vars\n",
      "............max_pooling2d\n",
      "...............vars\n",
      "............max_pooling2d_1\n",
      "...............vars\n",
      ".........vars\n",
      "...metrics\n",
      "......mean\n",
      ".........vars\n",
      "............0\n",
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      "......mean_1\n",
      ".........vars\n",
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      "......mean_2\n",
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      "............0\n",
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      "............0\n",
      "............1\n",
      "......mean_metric_wrapper_1\n",
      ".........vars\n",
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      "...optimizer\n",
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      "...vars\n",
      "Keras model archive saving:\n",
      "File Name                                             Modified             Size\n",
      "config.json                                    2023-02-17 16:59:17         6830\n",
      "metadata.json                                  2023-02-17 16:59:17           64\n",
      "variables.h5                                   2023-02-17 16:59:22    124143184\n",
      "Keras weights file (<HDF5 file \"variables.h5\" (mode r+)>) saving:\n",
      "...layers\n",
      "......dense\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
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      ".........vars\n",
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      "............0\n",
      "............1\n",
      "......input_layer\n",
      ".........vars\n",
      "......sequential\n",
      ".........layers\n",
      "............conv2d\n",
      "...............vars\n",
      "..................0\n",
      "..................1\n",
      "............conv2d_1\n",
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      "............flatten\n",
      "...............vars\n",
      "............max_pooling2d\n",
      "...............vars\n",
      "............max_pooling2d_1\n",
      "...............vars\n",
      ".........vars\n",
      "...metrics\n",
      "......mean\n",
      ".........vars\n",
      "............0\n",
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      "......mean_metric_wrapper\n",
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      "......mean_metric_wrapper_1\n",
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      "............0\n",
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      "...optimizer\n",
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      "...vars\n",
      "Keras model archive saving:\n",
      "File Name                                             Modified             Size\n",
      "config.json                                    2023-02-17 16:59:27         6848\n",
      "metadata.json                                  2023-02-17 16:59:27           64\n",
      "variables.h5                                   2023-02-17 16:59:31    124143184\n",
      "Keras weights file (<HDF5 file \"variables.h5\" (mode r+)>) saving:\n",
      "...layers\n",
      "......dense\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......dense_1\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......dense_2\n",
      ".........vars\n",
      "............0\n",
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      "......dense_3\n",
      ".........vars\n",
      "............0\n",
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      "......dense_4\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......dense_5\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......input_layer\n",
      ".........vars\n",
      "......sequential\n",
      ".........layers\n",
      "............conv2d\n",
      "...............vars\n",
      "..................0\n",
      "..................1\n",
      "............conv2d_1\n",
      "...............vars\n",
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      "..................1\n",
      "............conv2d_2\n",
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      "..................0\n",
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      "............dense\n",
      "...............vars\n",
      "..................0\n",
      "..................1\n",
      "............flatten\n",
      "...............vars\n",
      "............max_pooling2d\n",
      "...............vars\n",
      "............max_pooling2d_1\n",
      "...............vars\n",
      ".........vars\n",
      "...metrics\n",
      "......mean\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
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      "......mean_2\n",
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      "......mean_metric_wrapper_1\n",
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      "Keras model archive saving:\n",
      "File Name                                             Modified             Size\n",
      "config.json                                    2023-02-17 16:59:36         6848\n",
      "metadata.json                                  2023-02-17 16:59:36           64\n",
      "variables.h5                                   2023-02-17 16:59:42    124143184\n",
      "Keras weights file (<HDF5 file \"variables.h5\" (mode r+)>) saving:\n",
      "...layers\n",
      "......dense\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......dense_1\n",
      ".........vars\n",
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      "......dense_2\n",
      ".........vars\n",
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      "......dense_3\n",
      ".........vars\n",
