CNN_cifar_classifier.ipynb

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  "metadata": {
    "colab": {
      "name": "CNN_cifar_classifier.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "machine_shape": "hm"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5XHBpCexS5BU",
        "colab_type": "text"
      },
      "source": [
        "Imports"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YhcYG7nzFbeJ",
        "colab_type": "text"
      },
      "source": [
        "#### Ryan Martin\n",
        "#### CS 6140: Machine Learning\n",
        "#### 12/12/19\n",
        "#### Dr. Craig Martell\n",
        "\n",
        "\n",
        "\n",
        "# Convolutional Neural Network: CIFAR-10 Dataset\n",
        "## Frame the Problem and Look at the Big Picture:\n",
        "#### In this dataset, we are given 60000 32x32 colour images in 10 classes, with 6000 images per class and 50000 training images and 10000 test images. Each of these ten classes consist of the following objects: airplane, automobile, bird, cat, deer, dog, frog, horse, ship, and trucks. All these images of listed objects are mutually exclusive, so there are no overlapping classes such as automobiles and trucks because trucks contain only large commercial vehicles.\n",
        "#### The task in this project is to create a Convolutional Neural Network that will achieve ~75% accuracy without overfitting.\n",
        "\n",
        "#### For this project, we will be using the Keras library.\n",
        "\n",
        "## Get the Data:\n",
        "#### We will download the data in a way that will allow for us to access and monitor the training set and test set and validation set. By using ngrok, we can create a public URL to access the localhost of the ipython environment in Google Colab. This will make it possible for us to access the TensorBoard on our local machine and have more control over the batches of data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z0DRPKl39290",
        "colab_type": "code",
        "outputId": "dbc68567-0fb5-4d50-dd5b-ecebb5797a72",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        }
      },
      "source": [
        "import numpy as np\n",
        "import pickle\n",
        "import os\n",
        "import urllib\n",
        "import zipfile \n",
        "import tarfile\n",
        "from six.moves import urllib\n",
        "\n",
        "from __future__ import absolute_import\n",
        "from __future__ import division\n",
        "from __future__ import print_function\n",
        "\n",
        "# tensorflow imports\n",
        "import tensorflow as tf\n",
        "from tensorflow.contrib.training import HParams\n",
        "\n",
        "# tf.keras imports\n",
        "from tensorflow.keras import Model\n",
        "from tensorflow.keras.layers import Dense, Conv2D, Activation, Flatten\n",
        "from tensorflow.keras.layers import MaxPooling2D, Dropout\n",
        "from tensorflow.keras.layers import BatchNormalization, AveragePooling2D, Input\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "\n",
        "from keras.models import Sequential\n",
        "from keras.optimizers import adam\n",
        "from keras.utils import np_utils\n",
        "\n",
        "# import for showing the confusion matrix\n",
        "import itertools\n",
        "from sklearn.metrics import confusion_matrix\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<p style=\"color: red;\">\n",
              "The default version of TensorFlow in Colab will soon switch to TensorFlow 2.x.<br>\n",
              "We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now \n",
              "or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n",
              "<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BXSBbyK1FyBh",
        "colab_type": "text"
      },
      "source": [
        "#### Download and install [ngrok](https://ngrok.com/). "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L35DdtT_FOXP",
        "colab_type": "code",
        "outputId": "dd783bef-dc29-4a2a-f1cd-066684452a12",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        }
      },
      "source": [
        "! wget https://bin.equinox.io/c/4VmDzA7iaHb/ngrok-stable-linux-amd64.zip\n",
        "! unzip -o ngrok-stable-linux-amd64.zip"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2019-12-13 04:16:27--  https://bin.equinox.io/c/4VmDzA7iaHb/ngrok-stable-linux-amd64.zip\n",
            "Resolving bin.equinox.io (bin.equinox.io)... 52.204.223.154, 3.231.170.111, 34.227.147.55, ...\n",
            "Connecting to bin.equinox.io (bin.equinox.io)|52.204.223.154|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 13773305 (13M) [application/octet-stream]\n",
            "Saving to: ‘ngrok-stable-linux-amd64.zip’\n",
            "\n",
            "ngrok-stable-linux- 100%[===================>]  13.13M  19.2MB/s    in 0.7s    \n",
            "\n",
            "2019-12-13 04:16:28 (19.2 MB/s) - ‘ngrok-stable-linux-amd64.zip’ saved [13773305/13773305]\n",
            "\n",
            "Archive:  ngrok-stable-linux-amd64.zip\n",
            "  inflating: ngrok                   \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EB5P9HvKEblD",
        "colab_type": "text"
      },
      "source": [
        "#### Now we can run `ngrok` in the `ipython` environment and tunnel the data to a public, random URL."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YwZrIjZkEEYu",
        "colab_type": "code",
        "outputId": "6ae8318e-e2bf-4bce-e669-e707410c27bd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "# kill all running ngrok instances\n",
        "!pkill -f ngrok\n",
        "\n",
        "# Execute tensorboard\n",
        "!rm rf /tmp/checkpoints\n",
        "!mkdir /tmp/checkpoints\n",
        "LOG_DIR = '/tmp/checkpoints/'\n",
        "get_ipython().system_raw(\n",
        "    'tensorboard --logdir {} --host 0.0.0.0 --port 6006 &'\n",
        "    .format(LOG_DIR)\n",
        ")\n",
        "\n",
        "# execute ngrok\n",
        "get_ipython().system_raw('./ngrok http 6006 &')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "rm: cannot remove 'rf': No such file or directory\n",
            "rm: cannot remove '/tmp/checkpoints': No such file or directory\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gkCnSnAREdny",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "\n",
        "#### The code below outputs a URL containing the TensorBoard visualizations for the classifier we will train and evaluate."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3Imio4YREFzg",
        "colab_type": "code",
        "outputId": "421bebd3-092b-4198-baef-f3d6cf4e4819",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Do the tunneling\n",
        "! curl -s http://localhost:4040/api/tunnels | python3 -c \\\n",
        "    \"import sys, json; print(json.load(sys.stdin)['tunnels'][0]['public_url'])\""
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "https://2b3a9070.ngrok.io\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0KqmhIDXTKK7",
        "colab_type": "text"
      },
      "source": [
        "## Data download and preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tS4-fVqIxmh9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "hparams = HParams(\n",
        "    n_classes=10,  # number of different classes in dataset\n",
        "    learning_rate=1e-4,  # fixed learning rate\n",
        "    train_batch_size=32,  # training batch size\n",
        "    val_batch_size=32,  # validation batch size\n",
        "    test_batch_size=32,  # testing batch size\n",
        "    n_epochs=10,  # number of epochs to train\n",
        "    input_name='input_1',  # name of the input tensor for first layer of Keras model\n",
        "    data_dir='/tmp/cifar-data/',  # path to data directory\n",
        "    checkpoint_dir='/tmp/checkpoints/'  # path to model checkpoint directory\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VuC2mMKOIIRJ",
        "colab_type": "text"
      },
      "source": [
        "#### This code is for downloading and preprocessing of the CIFAR-10 dataset."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2EpQ-iJN94G1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# URL for the data-set on the internet.\n",
        "data_url = \"https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\"\n",
        "\n",
        "# Width and height of each image.\n",
        "img_size = 32\n",
        "\n",
        "# Number of channels in each image, 3 channels: Red, Green, Blue.\n",
        "num_channels = 3\n",
        "\n",
        "# Length of an image when flattened to a 1-dim array.\n",
        "img_size_flat = img_size * img_size * num_channels\n",
        "\n",
        "# Number of classes.\n",
        "num_classes = hparams.n_classes\n",
        "\n",
        "# Number of files for the training-set.\n",
        "_num_files_train = 5\n",
        "\n",
        "# Number of images for each batch-file in the training-set.\n",
        "_images_per_file = 10000\n",
        "\n",
        "# Total number of images in the training-set.\n",
        "# This is used to pre-allocate arrays for efficiency.\n",
        "_num_images_train = _num_files_train * _images_per_file\n",
        "\n",
        "\n",
        "def download(base_url, filename, download_dir):\n",
        "\n",
        "    # Path for local file.\n",
        "    save_path = os.path.join(download_dir, filename)\n",
        "\n",
        "    # Check if the file already exists, otherwise we need to download it now.\n",
        "    if not os.path.exists(save_path):\n",
        "        # Check if the download directory exists, otherwise create it.\n",
        "        if not os.path.exists(download_dir):\n",
        "            os.makedirs(download_dir)\n",
        "\n",
        "        print(\"Downloading\", filename, \"...\")\n",
        "\n",
        "        # Download the file from the internet.\n",
        "        url = base_url + filename\n",
        "        file_path, _ = urllib.request.urlretrieve(url=url,\n",
        "                                                  filename=save_path)\n",
        "\n",
        "        print(\" Done!\")\n",
        "\n",
        "\n",
        "def maybe_download_and_extract(url=data_url, download_dir=hparams.data_dir):\n",
        "\n",
        "    # Filename for saving the file downloaded from the internet.\n",
        "    # Use the filename from the URL and add it to the download_dir.\n",
        "    filename = url.split('/')[-1]\n",
        "    file_path = os.path.join(download_dir, filename)\n",
        "\n",
        "    # Check if the file already exists.\n",
        "    # If it exists then we assume it has also been extracted,\n",
        "    # otherwise we need to download and extract it now.\n",
        "    if not os.path.exists(file_path):\n",
        "        # Check if the download directory exists, otherwise create it.\n",
        "        if not os.path.exists(download_dir):\n",
        "            os.makedirs(download_dir)\n",
        "\n",
        "        # Download the file from the internet.\n",
        "        file_path, _ = urllib.request.urlretrieve(url=url,\n",
        "                                                  filename=file_path)\n",
        "\n",
        "        print()\n",
        "        print(\"Download finished. Extracting files.\")\n",
        "\n",
        "        if file_path.endswith(\".zip\"):\n",
        "            # Unpack the zip-file.\n",
        "            zipfile.ZipFile(file=file_path, mode=\"r\").extractall(download_dir)\n",
        "        elif file_path.endswith((\".tar.gz\", \".tgz\")):\n",
        "            # Unpack the tar-ball.\n",
        "            tarfile.open(name=file_path, mode=\"r:gz\").extractall(download_dir)\n",
        "\n",
        "        print(\"Done.\")\n",
        "    else:\n",
        "        print(\"Data has apparently already been downloaded and unpacked.\")\n",
        "\n",
        "\n",
        "def _get_file_path(filename=\"\"):\n",
        "\n",
        "    return os.path.join(hparams.data_dir, \"cifar-10-batches-py/\", filename)\n",
        "\n",
        "\n",
        "def _unpickle(filename):\n",
        "\n",
        "    # Create full path for the file.\n",
        "    file_path = _get_file_path(filename)\n",
        "\n",
        "    print(\"Loading data: \" + file_path)\n",
        "\n",
        "    with open(file_path, mode='rb') as file:\n",
        "        # In Python 3.X it is important to set the encoding,\n",
        "        # otherwise an exception is raised here.\n",
