Colab_notebook.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "kernelspec": {
      "display_name": "feedforward",
      "language": "python",
      "name": "feedforward"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.9"
    },
    "colab": {
      "name": "Copy of Capstone Project.ipynb",
      "provenance": [],
      "collapsed_sections": []
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rffwkDWTz_Fo"
      },
      "source": [
        "# Capstone Project\n",
        "## Image classifier for the SVHN dataset\n",
        "### Instructions\n",
        "\n",
        "In this notebook, you will create a neural network that classifies real-world images digits. You will use concepts from throughout this course in building, training, testing, validating and saving your Tensorflow classifier model.\n",
        "\n",
        "This project is peer-assessed. Within this notebook you will find instructions in each section for how to complete the project. Pay close attention to the instructions as the peer review will be carried out according to a grading rubric that checks key parts of the project instructions. Feel free to add extra cells into the notebook as required.\n",
        "\n",
        "### How to submit\n",
        "\n",
        "When you have completed the Capstone project notebook, you will submit a pdf of the notebook for peer review. First ensure that the notebook has been fully executed from beginning to end, and all of the cell outputs are visible. This is important, as the grading rubric depends on the reviewer being able to view the outputs of your notebook. Save the notebook as a pdf (you could download the notebook with File -> Download .ipynb, open the notebook locally, and then File -> Download as -> PDF via LaTeX), and then submit this pdf for review.\n",
        "\n",
        "### Let's get started!\n",
        "\n",
        "We'll start by running some imports, and loading the dataset. For this project you are free to make further imports throughout the notebook as you wish. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O3vI8jSIz_Fs"
      },
      "source": [
        "import tensorflow as tf\n",
        "from scipy.io import loadmat\n",
        "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, BatchNormalization\n",
        "from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping, LearningRateScheduler\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "gpu_options = tf.compat.v1.GPUOptions(allow_growth=True)\n",
        "sess = tf.compat.v1.Session(config=tf.compat.v1.ConfigProto(gpu_options=gpu_options))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pIUUGaQxpurj",
        "outputId": "6f9141e3-51d2-496f-e8dd-21b6dcd9b34e"
      },
      "source": [
        "tf.__version__"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'2.2.0'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8OrHY7TRz_Fx"
      },
      "source": [
        "For the capstone project, you will use the [SVHN dataset](http://ufldl.stanford.edu/housenumbers/). This is an image dataset of over 600,000 digit images in all, and is a harder dataset than MNIST as the numbers appear in the context of natural scene images. SVHN is obtained from house numbers in Google Street View images.\n",
        "\n",
        "* Y. Netzer, T. Wang, A. Coates, A. Bissacco, B. Wu and A. Y. Ng. \"Reading Digits in Natural Images with Unsupervised Feature Learning\". NIPS Workshop on Deep Learning and Unsupervised Feature Learning, 2011.\n",
        "\n",
        "The train and test datasets required for this project can be downloaded from [here](http://ufldl.stanford.edu/housenumbers/train.tar.gz) and [here](http://ufldl.stanford.edu/housenumbers/test.tar.gz). Once unzipped, you will have two files: `train_32x32.mat` and `test_32x32.mat`. You should store these files in Drive for use in this Colab notebook.\n",
        "\n",
        "Your goal is to develop an end-to-end workflow for building, training, validating, evaluating and saving a neural network that classifies a real-world image into one of ten classes."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YWdiz3n_z_Fy"
      },
      "source": [
        "# Load the dataset from your drive folder\n",
        "\n",
        "train = loadmat('/home/marcin/Pictures/capstone_project/train_32x32.mat')\n",
        "test = loadmat('/home/marcin/Pictures/capstone_project/test_32x32.mat')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Sot1IcuZz_F2"
      },
      "source": [
        "Both `train` and `test` are dictionaries with keys `X` and `y` for the input images and labels respectively."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_Q1n_Ai2z_F3"
      },
      "source": [
        "## 1. Inspect and preprocess the dataset\n",
        "* Extract the training and testing images and labels separately from the train and test dictionaries loaded for you.\n",
        "* Select a random sample of images and corresponding labels from the dataset (at least 10), and display them in a figure.\n",
        "* Convert the training and test images to grayscale by taking the average across all colour channels for each pixel. _Hint: retain the channel dimension, which will now have size 1._\n",
        "* Select a random sample of the grayscale images and corresponding labels from the dataset (at least 10), and display them in a figure."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-WIH5hyXz_F4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "494a5db4-bce5-4887-e09f-90eeeedf2727"
      },
      "source": [
        "train.keys()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['__header__', '__version__', '__globals__', 'X', 'y'])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zmGJK3xgz_F8"
      },
      "source": [
        "train_data, train_targets = train['X'], train['y']\n",
        "test_data, test_targets = test['X'], test['y']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6SR4gYffz_F_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "190ccb5a-0d65-42f9-cfb0-ddc00628d00a"
      },
      "source": [
        "print(train_data.shape)\n",
        "print(train_targets.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(32, 32, 3, 73257)\n",
            "(73257, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g1ZKj0qGwZAA",
        "outputId": "5e5d5984-8c7d-44cc-e9a6-b18a6d497656"
      },
      "source": [
        "print(test_data.shape)\n",
        "print(test_targets.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(32, 32, 3, 26032)\n",
            "(26032, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0VBo6oM_yeED",
        "outputId": "c0e12c75-558a-4aa2-d9e0-f1b5e839ba31"
      },
      "source": [
        "i=0\n",
        "for target in train_targets[:300]:\n",
        "  if target == 10:\n",
        "    print('index =', i)\n",
        "  i += 1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "index = 52\n",
            "index = 84\n",
            "index = 93\n",
            "index = 96\n",
            "index = 108\n",
            "index = 144\n",
            "index = 182\n",
            "index = 206\n",
            "index = 215\n",
            "index = 218\n",
            "index = 226\n",
            "index = 236\n",
            "index = 274\n",
            "index = 294\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "si9jozsszWFY"
      },
      "source": [
        "**Zeros are represented in targets as \"10\"**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UXYwWhHpz_GD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 183
        },
        "outputId": "ced200c3-1687-45cf-a31b-e658e572ad04"
      },
      "source": [
        "num_of_img = 12\n",
        "first_img_index = 51\n",
        "fig, ax = plt.subplots(1, num_of_img, figsize=(32, 3))\n",
        "\n",
        "for i in range(num_of_img):\n",
        "    ax[i].set_axis_off()\n",
        "    ax[i].imshow(train_data[..., i + first_img_index])\n",
        "    ax[i].set_title(train_targets[i + first_img_index][0], size=32)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2304x216 with 12 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vBFtvwUjC5Ju"
      },
      "source": [
        "**Instead 0's will be represented as 0**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dnmyfZkPznUm"
      },
      "source": [
        "i = 0\n",
        "for target in train_targets[:]:\n",
        "  if target == 10:\n",
        "    train_targets[i] = 0\n",
        "  i += 1\n",
        "\n",
        "k=0\n",
        "for target in test_targets[:]:\n",
        "  if target == 10:\n",
        "    test_targets[k] = 0\n",
        "  k += 1"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 183
        },
        "id": "pSsqjJRW0CwQ",
        "outputId": "6cdb48f9-4691-4063-b2f9-ec10ad949f04"
      },
      "source": [
        "num_of_img = 12\n",
        "first_img_index = 51\n",
        "fig, ax = plt.subplots(1, num_of_img, figsize=(32, 3))\n",
        "\n",
        "for i in range(num_of_img):\n",
        "    ax[i].set_axis_off()\n",
        "    ax[i].imshow(train_data[..., i + first_img_index])\n",
        "    ax[i].set_title(train_targets[i + first_img_index][0], size=32)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2304x216 with 12 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6pdtyJhfDVCF"
      },
      "source": [
        "**Converting images to grayscale**\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "auFZ63dtz_GH"
      },
      "source": [
        "def get_grayscale_image(data_images, samples=train_data.shape[3]):\n",
        "\n",
        "  data_images_grayscale = np.zeros((samples, train_data.shape[0],\n",
        "                             train_data.shape[1]))\n",
        "\n",
        "  for img_num in range(samples):\n",
        "    data_images_grayscale[img_num, ...] = np.average(data_images[:, :, :, img_num], \n",
        "                                                     axis=2)\n",
        "    \n",
        "  return data_images_grayscale[..., np.newaxis]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UfD019ON03L3",
        "outputId": "7b65698f-0968-4c5e-c864-ea6f05c8301b"
      },
      "source": [
        "test_data.shape[3]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "26032"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CGHZvq4zz_GK"
      },
      "source": [
        "train_data_grayscale = get_grayscale_image(train_data)\n",
        "test_data_grayscale = get_grayscale_image(test_data, samples=test_data.shape[3])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dhvdYNFm1MMH"
      },
      "source": [
        "**Training images**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 116
        },
        "id": "Lps-DEBdD2TZ",
        "outputId": "82d73029-021e-41f9-831d-5d7951c0ddea"
      },
      "source": [
        "num_of_img = 12\n",
        "first_img_index = 51\n",
        "fig, ax = plt.subplots(1, num_of_img, figsize=(32, 1))\n",
        "\n",
        "for i in range(num_of_img):\n",
        "    ax[i].set_axis_off()\n",
        "    ax[i].imshow(train_data_grayscale[i + first_img_index], cmap='gray')\n",