      "............0\n",
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      "......dense_4\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......dense_5\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......input_layer\n",
      ".........vars\n",
      "......sequential\n",
      ".........layers\n",
      "............conv2d\n",
      "...............vars\n",
      "..................0\n",
      "..................1\n",
      "............conv2d_1\n",
      "...............vars\n",
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      "..................1\n",
      "............conv2d_2\n",
      "...............vars\n",
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      "..................1\n",
      "............dense\n",
      "...............vars\n",
      "..................0\n",
      "..................1\n",
      "............flatten\n",
      "...............vars\n",
      "............max_pooling2d\n",
      "...............vars\n",
      "............max_pooling2d_1\n",
      "...............vars\n",
      ".........vars\n",
      "...metrics\n",
      "......mean\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......mean_1\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......mean_2\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......mean_metric_wrapper\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......mean_metric_wrapper_1\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "...optimizer\n",
      "......vars\n",
      ".........0\n",
      ".........1\n",
      ".........10\n",
      ".........11\n",
      ".........12\n",
      ".........13\n",
      ".........14\n",
      ".........15\n",
      ".........16\n",
      ".........17\n",
      ".........18\n",
      ".........19\n",
      ".........2\n",
      ".........20\n",
      ".........21\n",
      ".........22\n",
      ".........23\n",
      ".........24\n",
      ".........25\n",
      ".........26\n",
      ".........27\n",
      ".........28\n",
      ".........29\n",
      ".........3\n",
      ".........30\n",
      ".........31\n",
      ".........32\n",
      ".........33\n",
      ".........34\n",
      ".........35\n",
      ".........36\n",
      ".........37\n",
      ".........38\n",
      ".........39\n",
      ".........4\n",
      ".........40\n",
      ".........5\n",
      ".........6\n",
      ".........7\n",
      ".........8\n",
      ".........9\n",
      "...vars\n",
      "Keras model archive saving:\n",
      "File Name                                             Modified             Size\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "config.json                                    2023-02-17 16:59:54         6864\n",
      "metadata.json                                  2023-02-17 16:59:54           64\n",
      "variables.h5                                   2023-02-17 16:59:59    124143184\n",
      "Keras weights file (<HDF5 file \"variables.h5\" (mode r+)>) saving:\n",
      "...layers\n",
      "......dense\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......dense_1\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......dense_2\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......dense_3\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......dense_4\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......dense_5\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......input_layer\n",
      ".........vars\n",
      "......sequential\n",
      ".........layers\n",
      "............conv2d\n",
      "...............vars\n",
      "..................0\n",
      "..................1\n",
      "............conv2d_1\n",
      "...............vars\n",
      "..................0\n",
      "..................1\n",
      "............conv2d_2\n",
      "...............vars\n",
      "..................0\n",
      "..................1\n",
      "............dense\n",
      "...............vars\n",
      "..................0\n",
      "..................1\n",
      "............flatten\n",
      "...............vars\n",
      "............max_pooling2d\n",
      "...............vars\n",
      "............max_pooling2d_1\n",
      "...............vars\n",
      ".........vars\n",
      "...metrics\n",
      "......mean\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......mean_1\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......mean_2\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......mean_metric_wrapper\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......mean_metric_wrapper_1\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "...optimizer\n",
      "......vars\n",
      ".........0\n",
      ".........1\n",
      ".........10\n",
      ".........11\n",
      ".........12\n",
      ".........13\n",
      ".........14\n",
      ".........15\n",
      ".........16\n",
      ".........17\n",
      ".........18\n",
      ".........19\n",
      ".........2\n",
      ".........20\n",
      ".........21\n",
      ".........22\n",
      ".........23\n",
      ".........24\n",
      ".........25\n",
      ".........26\n",
      ".........27\n",
      ".........28\n",
      ".........29\n",
      ".........3\n",
      ".........30\n",
      ".........31\n",
      ".........32\n",
      ".........33\n",
      ".........34\n",
      ".........35\n",
      ".........36\n",
      ".........37\n",
      ".........38\n",
      ".........39\n",
      ".........4\n",
      ".........40\n",
      ".........5\n",
      ".........6\n",
      ".........7\n",
      ".........8\n",
      ".........9\n",
      "...vars\n",
      "Keras model archive saving:\n",