        "        data = pickle.load(file,encoding='bytes')\n",
        "\n",
        "    return data\n",
        "\n",
        "\n",
        "def _convert_images(raw):\n",
        "\n",
        "    # Convert the raw images from the data-files to floating-points.\n",
        "    raw_float = np.array(raw, dtype=float) / 255.0\n",
        "\n",
        "    # Reshape the array to 4-dimensions.\n",
        "    images = raw_float.reshape([-1, num_channels, img_size, img_size])\n",
        "\n",
        "    # Reorder the indices of the array.\n",
        "    images = images.transpose([0, 2, 3, 1])\n",
        "\n",
        "    return images\n",
        "\n",
        "\n",
        "def _load_data(filename):\n",
        "\n",
        "    # Load the pickled data-file.\n",
        "    data = _unpickle(filename)\n",
        "\n",
        "    # Get the raw images.\n",
        "    raw_images = data[b'data']\n",
        "\n",
        "    # Get the class-numbers for each image. Convert to numpy-array.\n",
        "    cls = np.array(data[b'labels'])\n",
        "\n",
        "    # Convert the images.\n",
        "    images = _convert_images(raw_images)\n",
        "\n",
        "    return images, cls\n",
        "\n",
        "\n",
        "def load_class_names():\n",
        "    # Load the class-names from the pickled file.\n",
        "    raw = _unpickle(filename=\"batches.meta\")[b'label_names']\n",
        "\n",
        "    # Convert from binary strings.\n",
        "    names = [x.decode('utf-8') for x in raw]\n",
        "\n",
        "    return names\n",
        "\n",
        "\n",
        "def load_training_data():\n",
        "\n",
        "    # Pre-allocate the arrays for the images and class-numbers for efficiency.\n",
        "    images = np.zeros(shape=[_num_images_train, img_size, img_size, num_channels], dtype=float)\n",
        "    cls = np.zeros(shape=[_num_images_train], dtype=int)\n",
        "\n",
        "    # Begin-index for the current batch.\n",
        "    begin = 0\n",
        "\n",
        "    # For each data-file.\n",
        "    for i in range(_num_files_train):\n",
        "        # Load the images and class-numbers from the data-file.\n",
        "        images_batch, cls_batch = _load_data(filename=\"data_batch_\" + str(i + 1))\n",
        "\n",
        "        # Number of images in this batch.\n",
        "        num_images = len(images_batch)\n",
        "\n",
        "        # End-index for the current batch.\n",
        "        end = begin + num_images\n",
        "\n",
        "        # Store the images into the array.\n",
        "        images[begin:end, :] = images_batch\n",
        "\n",
        "        # Store the class-numbers into the array.\n",
        "        cls[begin:end] = cls_batch\n",
        "\n",
        "        # The begin-index for the next batch is the current end-index.\n",
        "        begin = end\n",
        "\n",
        "    return images, cls\n",
        "\n",
        "\n",
        "def load_validation_data():\n",
        "\n",
        "    images, cls = _load_data(filename=\"test_batch\")\n",
        "\n",
        "    images = images[10000:, :, :, :]\n",
        "    cls = cls[10000:]\n",
        "\n",
        "    return images, cls\n",
        "\n",
        "def load_testing_data():\n",
        "\n",
        "\n",
        "    images, cls = _load_data(filename=\"test_batch\")\n",
        "\n",
        "    images = images[:10000, :, :, :]\n",
        "    cls = cls[:10000]\n",
        "\n",
        "    return images, cls"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AVDwmxHnAg4y",
        "colab_type": "text"
      },
      "source": [
        "### Preparation of the Data and Examination of the Data:\n",
        "\n",
        "#### We have 50000 training and 10000 test images. The images have a structure of (32,32,3) which correspond to (width, height, RGB). The validation set will be \"split\" with the training data. \n",
        "\n",
        "#### For each image there is a corresponding label, which is a class index.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kC1WjaOXAgBw",
        "colab_type": "code",
        "outputId": "84df9c29-687b-4aa1-91e3-8ce8153d1d24",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        }
      },
      "source": [
        "\n",
        "# Download and extract CIFAR-10 data\n",
        "maybe_download_and_extract()\n",
        "\n",
        "# training data\n",
        "x_train, y_train = load_training_data()\n",
        "\n",
        "# Validation data\n",
        "x_val, y_val = load_validation_data()\n",
        "\n",
        "# Testing data\n",
        "x_test, y_test = load_testing_data()\n",
        "\n",
        "print(np.array(x_train).shape)\n",
        "print(np.array(y_train).shape)\n",
        "print(x_test.shape)\n",
        "print(y_test.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Download finished. Extracting files.\n",
            "Done.\n",
            "Loading data: /tmp/cifar-data/cifar-10-batches-py/data_batch_1\n",
            "Loading data: /tmp/cifar-data/cifar-10-batches-py/data_batch_2\n",
            "Loading data: /tmp/cifar-data/cifar-10-batches-py/data_batch_3\n",
            "Loading data: /tmp/cifar-data/cifar-10-batches-py/data_batch_4\n",
            "Loading data: /tmp/cifar-data/cifar-10-batches-py/data_batch_5\n",
            "Loading data: /tmp/cifar-data/cifar-10-batches-py/test_batch\n",
            "Loading data: /tmp/cifar-data/cifar-10-batches-py/test_batch\n",
            "(50000, 32, 32, 3)\n",
            "(50000,)\n",
            "(10000, 32, 32, 3)\n",
            "(10000,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P1mmIMlWMb4_",
        "colab_type": "text"
      },
      "source": [
        "#### We will preprocess the data by transforming the features to float32 type, normalizing them, and one_hot_encoding the labels. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q3CN-tJ_e4MG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Transforming the features\n",
        "\n",
        "mean = np.mean(x_train,axis=(0,1,2,3))\n",
        "std = np.std(x_train,axis=(0,1,2,3))\n",
        "x_train = (x_train-mean)/(std+1e-7)\n",
        "x_test = (x_test-mean)/(std+1e-7)\n",
        "\n",
        "\n",
        "# One-hot encoding the labels\n",
        "nClasses = 10\n",
        "y_train = np_utils.to_categorical(y_train, nClasses)\n",
        "y_test = np_utils.to_categorical(y_test, nClasses)\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4a6EQeBijiSO",
        "colab_type": "code",
        "outputId": "fa73d474-0aa6-4e23-99ba-0df566ba103f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "print(\"Shape of training data:\")\n",
        "print(x_train.shape)\n",
        "print(y_train.shape)\n",
        "print(\"Shape of test data:\")\n",
        "print(x_test.shape)\n",
        "print(y_test.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Shape of training data:\n",
            "(50000, 32, 32, 3)\n",
            "(50000, 10)\n",
            "Shape of test data:\n",
            "(10000, 32, 32, 3)\n",
            "(10000, 10)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3-8FuYaeiBUY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "input_shape = (32,32,3)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o1C3HH6fNyHI",
        "colab_type": "text"
      },
      "source": [
        "#### These methods will help later in the stage of evaluating our model with training accuracy and validation accuracy to check for overfitting. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ndCVeOmEBz3A",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def plot_curves(history):\n",
        "  plt.figure(figsize=[8,6])\n",
        "  plt.plot(history.history['acc'])\n",
        "  plt.plot(history.history['val_acc'])\n",
        "  plt.legend(['Training Accuracy', 'Validation Accuracy'], \n",
        "           loc = 'lower right')\n",
        "  plt.xlabel('Epochs ')\n",
        "  plt.ylabel('Accuracy')\n",
        "  plt.title('Accuracy Curves')\n",
        "  plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PH25pmQzrNmN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def plot_losses(history):  \n",
        "    plt.figure(figsize=[8,6])\n",
        "    plt.plot(history.history['loss'])\n",
        "    plt.plot(history.history['val_loss'])\n",
        "    plt.title('model loss')\n",
        "    plt.ylabel('loss')\n",
        "    plt.xlabel('epoch')\n",
        "    plt.legend(['train', 'validation'], loc='upper left')\n",
        "    plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3e6SDwC5hp8p",
        "colab_type": "text"
      },
      "source": [
        "## Select and Train a Model\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1VlZjRIQhw_6",
        "colab_type": "text"
      },
      "source": [
        "### CNN Classifier\n",
        "\n",
        "#### A Convolutional Neural Network contains a convolutional layer and a pooling layer. The convolutional layer has neurons in the first layer, which are unconnected to the pixels in the input image but fully connected to the pixels in their receptive fields. Each neuron in the second convolutional layer is connected only to neurons in the small rectangle in the first layer. This architecture creates an environment for the network to concentrate on low-level features in the first hidden layer, and then assemble them to higher-level features in the next hidden layer. Because this heirarchial structure is common in real-world images, CNN's succeed in these image recognition problems. In addition to the convolutional layer, there are pooling layers that take a subsample of the input image in order to reduce the computational complexity and parameters, which limits the probability of overfitting. (See model below). \n",
        "\n",
        "#### We will first experiment with different weight decays. In this experiment, we will attempt to reduce the training error and test error by reducing the gap between training and test error. In changing the regularization hyperparameters, we can reduce effective model capacity. We will start at a small decay, and then decrease it to zero. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gu7FGlesAYko",
        "colab_type": "text"
      },
      "source": [
        "### CNN Model: Weight Decay 0.0005 "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-TAzH4anVNrL",
        "colab_type": "code",
        "outputId": "53fb7528-cfa0-4f61-b69f-c5173e5dc512",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        }
      },
      "source": [
        "from keras.layers import Dense, Flatten\n",
        "from keras.layers import Conv2D, MaxPooling2D\n",
        "from keras.optimizers import SGD\n",
        "\n",
        "model = Sequential()\n",
        "\n",
        "model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)))\n",
        "model.add(Conv2D(32, (3, 3), activation='relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "\n",
        "model.add(Flatten())\n",
        "model.add(Dense(256, activation='relu'))\n",
        "model.add(Dense(10, activation='softmax'))\n",
        "\n",
        "sgd = SGD(lr=0.01, decay=0.0005, momentum=0.9, nesterov=True)\n",
        "model.compile(loss='categorical_crossentropy', metrics=['accuracy'], optimizer=sgd)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:66: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:541: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4432: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4267: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:793: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3576: The name tf.log is deprecated. Please use tf.math.log instead.\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cPI05jjEWhBa",
        "colab_type": "code",
        "outputId": "99f8c640-14da-449e-e648-6b583ba5281f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "history = model.fit(x_train, y_train, batch_size=32, epochs=20, verbose=2, validation_split=0.2)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/math_grad.py:1424: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1033: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1020: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3005: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
            "\n",
            "Train on 40000 samples, validate on 10000 samples\n",