        "    ax[i].set_title(train_targets[i + first_img_index][0], size=32)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2304x72 with 12 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pfz5XTSB7agv"
      },
      "source": [
        "**Test images**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 116
        },
        "id": "_fG4k-5h1LPd",
        "outputId": "14f60b42-4fee-4c5d-fffe-1485a8e44c65"
      },
      "source": [
        "num_of_img = 12\n",
        "first_img_index = 51\n",
        "fig, ax = plt.subplots(1, num_of_img, figsize=(32, 1))\n",
        "\n",
        "for i in range(num_of_img):\n",
        "    ax[i].set_axis_off()\n",
        "    ax[i].imshow(test_data_grayscale[i + first_img_index], cmap='gray')\n",
        "    ax[i].set_title(test_targets[i + first_img_index][0], size=32)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2304x72 with 12 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jZLIaIh_9DTf"
      },
      "source": [
        "**Normalizing data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VaNqOYpar4Wi"
      },
      "source": [
        "train_data_grayscale = (train_data_grayscale - train_data_grayscale.mean()) / train_data_grayscale.std()\n",
        "test_data_grayscale = (test_data_grayscale - test_data_grayscale.mean()) / test_data_grayscale.std()\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EeIyFpMCd9ej",
        "outputId": "2989d797-c597-4173-8b2b-4be6bcb4ec83"
      },
      "source": [
        "train_data_grayscale[4, 3, :5]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.15943246],\n",
              "       [0.22006684],\n",
              "       [0.36828423],\n",
              "       [0.46934153],\n",
              "       [0.50976445]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a9QFAfUI9OZk"
      },
      "source": [
        "**One-hot encoding test and train targets**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nCDNFYGKVR7N"
      },
      "source": [
        "train_targets = tf.keras.utils.to_categorical(train_targets)\n",
        "test_targets = tf.keras.utils.to_categorical(test_targets)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x828j8OzbPDG",
        "outputId": "c9166aec-7c1e-42ad-e065-22cd12d66fca"
      },
      "source": [
        "train_targets.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(73257, 10)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e3PPuYiHccKZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9891e4b1-57e3-4f5e-b2c0-d15201d456c9"
      },
      "source": [
        "train_targets[1:5,]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0., 0., 0., 0., 0., 0., 0., 0., 0., 1.],\n",
              "       [0., 0., 1., 0., 0., 0., 0., 0., 0., 0.],\n",
              "       [0., 0., 0., 1., 0., 0., 0., 0., 0., 0.],\n",
              "       [0., 0., 1., 0., 0., 0., 0., 0., 0., 0.]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7e7iSyWXz_GN"
      },
      "source": [
        "## 2. MLP neural network classifier\n",
        "* Build an MLP classifier model using the Sequential API. Your model should use only Flatten and Dense layers, with the final layer having a 10-way softmax output. \n",
        "* You should design and build the model yourself. Feel free to experiment with different MLP architectures. _Hint: to achieve a reasonable accuracy you won't need to use more than 4 or 5 layers._\n",
        "* Print out the model summary (using the summary() method)\n",
        "* Compile and train the model (we recommend a maximum of 30 epochs), making use of both training and validation sets during the training run. \n",
        "* Your model should track at least one appropriate metric, and use at least two callbacks during training, one of which should be a ModelCheckpoint callback.\n",
        "* As a guide, you should aim to achieve a final categorical cross entropy training loss of less than 1.0 (the validation loss might be higher).\n",
        "* Plot the learning curves for loss vs epoch and accuracy vs epoch for both training and validation sets.\n",
        "* Compute and display the loss and accuracy of the trained model on the test set."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xWznFyz_GkdX"
      },
      "source": [
        "**Model structure and compilation**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l14VCBFVz_GO"
      },
      "source": [
        "def get_mlp_model(input_shape=train_data_grayscale[1].shape):\n",
        "  \n",
        "  model = tf.keras.models.Sequential([\n",
        "      Flatten(input_shape=input_shape),\n",
        "      Dense(64, activation='relu', name='dense_1'),\n",
        "      Dense(64, activation='relu', name='dense_2'),\n",
        "      Dense(10, activation='softmax', name='dense_3')\n",
        "  ])\n",
        "  return model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ebnulXlXEKTo",
        "outputId": "478a60fb-8400-4c1e-cd26-5c4c4479fe0d"
      },
      "source": [
        "print('Shape passed to get_mlp_model function:', \n",
        "      train_data_grayscale[1].shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Shape passed to get_mlp_model function: (32, 32, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "beEZO1kvz_GR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d3523da3-b9e0-4517-bf46-4c8ae2085503"
      },
      "source": [
        "model = get_mlp_model(train_data_grayscale[1].shape)\n",
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "flatten (Flatten)            (None, 1024)              0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 64)                65600     \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 64)                4160      \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 10)                650       \n",
            "=================================================================\n",
            "Total params: 70,410\n",
            "Trainable params: 70,410\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YxJXq3xYz_GU"
      },
      "source": [
        "lr = 0.001\n",
        "\n",
        "def compile_model(model):\n",
        "  model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=lr),\n",
        "                loss='categorical_crossentropy',\n",
        "                metrics=['accuracy'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pPPbzGhVz_GW"
      },
      "source": [
        "compile_model(model)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8xxcCjL4XKFb"
      },
      "source": [
        "**Callbacks**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J2EYFSZIXJze"
      },
      "source": [
        "def get_best_epoch_callback():\n",
        "  path='/home/marcin/Documents/Capstone_project/mlp_checkpoint_best'\n",
        "  \n",
        "  callback = ModelCheckpoint(path,\n",
        "                             verbose=1,\n",
        "                             save_best_only=True,\n",
        "                             save_weights_only=False)\n",
        "  return callback\n",
        "\n",
        "\n",
        "def get_early_stopping_callback(patience=2):\n",
        "  callback = EarlyStopping(monitor='val_loss',\n",
        "                           patience=patience,\n",
        "                           verbose=1)\n",
        "  return callback"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7b5_8VsCz_GZ"
      },
      "source": [
        "best_epoch_callback = get_best_epoch_callback()\n",
        "early_stopping_callback = get_early_stopping_callback()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cXdi8ZR2ZfdD"
      },
      "source": [
        "**Fitting the model**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b0kH6VYqz_Gc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "75b0cb47-0453-46c5-e53e-4e267c48d30f"
      },
      "source": [
        "history = model.fit(train_data_grayscale,\n",
        "                    train_targets,\n",
        "                    epochs=30,\n",
        "                    batch_size=128,\n",
        "                    validation_split=0.15,\n",
        "                    callbacks=[best_epoch_callback, early_stopping_callback])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/30\n",
            "485/487 [============================>.] - ETA: 0s - loss: 1.3759 - accuracy: 0.5547\n",
            "Epoch 00001: val_loss improved from inf to 1.04962, saving model to /home/marcin/Documents/Capstone_project/mlp_checkpoint_best\n",
            "WARNING:tensorflow:From /home/marcin/anaconda3/envs/tensorflow/lib/python3.7/site-packages/tensorflow/python/ops/resource_variable_ops.py:1817: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "If using Keras pass *_constraint arguments to layers.\n",
            "INFO:tensorflow:Assets written to: /home/marcin/Documents/Capstone_project/mlp_checkpoint_best/assets\n",
            "487/487 [==============================] - 1s 3ms/step - loss: 1.3747 - accuracy: 0.5551 - val_loss: 1.0496 - val_accuracy: 0.6744\n",
            "Epoch 2/30\n",
            "479/487 [============================>.] - ETA: 0s - loss: 0.9207 - accuracy: 0.7186\n",
            "Epoch 00002: val_loss improved from 1.04962 to 0.86651, saving model to /home/marcin/Documents/Capstone_project/mlp_checkpoint_best\n",
            "INFO:tensorflow:Assets written to: /home/marcin/Documents/Capstone_project/mlp_checkpoint_best/assets\n",
            "487/487 [==============================] - 1s 2ms/step - loss: 0.9197 - accuracy: 0.7189 - val_loss: 0.8665 - val_accuracy: 0.7410\n",
            "Epoch 3/30\n",
            "482/487 [============================>.] - ETA: 0s - loss: 0.8029 - accuracy: 0.7576\n",
            "Epoch 00003: val_loss improved from 0.86651 to 0.82173, saving model to /home/marcin/Documents/Capstone_project/mlp_checkpoint_best\n",
            "INFO:tensorflow:Assets written to: /home/marcin/Documents/Capstone_project/mlp_checkpoint_best/assets\n",
            "487/487 [==============================] - 1s 2ms/step - loss: 0.8026 - accuracy: 0.7576 - val_loss: 0.8217 - val_accuracy: 0.7513\n",
            "Epoch 4/30\n",
            "475/487 [============================>.] - ETA: 0s - loss: 0.7399 - accuracy: 0.7780\n",
            "Epoch 00004: val_loss improved from 0.82173 to 0.76369, saving model to /home/marcin/Documents/Capstone_project/mlp_checkpoint_best\n",
            "INFO:tensorflow:Assets written to: /home/marcin/Documents/Capstone_project/mlp_checkpoint_best/assets\n",
            "487/487 [==============================] - 1s 2ms/step - loss: 0.7394 - accuracy: 0.7780 - val_loss: 0.7637 - val_accuracy: 0.7669\n",
            "Epoch 5/30\n",
            "453/487 [==========================>...] - ETA: 0s - loss: 0.6881 - accuracy: 0.7952\n",
            "Epoch 00005: val_loss improved from 0.76369 to 0.72283, saving model to /home/marcin/Documents/Capstone_project/mlp_checkpoint_best\n",
            "INFO:tensorflow:Assets written to: /home/marcin/Documents/Capstone_project/mlp_checkpoint_best/assets\n",