      "File Name                                             Modified             Size\n",
      "config.json                                    2023-02-17 17:00:11         6844\n",
      "metadata.json                                  2023-02-17 17:00:11           64\n",
      "variables.h5                                   2023-02-17 17:00:14    124143184\n",
      "\n",
      "When gamma is: 0\n",
      "Classification accuracy (Task1) on the test set: 10.20\n",
      "Categorization accuracy (Task2) on the test set: 92.47\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1050x600 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "When gamma is: 0.3\n",
      "Classification accuracy (Task1) on the test set: 90.27\n",
      "Categorization accuracy (Task2) on the test set: 93.77\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1050x600 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "When gamma is: 0.5\n",
      "Classification accuracy (Task1) on the test set: 90.14\n",
      "Categorization accuracy (Task2) on the test set: 93.37\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1050x600 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "When gamma is: 0.7\n",
      "Classification accuracy (Task1) on the test set: 90.32\n",
      "Categorization accuracy (Task2) on the test set: 94.00\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1050x600 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "When gamma is: 1\n",
      "Classification accuracy (Task1) on the test set: 89.90\n",
      "Categorization accuracy (Task2) on the test set: 28.56\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1050x600 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# with open('history_collection.pkl', 'wb') as f:\n",
    "#     pickle.dump(history_collection, f)\n",
    "\n",
    "# with open('accuracies_mtl.pkl', 'wb') as f:\n",
    "#     pickle.dump(accuracies_mtl, f)\n",
    "\n",
    "# with open('history_collection.pkl', 'rb') as f:\n",
    "#     history_collection = pickle.load(f)\n",
    "\n",
    "# with open('accuracies_mtl.pkl', 'rb') as f:\n",
    "#     accuracies_mtl = pickle.load(f)\n",
    "\n",
    "def plot_learing_curve_MTL(history, title):\n",
    "    # plot the learning curves\n",
    "    fig = plt.figure(figsize=(7, 4), dpi=150)\n",
    "    fig.add_subplot(1,3,1)\n",
    "    plt.plot(history.history['loss'], label='train loss')\n",
    "    plt.plot(history.history['val_loss'], label='val loss')\n",
    "    plt.legend(loc= 'best', prop={'size': 5})\n",
    "    plt.grid(True)\n",
    "    plt.xlabel('epoch')\n",
    "    \n",
    "    fig.add_subplot(1,3,2)\n",
    "    plt.plot(history.history['classification_head_loss'], label='Task 1 Train loss')\n",
    "    plt.plot(history.history['categorization_head_loss'], label='Task 2 Train loss')\n",
    "    plt.plot(history.history['val_classification_head_loss'], label='Task 1 validation loss')\n",
    "    plt.plot(history.history['val_categorization_head_loss'], label='Task 2 validation loss')\n",
    "    plt.legend(loc=2, prop={'size': 5})\n",
    "    plt.grid(True)\n",
    "    plt.xlabel('epoch')\n",
    "    \n",
    "    fig.add_subplot(1,3,3)\n",
    "    plt.plot(history.history['classification_head_accuracy'], label='Task 1 accuracy')\n",
    "    plt.plot(history.history['categorization_head_accuracy'], label='Task 2 accuracy')\n",
    "    plt.legend(loc=2, prop={'size': 5})\n",
    "    plt.grid(True)\n",
    "    plt.ylim([0,1.0])\n",
    "    plt.xlabel('epoch')\n",
    "    plt.suptitle(title, fontsize=20)\n",
    "    plt.show()\n",
    "\n",
    "# # It can be used to reconstruct the model identically.\n",
    "for gamma, history, ac_mtl in zip([0, 0.3, 0.5 , 0.7, 1], history_collection, accuracies_mtl) :\n",
    "    print('\\nWhen gamma is:', gamma)\n",
    "    print('Classification accuracy (Task1) on the test set: %.2f' % (ac_mtl[0]*100))\n",
    "    print('Categorization accuracy (Task2) on the test set: %.2f' % (ac_mtl[1]*100))\n",
    "    # plot the learning curves\n",
    "    plot_learing_curve_MTL(history, f'Learning Curve for Task 1 and 2 when gamma ={gamma}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "MTL_model = load_model('6.2_mtl_cnn_0.3.h5')\n",
    "preds = MTL_model.predict(x_test, verbose = 0)\n",
    "with open('accuracies_mtl.pkl', 'rb') as f:\n",
    "    accuracies_mtl = pickle.load(f)\n",
    "classification_accuracy_MTL, categorization_accuracy_MTL = accuracies_mtl[1]\n",
    "    \n",
    "plt.figure(figsize=(8, 5))\n",
    "sns.heatmap(sklearn.metrics.confusion_matrix(np.argmax(preds['out_class'], axis=1), np.argmax(y_test_1, axis=1)), \n",
    "            cmap='icefire', annot=True, fmt=\"\", yticklabels=class_names, xticklabels=class_names, linewidths=0.1)\n",
    "plt.xlabel('predication', fontsize = 14)\n",
    "plt.ylabel('true', fontsize = 14)\n",
    "plt.title('Confusion Matrix for Task 1 Head; Multi-Task CNN,\\naccuracy= %.2f' %(classification_accuracy_MTL*100), fontsize=10);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 5))\n",
    "sns.heatmap(sklearn.metrics.confusion_matrix(np.argmax(preds['out_group'], axis=1), np.argmax(y_test_2, axis=1)), \n",
    "            cmap='icefire', annot=True, fmt=\"\", yticklabels=groups_names, xticklabels=groups_names, linewidths=0.1)\n",
    "plt.xlabel('predication', fontsize = 14)\n",
    "plt.ylabel('true', fontsize = 14)\n",
    "plt.title('Confusion Matrix for Task 2 Head; Multi-Task CNN,\\naccuracy= %.2f' %(categorization_accuracy_MTL*100), fontsize=15);"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "provenance": []
  },
  "gpuClass": "standard",
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}