            "Epoch 1/20\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:190: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:197: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:207: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:216: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:223: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n",
            "\n",
            " - 12s - loss: 1.3130 - acc: 0.5311 - val_loss: 1.0645 - val_acc: 0.6264\n",
            "Epoch 2/20\n",
            " - 6s - loss: 0.8885 - acc: 0.6877 - val_loss: 0.9405 - val_acc: 0.6656\n",
            "Epoch 3/20\n",
            " - 5s - loss: 0.6299 - acc: 0.7800 - val_loss: 0.9281 - val_acc: 0.6822\n",
            "Epoch 4/20\n",
            " - 5s - loss: 0.3931 - acc: 0.8654 - val_loss: 1.0345 - val_acc: 0.6885\n",
            "Epoch 5/20\n",
            " - 6s - loss: 0.1924 - acc: 0.9393 - val_loss: 1.2265 - val_acc: 0.6825\n",
            "Epoch 6/20\n",
            " - 6s - loss: 0.0740 - acc: 0.9814 - val_loss: 1.3996 - val_acc: 0.6957\n",
            "Epoch 7/20\n",
            " - 5s - loss: 0.0233 - acc: 0.9970 - val_loss: 1.5461 - val_acc: 0.6996\n",
            "Epoch 8/20\n",
            " - 5s - loss: 0.0087 - acc: 0.9995 - val_loss: 1.6417 - val_acc: 0.6995\n",
            "Epoch 9/20\n",
            " - 5s - loss: 0.0047 - acc: 0.9999 - val_loss: 1.7181 - val_acc: 0.7018\n",
            "Epoch 10/20\n",
            " - 5s - loss: 0.0036 - acc: 0.9999 - val_loss: 1.7616 - val_acc: 0.7012\n",
            "Epoch 11/20\n",
            " - 5s - loss: 0.0028 - acc: 1.0000 - val_loss: 1.7929 - val_acc: 0.7009\n",
            "Epoch 12/20\n",
            " - 5s - loss: 0.0025 - acc: 1.0000 - val_loss: 1.8200 - val_acc: 0.7006\n",
            "Epoch 13/20\n",
            " - 5s - loss: 0.0022 - acc: 1.0000 - val_loss: 1.8434 - val_acc: 0.7009\n",
            "Epoch 14/20\n",
            " - 5s - loss: 0.0020 - acc: 1.0000 - val_loss: 1.8579 - val_acc: 0.7016\n",
            "Epoch 15/20\n",
            " - 5s - loss: 0.0019 - acc: 1.0000 - val_loss: 1.8741 - val_acc: 0.7014\n",
            "Epoch 16/20\n",
            " - 5s - loss: 0.0018 - acc: 1.0000 - val_loss: 1.8862 - val_acc: 0.7019\n",
            "Epoch 17/20\n",
            " - 5s - loss: 0.0017 - acc: 1.0000 - val_loss: 1.8992 - val_acc: 0.7019\n",
            "Epoch 18/20\n",
            " - 5s - loss: 0.0016 - acc: 1.0000 - val_loss: 1.9093 - val_acc: 0.7016\n",
            "Epoch 19/20\n",
            " - 5s - loss: 0.0015 - acc: 1.0000 - val_loss: 1.9188 - val_acc: 0.7020\n",
            "Epoch 20/20\n",
            " - 5s - loss: 0.0015 - acc: 1.0000 - val_loss: 1.9278 - val_acc: 0.7024\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VMzvT61zkKLF",
        "colab_type": "text"
      },
      "source": [
        "#### In plotting the training accuracy and the validation accuracy, we can see the training accuracy curve intersects the validation accuracy curve in the plot below and creates a large gap between the two curves. This unfortunately means that our model has overfitted, and although our training accuracy has reached 100%, our testing data on evaluation would most likely not surpass 69% acccuracy because our loss score is way too high (2.04). This puts our accuracy below the industry standard of 75%. \n",
        "\n",
        "#### We can then continue our experiment in training CNN's with different hyperparameters to find a model that will generalize to new data at or above industry level. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wzlUzphSh6mR",
        "colab_type": "code",
        "outputId": "2391c9ab-ce95-46de-8866-f93d9510f248",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        }
      },
      "source": [
        "plot_curves(history)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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eZZUhx+t5hVx6Zlcy27YKuhwREWlBotrjiJlNAH4DJAO/d849UmP5/wPG+ZMZ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\nTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1Vp_DYqBh5vs",
        "colab_type": "code",
        "outputId": "eb3bb7f5-337f-437a-a1d4-35f3591f46c5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        }
      },
      "source": [
        "plot_losses(history)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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PgZLdcOE4Xe8uIhIABXwNDejYjGaNkpg0V73pK/XpI7DiYxh+P2R0DroaEZF6\nSQFfQwnxcZzZvSXvL1jHruLSoMuJPHm58P690O1c6Htl0NWIiNRbCvjDMKxHNtv3lPLxkvVBlxJZ\ndm3xLolLawXn/A28uxGKiEgAFPCHYdDRzWnSIFG96ct755dQtAoueAoaNA26GhGRek0BfxgS4+M4\no1tLpi1Yx56SsqDLiQzfvATfjodTboN2A4OuRkSk3lPAH6YRPbPYuquET5fpMD0blnl77+1OgJN+\nFXQ1IiKCAv6wDe6UQWpygg7Tl+yB134McfFw/pMQH7bxi0REpAYU8IcpOSGe07q2YOr8dRSX1uPD\n9NPvhdWz4JxHoWnboKsRERGfAv4IDO+ZzeYdxXy5fGPQpQRj+Qfw6d+g31XQ/dygqxERkRAK+CNw\nyjGZNEyKZ9LceniYfvt6eP1670Y2w+4LuhoRESlHAX8EUhLjObVLC6bOW0tpmQu6nLrjHLx5M+zc\n6N2KNqlR0BWJiEg5CvgjNKJHNuu37WHGinp0mP6rJ2HxZDjjLsjqGXQ1IiJSAQX8ERpybCbJCXH1\npzf92rneGO+dh8LxPwm6GhERqYQC/gg1Sk5gyLGZvDtvLWWxfph+zw547VpIaQKj/qlb0YqIRDAF\nfC0Y0TObdVt28/WqTUGXEl5TfweFC+G8x6FxZtDViIhIFRTwteAHXVqQFB/HpG9jeAjZBW9B7jgY\n9FPodFrQ1YiIyCEo4Csz5xUoWFCtRVNTEjmpcwbvzl2LczF4mL4oHyb+FLL7wA/+EHQ1IiJSDQr4\niuzZAe/dCePOhJWfV2uV4T2zyd+8kzl5ReGtra6VlcLrY7xb0l44DhKSgq5IRESqQQFfkaSGcM0k\naNQCnh0F8ycecpUzurYkIc5i76Y3nzwEKz+BEQ9A86ODrkZERKpJAV+Z9PZw7VTI7gXjr/Ku/a5C\nk4aJDOoUY4fpV30F0/8MPS6APpcFXY2IiNSAAr4qDZvBVRPh2OEw6Vfw37u8u7hVYkSPLFZu2MH8\nNVvqsMgw2VXkXRLXpDWc/bAuiRMRiTIK+ENJaggXPQf9R8PHf4U3boTS4goXPaNbS+IMJkd7b/o9\n2737zBflwwVPe9e9i4hIVFHAV0d8Apz9CAz5LXzzArx4CezedtBizRsnM/Co5kyauyZ6D9MXLoYn\nfwCL3/UGkWk7IOiKRETkMCjgq8sMhtzmjXu+bDr852zYVnjQYsN7ZrO8cDtLCg7+AhDxvn0Vxg6B\n7YVw5QQ4fkzQFYmIyGEKa8Cb2TgzKzCzuZW0m5k9amZLzWyOmfULabvazJb4j6vDWWeN9L8aLnkB\nChbC02fAhmUHNJ/ZvSVmMHdZD6wAACAASURBVCma7k1fshsm/do7557VA67/GI4+NeiqRETkCIR7\nD/4ZYFgV7cOBzv5jDPAvADNrBtwBHA8MAO4ws/SwVloTxw6D0W97HdGeHgr5s/Y1tUhN4bj2zaLn\nPPzm7+Hfw+GrsXDCzTD6Ha9jnYiIRLVqBbyZ/czM0vw97qfNbJaZDT3Ues65j4CqxlEdBTzrPF8A\nTc0sGzgTmOac2+ic2wRMo+ovCnWvTQ5cO83rhPfM2bDkvX1Nw3tmsWjdVpYVRvhh+iXvwRMne+fd\nL3oWzrwX4hODrkpERGpBdffgf+Sc2wIMBdKBK4H7auHzWwOrQl7n+fMqm38QMxtjZrlmlltYePA5\n8bDK6ATXvufdAObFi2H2CwAM65EFwLtzI3QvvqwU3r8Xnr8Q0lrD9R9Ct1FBVyUiIrWougG/9yLo\nEcBzzrl5IfMC5Zwb65zLcc7lZGYGMMJZakvvsHaHE+GNG+CjB8lOS6Fvu6aReR5+WyE8dx589Bfv\n5jXXTtMd6kREYlB1A36mmU3FC/gpZpYKlNXC5+cDbUNet/HnVTY/MqWkwWWvQM+L4P27YdKvOKt7\nC+at3sL3G3YEXd1+33/hHZJf9SWM/Aec+0/vFIOIiMSc6gb8tcDtwHHOuR1AInBNLXz+ROAq/9z+\nQKDIObcGmAIMNbN0v3PdUH9e5EpIgvOegEG3wIynuOz7P5DMHiZHwr3pnYPPH4NnzoKEZG+vvd+V\nQVclIiJhlFDN5U4AZjvntpvZFUA/4G+HWsnMXgSGABlmlofXMz4RwDn3ODAJ76jAUmAH/pcG59xG\nM7sbmOG/1V3Ouao660WGuDgYejektaLhu7/h9cYruXfOH7j+lAAPge/aAm/eBAsmQpezYdRj0KBp\ncPWIiEidsOrccc3M5gC9gV54l749BVzknDslrNXVUE5OjsvNzQ26DM/c1yl9bQzLSluQdt1Estp2\nqvsa1s71BsrZtALO+KN3GZzuKS8iEjPMbKZzLqeituoeoi9x3jeBUcA/nHOPAam1VWBM6nE+BaNe\nJMs2kfp/w2HdvLr9/K+fh6dO8+4rP/ptGPRThbuISD1S3YDfama/wbs87h0zi8M/1C6Vy+5zBv+T\ndj+7ikth3HBY8Un4P7R4J0z8Kbx5I7Q5Dn7yMbQfFP7PFRGRiFLdgL8Y2I13PfxavF7tD4StqhjS\nrc8JjNx5ByWNWnqXp814yrvzXVGed4vY2rRxuXf73FnPwkm/gqvehMYtavczREQkKlSrk51zbq2Z\nPQ8cZ2ZnA185554Nb2mxYUTPLB6alsGrvZ/ikmW3wTu/PHCB5CbQOBMatQh5bgGNMkKmM73npEaV\nf9CCt72hbM3gsvFwzJnh3TAREYlo1Qp4M7sIb4/9A7wb3PzdzH7tnHs1jLXFhE4tUunUojFvLNrB\nJde+DWu+gW3rvBHbthd4N57Z+1ywALZ9CLs2V/xmiQ33h33oF4LtBTDzGWjVF374H0hvX6fbKCIi\nkae6l8n9Du8a+AIAM8sE3gMU8NUwokcW/5i+lPU7S8lo0//QK5TsgR3rYVuB90VgW0HIlwH/C8Gm\nFZD3FezYAK4Mcq6FYX/2rnMXEZF6r7oBH7c33H0b0Fjy1Ta8ZzaPvr+UqfPWcdnx7Q69QkISpLXy\nHodSVgrFOyBZFzWIiMh+1Q3pd81sipmNNrPRwDt4N6mRauiSlUqH5g3Dc1e7uHiFu4iIHKRaAe+c\n+zUwFu9GN72Asc6528JZWCwxM4b3zOazZRvYtH1P0OWIiEg9UO3D7M6515xzv/AfE8JZVCwa0SOb\n0jLHtAXrgi5FRETqgSoD3sy2mtmWCh5bzWxLXRUZC3q0TqNNegMmR+IQsiIiEnOqDHjnXKpzLq2C\nR6pzLq2uiowFZsZZPbP5eMn6yBpCVkREYpJ6wtehH53YkYR446/TFgVdioiIxDgFfB1qmZbCjwZ3\n5M3Zq5mbXxR0OSIiEsMU8HXs+lOOpkmDRP4yRXvxIiISPgr4OtakQSI3n9qJjxYX8tnS9UGXIyIi\nMUoBH4ArT2hPqyYp3P/uQpxzQZcjIiIxSAEfgJTEeG494xi+ySti8ty1QZcjIiIxSAEfkAv6teGY\nlo15cMoiikvLgi5HRERijAI+IPFxxq/P7MLy9dsZn7sq6HJERCTGKOADdHrXFuS0T+dv7y1hx56S\noMsREZEYooAPkJlx2/AuFGzdzb8/XRF0OSIiEkMU8AE7rkMzTu/agsc/WKaR5kREpNYo4CPAr8/s\nwvY9Jfzzg6VBlyIiIjFCAR8Bjs1K5fx+bfjPZyvJ37wz6HJERCQGKOAjxM/POAYMHp62OOhSREQk\nBijgI0Trpg24+oT2vD4rj0VrtwZdjoiIRDkFfAS5cUgnGiUl8MCUhUGXIiIiUU4BH0HSGyXxkyFH\n896CAmas2Bh0OSIiEsXCGvBmNszMFpnZUjO7vYL2h81stv9YbGabQ9pKQ9omhrPOSPKjwR1pkZrM\nfZM1EI2IiBy+sAW8mcUDjwHDgW7ApWbWLXQZ59zPnXN9nHN9gL8Dr4c079zb5pwbGa46I02DpHh+\ndnpnZq7cxHsLCoIuR0REolQ49+AHAEudc8udc3uAl4BRVSx/KfBiGOuJGhfltOWojEb85d2FlJZp\nL15ERGounAHfGggdRSXPn3cQM2sPdATeD5mdYma5ZvaFmZ0bvjIjT2J8HL8681iWFGzjtVl5QZcj\nIiJRKFI62V0CvOqcKw2Z1945lwNcBjxiZkdXtKKZjfG/COQWFhbWRa11YniPLHq3bcrD0xazq7j0\n0CuIiIiECGfA5wNtQ1638edV5BLKHZ53zuX7z8uBD4C+Fa3onBvrnMtxzuVkZmYeac0Rw8y4bdix\nrCnaxbOfrwi6HBERiTLhDPgZQGcz62hmSXghflBveDPrAqQDn4fMSzezZH86AxgMzA9jrRFp0NEZ\nnHxMJo9NX0bRzuKgyxERkSgStoB3zpUANwNTgAXAeOfcPDO7y8xCe8VfArzkDrwmrCuQa2bfANOB\n+5xz9S7gAW4bdixFO4t54sNlQZciIiJRxGLpWuucnByXm5sbdBm17mcvfc2UeWv58Nen0jItJehy\nREQkQpjZTL+/2kEipZOdVOGXZxxLaZnjkfeWBF2KiIhECQV8FGjXvCGXH9+e8bmrWFa4LehyREQk\nCijgo8TNP+hESkIcD05ZFHQpIiISBRTwUSKjcTLXnXwUk+eu5evvNwVdjoiIRDgFfBT58UlH0bxR\nEve/q4FoRESkagr4KNI4OYFbTuvMF8s38uHi2Llrn4iI1D4FfJS5dEA72jVryP3vLqJMA9GIiEgl\nFPBRJikhjl8OPYYFa7Yw8ZvVQZcjIiIRSgEfhc7p1Ypu2Wk8OHURu0s0EI2IiBxMAR+F4uKM24Z3\nIW/TTl748vugyxERkQikgI9SJ3fOYNDRzfnH+0vZtrsk6HJERCTCKOCjlDecbBc2bN/Dkx8tD7oc\nERGJMAr4KNa7bVNG9MziyY+XU7h1d9DliIhIBFHAR7lfDT2WPSVl/HWqbmErIiL7KeCj3FGZjblm\ncAdezl3FnLzNQZcjIiIRQgEfA245rTPNGyVz58R5uvmNiIgACviYkJqSyG3DjmXW95t5Y3Z+0OWI\niEgEUMDHiAv6taF326b8efJCXTYnIiIK+FgRF2f8cWR3Crfu5u/vLwm6HBERCZgCPob0aduUH/Zv\nw7hPvmN54bagyxERkQAp4GPM/wzrQkpCPHe/PT/oUkREJEAK+BiTmZrMz07vzPRFhby/cF3Q5YiI\nSEAU8DHoqhM6cHRmI+56a75GmxMRqacU8DEoKSGOO87pzooNOxj3yYqgyxERkQAo4GPUycdkcka3\nlvz9/SWs27Ir6HJERKSOKeBj2P+e1Y2SMsd9kxcGXYqIiNQxBXwMa9e8IWNOOooJX+eTu2Jj0OWI\niEgdUsDHuBtPPZqstBTufGsepbpPvYhIvaGAj3ENkxL47VldmZu/hfG5q4IuR0RE6ogCvh44p1c2\nAzo044EpiyjaURx0OSIiUgfCGvBmNszMFpnZUjO7vYL20WZWaGaz/cePQ9quNrMl/uPqcNYZ68yM\nO0Z2Y/OOPTz83uKgyxERkToQtoA3s3jgMWA40A241My6VbDoy865Pv7jKX/dZsAdwPHAAOAOM0sP\nV631QfdWTbjs+HY898VKFq3dGnQ5IiISZuHcgx8ALHXOLXfO7QFeAkZVc90zgWnOuY3OuU3ANGBY\nmOqsN355xrE0Tk7gj2/Nwzl1uBMRiWXhDPjWQGivrjx/XnkXmNkcM3vVzNrWcF3MbIyZ5ZpZbmFh\nYW3UHbPSGyXxq6HH8NmyDbw7d23Q5YiISBgF3cnuLaCDc64X3l76f2r6Bs65sc65HOdcTmZmZq0X\nGGsuHdCOLlmp3PPOAnbu0X3qRURiVTgDPh9oG/K6jT9vH+fcBufcbv/lU0D/6q4rhychPo47R3Yn\nf/NOnvhoWdDliIhImIQz4GcAnc2so5klAZcAE0MXMLPskJcjgQX+9BRgqJml+53rhvrzpBYMPKo5\nZ/fK5l8fLCNv046gyxERkTAIW8A750qAm/GCeQEw3jk3z8zuMrOR/mK3mNk8M/sGuAUY7a+7Ebgb\n70vCDOAuf57Ukt+O6IoZ/GnSgkMvLCIiUcdiqTd1Tk6Oy83NDbqMqPH3/y7hr9MW88KPj2dQp4yg\nyxERkRoys5nOuZyK2oLuZCcBuu7ko2jbrAF3vjWPktKyoMsREZFapICvx1IS4/n9Wd1YvG4b//fF\nyqDLERGRWqSAr+eGdmvJSZ0zeGjaYjZs233oFUREJCoo4Os5M+OOc7qxY08pD07VfepFRGKFAl7o\n1CKVqwd14KUZ3zM3vyjockREpBYo4AWAn53emeaNkrhjou5TLyISCxTwAkBaSiL/c2YXZq7cxJuz\nVwddjoiIHCEFvOxzYf829GrThD9NWsC23SVBlyMiIkdAAS/7xMUZd47sTsHW3Tw2fWnQ5YiIyBFQ\nwMsB+rVL54J+bXj64+/4bv32oMsREZHDpICXg9w27FiSEuL4w5tz1eFORCRKKeDlIC3SUrhteBc+\nXrJed7gTEYlSCnip0BXHt+OUYzK5d9IClhduC7ocERGpIQW8VMjM+MuFvUhJjOfn47/RYDQiIlFG\nAS+VapmWwj3n9uCbVZv55wfLgi5HRERqQAEvVTq7VytG9WnFo/9dwrd5uo2tiEi0UMDLId01sgcZ\njZO59eWv2VVcGnQ5IiJSDQp4OaQmDRN58Ie9WVa4nfvfXRh0OSIiUg0KeKmWEztnMHpQB/796Qo+\nXbo+6HJEROQQFPBSbbcN68JRmY341SvfULSzOOhyRESkCgp4qbYGSfE8fFEfCrbu5s6J84IuR0RE\nqqCAlxrp3bYpP/1BJyZ8nc+kb9cEXY6IiFRCAS81dtOpnejdpgm/nfAtBVt2BV2OiIhUQAEvNZYY\nH8dDF/dhV3Ep//PaHA1IIyISgRTwcliOzmzMb4Z35YNFhbzw1fdBlyMiIuUo4OWwXTmwPSd1zuCe\ntxdo7HgRkQijgJfDFhdnPHBhbxLjjV+Mn60BaUREIogCXo5IVpMU7j63B19/v5nHP9SANCIikUIB\nL0dsVJ/WnN0rm0feW8LcfA1IIyISCcIa8GY2zMwWmdlSM7u9gvZfmNl8M5tjZv81s/YhbaVmNtt/\nTAxnnXLk7jm3B80bJ/Hzl2drQBoRkQgQtoA3s3jgMWA40A241My6lVvsayDHOdcLeBX4S0jbTudc\nH/8xMlx1Su1o2jCJv1zYmyUF23hgyqKgyxERqffCuQc/AFjqnFvunNsDvASMCl3AOTfdObfDf/kF\n0CaM9UiYnXJMJlcObM/Tn3zHZ8s0II2ISJDCGfCtgVUhr/P8eZW5Fpgc8jrFzHLN7AszOzccBUrt\n+82ILnTMaMSvxn/Dll0akEZEJCgR0cnOzK4AcoAHQma3d87lAJcBj5jZ0ZWsO8b/IpBbWFhYB9VK\nVRomJfDQRb1ZpwFpREQCFc6Azwfahrxu4887gJmdDvwOGOmc2713vnMu339eDnwA9K3oQ5xzY51z\nOc65nMzMzNqrXg5b33bp3DTkaF6flc+7czUgjYhIEMIZ8DOAzmbW0cySgEuAA3rDm1lf4Am8cC8I\nmZ9uZsn+dAYwGJgfxlqllv30tM70bN2E37z+LQVbNSCNiEhdC1vAO+dKgJuBKcACYLxzbp6Z3WVm\ne3vFPwA0Bl4pdzlcVyDXzL4BpgP3OecU8FEkMT6Ohy/uzY49pdz+2rcakEZEpI5ZLP3hzcnJcbm5\nuUGXISHGffIdd709nz+f35NLB7QLuhwRkZhiZjP9/moHiYhOdhK7Rg/qwOBOzbn77fms3KABaURE\n6ooCXsJq74A08XHGL8d/Q2lZ7BwxEhGJZAp4CbtWTRtw16ju5K7cxBMfaUAaEZG6oICXOnFun9aM\n6JnFX6cu5qWvvg+6HBGRmKeAlzphZtx/QS8GHd2c21//lvsmL6RMh+tFRMJGAS91JjUlkX+PPo7L\nj2/H4x8u46YXZrFzj0aeExEJBwW81KmE+DjuObcHvz+rK+/OW8slYz/XjXBERMJAAS91zsz48UlH\n8cQV/Vm8bhvnPfYZi9ZuDbosEZGYooCXwAztnsX460+guLSMC/71GR8u1mBBIiK1RQEvgerZpglv\n3jyYts0a8qNnZvDcFyuDLklEJCYo4CVw2U0a8MpPTuCUYzL53zfmcvfb83VDHBGRI6SAl4jQODmB\nJ6/KYfSgDjz9yXdc/9xMtu8uCbosEZGopYCXiBEfZ9w5sjt/HNmd9xeu46InPmdtkXrYi4gcDgW8\nRJyrB3Xg6auPY8X67Zz72KfMW10UdEkiIlFHAS8R6dQuLXj1hkHEGfzw8c/574J1QZckIhJVFPAS\nsbpmp/HGTYM5OrMx1z2by7hPvsM5db4TEakOBbxEtBZpKbx8/UBO79qSu96ezx0T51FSWhZ0WSIi\nEU8BLxGvYVICj1/RnzEnH8Wzn6/kx8/msnVXcdBliYhENAW8RIW4OOO3I7ryp/N68vGS9fzw8c/J\n37wz6LJERCKWAl6iymXHt+OZa44jf9NOzn3sU+bkbQ66JBGRiKSAl6hzUudMXr9xEMkJcVz0xOe8\nO3dt0CWJiEQcBbxEpc4tU5lw42C6Zqdxw/Mzuf65XKbMW8ueEnXAExEBSAi6AJHDlZmazIvXDeSR\n95bw2qw8psxbR9OGiZzTqxXn9WtN37ZNMbOgyxQRCYTF0nXFOTk5Ljc3N+gyJAAlpWV8snQ9r8/K\nZ+r8tewqLqNjRiPO69ua8/q2pm2zhkGXKCJS68xspnMup8I2BbzEmq27ipk8dy2vz8rji+UbARjQ\nsRnn923NiF7ZpKUkBlyhiEjtUMBLvZW3aQdvzl7Na7PyWF64neSEOE7v1pIL+rXmpM6ZJMarG4qI\nRC8FvNR7zjnm5BXx+qw83pqzho3b99C8URIj+7Tign5t6N4qTefrRSTqKOBFQuwpKePDxYVM+DqP\n9+YXsKe0jM4tGnN+vzac27cV2U0aBF2iiEi1KOBFKlG0o5h3vl3D67PyyF25CTMYdHRzzuvbhpOP\nySCjUTJxcdqzF5HIpIAXqYaVG7Yz4et8Xp+Vz/cbdwCQGG+0SE2hZVoyWU1SaJGaQlaTFLLSUmiZ\n5k23TEumYZKuOBWRuhdYwJvZMOBvQDzwlHPuvnLtycCzQH9gA3Cxc26F3/Yb4FqgFLjFOTflUJ+n\ngJfa4Jxj1veb+DaviLVbdlOwZRdr/ce6ol1s31N60DqpKQlk7Qt8/wtByJeArLQUmjdOJl5HA0Sk\nFlUV8GHb7TCzeOAx4AwgD5hhZhOdc/NDFrsW2OSc62RmlwD3AxebWTfgEqA70Ap4z8yOcc4d/JdV\npJaZGf3bN6N/+2YVtm/bXcLaol2s27LLe97qBb/3JWA3SwvWU7B1N6VlB355jo8zmjVKIiUxjqT4\nOJIT4klKiCM5IW7f8955SfFxJPvLJSUcvOz+5b3p+Lg44gzizPyHN0BPnHnbs2/e3uk49s0zM+L9\n+RayXpwZ5v97mIHBvmUqmhfnd1K0kHVDlxeRuhXO44oDgKXOueUAZvYSMAoIDfhRwJ3+9KvAP8z7\nSzAKeMk5txv4zsyW+u/3eRjrFamWxskJdGrRmE4tGle6TGmZY8O23d5e/5bd+/b+N2zfze7iMnaX\n7H2UsqekjG27S9iwrYw9pfvn7fGX2VNSRklZ9J9K2/uFwPzw3z/Pf+W3h863/U37viTYQcseuBwh\n80Pn7f+OYQe8Prh9f00Hzjv4vSvaxgqnQ9Yuv2pF71T+/Q9aphbe4+A6Dn6X6n4vq/Tfo8Jlq3qf\nKtoqfLdDr1edmg7njQ/1PpWt+qPBHTmnd6uaVHHYwhnwrYFVIa/zgOMrW8Y5V2JmRUBzf/4X5dZt\nXdGHmNkYYAxAu3btaqVwkSMVH2e0SEuhRVpKrbxfaZkLCf1SL/hLy9hd7D2Xljmcc5SWOcqcd5qh\nzEGZc5Q5h3P4bRW3lzlHWRn7li1zjlJ/2uEt70LWO2Aee9/TWw9ClvPnOW+mv+7eZWDv15a977N3\nxt73r2y50DOL+5YjdN7e93HlXh/YTrn2itY9qL2Czym/fCWTlD8lWtHXtvJnTcsvU533KD/TlZtx\n0GdU8Cbl16l8uYpVfPa38i+qVZ0trurrbU1OM9fka/Lh1uOtW/kSdXnvjajvGeScGwuMBe8cfMDl\niIRFfJzRICmeBknxgO7EJyKHFs6vEvlA25DXbfx5FS5jZglAE7zOdtVZV0RERCoRzoCfAXQ2s45m\nloTXaW5iuWUmAlf70xcC7zvv2MZE4BIzSzazjkBn4Ksw1ioiIhJTwnaI3j+nfjMwBe8yuXHOuXlm\ndheQ65ybCDwNPOd3otuI9yUAf7nxeB3ySoCb1INeRESk+nSjGxERkShV1XXwGkpLREQkBingRURE\nYpACXkREJAYp4EVERGKQAl5ERCQGKeBFRERikAJeREQkBingRUREYpACXkREJAbF1J3szKwQWFmL\nb5kBrK/F94sEsbhNEJvbpW2KHrG4XbG4TRB729XeOZdZUUNMBXxtM7Pcym4BGK1icZsgNrdL2xQ9\nYnG7YnGbIHa3qyI6RC8iIhKDFPAiIiIxSAFftbFBFxAGsbhNEJvbpW2KHrG4XbG4TRC723UQnYMX\nERGJQdqDFxERiUEKeMDMhpnZIjNbama3V9CebGYv++1fmlmHuq+y+sysrZlNN7P5ZjbPzH5WwTJD\nzKzIzGb7jz8EUWtNmdkKM/vWrzm3gnYzs0f9n9UcM+sXRJ3VZWbHhvwMZpvZFjO7tdwyUfGzMrNx\nZlZgZnND5jUzs2lmtsR/Tq9k3av9ZZaY2dV1V3XVKtmmB8xsof/7NcHMmlaybpW/q0GpZJvuNLP8\nkN+xEZWsW+XfyiBVsl0vh2zTCjObXcm6EfmzOmLOuXr9AOKBZcBRQBLwDdCt3DI3Ao/705cALwdd\n9yG2KRvo50+nAosr2KYhwNtB13oY27YCyKiifQQwGTBgIPBl0DXXYNvigbV417VG3c8KOBnoB8wN\nmfcX4HZ/+nbg/grWawYs95/T/en0oLenim0aCiT40/dXtE1+W5W/qxG2TXcCvzrEeof8Wxlp21Wu\n/a/AH6LpZ3WkD+3BwwBgqXNuuXNuD/ASMKrcMqOA//jTrwKnmZnVYY014pxb45yb5U9vBRYArYOt\nqs6MAp51ni+ApmaWHXRR1XQasMw5V5s3a6ozzrmPgI3lZof+3/kPcG4Fq54JTHPObXTObQKmAcPC\nVmgNVLRNzrmpzrkS/+UXQJs6L+wIVPJzqo7q/K0MTFXb5f+9vgh4sU6LCpgC3gu+VSGv8zg4DPct\n4//HLgKa10l1R8g/ndAX+LKC5hPM7Bszm2xm3eu0sMPngKlmNtPMxlTQXp2fZ6S6hMr/AEXjzwqg\npXNujT+9FmhZwTLR/DP7Ed4Ro4oc6nc10tzsn3YYV8mplGj+OZ0ErHPOLamkPdp+VtWigI9hZtYY\neA241Tm3pVzzLLxDwb2BvwNv1HV9h+lE51w/YDhwk5mdHHRBtcHMkoCRwCsVNEfrz+oAzjsWGjOX\n7ZjZ74AS4PlKFomm39V/AUcDfYA1eIezY8mlVL33Hk0/q2pTwEM+0DbkdRt/XoXLmFkC0ATYUCfV\nHSYzS8QL9+edc6+Xb3fObXHObfOnJwGJZpZRx2XWmHMu338uACbgHTYMVZ2fZyQaDsxyzq0r3xCt\nPyvfur2nSPznggqWibqfmZmNBs4GLve/uBykGr+rEcM5t845V+qcKwOepOJao+7nBPv+Zp8PvFzZ\nMtH0s6oJBTzMADqbWUd/L+oSYGK5ZSYCe3v2Xgi8X9l/6kjgn296GljgnHuokmWy9vYjMLMBeL8L\nkf6lpZGZpe6dxuvsNLfcYhOBq/ze9AOBopBDxJGs0j2MaPxZhQj9v3M18GYFy0wBhppZun9oeKg/\nLyKZ2TDgf4CRzrkdlSxTnd/ViFGun8p5VFxrdf5WRqLTgYXOubyKGqPtZ1UjQffyi4QHXs/rxXg9\nRH/nz7sL7z8wQAreodOlwFfAUUHX/P/t3c/LVFUcx/H3J4VIBU0wUBeGtlEhBKWF1sqdKxeKULqw\nNoIt2olQBP0DrYQEF5m6SmwjLsRn8YALsRBTdKO4EoQ2ERkYoV8X50xNVv5IHufxPu8XXJg5c+Zy\nvty59zv33pnzfUI879IuhV4BLvdlG7AP2Nf7fAxco/0S9gKwedLjfoq4Vvfx/tjHPtpW43EFONS3\n5VVg06TH/RRxLaQl7MVjbS/dtqJ9QbkD/EG7P/sR7bcqU8AN4BywtPfdBBwZe++Hff+6CeyddCxP\niOkm7V70aN8a/cNmBXDmcZ/V2bD8R0zH+v5yhZa0lz8aU3/+j2PlbFn+La7e/vVoXxrr+1Jsq+dd\nnMlOkqQB8hK9JEkDodJLwgAAAbdJREFUZIKXJGmATPCSJA2QCV6SpAEywUuSNEAmeEkzrlfEOz3p\ncUhziQlekqQBMsFL+lOS3Uku9rrYh5PMS3I3yZdJriWZSrKs992Q5MJYXfTXe/tbSc714jiXkqzp\nq1+U5GSvpX5iNldklIbABC8JgCRrgV3AlqraANwHPqDNtPdDVa0HpoHP+1u+AQ5U1du0WdBG7SeA\nQ9WK42ymzS4GrarhJ8A62uxhW2Y8KGkOmz/pAUiaNbYCG4Hv+8n1a7TiMA/4q1DHceBUksXAkqqa\n7u1HgW/7nN4rq+o7gKq6B9DXd7H6fOBJLgNvAudnPixpbjLBSxoJcLSqDv6tMfnskX7/d37r38ce\n38fjjzSjvEQvaWQK2JHkDYAkS5Osoh0ndvQ+7wPnq+oX4Ock7/X2PcB0Vf0K3E6yva/j1SQLXmgU\nkgC/QUvqqup6kk+Bs0leoVXl2g/8BrzTX/uJdp8eWvnXr3oCvwXs7e17gMNJvujr2PkCw5DUWU1O\n0mMluVtViyY9DknPxkv0kiQNkGfwkiQNkGfwkiQNkAlekqQBMsFLkjRAJnhJkgbIBC9J0gCZ4CVJ\nGqCHjhSWdueH8fUAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XcatjQ_rUw-T",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "score = model.evaluate(x_test, y_test, batch_size=128, verbose=0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KdnZxmTZVT8N",
        "colab_type": "code",
        "outputId": "03590c8e-65cf-452f-aa96-6d87e9982bff",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "print(model.metrics_names)\n",
        "print(score)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['loss', 'acc']\n",
            "[2.0442402618408204, 0.6917]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6tdHN0nxC7Yn",
        "colab_type": "text"
      },
      "source": [
        "### CNN Model: Weight Decay 0.00005\n",
        "\n",
        "#### Hypothesis: By lowering the weight decay, capacity will be increased, freeing the model parameters to become larger. This will reduce overfitting. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N1Go7ckLA7a6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.layers import Dense, Flatten\n",
        "from keras.layers import Conv2D, MaxPooling2D\n",
        "from keras.optimizers import SGD\n",
        "\n",
        "model = Sequential()\n",
        "\n",
        "model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)))\n",
        "model.add(Conv2D(32, (3, 3), activation='relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "\n",
        "model.add(Flatten())\n",
        "model.add(Dense(256, activation='relu'))\n",
        "model.add(Dense(10, activation='softmax'))\n",
        "\n",
        "sgd = SGD(lr=0.01, decay=0.00005, momentum=0.9, nesterov=True)\n",
        "model.compile(loss='categorical_crossentropy', metrics=['accuracy'], optimizer=sgd)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mt2MXYR3BQOY",
        "colab_type": "code",
        "outputId": "1e6abf7c-4e59-4f9c-bd18-3fde8bfd7944",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 714
        }
      },
      "source": [
        "history = model.fit(x_train, y_train, batch_size=32, epochs=20, verbose=2, validation_split=0.2)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 40000 samples, validate on 10000 samples\n",
            "Epoch 1/20\n",
            " - 6s - loss: 1.3417 - acc: 0.5187 - val_loss: 1.1004 - val_acc: 0.6181\n",
            "Epoch 2/20\n",
            " - 5s - loss: 0.9218 - acc: 0.6760 - val_loss: 0.9977 - val_acc: 0.6546\n",
            "Epoch 3/20\n",
            " - 5s - loss: 0.6496 - acc: 0.7707 - val_loss: 1.0004 - val_acc: 0.6650\n",
            "Epoch 4/20\n",
            " - 5s - loss: 0.3903 - acc: 0.8641 - val_loss: 1.1354 - val_acc: 0.6697\n",
            "Epoch 5/20\n",
            " - 5s - loss: 0.2098 - acc: 0.9276 - val_loss: 1.4239 - val_acc: 0.6679\n",
            "Epoch 6/20\n",
            " - 5s - loss: 0.1279 - acc: 0.9568 - val_loss: 1.5906 - val_acc: 0.6758\n",
            "Epoch 7/20\n",
            " - 5s - loss: 0.0786 - acc: 0.9742 - val_loss: 1.7957 - val_acc: 0.6780\n",
            "Epoch 8/20\n",
            " - 5s - loss: 0.0525 - acc: 0.9823 - val_loss: 2.1065 - val_acc: 0.6598\n",
            "Epoch 9/20\n",
            " - 5s - loss: 0.0523 - acc: 0.9828 - val_loss: 2.0240 - val_acc: 0.6797\n",
            "Epoch 10/20\n",
            " - 5s - loss: 0.0246 - acc: 0.9920 - val_loss: 2.2265 - val_acc: 0.6752\n",
            "Epoch 11/20\n",
            " - 5s - loss: 0.0144 - acc: 0.9957 - val_loss: 2.2057 - val_acc: 0.6813\n",
            "Epoch 12/20\n",
            " - 5s - loss: 0.0085 - acc: 0.9974 - val_loss: 2.2401 - val_acc: 0.6928\n",
            "Epoch 13/20\n",
            " - 5s - loss: 0.0020 - acc: 0.9996 - val_loss: 2.3406 - val_acc: 0.6980\n",
            "Epoch 14/20\n",
            " - 5s - loss: 7.4879e-04 - acc: 0.9999 - val_loss: 2.3244 - val_acc: 0.6962\n",
            "Epoch 15/20\n",
            " - 5s - loss: 2.0162e-04 - acc: 1.0000 - val_loss: 2.3398 - val_acc: 0.6984\n",
            "Epoch 16/20\n",
            " - 5s - loss: 1.4466e-04 - acc: 1.0000 - val_loss: 2.3572 - val_acc: 0.7002\n",
            "Epoch 17/20\n",
            " - 5s - loss: 1.2176e-04 - acc: 1.0000 - val_loss: 2.3707 - val_acc: 0.6997\n",
            "Epoch 18/20\n",
            " - 5s - loss: 1.0670e-04 - acc: 1.0000 - val_loss: 2.3823 - val_acc: 0.7007\n",
            "Epoch 19/20\n",
            " - 5s - loss: 9.5728e-05 - acc: 1.0000 - val_loss: 2.3926 - val_acc: 0.7008\n",
            "Epoch 20/20\n",
            " - 5s - loss: 8.7263e-05 - acc: 1.0000 - val_loss: 2.4014 - val_acc: 0.7008\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HKA9TWLYEAdf",
        "colab_type": "text"
      },
      "source": [
        "#### In comparing the curves below with the curves above, we have not solved our problem of overfitting by decreasing the decay. In seems that freeing the model parameters to become larger has actually made our overfitting problem worse. Our loss score is now 2.47 instead of 2.04. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TV-wAwShBUGh",
        "colab_type": "code",
        "outputId": "3706fbb9-10b8-4c59-9366-58aebc0d4545",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        }
      },
      "source": [
        "plot_curves(history)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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yvFeoSxIR6TAU4hI0Sz7N5/4FK0nvmsiLt57G8D7JoS5JRKRDUYhLm3PO8at3NvOTNzaQ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\nAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U9Bd3-pCBalm",
        "colab_type": "code",
        "outputId": "b460e9e1-795a-460f-f9f2-69e0d4be39b2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        }
      },
      "source": [
        "plot_losses(history)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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mwWwiIoGL+xBvnJbM+f3b8dbcdRTsKA+6nMg37V4o3KjBbCIiESDuQxzgssEd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\nPxx+9DacT7m/AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fwOJKDS5V1mI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "score = model.evaluate(x_test, y_test, batch_size=128, verbose=0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fm5uRarTV2XU",
        "colab_type": "code",
        "outputId": "c332363e-a915-4119-a9d4-9b1f40aa1c1f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "print(model.metrics_names)\n",
        "print(score)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['loss', 'acc']\n",
            "[2.4784544437408447, 0.6932]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FfKBSxMzEea-",
        "colab_type": "text"
      },
      "source": [
        "### CNN Model: Weight Decay 0.05\n",
        "\n",
        "#### Hypothesis: Since decreasing our weight decay increased overfitting, we will now increase our weight decay to hopefully get the opposite effect. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zqGBJpZuBFIU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.layers import Dense, Flatten\n",
        "from keras.layers import Conv2D, MaxPooling2D\n",
        "from keras.optimizers import SGD\n",
        "\n",
        "model = Sequential()\n",
        "\n",
        "model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)))\n",
        "model.add(Conv2D(32, (3, 3), activation='relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "\n",
        "model.add(Flatten())\n",
        "model.add(Dense(256, activation='relu'))\n",
        "model.add(Dense(10, activation='softmax'))\n",
        "\n",
        "sgd = SGD(lr=0.01, decay=0.05, momentum=0.9, nesterov=True)\n",
        "model.compile(loss='categorical_crossentropy', metrics=['accuracy'], optimizer=sgd)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3GaGP9ygBRNE",
        "colab_type": "code",