            "487/487 [==============================] - 1s 2ms/step - loss: 0.6896 - accuracy: 0.7953 - val_loss: 0.7228 - val_accuracy: 0.7859\n",
            "Epoch 6/30\n",
            "463/487 [===========================>..] - ETA: 0s - loss: 0.6611 - accuracy: 0.8024\n",
            "Epoch 00006: val_loss did not improve from 0.72283\n",
            "487/487 [==============================] - 1s 2ms/step - loss: 0.6619 - accuracy: 0.8019 - val_loss: 0.7338 - val_accuracy: 0.7816\n",
            "Epoch 7/30\n",
            "463/487 [===========================>..] - ETA: 0s - loss: 0.6291 - accuracy: 0.8109\n",
            "Epoch 00007: val_loss did not improve from 0.72283\n",
            "487/487 [==============================] - 1s 2ms/step - loss: 0.6294 - accuracy: 0.8108 - val_loss: 0.7418 - val_accuracy: 0.7815\n",
            "Epoch 00007: early stopping\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yk2mH3Npz_Gh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f052fbd5-b8ab-4631-b788-1e11406a7b36"
      },
      "source": [
        "test_loss, test_acc = model.evaluate(test_data_grayscale, test_targets)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "814/814 [==============================] - 1s 1ms/step - loss: 0.8324 - accuracy: 0.7619\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w0JxuytckIiZ",
        "outputId": "974bce33-a359-4bb9-8096-524baeeaaebd"
      },
      "source": [
        "print('Test loss = {:.03f}\\nTest accuracy = {:.03f}'.format(test_loss, test_acc))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test loss = 0.832\n",
            "Test accuracy = 0.762\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yAMjdNRDKkwv",
        "outputId": "b0e3b1f9-7afe-409b-e0da-d20c316e310e"
      },
      "source": [
        "history.history.keys()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "FCKlSPCdHHNf",
        "outputId": "3a757e98-6b61-4ce4-9879-f3c460c39371"
      },
      "source": [
        "df = pd.DataFrame(\n",
        "    {'val_loss': history.history['val_loss'],\n",
        "     'loss': history.history['loss'],\n",
        "     'accuracy': history.history['accuracy'],\n",
        "     'val_accuracy': history.history['val_accuracy']},\n",
        "    index=range(1, 8),\n",
        "    )\n",
        "\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   val_loss      loss  accuracy  val_accuracy\n",
              "1  1.049616  1.374741  0.555149      0.674402\n",
              "2  0.866513  0.919726  0.718860      0.741014\n",
              "3  0.821733  0.802591  0.757628      0.751297\n",
              "4  0.763693  0.739411  0.777960      0.766949\n",
              "5  0.722825  0.689612  0.795336      0.785877"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>val_loss</th>\n",
              "      <th>loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>val_accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.049616</td>\n",
              "      <td>1.374741</td>\n",
              "      <td>0.555149</td>\n",
              "      <td>0.674402</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.866513</td>\n",
              "      <td>0.919726</td>\n",
              "      <td>0.718860</td>\n",
              "      <td>0.741014</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.821733</td>\n",
              "      <td>0.802591</td>\n",
              "      <td>0.757628</td>\n",
              "      <td>0.751297</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.763693</td>\n",
              "      <td>0.739411</td>\n",
              "      <td>0.777960</td>\n",
              "      <td>0.766949</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.722825</td>\n",
              "      <td>0.689612</td>\n",
              "      <td>0.795336</td>\n",
              "      <td>0.785877</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "2lV5RNLpMg7h",
        "outputId": "cc855ab6-6e93-402a-99e4-fc60584d059d"
      },
      "source": [
        "df[['loss', 'val_loss']].plot(grid=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<AxesSubplot:>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "5FzNXkdCNu4T",
        "outputId": "bb4671e4-e086-4019-fdda-85c867791484"
      },
      "source": [
        "df[['accuracy', 'val_accuracy']].plot(grid=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<AxesSubplot:>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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RIuLj4ykoKCAyMpKpU6fy8ssv8/jjj+NwOCgrKyMxMZHc3Fzy8/OJiIhgzZo1TJ8+vdnz1c1v33BWzebmzZ80aRJnzpxh165dfPnll+34jXUexhhO5ZdbwX40jy0n8uun1R3aI9K6YncOVAoPcdP/dsZAzgHriv3oejizFYzDCsGBt8Gg22HgVAhPaP+5gkIgorv1aq1ah/MvheKm/3Jo6n3hqctfHLVNzI5WT7gxMBS2dGm4iCY/1F/UNLWsHcvdcQzn2/GOEEjbgbtp0LfCiBEjKCkpISkpiZ49e/LAAw8wc+ZMUlNTGTt2LEOHDnXpOM3tN2LECH7yk59wyy23EBgYyMiRI1m2bBkvvvgiCxcu5NVXXyUwMJCXX36ZyZMn89RTTzFp0iT69et3zXMvXryYOXPmkJSUxHXXXcfJkycBmp03H+Ab3/gGe/bscekxiJ3VhZJKPj+e57ztMZ+zRVbzRlJMV746IpEbBiZw/YAEukW6caBSZQmcSL981V7inCOwx2i4cZEV7kkT2nZDgqcEBEJYnPVqLWOgqqyJvyIuvz93PIPeScl1O1y5b7PL3LXclW1pZvnV21/Mu4gn/kYRc8XJfENqaqrZsePKb7WDBw8ybNiwFvftjLNXesKMGTNYtGgRU6dObXYbV/9N7DK7YFllDa+sTudi115sPpbHofPW1LnRXYO5fkA8NwxM4MaBCaTEu3GgkjGQd8R51b4OTm+B2mqryaJ/mvOq/TaI6tnqQ9vl38Uu9YD21UVEdhpjUpta50Nf+8oXFBUVMXHiRMaMGXPNkO8scksq+MfBXNZn5LDpWB5VNbV0CTrNxL5x/Gh6L24cmMCIXtHufSRdVRmc/Cccc3akFmVay7sPh8nfscK99yQI1KcnKddo0HvQvn37mD9//hXLQkJC2Lp1q5dK1LKYmBiOHDni7WJ41bHcUtZn5LA+4zy7zxRhDPSO68o3J6UQX3mOf52V5v6BSvnHL98hc2oTOCohONy6ar9xEQycBjG9WzyMUk3xq6BvzV0pvmDUqFHs2bPH28XwCF9s8mur2lrD7jOFrMvIYX1GDicuWPe0j0qKZtFtg5k2PJGhPSIREdLTc90T8tUVcHrT5bb2guPW8vhB8JWHrTtkUq63OkGVaie/CfrQ0FDy8/OJj4/3q7C3I2MM+fn5hIaGersobVZR7WDzsTzWZ+TwycFc8korCQoQJg+I59vX9+W2YYnuG6xUp/C0szlmPZzcCNXlEBQKfW+CSY/AoNsgrr97z6kUfhT0ycnJZGVlceHChWtuV1FR4dcB1JAv1yU0NJTk5OSWN/QhReVVfHool3UHcth49ALlVQ4iQoJIG9KNacMTSRvSneiubmz3rqmCzC1Wc8yxT+DCIWt5TAqM+6bV1t73Rgh28xeKUo34TdAHBwfXj+i8lvT0dJeeY+oP7FQXbzlTUM76jBzWZZxn+6lCHLWGxKgQ7hmfxLThPbiufxwhQW5sb7+Yfbmt/UQ6VJVCQDD0vQHGf8sK9/iBOhJUdSi/CXqlXGGM4UD2RdZl5LDuwPn6WyAHJ0bw77cMYNrwREYlRbtvOl9HDWRtc97++AnkOCfbi0qGUXOsYO93M4REuOd8SrWBBr3ye9WOWraeKGB9xnnWZ+SQXVxBgEBqShw/+dowpg1PpG9CeMsHclVprtUUc3QdHP/UGrwjgdBnMtz2Myvcuw/Tq3blMzTolV8qqahmw5ELrM/I4dNDuZRU1BAaHMBNg7rx+LTBTB3anXh3PTqvohjObIPTnzN+72pIP2Ytj0iEYTOtWx8HTLHmYFHKB2nQK7+Rc7HCeX97DluO51PlqCUuvAvTR/Rg2vBEbhrUja5d3NDeXpoLpz+3OlJPfw45+8HUggRiIgfCrT+1rtoTR+kc7sovaNArn2WM4ahz8NK6jBz2nikCICU+jAevT2Ha8B5MSIlt36hUY6DwpDW1QObn1s+6e9qDukJyKtz8Q6tZJvkr7N6yg7Sb09pdN6U6kga98imOWsPO04X17e2n8ssBGNM7hh9+dQjThie2b8722lrIPXBlsJeet9aFxliBPuFB6HM99ByjD9RQtqBBr7zuUpWDfx693N6eX1ZFcKBw/YAEHr6pP9OGJ5IY1cbxBDVVkL37cqif+cJqcweI7GXdx54y2Qr2bkO1KUbZkga98or80kr+cciaLOyfRy9QUV1LZGgQtw7tzrThidwyuBuRoW0YvFRZYnWcZm6xgv3sDqhxPpQlfhAMn2WFespka+CS3hmjOgENetVhTuWV1Xem7jhdQK2BntGh3Jfam2nDezCxXxxdglp5RV2WdznUT2+2HhpvHCAB1hztqf9iNcf0mQwR3TxTMaV8nAa98qiswnLePVLFL3dv4EhOKWA9cemxWwdx+/BERvSKcr293Rhryt66u2Eyt1hztQMEhkDyV+CmH1ih3nsihNjjuQRKtZcGvfKIimoHf9pwgj+mH6PaUcvEfpE8NWM404Yn0jsuzLWD1NZa88PUta9nbrGeRQoQEg19JsGYedYsj73G6UyPSjVDg165lTGGdRk5PLMmg6zCS3x9dE9ujS3i3jsmt7yzoxrO7bWu1k9/bnWcXiq01kX0uNxpmjLZeghHgJvnhFfKpjToldscv1DK4tUH+OfRPAYnRrB8wSSuH5BAenp60ztUlUHW9su3OmbtsKbuBWu63qFfvxzssf2041SpNnIp6EVkOvAiEAi8Yox5rtH6HwIPNDjmMKCbMaZARE4BJYADqGnumYbKf5VW1vD7fxzltc0nCQ0K5KkZw5k/OYXgwEYdq+UFV7avn9sLtTWAQI+RMG7+5av2yESv1EUpO2ox6EUkEPgDMA3IAraLyGpjTEbdNsaY54HnndvPBBYZYwoaHGaKMSbPrSVXXmeM4f092fxy7UFySyr5RmoyP5o+lITwLtbdMHlHIO8Ig458BAeegAsHrR0Du0DSBLj+e1b7eu+JOk+MUh7kyhX9ROCYMeYEgIisBGYBGc1sPw9Y4Z7iKV91ILuYn7//JbmZh5mVUMS3x1STVLMWVlrhTkVR/baJgV2h3w0waraz43Q8BPvmA1WUsiNp6dmfIjIbmG6Medj5eT4wyRjzWBPbhmFd9Q+su6IXkZNAIWCAPxljljZznoXAQoDExMQJK1eubFOFSktLiYiwx9zfvlKXwJpywsrPElaeRVBJFsUXMomqOEs/OU+wOOq3q+wSS3lYEuVhyc5XEuVhvcmvDiEiMsqLNXAfX/k3cQe71MUu9YD21WXKlCk7m2sad+WKvqkesOa+HWYCmxs129xgjMkWke7AehE5ZIzZeNUBrS+ApQCpqakmLS3NhaJdLT09nbbu62s6tC7GWE9HyjsCeUfrm13IOwIl5+o3qyGA07WJVMcOxDF0NsE9h0LCYIgfSEjXGEKAWG/Ww8O0Lr7HLvUAz9XFlaDPAno3+JwMZDez7VwaNdsYY7KdP3NFZBVWU9BVQa86SE0lFJy4HOIXnD/zj1mPvasTEgUJg6D/FLKCknnjSDCf5cWQmDKUp+4ay9Ae9rhCV6ozcCXotwODRKQfcBYrzO9vvJGIRAO3AN9ssCwcCDDGlDjf3w783B0FVy0oL7jyqrzuKr3wlDW3ep3o3lag9/mm9TNhsPWKSCS3tJLnPjzEe1vP0iMqlJ/MHcaM0T3bPnOkUsorWgx6Y0yNiDwGfIx1e+VrxpgDIvKIc/0S56Z3A+uMMWUNdk8EVjmDIQhYboz5yJ0V6NRqHdaUAFc0tTjflze4ySkwxHogdY/RMHK2M8wHWcuaeJZptaOWNzed5LefHKWqppbvpA3g0SkDCQ/RYRdK+SOX/s81xqwF1jZatqTR5zeANxotOwGMaVcJlTWwKP/Y1YGef+zyzIwAYfFWiA/92uUr84RB1iyNLo4i3XQ0j8UfHOBYbilThnTjqZkj6OfO560qpTqcXqL5oktFsOk3jM7YCLvzoPjM5XUSYAV3wmDon2b97DbEmoI3PL7Np8wqLOfZNQf56MB5UuLDePXBVKYO00FLStmBBr2vqSiGv9wD2XsIDu8L/a6DhG9dvkKP6+/We9AbTj4WIMIPvzqEf72xH6HBOo+MUnahQe9LKkvgL7OtqQG+8RY7cyI8dtuYMYb1GTn8vMHkYz/52jB6xXT1yPmUUt6jQe8rKkth2Rw4uxPmvA7DZkBOukdOdfxCKT/7IIONRy5cMfmYUsqeNOh9QVUZLP8GnNkK975qPe7OA1yefEwpZSsa9N5WVQ7L77Nmc7znzzDyHrefotnJxyL0QR1KdQYa9N5UfQlWzoNTm+DuP1mTfrnZgexiFq8+wPZThYxJjuZP8ycwrk/jSQqUUnamQe8t1RWw8gE4sQHu+iOMuc+thy8qr+LX646wbOtpYsK68N/3jmLOhN4EBOioVqU6Gw16b6iphLfnw/F/wJ2/h7FXzSjRZo5aw8rtmbzw8WGKL1Xzrcl9WXTbYKLDgt12DqWUf9Gg72g1VfD2g3B0Hcz4LYz/ltsOvfN0IU+v3s/+sxeZ1C+On80aoZOPKaU06DuUoxreeQiOfAhfewFSH3LLYXNLKqzJx3ZZk4/9ft44nXxMKVVPg76jOKrhnX+BQ2tg+n/DxAXtPmS1o5Y3Pz+lk48ppa5JE6EjOGrgvYVwcDV89Zdw3SPtPqROPqaUcpUGvafVOuBvj8CB92Daz2Hyo+06XFZhOb/4+0E+3K+TjymlXKNB70m1Dvjbd2DfX2Hq03DD99t8qLrJx17ecAxBJx9TSrlOg95Tamth9Xfhy5Uw5adw0w/adBidfEwp1V4a9J5QWwtrvg97lsEtT8AtP2zTYc6V1vLg69t18jGlVLto0LubMfD3H8Cut+Cm/4C0J1p9CEet4fmPD/PnzZcIC6nWyceUUu2iQe9OxsDaH8LO1+GGx+HWn0Ib7mVfvfcsSzYc54ZeQbz4L2k6+ZhSql006N3FGPjoP2H7n2HyY3Db4jaFPMDyrZn0Swjn4VFGQ14p1W7aFuAOxsC6n8LWl2HSv8Ptz7Y55I/mlLD9VCHzJvbWka1KKbfQoG8vY+CTp2HLSzBxIUz/f20OeYDl2zLpEhjAveOT3VhIpVRnpkHfHsbAp8/A5hch9V/hjl+1K+Qrqh28uzOLr47sQbw22Sil3ESDvj3Sn4N//hrGP2hNUtbOppa1+85xsaKGeRN7u6mASimlQd92G34FG56Dsd+0phsOaP+vsq4TdnL/+PaXTymlnDTo22LjC/DZL2DMPLjzd24J+SM5Jew4rZ2wSin306BvrU2/tdrlR82BWX+AAPfMNbN8q9UJO3uCNtsopdxLg741Pn/JusNmxD1w1xK3hXxFtYP3dmUxfWQP4sK7uOWYSilVR4PeVV8sgXU/geGz4J4/Q6D7xpr9/cu6Ttg+bjumUkrV0aB3xbY/w0c/hqEz4N5X3RryYN073z8hnOv6x7n1uEopBS4GvYhMF5HDInJMRK6apUtEfigie5yv/SLiEJE4V/b1eTteg7X/AUO+BrNfh8Bgtx7+8PkSdp4uZN7EPtoJq5TyiBaDXkQCgT8AdwDDgXkiMrzhNsaY540xY40xY4H/BDYYYwpc2den7XoL1iyCQV+FOW9AkPvbz1fUjYSdoCNhlVKe4coV/UTgmDHmhDGmClgJzLrG9vOAFW3c13fsXgarvwcDb4NvvAVB7h+peqnKwbvaCauU8jBXGpuTgDMNPmcBk5raUETCgOnAY23YdyGwECAxMZH09HQXina10tLSNu9bJ/F8OkMP/ZbC2DHs77mQ2s1ftOt4zdl0tpqSihqGhxQ0WWZ31MUX2KUeoHXxRXapB3iuLq4EfVMNx6aZbWcCm40xBa3d1xizFFgKkJqaatLS0lwo2tXS09Np674AfPlX2PAi9LuJuHn/x81dwtp+rBb87o+b6d8tmH+7+5Ym2+fbXRcfYZd6gNbFF9mlHuC5urjSdJMFNBzFkwxkN7PtXC4327R2X+/b/y6sWgh9rod5K8GDIX/o/EV2ZRZxv3bCKqU8zJWg3w4MEpF+ItIFK8xXN95IRKKBW4D3W7uvT8h4H95dAL0nwf3/B13CPXq6FVt1OmKlVMdosenGGFMjIo8BHwOBwGvGmAMi8ohz/RLnpncD64wxZS3t6+5KtNvBNfDOv0ByKjzwVwiJ8OjpLlU5eG/3We4Y1YNY7YRVSnmYSyN/jDFrgbWNli1p9PkN4A1X9vUphz+Ev34beo6FB96BkEiPn3LNl9mUVNRwv46EVUp1gM49MvbIOnj7W9BjFMx/D0KjOuS0y7dlMqBbOBP76UhYpZTndd6gP/YJ/N83ofswZ8hHd8hpD567yO7MIh0Jq5TqMJ0z6I9/BisfgG6DYf7foGtsh516xbZMugRpJ6xSquN0vqA/uRFWzIO4ATD/fQjruOaTS1UOVu06y9dGaiesUqrjdK6gP7UZlt8HsX3hwdUQ3rGP7Pvgy2xKKmu4f1JKh55XKdW5dZ6gP70Fls2B6GRnyCd0eBGWb81kYPcIvtK345qKlFKqcwT9mW2wbDZE9YQHP4CI7h1ehIzsi+w5o52wSqmOZ/+gz9oB/3sPRCTCg2sgsodXinG5EzbJK+dXSnVe9g76s7uskA+Pt67ko3p6pRjlVTX8bfdZvj6qJzFh2gmrlOpY9g367D3wv3dB12jrSj7ae1fSa/aec3bC6khYpVTHs2fQn99nhXxIlBXyMb1b3MWTlm2zOmFTU7QTVinV8WwX9OGlp+DNOyE4zGquifXurYwHsovZe0anI1ZKeY+9gj73EGP2PmU99u/BDyCun7dLxIptmYQEBXCPdsIqpbzEPkFfXgBvzsRIgNVcEz/A2yVydsJmayesUsqrXJqm2C+ExcFNP2BvfiQTEwZ6uzQAfLA3m1LthFVKeZl9rugBrvt3ysN9Z7Kw5dvOMKh7BBO0E1Yp5UX2CnofUt8JO0k7YZVS3qVB7yHLtzo7Ycf5zl8YSqnOSYPeA8oqa3h/TzZfH92T6LBgbxdHKdXJadB7QF0n7APaCauU8gEa9B6wYlsmgxMjGN9HO2GVUt6nQe9m+88WszerWEfCKqV8hga9my13joS9WzthlVI+QoPejcoqa3h/91lmjO6lnbBKKZ+hQe9Gq/dmU1bl4P5J3p0tUymlGtKgd6MV2zIZkhipnbBKKZ+iQe8m+88W82VWsY6EVUr5HA16N1m2NZPQ4ADuGqfTESulfIsGvRuUVtaweo+zE7ardsIqpXyLS0EvItNF5LCIHBORJ5rZJk1E9ojIARHZ0GD5KRHZ51y3w10F9yWr91idsPMm6khYpZTvaXE+ehEJBP4ATAOygO0istoYk9Fgmxjgj8B0Y0ymiHRvdJgpxpg89xXbt6zYlsnQHpGM7xPj7aIopdRVXLminwgcM8acMMZUASuBWY22uR94zxiTCWCMyXVvMX3Xvqxi9p3VTlillO9yJeiTgDMNPmc5lzU0GIgVkXQR2Ski32qwzgDrnMsXtq+4vmf5NqsTdtZY7YRVSvkmVx4l2NRlqmniOBOAqUBXYIuIfGGMOQLcYIzJdjbnrBeRQ8aYjVedxPoSWAiQmJhIenp6K6pxWWlpaZv3ba1LNYZVO8v5So8gdm/d7Pbjd2RdPMku9QCtiy+ySz3Ac3VxJeizgIZDPZOB7Ca2yTPGlAFlIrIRGAMcMcZkg9WcIyKrsJqCrgp6Y8xSYClAamqqSUtLa2VVLOnp6bR139ZatvU0FY79LJo10SODpDqyLp5kl3qA1sUX2aUe4Lm6uNJ0sx0YJCL9RKQLMBdY3Wib94GbRCRIRMKAScBBEQkXkUgAEQkHbgf2u6/43lXXCTuud4y3i6KUUs1q8YreGFMjIo8BHwOBwGvGmAMi8ohz/RJjzEER+Qj4EqgFXjHG7BeR/sAqZydlELDcGPORpyrTkb7MKmL/2Yv8fNYI7YRVSvk0V5puMMasBdY2Wrak0efngecbLTuB1YRjOyu2ZdI1OFBHwiqlfJ6OjG2Dkopq3t+TzcwxPYkK1ZGwSinfpkHfBu/vyaZcR8IqpfyEBn0rGWNYvjWTYT2jGKudsEopP6BB30pfZhWTce4i90/srZ2wSim/oEHfSnWdsLO0E1Yp5Sc06FuhpKKa1XuzuXNML+2EVUr5DQ36VvhbXSfsJO2EVUr5Dw16F9V1wg7vGcWY5GhvF0cppVymQe+ivVnFHDx3kXk6HbFSys9o0LtoxVbnSNixvbxdFKWUahUNehdcbNAJG6mdsEopP6NB74L392RzqdrB/doJq5TyQxr0LajrhB3RK4rR2gmrlPJDGvQt2HOmyOqEnaidsEop/6RB34IV2zIJ6xLILO2EVUr5KQ36a7hYUc0He89pJ6xSyq9p0F/D+7vPaiesUsrvadA3wxjDsq2ZjEyKYnRyjLeLo5RSbaZB34zdZ4o4dL5EHy6ilPJ7GvTNWLG1rhNWpyNWSvk3DfomFF+q5oMvs5k1thcRIS49P10ppXyWBn0T3t9zlorqWu6fmOLtoiilVLtp0DdSNxJ2ZFIUo3QkrFLKBjToG9mVaXXC6tW8UsouNOgbWbEtk/AugdypI2GVUjahQd9A8aVq1nyZzZ1jk7QTVillGxr0Dfxtt9UJ+4COhFVK2YgGvVNdJ+yopGhGJmknrFLKPjTonXZlFnE4p0TntVFK2Y4GvdPyrVYn7Mwx2gmrlLIXl4JeRKaLyGEROSYiTzSzTZqI7BGRAyKyoTX7eltxudUJO2ucdsIqpeynxVQTkUDgD8A0IAvYLiKrjTEZDbaJAf4ITDfGZIpId1f39QWrdmdRWVPL/TqBmVLKhly5op8IHDPGnDDGVAErgVmNtrkfeM8YkwlgjMltxb5eZYxh+bZMRidrJ6xSyp5caadIAs40+JwFTGq0zWAgWETSgUjgRWPMWy7uC4CILAQWAiQmJpKenu5C0a5WWlraqn2PFjo4klPBQyO6tPmcntLauvgqu9QDtC6+yC71AM/VxZWgb+qJ2KaJ40wApgJdgS0i8oWL+1oLjVkKLAVITU01aWlpLhTtaunp6bRm39Vv7yEiJIf/+MYUwn2sfb61dfFVdqkHaF18kV3qAZ6riyvJlgX0bvA5GchuYps8Y0wZUCYiG4ExLu7rNcXl1fz9y3PMnpDscyGvlFLu4kob/XZgkIj0E5EuwFxgdaNt3gduEpEgEQnDap456OK+XvNeXSes3juvlLKxFi9jjTE1IvIY8DEQCLxmjDkgIo841y8xxhwUkY+AL4Fa4BVjzH6Apvb1UF1apW4k7JjkaEb00k5YpZR9udReYYxZC6xttGxJo8/PA8+7sq8v2Hm6kKO5pfz3vaO8XRSllPKoTjsydvnWTCJCgpgxWkfCKqXsrVMGfVF5FWv2neOucb20E1YpZXudMujf23WWqppa5ulIWKVUJ9Dpgr5uJOyY3jHaCauU6hQ6XdDvOF3IsdxSHtCreaVUJ9Hpgn751kwiQ4KYMaant4uilFIdolMFfVF5FX/fd467xiUR1kU7YZVSnUOnCvp3tRNWKdUJdZqgt0bCnmZs7xiG94rydnGUUqrDdJqg336qkOMXynReG6VUp9Npgn751tNWJ+xo7YRVSnUunSLoC8uqWLv/PHeP105YpVTn0ymC/t1dWdoJq5TqtGwf9HUjYcf1iWFYT+2EVUp1PrYP+m0nCzhxoUyv5pVSnZbtg375tkwiQ4OYqdMRK6U6KVsHfWFZFR/uO88945Lo2iXQ28VRSimvsHXQv7sriypHLfP03nmlVCdm26Cv64Qd3yeGoT20E1Yp1XnZNui3aiesUkoBNg765VutTlh9JqxSqrOzZdAXlFXx0X7thFVKKbBp0L+70+qEvX9SireLopRSXme7oDfGsGJbJhNSYhnSI9LbxVFKKa+zXdAfKqjlRJ52wiqlVB3bBX36mWqiQnU6YqWUqmOroM8vrWRnjoN7xicTGqydsEopBTYL+nd3ZVFj0KdIKaVUA7YJeqsT9gyDYgIYnKidsEopVcc2j1sqr3IwqV8csVW53i6KUkr5FJeu6EVkuogcFpFjIvJEE+vTRKRYRPY4X081WHdKRPY5l+9wZ+EbCg8J4rl7RzOpp22+u5RSyi1aTEURCQT+AEwDsoDtIrLaGJPRaNN/GmNmNHOYKcaYvPYVVSmlVFu4ckU/EThmjDlhjKkCVgKzPFsspZRS7iLGmGtvIDIbmG6Medj5eT4wyRjzWINt0oB3sa74s4H/MMYccK47CRQCBviTMWZpM+dZCCwESExMnLBy5co2Vai0tJSIiIg27etr7FIXu9QDtC6+yC71gPbVZcqUKTuNMalNrjTGXPMFzAFeafB5PvD7RttEARHO918DjjZY18v5szuwF7i5pXNOmDDBtNVnn33W5n19jV3qYpd6GKN18UV2qYcx7asLsMM0k6muNN1kAb0bfE7Gumpv+GVx0RhT6ny/FggWkQTn52znz1xgFVZTkFJKqQ7iStBvBwaJSD8R6QLMBVY33EBEeoiION9PdB43X0TCRSTSuTwcuB3Y784KKKWUurYW77oxxtSIyGPAx0Ag8Jox5oCIPOJcvwSYDfy7iNQAl4C5xhgjIonAKud3QBCw3BjzkYfqopRSqgku3XTubI5Z22jZkgbvXwJeamK/E8CYdpZRKaVUO7R41403iMgF4HQbd08A7HLPvl3qYpd6gNbFF9mlHtC+uqQYY7o1tcIng749RGSHae4WIz9jl7rYpR6gdfFFdqkHeK4utpnUTCmlVNM06JVSyubsGPRNjrz1U3api13qAVoXX2SXeoCH6mK7NnqllFJXsuMVvVJKqQY06JVSyuZsE/Qi8pqI5IqIX0+xICK9ReQzETkoIgdE5PveLlNbiUioiGwTkb3OuvzM22VqDxEJFJHdIrLG22Vpj456GFBHEJEYEXlHRA45/5+Z7O0ytYWIDGnw4KY9InJRRB532/Ht0kYvIjcDpcBbxpiR3i5PW4lIT6CnMWaXc56gncBd5uoHvfg85/xH4caYUhEJBjYB3zfGfOHlorWJiPwASAWiTPMP2fF5InIKSDU2eBiQiLyJ9dCjV5xzcYUZY4q8XKx2cT7s6SzWdPBtHTh6Bdtc0RtjNgIF3i5HexljzhljdjnflwAHgSTvlqptnLOnljo/BjtffnllISLJwNeBV7xdFmURkSjgZuBVAGNMlb+HvNNU4Li7Qh5sFPR2JCJ9gXHAVi8Xpc2czR17gFxgvTHGX+vyW+BHQK2Xy+EOBlgnIjudD/zxV/2BC8Drzia1V5yz5Pq7ucAKdx5Qg95HiUgE1lO7HjfGXPR2edrKGOMwxozFeo7BRBHxu2Y1EZkB5Bpjdnq7LG5ygzFmPHAH8Kiz2dMfBQHjgZeNMeOAMuAJ7xapfZzNT3cCf3XncTXofZCzPftdYJkx5j1vl8cdnH9SpwPTvVuSNrkBuNPZtr0SuFVE/uLdIrWdjR4GlAVkNfgr8R2s4PdndwC7jDE57jyoBr2PcXZgvgocNMb8xtvlaQ8R6SYiMc73XYHbgENeLVQbGGP+0xiTbIzpi/Vn9afGmG96uVhtYqeHARljzgNnRGSIc9FUwO9uWmhkHm5utgEX56P3ByKyAkgDEkQkC3jaGPOqd0vVJjdgPZd3n7NtG+BJ5zMB/E1P4E3nXQQBwNvGGL++NdEG7PYwoO8Cy5xNHieAh7xcnjYTkTBgGvBvbj+2XW6vVEop1TRtulFKKZvToFdKKZvToFdKKZvToFdKKZvToFdKKZvToFdKKZvToFdKKZv7//MlEI0V0AQ1AAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ncPtDtCLz_Gg"
      },
      "source": [
        "## 3. CNN neural network classifier\n",
        "* Build a CNN classifier model using the Sequential API. Your model should use the Conv2D, MaxPool2D, BatchNormalization, Flatten, Dense and Dropout layers. The final layer should again have a 10-way softmax output. \n",
        "* You should design and build the model yourself. Feel free to experiment with different CNN architectures. _Hint: to achieve a reasonable accuracy you won't need to use more than 2 or 3 convolutional layers and 2 fully connected layers.)_\n",
        "* The CNN model should use fewer trainable parameters than your MLP model.\n",
        "* Compile and train the model (we recommend a maximum of 30 epochs), making use of both training and validation sets during the training run.\n",
        "* Your model should track at least one appropriate metric, and use at least two callbacks during training, one of which should be a ModelCheckpoint callback.\n",
        "* You should aim to beat the MLP model performance with fewer parameters!\n",
        "* Plot the learning curves for loss vs epoch and accuracy vs epoch for both training and validation sets.\n",
        "* Compute and display the loss and accuracy of the trained model on the test set."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uYO1hzVMG-vp"
      },
      "source": [
        "**Model structure and compilation**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jDUgbpVwVxnX",
        "outputId": "313d3cc1-3794-4d4b-80e3-27565430709a"
      },
      "source": [
        "print('input_shape =', train_data_grayscale[1].shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "input_shape = (32, 32, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lbgRgZ5cz_Gn"
      },
      "source": [
        "def get_cnn_model(input_shape=train_data_grayscale[1].shape):\n",
        "  model = tf.keras.models.Sequential([\n",
        "                                      \n",
        "                      Conv2D(16, (3, 3), input_shape=(input_shape), \n",
        "                             activation='relu',\n",
        "                             bias_initializer='zeros'),\n",
        "\n",
        "                      MaxPooling2D((2, 2)),\n",
        "\n",
        "                      BatchNormalization(),\n",
        "\n",
        "                      Conv2D(32, (3,3), activation='relu'),\n",
        "\n",
        "                      MaxPooling2D((2, 2)),\n",
        "\n",
        "                      BatchNormalization(),\n",
        "\n",
        "                      Conv2D(64, (3,3), activation='relu'),\n",
        "\n",
        "                      BatchNormalization(),\n",
        "\n",
        "                      Flatten(),\n",
        "\n",
        "                      Dense(32, activation='relu', kernel_regularizer='l2'),\n",
        "\n",
        "                      Dense(10, activation='softmax'),\n",
        "  ])\n",
        "\n",
        "  return model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VytQECDVz_Gv"
      },
      "source": [
        "def compile_model(model, lr=0.0001):\n",
        "  model.compile(optimizer=tf.keras.optimizers.RMSprop(learning_rate=lr),\n",
        "                loss='categorical_crossentropy',\n",
        "                metrics=['accuracy'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8OH2OBqPHdgD"
      },
      "source": [
        "**Callbacks**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rDhdleawR67r"
      },
      "source": [
        "def get_best_epoch_callback_cnn():\n",
        "  path='/home/marcin/Documents/Capstone_project/cnn_checkpoint_best'\n",
        "  \n",
        "  callback = ModelCheckpoint(path,\n",
        "                             verbose=1,\n",
        "                             save_best_only=True,\n",
        "                             save_weights_only=False)\n",
        "  return callback\n",
        "\n",
        "\n",
        "def get_early_stopping_callback_cnn(patience=2, monitor='val_loss'):\n",
        "  callback = EarlyStopping(monitor=monitor,\n",
        "                           patience=patience,\n",
        "                           verbose=1)\n",
        "  return callback"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kJd52nE58y9V"
      },
      "source": [
        "cnn_best_epoch_callback = get_best_epoch_callback_cnn()\n",
        "cnn_early_stopping_callback = get_early_stopping_callback_cnn(3)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8MQARJcCHuoh"
      },
      "source": [
        "**Fitting the model**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nkmS2vV2z_Gs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3a144fc4-0275-46bc-e3cd-03152789625b"
      },
      "source": [
        "model = get_cnn_model(train_data_grayscale[1].shape)\n",
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_3 (Conv2D)            (None, 30, 30, 16)        160       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 15, 15, 16)        0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_3 (Batch (None, 15, 15, 16)        64        \n",
            "_________________________________________________________________\n",
            "conv2d_4 (Conv2D)            (None, 13, 13, 32)        4640      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2 (None, 6, 6, 32)          0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_4 (Batch (None, 6, 6, 32)          128       \n",
            "_________________________________________________________________\n",
            "conv2d_5 (Conv2D)            (None, 4, 4, 64)          18496     \n",
            "_________________________________________________________________\n",
            "batch_normalization_5 (Batch (None, 4, 4, 64)          256       \n",
            "_________________________________________________________________\n",
            "flatten_2 (Flatten)          (None, 1024)              0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 32)                32800     \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 10)                330       \n",
            "=================================================================\n",
            "Total params: 56,874\n",
            "Trainable params: 56,650\n",
            "Non-trainable params: 224\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "60mJypwQz_Gx"
      },
      "source": [
        "compile_model(model)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w2v80qosz_G0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5a06bccd-7195-41cf-b786-041412cf6fa3"
      },
      "source": [
        "epochs=30\n",
        "\n",
        "history = model.fit(train_data_grayscale,\n",
        "                    train_targets,\n",
        "                    epochs=epochs,\n",
        "                    batch_size=128,\n",
        "                    validation_split=0.15,\n",
        "                    callbacks=[cnn_best_epoch_callback, cnn_early_stopping_callback])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/30\n",
            "480/487 [============================>.] - ETA: 0s - loss: 1.2085 - accuracy: 0.7786\n",
            "Epoch 00001: val_loss improved from 1.56558 to 1.05020, saving model to /home/marcin/Documents/Capstone_project/cnn_checkpoint_best\n",
            "INFO:tensorflow:Assets written to: /home/marcin/Documents/Capstone_project/cnn_checkpoint_best/assets\n",
            "487/487 [==============================] - 3s 6ms/step - loss: 1.2063 - accuracy: 0.7792 - val_loss: 1.0502 - val_accuracy: 0.8182\n",
            "Epoch 2/30\n",
            "472/487 [============================>.] - ETA: 0s - loss: 0.9371 - accuracy: 0.8372\n",
            "Epoch 00002: val_loss improved from 1.05020 to 0.86046, saving model to /home/marcin/Documents/Capstone_project/cnn_checkpoint_best\n",
            "INFO:tensorflow:Assets written to: /home/marcin/Documents/Capstone_project/cnn_checkpoint_best/assets\n",
            "487/487 [==============================] - 3s 6ms/step - loss: 0.9342 - accuracy: 0.8376 - val_loss: 0.8605 - val_accuracy: 0.8488\n",
            "Epoch 3/30\n",
            "477/487 [============================>.] - ETA: 0s - loss: 0.7820 - accuracy: 0.8615\n",
            "Epoch 00003: val_loss improved from 0.86046 to 0.74323, saving model to /home/marcin/Documents/Capstone_project/cnn_checkpoint_best\n",
            "INFO:tensorflow:Assets written to: /home/marcin/Documents/Capstone_project/cnn_checkpoint_best/assets\n",
            "487/487 [==============================] - 3s 6ms/step - loss: 0.7815 - accuracy: 0.8613 - val_loss: 0.7432 - val_accuracy: 0.8627\n",
            "Epoch 4/30\n",
            "480/487 [============================>.] - ETA: 0s - loss: 0.6778 - accuracy: 0.8751\n",
            "Epoch 00004: val_loss improved from 0.74323 to 0.66292, saving model to /home/marcin/Documents/Capstone_project/cnn_checkpoint_best\n",
            "INFO:tensorflow:Assets written to: /home/marcin/Documents/Capstone_project/cnn_checkpoint_best/assets\n",
            "487/487 [==============================] - 3s 6ms/step - loss: 0.6766 - accuracy: 0.8753 - val_loss: 0.6629 - val_accuracy: 0.8726\n",
            "Epoch 5/30\n",
            "476/487 [============================>.] - ETA: 0s - loss: 0.5995 - accuracy: 0.8854\n",
            "Epoch 00005: val_loss improved from 0.66292 to 0.60230, saving model to /home/marcin/Documents/Capstone_project/cnn_checkpoint_best\n",
            "INFO:tensorflow:Assets written to: /home/marcin/Documents/Capstone_project/cnn_checkpoint_best/assets\n",
            "487/487 [==============================] - 3s 6ms/step - loss: 0.6000 - accuracy: 0.8852 - val_loss: 0.6023 - val_accuracy: 0.8792\n",
            "Epoch 6/30\n",
            "479/487 [============================>.] - ETA: 0s - loss: 0.5420 - accuracy: 0.8926\n",
            "Epoch 00006: val_loss improved from 0.60230 to 0.55425, saving model to /home/marcin/Documents/Capstone_project/cnn_checkpoint_best\n",
            "INFO:tensorflow:Assets written to: /home/marcin/Documents/Capstone_project/cnn_checkpoint_best/assets\n",
            "487/487 [==============================] - 3s 6ms/step - loss: 0.5420 - accuracy: 0.8927 - val_loss: 0.5542 - val_accuracy: 0.8857\n",
            "Epoch 7/30\n",
            "479/487 [============================>.] - ETA: 0s - loss: 0.4973 - accuracy: 0.8986\n",
            "Epoch 00007: val_loss improved from 0.55425 to 0.51523, saving model to /home/marcin/Documents/Capstone_project/cnn_checkpoint_best\n",
            "INFO:tensorflow:Assets written to: /home/marcin/Documents/Capstone_project/cnn_checkpoint_best/assets\n",
            "487/487 [==============================] - 3s 6ms/step - loss: 0.4972 - accuracy: 0.8986 - val_loss: 0.5152 - val_accuracy: 0.8891\n",
            "Epoch 8/30\n",
            "481/487 [============================>.] - ETA: 0s - loss: 0.4612 - accuracy: 0.9037\n",
            "Epoch 00008: val_loss improved from 0.51523 to 0.48582, saving model to /home/marcin/Documents/Capstone_project/cnn_checkpoint_best\n",
            "INFO:tensorflow:Assets written to: /home/marcin/Documents/Capstone_project/cnn_checkpoint_best/assets\n",
            "487/487 [==============================] - 3s 6ms/step - loss: 0.4612 - accuracy: 0.9036 - val_loss: 0.4858 - val_accuracy: 0.8931\n",
            "Epoch 9/30\n",
            "486/487 [============================>.] - ETA: 0s - loss: 0.4321 - accuracy: 0.9082\n",
            "Epoch 00009: val_loss improved from 0.48582 to 0.46522, saving model to /home/marcin/Documents/Capstone_project/cnn_checkpoint_best\n",
            "INFO:tensorflow:Assets written to: /home/marcin/Documents/Capstone_project/cnn_checkpoint_best/assets\n",
            "487/487 [==============================] - 3s 7ms/step - loss: 0.4322 - accuracy: 0.9081 - val_loss: 0.4652 - val_accuracy: 0.8953\n",
            "Epoch 10/30\n",
            "472/487 [============================>.] - ETA: 0s - loss: 0.4091 - accuracy: 0.9126\n",
            "Epoch 00010: val_loss improved from 0.46522 to 0.44529, saving model to /home/marcin/Documents/Capstone_project/cnn_checkpoint_best\n",
            "INFO:tensorflow:Assets written to: /home/marcin/Documents/Capstone_project/cnn_checkpoint_best/assets\n",
            "487/487 [==============================] - 3s 6ms/step - loss: 0.4084 - accuracy: 0.9127 - val_loss: 0.4453 - val_accuracy: 0.8980\n",
            "Epoch 11/30\n",
            "486/487 [============================>.] - ETA: 0s - loss: 0.3880 - accuracy: 0.9153\n",
            "Epoch 00011: val_loss improved from 0.44529 to 0.42932, saving model to /home/marcin/Documents/Capstone_project/cnn_checkpoint_best\n",
            "INFO:tensorflow:Assets written to: /home/marcin/Documents/Capstone_project/cnn_checkpoint_best/assets\n",
            "487/487 [==============================] - 3s 6ms/step - loss: 0.3879 - accuracy: 0.9153 - val_loss: 0.4293 - val_accuracy: 0.9001\n",
            "Epoch 12/30\n",
            "483/487 [============================>.] - ETA: 0s - loss: 0.3707 - accuracy: 0.9184\n",
            "Epoch 00012: val_loss improved from 0.42932 to 0.41675, saving model to /home/marcin/Documents/Capstone_project/cnn_checkpoint_best\n",
            "INFO:tensorflow:Assets written to: /home/marcin/Documents/Capstone_project/cnn_checkpoint_best/assets\n",
            "487/487 [==============================] - 3s 6ms/step - loss: 0.3707 - accuracy: 0.9184 - val_loss: 0.4167 - val_accuracy: 0.9013\n",
            "Epoch 13/30\n",
            "477/487 [============================>.] - ETA: 0s - loss: 0.3556 - accuracy: 0.9218\n",
            "Epoch 00013: val_loss improved from 0.41675 to 0.40844, saving model to /home/marcin/Documents/Capstone_project/cnn_checkpoint_best\n",
            "INFO:tensorflow:Assets written to: /home/marcin/Documents/Capstone_project/cnn_checkpoint_best/assets\n",
            "487/487 [==============================] - 3s 6ms/step - loss: 0.3551 - accuracy: 0.9220 - val_loss: 0.4084 - val_accuracy: 0.9028\n",
            "Epoch 14/30\n",
            "478/487 [============================>.] - ETA: 0s - loss: 0.3425 - accuracy: 0.9238\n",
            "Epoch 00014: val_loss improved from 0.40844 to 0.40068, saving model to /home/marcin/Documents/Capstone_project/cnn_checkpoint_best\n",
            "INFO:tensorflow:Assets written to: /home/marcin/Documents/Capstone_project/cnn_checkpoint_best/assets\n",
            "487/487 [==============================] - 3s 6ms/step - loss: 0.3421 - accuracy: 0.9239 - val_loss: 0.4007 - val_accuracy: 0.9041\n",
            "Epoch 15/30\n",
            "481/487 [============================>.] - ETA: 0s - loss: 0.3309 - accuracy: 0.9271\n",
            "Epoch 00015: val_loss improved from 0.40068 to 0.39145, saving model to /home/marcin/Documents/Capstone_project/cnn_checkpoint_best\n",
            "INFO:tensorflow:Assets written to: /home/marcin/Documents/Capstone_project/cnn_checkpoint_best/assets\n",
            "487/487 [==============================] - 3s 7ms/step - loss: 0.3308 - accuracy: 0.9271 - val_loss: 0.3915 - val_accuracy: 0.9045\n",
            "Epoch 16/30\n",
            "485/487 [============================>.] - ETA: 0s - loss: 0.3203 - accuracy: 0.9295\n",
            "Epoch 00016: val_loss improved from 0.39145 to 0.38659, saving model to /home/marcin/Documents/Capstone_project/cnn_checkpoint_best\n",
            "INFO:tensorflow:Assets written to: /home/marcin/Documents/Capstone_project/cnn_checkpoint_best/assets\n",
            "487/487 [==============================] - 3s 6ms/step - loss: 0.3204 - accuracy: 0.9295 - val_loss: 0.3866 - val_accuracy: 0.9054\n",
            "Epoch 17/30\n",
            "480/487 [============================>.] - ETA: 0s - loss: 0.3108 - accuracy: 0.9320\n",
            "Epoch 00017: val_loss improved from 0.38659 to 0.38075, saving model to /home/marcin/Documents/Capstone_project/cnn_checkpoint_best\n",
            "INFO:tensorflow:Assets written to: /home/marcin/Documents/Capstone_project/cnn_checkpoint_best/assets\n",
            "487/487 [==============================] - 3s 6ms/step - loss: 0.3108 - accuracy: 0.9319 - val_loss: 0.3808 - val_accuracy: 0.9063\n",
            "Epoch 18/30\n",
            "480/487 [============================>.] - ETA: 0s - loss: 0.3026 - accuracy: 0.9345\n",
            "Epoch 00018: val_loss improved from 0.38075 to 0.37608, saving model to /home/marcin/Documents/Capstone_project/cnn_checkpoint_best\n",
            "INFO:tensorflow:Assets written to: /home/marcin/Documents/Capstone_project/cnn_checkpoint_best/assets\n",
            "487/487 [==============================] - 3s 6ms/step - loss: 0.3028 - accuracy: 0.9343 - val_loss: 0.3761 - val_accuracy: 0.9060\n",
            "Epoch 19/30\n",
            "479/487 [============================>.] - ETA: 0s - loss: 0.2945 - accuracy: 0.9352\n",
            "Epoch 00019: val_loss did not improve from 0.37608\n",
            "487/487 [==============================] - 2s 4ms/step - loss: 0.2942 - accuracy: 0.9353 - val_loss: 0.3788 - val_accuracy: 0.9063\n",