        "outputId": "ffd8cd77-07b4-4791-a026-526cf00c8743",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 714
        }
      },
      "source": [
        "history = model.fit(x_train, y_train, batch_size=32, epochs=20, verbose=2, validation_split=0.2)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 40000 samples, validate on 10000 samples\n",
            "Epoch 1/20\n",
            " - 6s - loss: 1.5949 - acc: 0.4345 - val_loss: 1.4827 - val_acc: 0.4779\n",
            "Epoch 2/20\n",
            " - 5s - loss: 1.4209 - acc: 0.5011 - val_loss: 1.4334 - val_acc: 0.4935\n",
            "Epoch 3/20\n",
            " - 5s - loss: 1.3764 - acc: 0.5154 - val_loss: 1.4037 - val_acc: 0.5032\n",
            "Epoch 4/20\n",
            " - 5s - loss: 1.3501 - acc: 0.5264 - val_loss: 1.3871 - val_acc: 0.5085\n",
            "Epoch 5/20\n",
            " - 5s - loss: 1.3314 - acc: 0.5340 - val_loss: 1.3739 - val_acc: 0.5149\n",
            "Epoch 6/20\n",
            " - 5s - loss: 1.3168 - acc: 0.5387 - val_loss: 1.3642 - val_acc: 0.5182\n",
            "Epoch 7/20\n",
            " - 5s - loss: 1.3053 - acc: 0.5440 - val_loss: 1.3547 - val_acc: 0.5216\n",
            "Epoch 8/20\n",
            " - 5s - loss: 1.2954 - acc: 0.5465 - val_loss: 1.3476 - val_acc: 0.5238\n",
            "Epoch 9/20\n",
            " - 6s - loss: 1.2870 - acc: 0.5483 - val_loss: 1.3416 - val_acc: 0.5300\n",
            "Epoch 10/20\n",
            " - 6s - loss: 1.2797 - acc: 0.5511 - val_loss: 1.3358 - val_acc: 0.5297\n",
            "Epoch 11/20\n",
            " - 5s - loss: 1.2729 - acc: 0.5549 - val_loss: 1.3308 - val_acc: 0.5332\n",
            "Epoch 12/20\n",
            " - 5s - loss: 1.2670 - acc: 0.5555 - val_loss: 1.3265 - val_acc: 0.5344\n",
            "Epoch 13/20\n",
            " - 5s - loss: 1.2616 - acc: 0.5572 - val_loss: 1.3218 - val_acc: 0.5362\n",
            "Epoch 14/20\n",
            " - 5s - loss: 1.2565 - acc: 0.5593 - val_loss: 1.3189 - val_acc: 0.5384\n",
            "Epoch 15/20\n",
            " - 5s - loss: 1.2522 - acc: 0.5607 - val_loss: 1.3156 - val_acc: 0.5374\n",
            "Epoch 16/20\n",
            " - 5s - loss: 1.2480 - acc: 0.5629 - val_loss: 1.3116 - val_acc: 0.5405\n",
            "Epoch 17/20\n",
            " - 5s - loss: 1.2441 - acc: 0.5642 - val_loss: 1.3087 - val_acc: 0.5402\n",
            "Epoch 18/20\n",
            " - 5s - loss: 1.2406 - acc: 0.5650 - val_loss: 1.3057 - val_acc: 0.5426\n",
            "Epoch 19/20\n",
            " - 5s - loss: 1.2369 - acc: 0.5657 - val_loss: 1.3030 - val_acc: 0.5408\n",
            "Epoch 20/20\n",
            " - 5s - loss: 1.2339 - acc: 0.5664 - val_loss: 1.3009 - val_acc: 0.5438\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wyozMTWSFbds",
        "colab_type": "text"
      },
      "source": [
        "#### The results of this model are interesting. We increased our decay to .05 and have greatly reduced our gap in training accuracy and validation accuracy. However, our overfitting is still somewhat significant and our accuracy is too low for industry standards. We will continue our experimentation with hyperparameter tuning. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3K1WlK40BXoX",
        "colab_type": "code",
        "outputId": "98444137-75ec-48a5-900b-dd558c7c88a2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        }
      },
      "source": [
        "plot_curves(history)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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32/9x1Sxyh6WEuqS+o2oHvPwjWPuiN6jtG//0VoUTEdkHBbwcUH1jM7f9azWP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\nWsErpZRSDvR/t90T/g2b0loAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W1S212FMBbg9",
        "colab_type": "code",
        "outputId": "056b4857-8b48-47ec-a00d-9c3ec0504a9c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        }
      },
      "source": [
        "plot_losses(history)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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2AAudc1X9fV5RUZFbv379UP4ThlV7Ryfv/cka6prbef6fLyEuOvLUNzkHb/0G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\nWYb+B4l8QOmPIMxEAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xv2DdFzWV_Ax",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "score = model.evaluate(x_test, y_test, batch_size=128, verbose=0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3xQYxziPWBur",
        "colab_type": "code",
        "outputId": "e72ec3cf-2263-47a6-eb50-1385c7b712c4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "print(model.metrics_names)\n",
        "print(score)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['loss', 'acc']\n",
            "[1.284461966896057, 0.5452]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M5pQjy-gF-Ka",
        "colab_type": "text"
      },
      "source": [
        "### CNN Model: Weight Decay 0.0\n",
        "\n",
        "#### We will train our model with 0.0 decay to see what the effect is on the model. \n",
        "\n",
        "####Hypothesis: Decreasing the weight decay to 0.0 will free the model parameters to become larger, which might increase overfitting the most we have seen so far. \n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Uh4gs_ibBK0l",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.layers import Dense, Flatten\n",
        "from keras.layers import Conv2D, MaxPooling2D\n",
        "from keras.optimizers import SGD\n",
        "\n",
        "model = Sequential()\n",
        "\n",
        "model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)))\n",
        "model.add(Conv2D(32, (3, 3), activation='relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "\n",
        "model.add(Flatten())\n",
        "model.add(Dense(256, activation='relu'))\n",
        "model.add(Dense(10, activation='softmax'))\n",
        "\n",
        "sgd = SGD(lr=0.01, decay=0.0, momentum=0.9, nesterov=True)\n",
        "model.compile(loss='categorical_crossentropy', metrics=['accuracy'], optimizer=sgd)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2o6PMXwCBSCQ",
        "colab_type": "code",
        "outputId": "2c142718-22d3-430d-cc83-64fa7ee58630",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 714
        }
      },
      "source": [
        "history = model.fit(x_train, y_train, batch_size=32, epochs=20, verbose=2, validation_split=0.2)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 40000 samples, validate on 10000 samples\n",
            "Epoch 1/20\n",
            " - 6s - loss: 1.3340 - acc: 0.5236 - val_loss: 1.0779 - val_acc: 0.6144\n",
            "Epoch 2/20\n",
            " - 5s - loss: 0.9263 - acc: 0.6755 - val_loss: 0.9950 - val_acc: 0.6570\n",
            "Epoch 3/20\n",
            " - 5s - loss: 0.6771 - acc: 0.7611 - val_loss: 1.0385 - val_acc: 0.6583\n",
            "Epoch 4/20\n",
            " - 5s - loss: 0.4344 - acc: 0.8484 - val_loss: 1.1560 - val_acc: 0.6645\n",
            "Epoch 5/20\n",
            " - 5s - loss: 0.2791 - acc: 0.9048 - val_loss: 1.4351 - val_acc: 0.6634\n",
            "Epoch 6/20\n",
            " - 5s - loss: 0.2131 - acc: 0.9273 - val_loss: 1.6085 - val_acc: 0.6548\n",
            "Epoch 7/20\n",
            " - 5s - loss: 0.1714 - acc: 0.9428 - val_loss: 1.8872 - val_acc: 0.6466\n",
            "Epoch 8/20\n",
            " - 5s - loss: 0.1360 - acc: 0.9550 - val_loss: 1.9527 - val_acc: 0.6544\n",
            "Epoch 9/20\n",
            " - 5s - loss: 0.1459 - acc: 0.9530 - val_loss: 1.9017 - val_acc: 0.6678\n",
            "Epoch 10/20\n",
            " - 5s - loss: 0.1174 - acc: 0.9628 - val_loss: 2.2001 - val_acc: 0.6591\n",
            "Epoch 11/20\n",
            " - 5s - loss: 0.1168 - acc: 0.9645 - val_loss: 2.3923 - val_acc: 0.6601\n",
            "Epoch 12/20\n",
            " - 5s - loss: 0.1202 - acc: 0.9644 - val_loss: 2.3862 - val_acc: 0.6446\n",
            "Epoch 13/20\n",
            " - 5s - loss: 0.1135 - acc: 0.9662 - val_loss: 2.4839 - val_acc: 0.6488\n",
            "Epoch 14/20\n",
            " - 5s - loss: 0.1025 - acc: 0.9704 - val_loss: 2.4499 - val_acc: 0.6395\n",
            "Epoch 15/20\n",
            " - 5s - loss: 0.0960 - acc: 0.9716 - val_loss: 2.6770 - val_acc: 0.6436\n",
            "Epoch 16/20\n",
            " - 5s - loss: 0.1397 - acc: 0.9620 - val_loss: 2.8315 - val_acc: 0.6292\n",
            "Epoch 17/20\n",
            " - 5s - loss: 0.1357 - acc: 0.9630 - val_loss: 2.7127 - val_acc: 0.6382\n",
            "Epoch 18/20\n",
            " - 5s - loss: 0.1152 - acc: 0.9690 - val_loss: 2.8563 - val_acc: 0.6425\n",
            "Epoch 19/20\n",
            " - 5s - loss: 0.1185 - acc: 0.9692 - val_loss: 3.1674 - val_acc: 0.6253\n",
            "Epoch 20/20\n",
            " - 5s - loss: 0.1465 - acc: 0.9614 - val_loss: 3.0073 - val_acc: 0.6177\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i9TQOEsLHFvo",
        "colab_type": "text"
      },
      "source": [
        "#### As expected, the model has severly overfitted with a loss of 3.06, the highest so far."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sSwZ0LCaBYio",
        "colab_type": "code",
        "outputId": "2ab136c9-cb93-42fe-d100-d0a48156ec22",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        }
      },
      "source": [
        "plot_curves(history)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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wJTkFnH1aMn26R/u7HBER6WSO2Z3uXZ3t/3VQLdLCqu2HKCyp5orx6f4uRURE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\nd+JmZmaZciduZmaWKXfiZmZmmfofsnRcMTH7A7EAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uGVc-6fIBcSx",
        "colab_type": "code",
        "outputId": "f108e8fc-9c8a-4fdb-e8df-c88f12d3dfad",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        }
      },
      "source": [
        "plot_losses(history)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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VkNIUup0MXU6AjsO9ID0YcYleSLcd7F014pw3aMmqb+Dnb7yj9iUfesvGxEOL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\nKAdxMzOzQv0NIKhan/2VT0sAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PYWGirsTWEAk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "score = model.evaluate(x_test, y_test, batch_size=128, verbose=0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pF9NbWGFWGn2",
        "colab_type": "code",
        "outputId": "df8c5fbb-11bc-47c5-fc60-8fb3b1996605",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "print(model.metrics_names)\n",
        "print(score)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['loss', 'acc']\n",
            "[3.0628340278625488, 0.6155]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gVnTLZqklIYD",
        "colab_type": "text"
      },
      "source": [
        "### CNN with Dropout:\n",
        "\n",
        "#### CNN's and other Deep Neural Networks have hundreds of thousands of parameters, which make them prone to overfitting. Certain regularization techniques, such as Dropout, can prevent overfitting so the models can generalize better. \n",
        "\n",
        "#### Dropout works by masking a random subset of a neural network for every input of probability p and scaling up the rest of the inputs by 1(1 - p). During training, masking prevents gradient backpropagation through the masked outputs. \n",
        "\n",
        "#### During testing, dropout will set p to zero, so that the expected layer of outputs are the same during training and testing. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IV0NV5SMHWge",
        "colab_type": "text"
      },
      "source": [
        "### CNN Model: Dropout Layer with 0.0 Weight Decay\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "58GlufxOXUNA",
        "colab_type": "code",
        "outputId": "c0720b92-4415-4e6d-aefe-540fee8b7a26",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "source": [
        "from keras.layers import Dense, Dropout, Flatten\n",
        "from keras.layers import Conv2D, MaxPooling2D\n",
        "\n",
        "model = Sequential()\n",
        "\n",
        "model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)))\n",
        "model.add(Conv2D(32, (3, 3), activation='relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "# Dropout layer added here\n",
        "model.add(Dropout(0.25))\n",
        "\n",
        "model.add(Flatten())\n",
        "model.add(Dense(256, activation='relu'))\n",
        "# Dropout layer added here\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(10, activation='softmax'))\n",
        "\n",
        "sgd = SGD(lr=0.01, decay=0.0, momentum=0.9, nesterov=True)\n",