            "Epoch 20/30\n",
            "478/487 [============================>.] - ETA: 0s - loss: 0.2875 - accuracy: 0.9377\n",
            "Epoch 00020: val_loss improved from 0.37608 to 0.36650, saving model to /home/marcin/Documents/Capstone_project/cnn_checkpoint_best\n",
            "INFO:tensorflow:Assets written to: /home/marcin/Documents/Capstone_project/cnn_checkpoint_best/assets\n",
            "487/487 [==============================] - 3s 6ms/step - loss: 0.2875 - accuracy: 0.9377 - val_loss: 0.3665 - val_accuracy: 0.9085\n",
            "Epoch 21/30\n",
            "481/487 [============================>.] - ETA: 0s - loss: 0.2789 - accuracy: 0.9396\n",
            "Epoch 00021: val_loss improved from 0.36650 to 0.36359, saving model to /home/marcin/Documents/Capstone_project/cnn_checkpoint_best\n",
            "INFO:tensorflow:Assets written to: /home/marcin/Documents/Capstone_project/cnn_checkpoint_best/assets\n",
            "487/487 [==============================] - 3s 6ms/step - loss: 0.2800 - accuracy: 0.9393 - val_loss: 0.3636 - val_accuracy: 0.9103\n",
            "Epoch 22/30\n",
            "478/487 [============================>.] - ETA: 0s - loss: 0.2725 - accuracy: 0.9411\n",
            "Epoch 00022: val_loss improved from 0.36359 to 0.36314, saving model to /home/marcin/Documents/Capstone_project/cnn_checkpoint_best\n",
            "INFO:tensorflow:Assets written to: /home/marcin/Documents/Capstone_project/cnn_checkpoint_best/assets\n",
            "487/487 [==============================] - 3s 7ms/step - loss: 0.2731 - accuracy: 0.9409 - val_loss: 0.3631 - val_accuracy: 0.9095\n",
            "Epoch 23/30\n",
            "480/487 [============================>.] - ETA: 0s - loss: 0.2670 - accuracy: 0.9432\n",
            "Epoch 00023: val_loss improved from 0.36314 to 0.36169, saving model to /home/marcin/Documents/Capstone_project/cnn_checkpoint_best\n",
            "INFO:tensorflow:Assets written to: /home/marcin/Documents/Capstone_project/cnn_checkpoint_best/assets\n",
            "487/487 [==============================] - 3s 6ms/step - loss: 0.2669 - accuracy: 0.9433 - val_loss: 0.3617 - val_accuracy: 0.9096\n",
            "Epoch 24/30\n",
            "479/487 [============================>.] - ETA: 0s - loss: 0.2609 - accuracy: 0.9449\n",
            "Epoch 00024: val_loss improved from 0.36169 to 0.36018, saving model to /home/marcin/Documents/Capstone_project/cnn_checkpoint_best\n",
            "INFO:tensorflow:Assets written to: /home/marcin/Documents/Capstone_project/cnn_checkpoint_best/assets\n",
            "487/487 [==============================] - 3s 6ms/step - loss: 0.2612 - accuracy: 0.9448 - val_loss: 0.3602 - val_accuracy: 0.9109\n",
            "Epoch 25/30\n",
            "480/487 [============================>.] - ETA: 0s - loss: 0.2558 - accuracy: 0.9462\n",
            "Epoch 00025: val_loss improved from 0.36018 to 0.35918, saving model to /home/marcin/Documents/Capstone_project/cnn_checkpoint_best\n",
            "INFO:tensorflow:Assets written to: /home/marcin/Documents/Capstone_project/cnn_checkpoint_best/assets\n",
            "487/487 [==============================] - 3s 6ms/step - loss: 0.2559 - accuracy: 0.9462 - val_loss: 0.3592 - val_accuracy: 0.9102\n",
            "Epoch 26/30\n",
            "479/487 [============================>.] - ETA: 0s - loss: 0.2507 - accuracy: 0.9474\n",
            "Epoch 00026: val_loss improved from 0.35918 to 0.35439, saving model to /home/marcin/Documents/Capstone_project/cnn_checkpoint_best\n",
            "INFO:tensorflow:Assets written to: /home/marcin/Documents/Capstone_project/cnn_checkpoint_best/assets\n",
            "487/487 [==============================] - 3s 6ms/step - loss: 0.2505 - accuracy: 0.9473 - val_loss: 0.3544 - val_accuracy: 0.9127\n",
            "Epoch 27/30\n",
            "479/487 [============================>.] - ETA: 0s - loss: 0.2449 - accuracy: 0.9487\n",
            "Epoch 00027: val_loss did not improve from 0.35439\n",
            "487/487 [==============================] - 2s 4ms/step - loss: 0.2452 - accuracy: 0.9487 - val_loss: 0.3572 - val_accuracy: 0.9096\n",
            "Epoch 28/30\n",
            "481/487 [============================>.] - ETA: 0s - loss: 0.2399 - accuracy: 0.9509\n",
            "Epoch 00028: val_loss improved from 0.35439 to 0.35296, saving model to /home/marcin/Documents/Capstone_project/cnn_checkpoint_best\n",
            "INFO:tensorflow:Assets written to: /home/marcin/Documents/Capstone_project/cnn_checkpoint_best/assets\n",
            "487/487 [==============================] - 3s 6ms/step - loss: 0.2402 - accuracy: 0.9507 - val_loss: 0.3530 - val_accuracy: 0.9112\n",
            "Epoch 29/30\n",
            "480/487 [============================>.] - ETA: 0s - loss: 0.2357 - accuracy: 0.9515\n",
            "Epoch 00029: val_loss did not improve from 0.35296\n",
            "487/487 [==============================] - 2s 4ms/step - loss: 0.2358 - accuracy: 0.9516 - val_loss: 0.3532 - val_accuracy: 0.9113\n",
            "Epoch 30/30\n",
            "477/487 [============================>.] - ETA: 0s - loss: 0.2314 - accuracy: 0.9521\n",
            "Epoch 00030: val_loss did not improve from 0.35296\n",
            "487/487 [==============================] - 2s 4ms/step - loss: 0.2314 - accuracy: 0.9521 - val_loss: 0.3563 - val_accuracy: 0.9110\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MYVKCp9gH668"
      },
      "source": [
        "**Graphs**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "GSGl0Rb4GKEd",
        "outputId": "0a9b73e0-331c-462b-d3c1-7b8480ca9281"
      },
      "source": [
        "\n",
        "df = pd.DataFrame(\n",
        "    {'val_loss': history.history['val_loss'],\n",
        "     'loss': history.history['loss'],\n",
        "     'accuracy': history.history['accuracy'],\n",
        "     'val_accuracy': history.history['val_accuracy']},\n",
        "    index=range(1, 30 + 1),\n",
        "    )\n",
        "\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   val_loss      loss  accuracy  val_accuracy\n",
              "1  1.050195  1.206298  0.779180      0.818182\n",
              "2  0.860463  0.934195  0.837605      0.848758\n",
              "3  0.743228  0.781543  0.861293      0.862681\n",
              "4  0.662919  0.676608  0.875313      0.872600\n",
              "5  0.602303  0.600035  0.885174      0.879152"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>val_loss</th>\n",
              "      <th>loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>val_accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.050195</td>\n",
              "      <td>1.206298</td>\n",
              "      <td>0.779180</td>\n",
              "      <td>0.818182</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.860463</td>\n",
              "      <td>0.934195</td>\n",
              "      <td>0.837605</td>\n",
              "      <td>0.848758</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.743228</td>\n",
              "      <td>0.781543</td>\n",
              "      <td>0.861293</td>\n",
              "      <td>0.862681</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.662919</td>\n",
              "      <td>0.676608</td>\n",
              "      <td>0.875313</td>\n",
              "      <td>0.872600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.602303</td>\n",
              "      <td>0.600035</td>\n",
              "      <td>0.885174</td>\n",
              "      <td>0.879152</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 562
        },
        "id": "7AqcqFakGgWd",
        "outputId": "18c7c121-7eb6-408b-ea2e-501cae118905"
      },
      "source": [
        "df[['loss', 'val_loss']].plot(grid=True, ylim=(0, 2), title='Losses vs epochs')\n",
        "df[['accuracy', 'val_accuracy']].plot(grid=True, ylim=(0.7, 1), title='Accuracies vs epochs')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<AxesSubplot:title={'center':'Accuracies vs epochs'}>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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3VXWMO9wNICI+4BHgPGAEMF1ERrSkscYYY45cs4leVZcAe45i2+OAjaqar6qVwFxgylFsxxhjTAtINA/5F5EBwL9UdWQDdROBF4ECoBD4uaquFZGpwGRVvc5dbgYwXlVvauQzZgGzALKyssbOnTu3ri4UCpGcnHxEgbV3XovJa/GA92LyWjzgvZhaEs+kSZNWqGpOQ3Wt8Tz6lUB/VQ2JyPnAK8AQoKH3gTW6V1HV2cBsgJycHJ04cWJdXW5uLpHzXuC1mLwWD3gvJq/FA96Lqa3iafFVN6paoqohd3oBEBCR7jhH+H0jFs3GOeI3xhhzDLU40YtIT3Hf5isi49xt7gaWAUNEZKCIxAPTgPkt/TxjjDFHptmuGxF5DpgIdBeRAuB3QABAVR8HpgI3ikgYOABMU6fjPywiNwGLAB8wR1XXtkkUxhhjGtVsolfV6c3UPww83EjdAmDB0TXNGGNMa7A7Y40xxuMs0RtjjMdZojfGGI+zRG+MMR5nid4YYzzOEr0xxnicJXpjjPE4S/TGGONxluiNMcbjLNEbY4zHWaI3xhiPs0RvjDEeZ4neGGM8zhK9McZ4nCV6Y4zxOEv0xhjjcZbojTHG4yzRG2OMxzWb6EVkjogUichnjdRfISKfusP7IjI6om6TiKwRkVUisrw1G26MMSY60RzRPwlMbqL+K+BMVT0R+AMwu179JFUdo6o5R9dEY4wxLRHNy8GXiMiAJurfj5j9EMhuhXYZY4xpJaKqzS/kJPp/qerIZpb7OTBMVa9z578C9gIK/FlV6x/tR647C5gFkJWVNXbu3Ll1daFQiOTk5Gbb2ZF4LSavxQPei8lr8YD3YmpJPJMmTVrRaM+JqjY7AAOAz5pZZhKQB2RElPV2x5nAamBCNJ83duxYjbR48WL1Gq/F5LV4VL0Xk9fiUfVeTC2JB1iujeTUVrnqRkROBP4KTFHV3RE7kUJ3XAS8DIxrjc8zxhgTvRYnehHpB7wEzFDVDRHlSSKSUjsNnAM0eOVOaykuq2R3qKItP8IYYzqcaC6vfA74ABgqIgUicq2I3CAiN7iL3AlkAI/Wu4wyC1gqIquBj4HXVXVhG8QAQFllmG/c9zaz381vq48wxpgOKZqrbqY3U38dcF0D5fnA6MPXaBuJ8X5OHZTBa6sK+eW5w4iLk2P10cYY06556s7YKWN6U7ivnI837Yl1U4wxpt3wVKL/9ogsEuN9vLqqMNZNMcaYdsNTiT4x3s85I7JYsGYbleGaWDfHGGPaBU8leoApJ/Vh34EqctcXxbopxhjTLngu0X/zuO5kJMVb940xxrg8l+j9vjguOLEXb+XtoLS8KtbNMcaYmPNcoge4aEwfKsI1LPxse6ybYowxMefJRH9yv670S09k/mrrvjHGGE8mehFhypjevLdxF0Wl5bFujjHGxJQnEz3AlDF9qFF4bfW2WDfFGGNiyrOJ/rjMZEb2SeXVVVtj3RRjjIkpzyZ6gCmj+/BpwT7yd4Zi3RRjjIkZTyf6C0f3RgResWvqjTGdmKcTfc+0IKcNymD+qq21b7wyxphOx1uJvqQQ9u86pOi7Y/qwaXcZqwv2xahRxhgTW95J9OX74OFT4J37DymePKon8f44XvnETsoaYzon7yT6YBqMvBRW/A32HUzqqcEAZw3L5F+fFhKutidaGmM6H+8keoAJPwdVePc/DimeMqY3u0KVvPfl7kZWNMYY74rmnbFzRKRIRBp8sbc4/iQiG0XkUxE5OaJusoisd+tub82GN6hrPzh5Jqx8GvZuriueODSTlKCfV637xhjTCUVzRP8kMLmJ+vOAIe4wC3gMQER8wCNu/QhguoiMaEljo/LNn4HEHXJUHwz4OH9kLxat3c6Byuo2b4IxxrQnzSZ6VV0CNPUS1inA39XxIdBVRHoB44CNqpqvqpXAXHfZtpXWB3KugU+egT35Bxt5Um/2V1bzVt6ONm+CMca0J/5W2EYfYEvEfIFb1lD5+MY2IiKzcH4RkJWVRW5ubl1dKBQ6ZL458XHjGS9z2DnvZ6wb/hMAalTpliA88danpOzdEPW22sqRxtTeeS0e8F5MXosHvBdTW8XTGoleGijTJsobpKqzgdkAOTk5OnHixLq63NxcIuejs4KeHz5Kz+89AN2PA+B7B/KYs/QrRp/yDbolxR/h9lrX0cXUfnktHvBeTF6LB7wXU1vF0xpX3RQAfSPms4HCJsqPjdNvAX8Q3vn3uqKLRvcmXKO8vsaeaGmM6TxaI9HPB2a6V9+cCuxT1W3AMmCIiAwUkXhgmrvssZHcA8bNgjX/hJ3rATihdyrHZSbbEy2NMZ1KNJdXPgd8AAwVkQIRuVZEbhCRG9xFFgD5wEbgL8APAVQ1DNwELALygOdVdW0bxNC4b/wY4pMg977aWPjumN4s27SXgr1lx7QpxhgTK9FcdTNdVXupakBVs1X1CVV9XFUfd+tVVX+kqoNVdZSqLo9Yd4GqHu/W3dOWgTQoKQPG3wBrX4Idzj5mypg+APaaQWNMp+GtO2MbctqPICEVcv8IQN/0RMb278arnxTaEy2NMZ2C9xN9YrqT7PNeg22rAfh+Tjbrd5Ty2qd2UtYY433eT/QAp97oPPTM7aufOrYvJ2ancfdraykuq4xx44wxpm11jkQfTINv3AzrF8DWFfjihD9eMoq9ZVX8ccG6WLfOGGPaVOdI9OCclO2SDoudvvoTeqdx3TcHMm/5Fj7Mt6daGmO8q/Mk+oQUOP0nsPFN2PIxALecdTx907vwq5fXUF5lDzszxnhT50n0AOOuh8TusPheALrE+7jnu6PI37mfR3O/jHHjjDGmbXSuRB+fBGfcCvmLYfP7AEw4vgcXn9SHx3I38sWO0hg30BhjWl/nSvQAOT+A5Cx4+x7nbVTAb74znKQEP3e8tIaaGru23hjjLZ0v0ccnwpm/gM1L4b3/BiAjOYFfnz+c5Zv38tyyr2PbPmOMaWWdL9ED5FzrvEj8rd/D+v8FYOrYbE4blMF9C9axo6Q8xg00xpjW0zkTvQhc9DD0Gg0vXgdFeYgI914yiorqGn7/2rF99poxxrSlzpnowenCmfasc4L2uWlQtoeB3ZP4yVlDWLBmO29+bq8cNMZ4Q+dN9OC8X/ayZ6BkGzw/E6qruP6bgxialcKdr35GqCIc6xYaY0yLde5ED9D3FLjoT7DpXVh4O/H+OO69ZBTbS8r5j0XrY906Y4xpMUv0AKOnOS8pWfZXWPYEY/t348rx/Xnqg02s2lIc69YZY0yLWKKvdfZdMOQc+N9fwFfvctvkoWSmJHDHS2uoqq6JdeuMMeaoWaKvFeeDS/8K6YPh+ZmklhXw+4tGkrethAff3BDr1hljzFGLKtGLyGQRWS8iG0Xk9gbqbxORVe7wmYhUi0i6W7dJRNa4dcsP33o7EkyD6c+B1sDcy5k8JInv52TzaO6XPPneV7FunTHGHJVoXg7uAx4BzgNGANNFZETkMqr6gKqOUdUxwB3AO6q6J2KRSW59Tus1vY1kDIbvPwU718NLs7j3uydw9vAs7nrtc15dtTXWrTPGmCMWzRH9OGCjquaraiUwF5jSxPLTgedao3ExM2giTP4jrF+AP/ceHr78JMYNTOdnz69m8fqiWLfOGGOOiDT3gmwRmQpMVtXr3PkZwHhVvamBZROBAuC42iN6EfkK2Aso8GdVnd3I58wCZgFkZWWNnTt3bl1dKBQiOTn5yKNrCVWO3/Aovbe9wfrjb+TLHudy38flbN9fw22nBBnSzdeizcckpjbktXjAezF5LR7wXkwtiWfSpEkrGu01UdUmB+B7wF8j5mcA/9PIspcBr9Ur6+2OM4HVwITmPnPs2LEaafHixRoTVRWqT1+q+rtU1YW/0qLi/Xrm/W/rqN8t1HXbSlq06ZjF1Ea8Fo+q92LyWjyq3oupJfEAy7WRnBpN100B0DdiPhsobGTZadTrtlHVQndcBLyM0xXUMfjjYfpcGPf/4IOH6fHaTP5x5XCCAR8znviILXvKYt1CY4xpVjSJfhkwREQGikg8TjKfX38hEUkDzgRejShLEpGU2mngHOCz1mj4MePzw/n3wwUPQv5isl+8iLlTsyivqmbGEx+xs7Qi1i00xpgmNZvoVTUM3AQsAvKA51V1rYjcICI3RCx6MfCGqu6PKMsClorIauBj4HVVXdh6zT+Gcn4AM16G0A4GvXIh/zyvmu0l5Vz9t48pKa+KdeuMMaZRUV1Hr6oLVPV4VR2sqve4ZY+r6uMRyzypqtPqrZevqqPd4YTadTusgRPg+rchqQdDF83glVM3sn57Kdc/tdxeLm6MabfsztgjlTEYrnsLBp7JsGW/YdGwBSz/aic3P/cJYXtUgjGmHbJEfzSCaXD58zD+RgbnP82SPo/x4edf8csX11iyN8a0O5boj5bPD+fdBxc+RJ+9H/NOt39j+SfLmTnnY3aH7AStMab9sETfUmOvhpmvkk4Jbyb9jkFfv8CU/3mXNQX7Yt0yY4wBLNG3jgFnwPVvE99nNP/m+wsPV/6GXzz+PP9cviXWLTPGGEv0rSZ9IFz9L7joYU5M2MZr/tvZ9spv+P1LK6gMW7+9MSZ2LNG3JhE4eQZxNy0nbtQl/Nj/CjNWXc69jzxOUUl5rFtnjOmkLNG3heQexF36F5jxMlkp8dy19w4+fvAyVq3fGOuWGWM6IUv0bWnwt0i65WN2nXQTk/Vd+j17Ju+9+D9ojXXlGGOOHUv0bS3Qhe5T7uHANbnsDvbj9DW/YeN/nkWgZHOsW2aM6SQs0R8jKf1PZNAvlvLmoNvJCq3jtJU/oWjOdNjxeaybZozxOEv0x5DP5+PbM+9g7ffe4Umm0GXzYnjsNA48cwXsWBvr5hljPMoSfQycNvJ4+k64mnmnv86jNRdTveH/4LFvUDN3BmzvWE9xNsa0f5boYyQQJ1x3zlguuvVRfjvwWR4KX0zZurfg8dNh3pWwfU2sm2iM8QhL9DGW3S2RB6/+FifOuJ/LEmfzUPgSDqx/Gx4/A+ZeAdtWx7qJxpgOzhJ9OzFpaCYv3no+cZN+xYTKh3hEp1KxMRf+PAGeOAdWPQdVB2LdTGNMB2SJvh0JBnzcfNYQXvrpd1g1+EZOCT3Io/E/oGTPDnjlBvjPYbDwDti5IdZNNcZ0IP5YN8Acrm96In+ZmcPb6/rywKJe3L/tLM5P/oKfpb7PoI//gnz4KPQ/A3KugeEXgj8h1k02xrRjUR3Ri8hkEVkvIhtF5PYG6ieKyD4RWeUOd0a7rmnct4ZlseDHZ/DUD8azN/M0zvr6aibWPEpu3x9RXfw1vHgt/NdwePNO2JMf6+YaY9qpZo/oRcQHPAJ8GygAlonIfFWtf6fPu6p6wVGuaxohIpx5fA/OPL4Hq7cU8+clX3LNZ0nE+07nl0O2c5m8SdL7D8N7D0H2KTD0PBh6PvQY5jxkzRjT6UXTdTMO2Kiq+QAiMheYAkSTrFuyrqlndN+uPHrFWDbt2s/sd/O5b0Ucf6iewbRh1/DjjGX0KnwL/u9uZ+g2wEn4Q8+DfqeBLxDr5htjYkRUtekFRKYCk1X1Ond+BjBeVW+KWGYi8CLOUXsh8HNVXRvNuhHbmAXMAsjKyho7d+7curpQKERycnILwmx/WiOm4ooa3toc5u2vqygLw6C0OM7L2sfkwCp67/2Ybns/JU6rqPInsSd9LLu6j2NP+slU+5NaKYqD7Dtq/7wWD3gvppbEM2nSpBWqmtNQXTRH9A39/q+/d1gJ9FfVkIicD7wCDIlyXadQdTYwGyAnJ0cnTpxYV5ebm0vkvBe0VkzfBUIVYeYt28I/l2/hkQ01/MV3Jt8e8X0um9SN0+M+I/DFQrI2LCSraAnE+Z0j/OxToM9YyM6BlJ4tbod9R+2f1+IB78XUVvFEk+gLgL4R89k4R+11VLUkYnqBiDwqIt2jWde0XHKCn2vPGMgPTh/A2sISXlhRwPzVhby+Zhs9Urpw8Uk3cemV9zA0vB7WL4D8XHj/T1ATdjaQmg19TnaSfp8c6D0G4lv/qN8YExvRJPplwBARGQhsBaYBl0cuICI9gR2qqiIyDudqnt1AcXPrmtYjIozsk8bIPmn86vzhLF5fxIsrCpiz9CtmL8lnZJ9Upp58NRdd+WvS46udxywULIety2HrCsib724oDjJHOEf8fU6G3idD5nDr5zemg2o20atqWERuAhYBPmCO2/9+g1v/ODAVuFFEwsABYJo6nf8NrttGsZgI8f44zj2hJ+ee0JPdoQrmry7khRUF3PXa5/zh9TzG9u/G2cMzOWv4TAaf9kNnpf27YOtKJ/EXLIfPX4WVTzl1/iD0PPFg4u9zMqQPhji7586Y9i6qG6ZUdQGwoF7Z4xHTDwMPR7uuObYykhO45vSBXHP6QPK2lfD6p9t4K28H9y5Yx70L1jGwexJnDcvkrOFZ5Aw+m8Dx5zgrqsLer5zkX/iJM175NHzkfvUJqdBrNPQ5mR5746Eoy0n+/vjYBWuMOYzdGdvJDO+VyvBeqfz83KEU7C3j7XVFvJVXxN8/2Mxfl35FatDPmUMzOXt4JhOPzyQtfRCkD4JRU50N1FTDzvVQuNLdAayEDx7lhJoq+PwB52Rv+mDoMdTp7ukx1LmmP+M4u4PXmBixRN+JZXdLZOZpA5h52gBCFWGWfrGTt/KKWLyuiNdWF+KLE07MTmP8wAzGD0onp383UoIByBrhDCdd6WwoXMHy/32GnP4psDPP2RHsWAvr/gXqvh9XfM4Oo8dQyBgMXftDt/7QbSCk9bVfAca0IUv0BnCu3Jk8sheTR/aiukZZtaWYxeuK+CB/N399N5/H3/mSOIGRfdI4dVAG4wemkzMgnbQuAfAnEEoZBCdOPHSjVeWweyPsXOcMRe5O4Is3oLoyYkGB1D5O4q/bAQxwdgDJmZDUHYJd7U5fY46SJXpzGF+cMLZ/N8b27wZAWWWYlZuL+eir3XyUv4cn39vE7CX5iMCIXqmMH5hBUlmY4SXlZKUGD24oEISeI50hUk0NlG6D4s2wdxPsdcfFm51LP0sbuAI3zg9JPZykn9QjYnDnE7tDYgYkZTjjhFTbMRjjskRvmpUY7+eMId05Y0h3AMqrqvnk64OJ/5mPNlMRruF/Pvk/eqYGGd03jTF9uzG6bxqj+qQ53T2R4uIgrY8z9P/G4R9YVQ77tjjD/t2wf2fEsMsZ7/7Sma7a33CjffFOwo8ckro7O4SULEiOHDLt0lHjaZbozRELBnycNjiD0wZnAFARrubp13KJ6zGI1QXFrN5SzKK1OwDnoPq4HsmM7tuV0X27Mia7K8f3TCbB72v8AwJB6D7EGZpTud9J+GW7oGxPxPRud3qPM7/9U2e+vLjh7SRmHJb8+23bAx+th/hkSEh2xynOOD7p4LSdXzDtnCV602IJfh/HdfMx8YyBdWV791fy6dZ9rN5SXNff/8KKAgD8ccKgHkkM65nK0J4pDO+VwrCeqfRKCyJH2t0Sn+QM3fpHt3y40vlFENoOoSIodceR87u/hNAOBlVXwFdRbNOXAMFUCKY5Q0LttDtOSDs4H7nTiE86dNzcDkPVuZs5XOGc46gdJ3W3O5lNkyzRmzbRLSm+7vHKAKpKwd4DrC4oJm9bCeu3l7Ji817mrz7YH58a9DOsZyrD3MQ/tGcKx2UmOyd8W4s//mC3UVNUWfL2m0w49WSoKIXKkPProSIElaXuOOSMK0qcoXwflLvTJVud6fJ9EI7yFZC++INJ3xcfkcwrnHG4gkYeFeV0SXXtd/CEdt30AOja1y5t7eQs0ZtjQkTom55I3/RELjixd115SXkVG7aXkre9lHXuDuCllVsJVWyuW6ZHSgJDMpM5rt7QIznhyH8BRN9ganzx7sne7i3bVrjy4I7gkB1GqIH5/c44XOHcjeyPd34x+OOd+dppX4KTvH0BCO1wTmgXf+28TD7vX1BTdWgbUnpxMkmQn3lwW/6Eg9upna8ti4sDxHkchog77c7XTiMQ53PK4vz1hvplvgaWqV/mzovv4HbF52yrbtp36LSJiiV6E1OpwQA5A5xLNWvVHv1v2FHKxqIQG4tCfFEU4uWVWymtCEes62dIVgrH9UhmQPck+qUn0j8jkX4ZiaTWPwEcS/548LfCDiNaNdVOF1Tx5oM7gOLNVH2d5yTHyjI4sNf9lVDu7IjC5QfntfrYtLMVfDMuHj5OjegOq+0aS4L4lIPTvviILq/yg7+QIn8t1dbX/bJKhEBS49P+BOp+YdU97r3+vDtdXXHo3zhcAVUHDp0PlzN0TwnE6OmVxhxTkUf/Zw3PqitXVXaUVLjJv5Qv3J3AW3k72L2/8pBtdE0M0D89kX4ZSfRL70L/9CT6ZTjbzEpJwO/z8DN64nwNXtW0JtpH4FaH3Rvd1ElSkdO487XTNTXOjqGm2jl/UBM+OK0RZdX15g9ZvoF5rXHKtCZiuvrQ8powW/PX0y8r3f1FtN/pVivbA8VbDnatVZY6y8f53V9F8Qd/LR0ynwCBNCfZl+2C4jKoKnN/aZU5ybo11f2KCtYNwXDbnNi3RG86DBGhZ1qQnmnBuks9a5WWV/H1njK27Clj8+4yNrvTq7cUs2DNNqprDh5h+eKEnqlBencN0rtrF2dIi5ju2oXUYCf+r+HrOLHnSy79mtt51e6sWtrVUx12LuetdHcA4XIOdmNB3es3Iudrp2t3JoHgwZ1LA92Oq3NzaSaao9JxvlFjmpASDHBC7zRO6J12WF1VdQ3bisvZvGc/W/YcYNu+A2wtPkBh8QE++drZEVRVH3qSMznBT6q/msFffkRWapBeacG6cc+0ID1Tg6QnxbfdOQLTekScPv2W8vnB515B1cFYojeeF/DF0c/tu29ITY2yK1ThJv9ytu07QMHeA3z25RZKy8N8sWMXRaXl1NS74CXeH0dWagI9U4NkpgbJTEkgq944MzVIatBvOwQTU5boTacXFydOok4NclK/g+W5uTuZOPF0AMLVNewKVbK9pJzt+w6wbV+5O+0MeYUl5JaUs7/y8BOZCf64usSfkRxPRnICGUnxpLtD9+QE0pPiyUiKp1tSPAEvnz8wMWGJ3pgo+H1xdecH6Nu10eVCFWGKSsopKq1gR0k5O91x7Xz+zv0s37SXvWWVh/1CqJXWJUCGuwPonuKMM5IOTndPTqBHsrPTSEqw/8KmefavxJhWlJzgJ7lHMoN6JDe5XHWNUlxWyZ79leze745DFRHTlewMVbB+eynvhXaz70BVg9vpEvDRLTFAWmI8aV38dO0ST1qXAF0TA6S647QuAbp2iWfTvmq2Fh8gPTGeLvF2DXpnYonemBjwxYnThZOcQBRP9KEyXMPu/RV1O4BdpRXsClWyK1RBcVkV+w5Use9AJfm7QhSXVVF8oIrKcM1h27nrg7cBCAbiSE90uorSk+LpluiMuyYG3HE8XbsE6JbolHVNDJCcYOcaOqqoEr2ITAYewnnv619V9b569VcAv3RnQ8CNqrrardsElALVQFhVc1qn6cZ0HvH+OHqldaFXWpeo1ymvqmbfgaq6HcG7H68ke9Dx7NlfxV7318Te/ZXsKatky54y9uyvpKQ83Oj2fHFCV/dXQtfEeLq5vxrSugRIDbrjLgFSg/666dpxUrzPdhIx1GyiFxEf8AjwbaAAWCYi81X184jFvgLOVNW9InIeMBsYH1E/SVV3tWK7jTHNCAZ8BAO+uncElG32M/GUfk2uU1Vd4+4YKtlb5uwk9pZVsq+simK3rHa6sLicvG2llByoOuSO5Yb44oSUoJ+UoJ/UYMCdDtRNpwb9pHY5WJ6c4Cc56CfFHScn+EmK9xMXZzuLoxHNEf04YKOq5gOIyFxgClCX6FX1/YjlPwSyW7ORxphjI+CLo0dKAj1SjuwhaOHqGkIVYfYdqKLkQJiS8ip3urZbqYrS8jCl5c64pLyKLXvK6qZDFeFDnhrQmOQEN+kn+EgOBgiXHWBewYqDO4ZgoG7nkOLuIJxxoG6HkZzgx9fJdhjRJPo+wJaI+QIOPVqv71rgfyPmFXhDRBT4s6rOPuJWGmPaNb8vzunXTzy6W/hrapRQZbhuZ7C/wpkOVYQJueP68yXlVWwrhS93hpy68jChyuh2GInxvoZ/NSQ480kRZbXlkdMpQWfcJeDrEDsN0Wb+KiLyPeBcVb3OnZ8BjFPVmxtYdhLwKHCGqu52y3qraqGIZAJvAjer6pIG1p0FzALIysoaO3fu3Lq6UChEcnLTVzF0NF6LyWvxgPdi8lo8cHhMNapUVMOBsHIg7I6r3Olqpby2zK0vj1iudrq82hk3dvlrffFxkOCDBL84Y58zDkbMB+vVB/0NL1dTUUbv9KP7jiZNmrSisXOg0RzRFwB9I+azgcNe6ikiJwJ/Bc6rTfIAqlrojotE5GWcrqDDEr17pD8bICcnRyMfvpQb7cOYOhCvxeS1eMB7MXktHmi7mFSVinDNIb8g9le4vygqwuyvqCZUUUVZZTUHKqvZXxmmrLKasgpn2imrZk+ls+yByjBlVc3/2kiJF9bc3frxRJPolwFDRGQgsBWYBlweuYCI9ANeAmao6oaI8iQgTlVL3elzgLtbq/HGGNMWRKTuZHb35NZ5aYuqUl5VE7EjqN0J1O4owuTlrWuVz6qv2USvqmERuQlYhHN55RxVXSsiN7j1jwN3AhnAo+4lVLWXUWYBL7tlfuBZVV3YJpEYY0w7JiJ0ifc1ebNat30b2+Szo7qOXlUXAAvqlT0eMX0dcF0D6+UDo1vYRmOMMS1gT08yxhiPs0RvjDEeZ4neGGM8zhK9McZ4nCV6Y4zxOEv0xhjjcZbojTHG4yzRG2OMx1miN8YYj7NEb4wxHmeJ3hhjPM4SvTHGeJwlemOM8ThL9MYY43GW6I0xxuMs0RtjjMdZojfGGI+zRG+MMR5nid4YYzwuqkQvIpNFZL2IbBSR2xuoFxH5k1v/qYicHO26xhhj2laziV5EfMAjwHnACGC6iIyot9h5wBB3mAU8dgTrGmOMaUPRHNGPAzaqar6qVgJzgSn1lpkC/F0dHwJdRaRXlOsaY4xpQ/4olukDbImYLwDGR7FMnyjXBUBEZuH8GgAIicj6iOruwK4o2tqReC0mr8UD3ovJa/GA92JqSTz9G6uIJtFLA2Ua5TLRrOsUqs4GZjfYAJHlqprTVCM7Gq/F5LV4wHsxeS0e8F5MbRVPNIm+AOgbMZ8NFEa5THwU6xpjjGlD0fTRLwOGiMhAEYkHpgHz6y0zH5jpXn1zKrBPVbdFua4xxpg21OwRvaqGReQmYBHgA+ao6loRucGtfxxYAJwPbATKgGuaWvco2tlgl04H57WYvBYPeC8mr8UD3oupTeIR1Qa7zI0xxniE3RlrjDEeZ4neGGM8rt0neq89QkFENonIGhFZJSLLY92eoyEic0SkSEQ+iyhLF5E3ReQLd9wtlm08Eo3Ec5eIbHW/p1Uicn4s23ikRKSviCwWkTwRWSsiP3HLO+T31EQ8HfZ7EpGgiHwsIqvdmH7vlrf6d9Su++jdRyhsAL6NcwnnMmC6qn4e04a1gIhsAnJUtcPe5CEiE4AQzt3QI92y+4E9qnqfu0Pupqq/jGU7o9VIPHcBIVX9j1i27Wi5d6b3UtWVIpICrAC+C1xNB/yemojn+3TQ70lEBEhS1ZCIBIClwE+AS2jl76i9H9HbIxTaIVVdAuypVzwFeMqdfgrnP2GH0Eg8HZqqblPVle50KZCHc6d6h/yemoinw3IfGRNyZwPuoLTBd9TeE31jj1boyBR4Q0RWuI998Ios994J3HFmjNvTGm5yn8Y6p6N0cTRERAYAJwEf4YHvqV480IG/JxHxicgqoAh4U1Xb5Dtq74k+6kcodCCnq+rJOE/0/JHbbWDan8eAwcAYYBvwnzFtzVESkWTgReAWVS2JdXtaqoF4OvT3pKrVqjoG56kB40RkZFt8TntP9NE8fqFDUdVCd1wEvIzTPeUFO9x+1Nr+1KIYt6dFVHWH+5+wBvgLHfB7cvt9XwSeUdWX3OIO+z01FI8XvicAVS0GcoHJtMF31N4TvaceoSAiSe6JJEQkCTgH+KzptTqM+cBV7vRVwKsxbEuL1f5Hc11MB/ue3BN9TwB5qvpfEVUd8ntqLJ6O/D2JSA8R6epOdwHOBtbRBt9Ru77qBsC9XOq/OfgIhXti26KjJyKDcI7iwXn8xLMdMR4ReQ6YiPNI1R3A74BXgOeBfsDXwPdUtUOc4Gwknok43QEKbAL+X22/aUcgImcA7wJrgBq3+Fc4/dod7ntqIp7pdNDvSUROxDnZ6sM56H5eVe8WkQxa+Ttq94neGGNMy7T3rhtjjDEtZIneGGM8zhK9McZ4nCV6Y4zxOEv0xhjjcZbojTHG4yzRG2OMx/1/tpZdFvvTvkUAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YK35MbdYIEIn"
      },
      "source": [
        "**Model evaluation on test set**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eoAFsrFJII5k",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0969cbd7-fba4-4e0d-8ca0-468eeae68ead"
      },
      "source": [
        "test_loss, test_accuracy = model.evaluate(test_data_grayscale, test_targets)\n",
        "print('Test loss = {:.03f}'.format(test_loss))\n",
        "print('Test accuracy = {:.03f}'.format(test_accuracy))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "814/814 [==============================] - 1s 1ms/step - loss: 0.3831 - accuracy: 0.9029\n",
            "Test loss = 0.383\n",
            "Test accuracy = 0.903\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3iBYFPWUz_G4"
      },
      "source": [
        "## 4. Get model predictions\n",
        "* Load the best weights for the MLP and CNN models that you saved during the training run.\n",
        "* Randomly select 5 images and corresponding labels from the test set and display the images with their labels.\n",
        "* Alongside the image and label, show each model’s predictive distribution as a bar chart, and the final model prediction given by the label with maximum probability."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XMYYWs0oz_G5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a48b0786-8761-4d6f-c376-db9781da16af"
      },
      "source": [
        "! ls -lh /home/marcin/Documents/Capstone_project"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total 8,0K\r\n",
            "drwxr-xr-x 4 marcin marcin 4,0K sty  8 18:04 cnn_checkpoint_best\r\n",
            "drwxr-xr-x 4 marcin marcin 4,0K sty  8 17:54 mlp_checkpoint_best\r\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8GoahZ8aXLBw"