        "model.compile(loss='categorical_crossentropy', metrics=['accuracy'], optimizer=sgd)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:148: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3733: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iqAgAO0oXWJz",
        "colab_type": "code",
        "outputId": "5a1775bd-4763-42d6-d527-306a216dae34",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 714
        }
      },
      "source": [
        "history = model.fit(x_train, y_train, batch_size=32, epochs=20, verbose=2, validation_split=0.2)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 40000 samples, validate on 10000 samples\n",
            "Epoch 1/20\n",
            " - 6s - loss: 1.5348 - acc: 0.4446 - val_loss: 1.2440 - val_acc: 0.5582\n",
            "Epoch 2/20\n",
            " - 6s - loss: 1.2106 - acc: 0.5722 - val_loss: 1.0371 - val_acc: 0.6323\n",
            "Epoch 3/20\n",
            " - 6s - loss: 1.0917 - acc: 0.6183 - val_loss: 1.0765 - val_acc: 0.6179\n",
            "Epoch 4/20\n",
            " - 6s - loss: 1.0153 - acc: 0.6421 - val_loss: 0.9942 - val_acc: 0.6554\n",
            "Epoch 5/20\n",
            " - 6s - loss: 0.9366 - acc: 0.6709 - val_loss: 0.9812 - val_acc: 0.6597\n",
            "Epoch 6/20\n",
            " - 6s - loss: 0.8757 - acc: 0.6895 - val_loss: 0.9550 - val_acc: 0.6714\n",
            "Epoch 7/20\n",
            " - 6s - loss: 0.8260 - acc: 0.7063 - val_loss: 0.9890 - val_acc: 0.6677\n",
            "Epoch 8/20\n",
            " - 6s - loss: 0.7834 - acc: 0.7231 - val_loss: 0.9575 - val_acc: 0.6735\n",
            "Epoch 9/20\n",
            " - 6s - loss: 0.7454 - acc: 0.7377 - val_loss: 0.9877 - val_acc: 0.6707\n",
            "Epoch 10/20\n",
            " - 6s - loss: 0.7242 - acc: 0.7504 - val_loss: 0.9958 - val_acc: 0.6700\n",
            "Epoch 11/20\n",
            " - 6s - loss: 0.6962 - acc: 0.7594 - val_loss: 1.0299 - val_acc: 0.6704\n",
            "Epoch 12/20\n",
            " - 6s - loss: 0.6558 - acc: 0.7725 - val_loss: 1.0088 - val_acc: 0.6737\n",
            "Epoch 13/20\n",
            " - 6s - loss: 0.6333 - acc: 0.7807 - val_loss: 0.9921 - val_acc: 0.6780\n",
            "Epoch 14/20\n",
            " - 6s - loss: 0.6195 - acc: 0.7857 - val_loss: 1.0414 - val_acc: 0.6683\n",
            "Epoch 15/20\n",
            " - 6s - loss: 0.6025 - acc: 0.7913 - val_loss: 1.0596 - val_acc: 0.6700\n",
            "Epoch 16/20\n",
            " - 6s - loss: 0.5820 - acc: 0.7982 - val_loss: 1.0554 - val_acc: 0.6682\n",
            "Epoch 17/20\n",
            " - 6s - loss: 0.5814 - acc: 0.8012 - val_loss: 1.0831 - val_acc: 0.6685\n",
            "Epoch 18/20\n",
            " - 6s - loss: 0.5710 - acc: 0.8051 - val_loss: 1.0620 - val_acc: 0.6750\n",
            "Epoch 19/20\n",
            " - 6s - loss: 0.5463 - acc: 0.8133 - val_loss: 1.0937 - val_acc: 0.6699\n",
            "Epoch 20/20\n",
            " - 6s - loss: 0.5432 - acc: 0.8146 - val_loss: 1.2115 - val_acc: 0.6570\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6klQy85Lr4tS",
        "colab_type": "text"
      },
      "source": [
        "#### As seen in our accuracy curves and loss curves, we did prevent over fitting to an extent with dropout, but our validation accuracy with no decay suffered. \n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8w9rBwahh1QI",
        "colab_type": "code",
        "outputId": "ae8407d1-3162-44fc-9c92-f6a9de4fa9ad",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        }
      },
      "source": [
        "plot_curves(history)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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f2z+BW04ZwWnpvUnvm6B70KXLUsCLSIdTULqXP3+0jhcW5VFX75g2PIWrjh/CKUf1ol9S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\nAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wSnXJbRsh11L",
        "colab_type": "code",
        "outputId": "7281a4b4-2392-4eba-e053-31dcd4b915c9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        }
      },
      "source": [
        "plot_losses(history)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Vr8OS52HnIggJd6PtIVe7u3C8damwPgp2uH9PN3zsGuSqy795rtMgGHmb+/ev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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NoiI2-j9WI73",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "score = model.evaluate(x_test, y_test, batch_size=128, verbose=0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m6WZ581AWLzp",
        "colab_type": "code",
        "outputId": "e1b1659a-3e1c-4e20-ff27-3a4e7531d4a4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "print(model.metrics_names)\n",
        "print(score)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['loss', 'acc']\n",
            "[1.2254878425598144, 0.6513]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FTtuDDJtId8z",
        "colab_type": "text"
      },
      "source": [
        "### CNN Model: Dropout Layer with Weight Decay\n",
        "\n",
        "####Hypothesis: With these finely tuned parameters, we will most likely see our most accurate and least overfitted model yet. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rjkSyq3BIQC8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.layers import Dense, Dropout, Flatten\n",
        "from keras.layers import Conv2D, MaxPooling2D\n",
        "\n",
        "model = Sequential()\n",
        "\n",
        "model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)))\n",
        "model.add(Conv2D(32, (3, 3), activation='relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "# Dropout layer added here\n",
        "model.add(Dropout(0.25))\n",
        "\n",
        "model.add(Flatten())\n",
        "model.add(Dense(256, activation='relu'))\n",
        "# Dropout layer added here\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(10, activation='softmax'))\n",
        "\n",
        "sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)\n",
        "model.compile(loss='categorical_crossentropy', metrics=['accuracy'], optimizer=sgd)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3z021mGmI_dp",
        "colab_type": "code",
        "outputId": "8666aa4d-1b66-46a6-cc45-a08eba7e0509",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 714
        }
      },
      "source": [
        "history = model.fit(x_train, y_train, batch_size=32, epochs=20, verbose=2, validation_split=0.2)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 40000 samples, validate on 10000 samples\n",
            "Epoch 1/20\n",
            " - 6s - loss: 1.5476 - acc: 0.4405 - val_loss: 1.2485 - val_acc: 0.5524\n",
            "Epoch 2/20\n",
            " - 6s - loss: 1.2266 - acc: 0.5655 - val_loss: 1.0753 - val_acc: 0.6266\n",
            "Epoch 3/20\n",
            " - 6s - loss: 1.1071 - acc: 0.6093 - val_loss: 1.0251 - val_acc: 0.6446\n",
            "Epoch 4/20\n",
            " - 6s - loss: 1.0144 - acc: 0.6442 - val_loss: 1.0270 - val_acc: 0.6362\n",
            "Epoch 5/20\n",
            " - 6s - loss: 0.9389 - acc: 0.6681 - val_loss: 0.9826 - val_acc: 0.6585\n",
            "Epoch 6/20\n",
            " - 6s - loss: 0.8780 - acc: 0.6929 - val_loss: 1.0105 - val_acc: 0.6501\n",
            "Epoch 7/20\n",
            " - 6s - loss: 0.8212 - acc: 0.7125 - val_loss: 0.9717 - val_acc: 0.6721\n",
            "Epoch 8/20\n",
            " - 6s - loss: 0.7776 - acc: 0.7255 - val_loss: 0.9338 - val_acc: 0.6875\n",
            "Epoch 9/20\n",
            " - 6s - loss: 0.7407 - acc: 0.7419 - val_loss: 0.9689 - val_acc: 0.6774\n",
            "Epoch 10/20\n",
            " - 6s - loss: 0.7066 - acc: 0.7539 - val_loss: 0.9714 - val_acc: 0.6744\n",
            "Epoch 11/20\n",
            " - 6s - loss: 0.6661 - acc: 0.7676 - val_loss: 0.9925 - val_acc: 0.6774\n",
            "Epoch 12/20\n",
            " - 6s - loss: 0.6535 - acc: 0.7719 - val_loss: 1.0358 - val_acc: 0.6691\n",
            "Epoch 13/20\n",
            " - 6s - loss: 0.6218 - acc: 0.7871 - val_loss: 1.0339 - val_acc: 0.6711\n",
            "Epoch 14/20\n",
            " - 6s - loss: 0.6077 - acc: 0.7912 - val_loss: 1.0599 - val_acc: 0.6730\n",
            "Epoch 15/20\n",
            " - 6s - loss: 0.5877 - acc: 0.7978 - val_loss: 1.0986 - val_acc: 0.6655\n",
            "Epoch 16/20\n",
            " - 6s - loss: 0.5725 - acc: 0.8024 - val_loss: 1.0452 - val_acc: 0.6857\n",
            "Epoch 17/20\n",
            " - 6s - loss: 0.5649 - acc: 0.8075 - val_loss: 1.0380 - val_acc: 0.6824\n",
            "Epoch 18/20\n",
            " - 6s - loss: 0.5484 - acc: 0.8142 - val_loss: 1.1226 - val_acc: 0.6693\n",
            "Epoch 19/20\n",
            " - 6s - loss: 0.5446 - acc: 0.8140 - val_loss: 1.1020 - val_acc: 0.6775\n",
            "Epoch 20/20\n",
            " - 6s - loss: 0.5360 - acc: 0.8186 - val_loss: 1.1107 - val_acc: 0.6685\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8GAXNG3yK5As",
        "colab_type": "text"
      },
      "source": [
        "#### The results of these curves were also disappointing. We see no reduction in overfitting from the previous model and the accuracy still lingers at around 67%. We must try different techniques such as regularization to create better results. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tYJyNey_KReV",
        "colab_type": "code",
        "outputId": "65a4bb8c-adca-4660-8064-0b36d1d267e1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        }
      },
      "source": [
        "plot_curves(history)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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IhE11jY/s/L3fCvHMXSUUVxxYT71zXAxDe8ZzYloPhvaMZ2iPeIb2jGdAt45ahlXkEBTw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\nAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XGPkkIkVKStx",
        "colab_type": "code",
        "outputId": "523dd957-d2d7-4a4b-fd97-eb1c5746d410",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        }
      },
      "source": [
        "plot_losses(history)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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M7hcXEZFGQSFeB5FhIfRITdDiNhERaRQU4nWUlZ7E4pwCKquPuuW7iIiITynE6ygrI4ny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\nM6spd+JmZmY19X9tNhN6WRcr6AAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1JrxDH7WWOKf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "score = model.evaluate(x_test, y_test, batch_size=128, verbose=0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZfcaEGwpWO6p",
        "colab_type": "code",
        "outputId": "50ffb4e9-c911-4ca0-c52d-50e5db954c92",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "print(model.metrics_names)\n",
        "print(score)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['loss', 'acc']\n",
            "[1.1180916896820068, 0.6651]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RA-wxiXdsPHh",
        "colab_type": "text"
      },
      "source": [
        "## Batch Normalization: \n",
        "\n",
        "#### Batch Normalization is a technique that addresses the issue of vanishing/exploding gradients, and also the problem involving the distribution of each layer's inputs changing during training while the parameters of the previous layer change synchronously. This is known as the *Internal Covariate Shift* problem. \n",
        "\n",
        "#### Batch Normalization accomplishes this task by adding an operation in the model just before the activation function of each layer, simply zero-centering and normalizing the inputs, and then scaling and shifting the result with two new parameters per layer. This operation basically forces the model to learn the optimal scale and mean of the inputs per layer. \n",
        "\n",
        "#### The following equations show the step by step process the algorithm performs to estimate the inputs mean and standard deviation by evaluating the mean and deviation of the inputs over the current mini-batch. \n",
        "\n",
        "#### Step 1: Finds the empirical mean over the whole mini-batch. \n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LD6NYAAHvnZO",
        "colab_type": "code",
        "outputId": "f82454d0-3938-4907-f032-8cc22bedcc27",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 96
        }
      },
      "source": [
        "from IPython.display import Math, HTML\n",
        "display(HTML(\"<script src='https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.3/\"\n",
        "               \"latest.js?config=default'></script>\"))\n",
        "\n",