      },
      "source": [
        "test_data_raw, test_targets_raw = test['X'], test['y']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qpdg7pVX33SW"
      },
      "source": [
        "**Predictions for MLP model**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Or__wSma7TG_"
      },
      "source": [
        "from tensorflow.keras.models import load_model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "--GPiXsk4ChN"
      },
      "source": [
        "best_mlp_model = load_model('/home/marcin/Documents/Capstone_project/mlp_checkpoint_best')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "HLgzA09a4ChO",
        "outputId": "df5a8f5c-6dbf-42b8-ef01-0758fd69f9a4"
      },
      "source": [
        "image_indexes = [32, 435, 4533, 7567, 25543]\n",
        "\n",
        "df_probabilities = pd.DataFrame({}, index=range(0, 10))\n",
        "\n",
        "def get_prediction(id):\n",
        "  test_img = test_data_grayscale[id, ...]\n",
        "  preds = best_mlp_model.predict(test_img[np.newaxis, ...])\n",
        "  return preds\n",
        "\n",
        "for i in range(len(image_indexes)):\n",
        "  df_probabilities.insert(i, str(image_indexes[i]), \n",
        "                          get_prediction(image_indexes[i])[0])\n",
        "\n",
        "df_probabilities"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         32           435      4533      7567         25543\n",
              "0  0.018219  6.451244e-05  0.078535  0.000015  2.087810e-13\n",
              "1  0.020115  5.331291e-03  0.031063  0.000468  2.259156e-11\n",
              "2  0.027306  1.511783e-01  0.002295  0.000029  1.636767e-09\n",
              "3  0.024261  1.769177e-03  0.010476  0.042667  9.999965e-01\n",
              "4  0.077036  6.841851e-07  0.573967  0.000032  9.741660e-11\n",
              "5  0.148101  3.620827e-06  0.012158  0.953533  9.716682e-07\n",
              "6  0.383293  8.621002e-07  0.166725  0.001017  6.700790e-12\n",
              "7  0.016661  8.412678e-01  0.001838  0.000477  7.858468e-13\n",
              "8  0.248031  3.160462e-04  0.106957  0.001240  2.467407e-06\n",
              "9  0.036976  6.771163e-05  0.015986  0.000521  4.047356e-08"
            ],
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>32</th>\n",
              "      <th>435</th>\n",
              "      <th>4533</th>\n",
              "      <th>7567</th>\n",
              "      <th>25543</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.018219</td>\n",
              "      <td>6.451244e-05</td>\n",
              "      <td>0.078535</td>\n",
              "      <td>0.000015</td>\n",
              "      <td>2.087810e-13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.020115</td>\n",
              "      <td>5.331291e-03</td>\n",
              "      <td>0.031063</td>\n",
              "      <td>0.000468</td>\n",
              "      <td>2.259156e-11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.027306</td>\n",
              "      <td>1.511783e-01</td>\n",
              "      <td>0.002295</td>\n",
              "      <td>0.000029</td>\n",
              "      <td>1.636767e-09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.024261</td>\n",
              "      <td>1.769177e-03</td>\n",
              "      <td>0.010476</td>\n",
              "      <td>0.042667</td>\n",
              "      <td>9.999965e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.077036</td>\n",
              "      <td>6.841851e-07</td>\n",
              "      <td>0.573967</td>\n",
              "      <td>0.000032</td>\n",
              "      <td>9.741660e-11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.148101</td>\n",
              "      <td>3.620827e-06</td>\n",
              "      <td>0.012158</td>\n",
              "      <td>0.953533</td>\n",
              "      <td>9.716682e-07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.383293</td>\n",
              "      <td>8.621002e-07</td>\n",
              "      <td>0.166725</td>\n",
              "      <td>0.001017</td>\n",
              "      <td>6.700790e-12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.016661</td>\n",
              "      <td>8.412678e-01</td>\n",
              "      <td>0.001838</td>\n",
              "      <td>0.000477</td>\n",
              "      <td>7.858468e-13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.248031</td>\n",
              "      <td>3.160462e-04</td>\n",
              "      <td>0.106957</td>\n",
              "      <td>0.001240</td>\n",
              "      <td>2.467407e-06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.036976</td>\n",
              "      <td>6.771163e-05</td>\n",
              "      <td>0.015986</td>\n",
              "      <td>0.000521</td>\n",
              "      <td>4.047356e-08</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "WMQiYsbz4ChP",
        "outputId": "ac3060a9-a3f1-4e0e-b7bc-6ee564861fbe"
      },
      "source": [
        "for i in range(5):\n",
        "  fig, (ax, ax2) = plt.subplots(ncols=2)\n",
        "\n",
        "  ax.imshow(test_data_raw[..., image_indexes[i]])\n",
        "  ax.set_title(test_targets_raw[image_indexes[i]][0])\n",
        "\n",
        "  max_probability = 'Predicted number = '\\\n",
        "  + str(df_probabilities[str(image_indexes[i])].argmax()) \\\n",
        "  +'\\nwith probability = ' + str(df_probabilities[str(image_indexes[i])].max())\n",
        "  ax2 = df_probabilities[str(image_indexes[i])].plot.bar(\n",
        "      xlabel='Digit', title=max_probability , ylim=(0,1),\n",
        "      grid=True, legend=False)\n",
        "  \n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xrl9cccE0cvQ"
      },
      "source": [
        "**Predictions for CNN model**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8yUPWbFCz_G8"
      },
      "source": [
        "best_cnn_model = load_model('/home/marcin/Documents/Capstone_project/cnn_checkpoint_best')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "qcOc_-qOrlI3",
        "outputId": "02544e6d-863f-4d9a-ecdc-30e6d1880df5"
      },
      "source": [
        "image_indexes = [32, 435, 4533, 7567, 25543]\n",
        "\n",
        "df_probabilities = pd.DataFrame({}, index=range(0, 10))\n",
        "\n",
        "def get_prediction(id):\n",
        "  test_img = test_data_grayscale[id, ...]\n",
        "  preds = best_cnn_model.predict(test_img[np.newaxis, ...])\n",
        "  return preds\n",
        "\n",
        "for i in range(len(image_indexes)):\n",
        "  df_probabilities.insert(i, str(image_indexes[i]), \n",
        "                          get_prediction(image_indexes[i])[0])\n",
        "\n",
        "df_probabilities"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         32           435      4533          7567     25543\n",
              "0  0.001272  4.220291e-06  0.000856  1.279206e-06  0.000009\n",
              "1  0.000336  4.255832e-05  0.002325  3.161107e-07  0.000004\n",
              "2  0.001307  2.453201e-05  0.000710  7.011399e-07  0.000032\n",
              "3  0.001497  2.948743e-06  0.000845  8.894220e-05  0.999647\n",
              "4  0.002840  7.787302e-07  0.982367  1.754315e-07  0.000011\n",
              "5  0.181843  7.172712e-07  0.000978  9.984167e-01  0.000017\n",
              "6  0.497702  6.652152e-06  0.002614  1.372810e-03  0.000002\n",
              "7  0.000198  9.999162e-01  0.000138  7.342958e-07  0.000029\n",
              "8  0.309153  2.995431e-07  0.008833  1.120556e-04  0.000177\n",
              "9  0.003852  9.593099e-07  0.000333  6.427829e-06  0.000073"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>32</th>\n",
              "      <th>435</th>\n",
              "      <th>4533</th>\n",
              "      <th>7567</th>\n",
              "      <th>25543</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.001272</td>\n",
              "      <td>4.220291e-06</td>\n",
              "      <td>0.000856</td>\n",
              "      <td>1.279206e-06</td>\n",
              "      <td>0.000009</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.000336</td>\n",
              "      <td>4.255832e-05</td>\n",
              "      <td>0.002325</td>\n",
              "      <td>3.161107e-07</td>\n",
              "      <td>0.000004</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.001307</td>\n",
              "      <td>2.453201e-05</td>\n",
              "      <td>0.000710</td>\n",
              "      <td>7.011399e-07</td>\n",
              "      <td>0.000032</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.001497</td>\n",
              "      <td>2.948743e-06</td>\n",
              "      <td>0.000845</td>\n",
              "      <td>8.894220e-05</td>\n",
              "      <td>0.999647</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.002840</td>\n",
              "      <td>7.787302e-07</td>\n",
              "      <td>0.982367</td>\n",
              "      <td>1.754315e-07</td>\n",
              "      <td>0.000011</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.181843</td>\n",
              "      <td>7.172712e-07</td>\n",
              "      <td>0.000978</td>\n",
              "      <td>9.984167e-01</td>\n",
              "      <td>0.000017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.497702</td>\n",
              "      <td>6.652152e-06</td>\n",
              "      <td>0.002614</td>\n",
              "      <td>1.372810e-03</td>\n",
              "      <td>0.000002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.000198</td>\n",
              "      <td>9.999162e-01</td>\n",
              "      <td>0.000138</td>\n",
              "      <td>7.342958e-07</td>\n",
              "      <td>0.000029</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.309153</td>\n",
              "      <td>2.995431e-07</td>\n",
              "      <td>0.008833</td>\n",
              "      <td>1.120556e-04</td>\n",
              "      <td>0.000177</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.003852</td>\n",
              "      <td>9.593099e-07</td>\n",
              "      <td>0.000333</td>\n",
              "      <td>6.427829e-06</td>\n",
              "      <td>0.000073</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W48syko0z_G-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "ef02c00d-bf63-4761-951e-79c4cba2f11a"
      },
      "source": [
        "for i in range(5):\n",
        "  fig, (ax, ax2) = plt.subplots(ncols=2)\n",
        "\n",
        "  ax.imshow(test_data_raw[..., image_indexes[i]])\n",
        "  ax.set_title(test_targets_raw[image_indexes[i]][0])\n",
        "\n",
        "  max_probability = 'Predicted number = '\\\n",
        "  + str(df_probabilities[str(image_indexes[i])].argmax()) \\\n",
        "  +'\\nIts probability = ' + str(df_probabilities[str(image_indexes[i])].max())\n",
        "  ax2 = df_probabilities[str(image_indexes[i])].plot.bar(\n",
        "      xlabel='Digit', title=max_probability , ylim=(0,1),\n",
        "      grid=True, legend=False)\n",
        "  \n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}