        "Math(r'\\mu_B \\leftarrow \\frac{1}{m}\\sum_{i = 1}^{m}x_i')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<script src='https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.3/latest.js?config=default'></script>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/latex": "$$\\mu_B \\leftarrow \\frac{1}{m}\\sum_{i = 1}^{m}x_i$$",
            "text/plain": [
              "<IPython.core.display.Math object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jg2XGZzD1wtY",
        "colab_type": "text"
      },
      "source": [
        "#### Step 2: Finds the empirical standard deviation evaluated over the whole mini-batch."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SMHioAfOv6rF",
        "colab_type": "code",
        "outputId": "ab099211-3ef7-4a28-d2c5-69896d77f07f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 96
        }
      },
      "source": [
        "from IPython.display import Math, HTML\n",
        "display(HTML(\"<script src='https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.3/\"\n",
        "               \"latest.js?config=default'></script>\"))\n",
        "\n",
        "Math(r'\\sigma_B^2 \\leftarrow \\frac{1}{m} \\sum_{i=1}^{m}(x_i - \\mu_B)^2')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<script src='https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.3/latest.js?config=default'></script>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/latex": "$$\\sigma_B^2 \\leftarrow \\frac{1}{m} \\sum_{i=1}^{m}(x_i - \\mu_B)^2$$",
            "text/plain": [
              "<IPython.core.display.Math object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S4xkeaWI18F5",
        "colab_type": "text"
      },
      "source": [
        "#### Step 3: Produces the zero-centered, normalized input."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hB5GhCMTwA0L",
        "colab_type": "code",
        "outputId": "b9a5c6a3-f2fb-4378-bc81-951665f4d336",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 99
        }
      },
      "source": [
        "from IPython.display import Math, HTML\n",
        "display(HTML(\"<script src='https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.3/\"\n",
        "               \"latest.js?config=default'></script>\"))\n",
        "\n",
        "Math(r'\\hat{x_i} \\leftarrow \\frac{x_i - \\mu_B}{\\sqrt{\\sigma_B^2 + \\epsilon}}')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<script src='https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.3/latest.js?config=default'></script>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/latex": "$$\\hat{x_i} \\leftarrow \\frac{x_i - \\mu_B}{\\sqrt{\\sigma_B^2 + \\epsilon}}$$",
            "text/plain": [
              "<IPython.core.display.Math object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zAavsoZl2IXa",
        "colab_type": "text"
      },
      "source": [
        "#### Step 4: The entire output of the Batch Normalization algorithm: it returns a scaled and shifted version of the inputs. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0fIXJsw8wXJe",
        "colab_type": "code",
        "outputId": "dc19903a-ea34-4bde-f1aa-536af9cc22ae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "from IPython.display import Math, HTML\n",
        "display(HTML(\"<script src='https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.3/\"\n",
        "               \"latest.js?config=default'></script>\"))\n",
        "\n",
        "Math(r'y_i \\leftarrow \\gamma \\hat{x_i} + \\beta \\equiv \\mbox{BN}_{\\gamma,\\beta}(x_i)')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<script src='https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.3/latest.js?config=default'></script>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/latex": "$$y_i \\leftarrow \\gamma \\hat{x_i} + \\beta \\equiv \\mbox{BN}_{\\gamma,\\beta}(x_i)$$",
            "text/plain": [
              "<IPython.core.display.Math object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y9AcQ_vS2bek",
        "colab_type": "text"
      },
      "source": [
        "#### We will now continue optimizing our model with Batch Normalization, while keeping our Dropout rate with decay and CNN layers the same. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wA-kwlzUX_F8",
        "colab_type": "code",
        "outputId": "47ee0019-3786-41be-9ca2-83deb002098d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        }
      },
      "source": [
        "from keras.layers import Dense, Flatten, Activation\n",
        "from keras.layers import Conv2D, MaxPooling2D\n",
        "from keras.layers.normalization import BatchNormalization\n",
        "\n",
        "model = Sequential()\n",
        "\n",
        "model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)))\n",
        "model.add(Conv2D(32, (3, 3)))\n",
        "# Batch normalization layer added here\n",
        "model.add(BatchNormalization())\n",
        "model.add(Activation('relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "model.add(Dropout(0.25))\n",
        "\n",
        "model.add(Flatten())\n",
        "model.add(Dense(256))\n",
        "# Batch normalization layer added here\n",
        "model.add(BatchNormalization())\n",
        "model.add(Activation('relu'))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(10, activation='softmax'))\n",
        "\n",
        "sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)\n",
        "model.compile(loss='categorical_crossentropy', metrics=['accuracy'], optimizer=sgd)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:2041: The name tf.nn.fused_batch_norm is deprecated. Please use tf.compat.v1.nn.fused_batch_norm instead.\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a2dLQaKDYGgB",
        "colab_type": "code",
        "outputId": "211a3513-9423-408a-dbfe-335ad3db60c9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 714
        }
      },
      "source": [
        "history = model.fit(x_train, y_train, batch_size=32, epochs=20, verbose=2, validation_split=0.2)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 40000 samples, validate on 10000 samples\n",
            "Epoch 1/20\n",
            " - 8s - loss: 1.5453 - acc: 0.4478 - val_loss: 1.1670 - val_acc: 0.5828\n",
            "Epoch 2/20\n",
            " - 8s - loss: 1.2015 - acc: 0.5712 - val_loss: 1.0032 - val_acc: 0.6448\n",
            "Epoch 3/20\n",
            " - 8s - loss: 1.0536 - acc: 0.6253 - val_loss: 0.9519 - val_acc: 0.6643\n",
            "Epoch 4/20\n",
            " - 8s - loss: 0.9535 - acc: 0.6581 - val_loss: 0.9228 - val_acc: 0.6751\n",
            "Epoch 5/20\n",
            " - 8s - loss: 0.8838 - acc: 0.6873 - val_loss: 0.8669 - val_acc: 0.7003\n",
            "Epoch 6/20\n",
            " - 8s - loss: 0.8218 - acc: 0.7087 - val_loss: 0.8469 - val_acc: 0.7079\n",
            "Epoch 7/20\n",
            " - 8s - loss: 0.7667 - acc: 0.7290 - val_loss: 0.8408 - val_acc: 0.7122\n",
            "Epoch 8/20\n",
            " - 8s - loss: 0.7225 - acc: 0.7417 - val_loss: 0.8292 - val_acc: 0.7109\n",
            "Epoch 9/20\n",
            " - 8s - loss: 0.6767 - acc: 0.7589 - val_loss: 0.8169 - val_acc: 0.7234\n",
            "Epoch 10/20\n",
            " - 8s - loss: 0.6403 - acc: 0.7722 - val_loss: 0.7874 - val_acc: 0.7317\n",
            "Epoch 11/20\n",
            " - 8s - loss: 0.6133 - acc: 0.7821 - val_loss: 0.8363 - val_acc: 0.7196\n",
            "Epoch 12/20\n",
            " - 8s - loss: 0.5751 - acc: 0.7931 - val_loss: 0.8205 - val_acc: 0.7286\n",
            "Epoch 13/20\n",
            " - 8s - loss: 0.5480 - acc: 0.8050 - val_loss: 0.8223 - val_acc: 0.7205\n",
            "Epoch 14/20\n",
            " - 8s - loss: 0.5243 - acc: 0.8130 - val_loss: 0.8562 - val_acc: 0.7152\n",
            "Epoch 15/20\n",
            " - 8s - loss: 0.4956 - acc: 0.8214 - val_loss: 0.8272 - val_acc: 0.7306\n",
            "Epoch 16/20\n",
            " - 8s - loss: 0.4802 - acc: 0.8290 - val_loss: 0.8033 - val_acc: 0.7347\n",
            "Epoch 17/20\n",
            " - 8s - loss: 0.4602 - acc: 0.8336 - val_loss: 0.8109 - val_acc: 0.7363\n",
            "Epoch 18/20\n",
            " - 8s - loss: 0.4359 - acc: 0.8444 - val_loss: 0.8502 - val_acc: 0.7311\n",
            "Epoch 19/20\n",
            " - 8s - loss: 0.4264 - acc: 0.8464 - val_loss: 0.8340 - val_acc: 0.7344\n",
            "Epoch 20/20\n",
            " - 8s - loss: 0.4035 - acc: 0.8540 - val_loss: 0.8023 - val_acc: 0.7432\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5ylAi23S26PR",
        "colab_type": "text"
      },
      "source": [
        "#### In viewing the plots, we see a slight improvement in the loss and accuracy curves. Our validation accuracy is also increasing, which means that our model with generalize more with new data at around 73% accuracy, but we might need to tune our dropout level to reach a better overall evaluation. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mg9vDcNEcfGW",
        "colab_type": "code",
        "outputId": "f43bfdec-ca91-4825-b6ae-4023a4e8db6a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        }
      },
      "source": [
        "plot_curves(history)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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28DDQO7rP7e4+P/reHcANQBNwm7u/EGatItK5baio4bFFm3mycAvVtQ0M7NWVmRMHcubo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\nTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z7Df4Bdjbelm",
        "colab_type": "code",
        "outputId": "3a16a5dc-8d72-43b2-ad64-03bf87188ea8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        }
      },
      "source": [
        "plot_losses(history)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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tZi1hLax/DT76D6gqgZQRziYrQ2ZBdFKrTr2jsII5/1iOBf5x/SjO653QNjWL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\nPv3k+89SWwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O76yzxw-WUYd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "score = model.evaluate(x_test, y_test, batch_size=128, verbose=0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sUI_LgOSWXwS",
        "colab_type": "code",
        "outputId": "497534de-1388-44b1-f8fd-b153e358a8d9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "print(model.metrics_names)\n",
        "print(score)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['loss', 'acc']\n",
            "[0.8316235427856445, 0.7291]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aQlg7BYkkRDK",
        "colab_type": "text"
      },
      "source": [
        "#### We will train the same model, but with a minor change to the second dropout instance. We will decrease it from .5 to .4"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UHehDH-r9TIU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.layers import Dense, Flatten, Activation\n",
        "from keras.layers import Conv2D, MaxPooling2D\n",
        "from keras.layers.normalization import BatchNormalization\n",
        "\n",
        "model = Sequential()\n",
        "\n",
        "model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)))\n",
        "model.add(Conv2D(32, (3, 3)))\n",
        "# Batch normalization layer added here\n",
        "model.add(BatchNormalization())\n",
        "model.add(Activation('relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "model.add(Dropout(0.25))\n",
        "\n",
        "model.add(Flatten())\n",
        "model.add(Dense(256))\n",
        "# Batch normalization layer added here\n",
        "model.add(BatchNormalization())\n",
        "model.add(Activation('relu'))\n",
        "model.add(Dropout(0.4))\n",
        "model.add(Dense(10, activation='softmax'))\n",
        "\n",
        "sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)\n",
        "model.compile(loss='categorical_crossentropy', metrics=['accuracy'], optimizer=sgd)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wev0WJuk9bUs",
        "colab_type": "code",
        "outputId": "8c7be381-7415-4958-932e-611f3205c190",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 714
        }
      },
      "source": [
        "history = model.fit(x_train, y_train, batch_size=32, epochs=20, verbose=2, validation_split=0.2)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 40000 samples, validate on 10000 samples\n",
            "Epoch 1/20\n",
            " - 9s - loss: 1.4730 - acc: 0.4755 - val_loss: 1.1305 - val_acc: 0.5951\n",
            "Epoch 2/20\n",
            " - 8s - loss: 1.1309 - acc: 0.5995 - val_loss: 1.0290 - val_acc: 0.6260\n",
            "Epoch 3/20\n",
            " - 8s - loss: 0.9775 - acc: 0.6537 - val_loss: 0.8819 - val_acc: 0.6882\n",
            "Epoch 4/20\n",
            " - 8s - loss: 0.8868 - acc: 0.6881 - val_loss: 0.9065 - val_acc: 0.6826\n",
            "Epoch 5/20\n",
            " - 8s - loss: 0.8119 - acc: 0.7119 - val_loss: 0.8484 - val_acc: 0.7003\n",
            "Epoch 6/20\n",
            " - 8s - loss: 0.7441 - acc: 0.7369 - val_loss: 0.8487 - val_acc: 0.7025\n",
            "Epoch 7/20\n",
            " - 8s - loss: 0.6935 - acc: 0.7543 - val_loss: 0.8050 - val_acc: 0.7181\n",
            "Epoch 8/20\n",
            " - 8s - loss: 0.6400 - acc: 0.7752 - val_loss: 0.8058 - val_acc: 0.7163\n",
            "Epoch 9/20\n",
            " - 8s - loss: 0.5966 - acc: 0.7884 - val_loss: 0.8265 - val_acc: 0.7197\n",
            "Epoch 10/20\n",
            " - 8s - loss: 0.5477 - acc: 0.8042 - val_loss: 0.7941 - val_acc: 0.7299\n",
            "Epoch 11/20\n",
            " - 8s - loss: 0.5132 - acc: 0.8170 - val_loss: 0.8757 - val_acc: 0.7125\n",
            "Epoch 12/20\n",
            " - 8s - loss: 0.4911 - acc: 0.8250 - val_loss: 0.8200 - val_acc: 0.7236\n",
            "Epoch 13/20\n",
            " - 8s - loss: 0.4553 - acc: 0.8385 - val_loss: 0.8391 - val_acc: 0.7249\n",
            "Epoch 14/20\n",
            " - 8s - loss: 0.4279 - acc: 0.8469 - val_loss: 0.8352 - val_acc: 0.7297\n",
            "Epoch 15/20\n",
            " - 8s - loss: 0.3953 - acc: 0.8584 - val_loss: 0.8462 - val_acc: 0.7347\n",
            "Epoch 16/20\n",
            " - 8s - loss: 0.3797 - acc: 0.8655 - val_loss: 0.8510 - val_acc: 0.7357\n",
            "Epoch 17/20\n",
            " - 8s - loss: 0.3627 - acc: 0.8702 - val_loss: 0.8963 - val_acc: 0.7250\n",
            "Epoch 18/20\n",
            " - 8s - loss: 0.3498 - acc: 0.8775 - val_loss: 0.8909 - val_acc: 0.7283\n",
            "Epoch 19/20\n",
            " - 8s - loss: 0.3290 - acc: 0.8828 - val_loss: 0.9113 - val_acc: 0.7256\n",
            "Epoch 20/20\n",
            " - 8s - loss: 0.3119 - acc: 0.8896 - val_loss: 0.8866 - val_acc: 0.7347\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CDprhT-QkiSQ",
        "colab_type": "text"
      },
      "source": [
        "#### Interestingly, a minor change to the dropout brought the training and accuracy curves closer together, which means that model is generalizing better than before. This is because dropout trains an essemble of all subnetworks that can be constructed by removing non-output units from the base network, which effectively prevents overfitting by reducing the correlation between neurons.\n",
        "\n",
        "#### As a result, our model's loss has dropped to .90 and achieved an accuracy of 72.69%, which is almost at industry standard. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UmAKsi8y9uZx",
        "colab_type": "code",
        "outputId": "2c22d009-3905-4f1f-f183-fccfe4e347df",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        }
      },
      "source": [
        "plot_curves(history)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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0uecudx5LPMEJ9LGXQt8jLHvcuM8TyJ5Q3h/QzcO6+Qei/YIjIWKwc966cqgu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\nAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U_UrwKBT9wpr",
        "colab_type": "code",
        "outputId": "73131bbc-c77d-4926-bef7-a375091c313e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        }
      },
      "source": [
        "plot_losses(history)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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5z8ekugL9dEg/2anXw5oa4itxutEHAC8DzwMXWWvHuLnOE2pVIQ7OXuNvXQqT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\nAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lQiU5mHTWZ2D",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "score = model.evaluate(x_test, y_test, batch_size=128, verbose=0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-GrRmgkrWajS",
        "colab_type": "code",
        "outputId": "4cad64e9-05fc-4447-8a2a-1f62515d6621",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "print(model.metrics_names)\n",
        "print(score)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['loss', 'acc']\n",
            "[0.9044249374389648, 0.7269]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ocSHNY21eDQj",
        "colab_type": "text"
      },
      "source": [
        "### Final Network Architecture: Deep CNN with Data Augmentation and RMSProp Optimization\n",
        "\n",
        "#### We can increase the accuracy of the training with multiple convolutional layers that can generalize better and learn intermediary features. Building a Deep Neural Network can be advantageous, but it runs the risk of overfitting very quickly. \n",
        "\n",
        "#### We will deal with the issue of overfitting with data augmentation to increase the size of the training set by adding extra copies of the training examples that have been modified with transformations that do not change the actual class. This will help our model generalize because there will be more instances for it to learn on. \n",
        "\n",
        "#### Our next challenge will be to carefully design our model in regards to regularization and fine tuning our hyperparameters. The learning rate is one of the most difficult hyperparamters because it significantly affects the model's performance. We can deal with this issue by optimizing our model with RMSProp, which will algorithmically change the gradient accumulation into an exponentially weighted moving average. Since RMSProp uses a decaying average, it can discard history to converge more rapidly after finding the convex bowl. We will need this because we will be training with over 300,000 parameters. \n",
        "\n",
        "#### We will also run this model on the testing data as validation because we already know that it will perform better than the rest. The Dropout rate will start a .4 and decrease slightly over the layers. We will also continue using a small amount of decay and Batch Normalization. Basically, this model is just a deeper version of the best model we trained above. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CtODHDFaI5xF",
        "colab_type": "code",
        "outputId": "955e8341-729c-405f-e020-f3219822afc0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "import keras\n",
        "from keras.models import Sequential\n",
        "from keras.utils import np_utils\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from keras.layers import Dense, Activation, Flatten, Dropout, BatchNormalization\n",
        "from keras.layers import Conv2D, MaxPooling2D\n",
        "from keras.datasets import cifar10\n",
        "from keras import regularizers\n",
        "from keras.callbacks import LearningRateScheduler\n",
        "import numpy as np\n",
        "\n",
        "def lr_schedule(epoch):\n",
        "    lrate = 0.001\n",
        "    if epoch > 75:\n",
        "        lrate = 0.0005\n",
        "    elif epoch > 100:\n",
        "        lrate = 0.0003        \n",
        "    return lrate\n",
        "\n",
        "weight_decay = 1e-4\n",
        "model = Sequential()\n",
        "model.add(Conv2D(32, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay), input_shape=input_shape))\n",
        "model.add(Activation('elu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Conv2D(32, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))\n",
        "model.add(Activation('elu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "model.add(Dropout(0.2))\n",
        "\n",
        "model.add(Conv2D(64, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))\n",
        "model.add(Activation('elu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Conv2D(64, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))\n",
        "model.add(Activation('elu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "model.add(Dropout(0.3))\n",
        "\n",
        "model.add(Conv2D(128, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))\n",
        "model.add(Activation('elu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Conv2D(128, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))\n",
        "model.add(Activation('elu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "model.add(Dropout(0.4))\n",
        "\n",
        "model.add(Flatten())\n",
        "model.add(Dense(num_classes, activation='softmax'))\n",
        "\n",
        "model.summary()\n",
        "\n",
        "#data augmentation\n",
        "datagen = ImageDataGenerator(\n",
        "    rotation_range=15,\n",
        "    width_shift_range=0.1,\n",
        "    height_shift_range=0.1,\n",
        "    horizontal_flip=True,\n",
        "    )\n",
        "datagen.fit(x_train)\n",
        "\n",
        "#training\n",
        "batch_size = 64\n",
        "\n",
        "opt_rms = keras.optimizers.rmsprop(lr=0.001,decay=1e-6)\n",
        "model.compile(loss='categorical_crossentropy', optimizer=opt_rms, metrics=['accuracy'])\n",
        "history = model.fit_generator(datagen.flow(x_train, y_train, batch_size=batch_size),\\\n",
        "                    steps_per_epoch=x_train.shape[0] // batch_size,epochs=125,\\\n",
        "                    verbose=1,validation_data=(x_test,y_test),callbacks=[LearningRateScheduler(lr_schedule)])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_9\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_17 (Conv2D)           (None, 32, 32, 32)        896       \n",
            "_________________________________________________________________\n",
            "activation_5 (Activation)    (None, 32, 32, 32)        0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_5 (Batch (None, 32, 32, 32)        128       \n",
            "_________________________________________________________________\n",
            "conv2d_18 (Conv2D)           (None, 32, 32, 32)        9248      \n",
            "_________________________________________________________________\n",
            "activation_6 (Activation)    (None, 32, 32, 32)        0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_6 (Batch (None, 32, 32, 32)        128       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_9 (MaxPooling2 (None, 16, 16, 32)        0         \n",
            "_________________________________________________________________\n",
            "dropout_9 (Dropout)          (None, 16, 16, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_19 (Conv2D)           (None, 16, 16, 64)        18496     \n",
            "_________________________________________________________________\n",
            "activation_7 (Activation)    (None, 16, 16, 64)        0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_7 (Batch (None, 16, 16, 64)        256       \n",
            "_________________________________________________________________\n",
            "conv2d_20 (Conv2D)           (None, 16, 16, 64)        36928     \n",
            "_________________________________________________________________\n",
            "activation_8 (Activation)    (None, 16, 16, 64)        0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_8 (Batch (None, 16, 16, 64)        256       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_10 (MaxPooling (None, 8, 8, 64)          0         \n",
            "_________________________________________________________________\n",
            "dropout_10 (Dropout)         (None, 8, 8, 64)          0         \n",
            "_________________________________________________________________\n",
            "conv2d_21 (Conv2D)           (None, 8, 8, 128)         73856     \n",
            "_________________________________________________________________\n",
            "activation_9 (Activation)    (None, 8, 8, 128)         0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_9 (Batch (None, 8, 8, 128)         512       \n",
            "_________________________________________________________________\n",
            "conv2d_22 (Conv2D)           (None, 8, 8, 128)         147584    \n",
            "_________________________________________________________________\n",
            "activation_10 (Activation)   (None, 8, 8, 128)         0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_10 (Batc (None, 8, 8, 128)         512       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_11 (MaxPooling (None, 4, 4, 128)         0         \n",
            "_________________________________________________________________\n",
            "dropout_11 (Dropout)         (None, 4, 4, 128)         0         \n",
            "_________________________________________________________________\n",
            "flatten_9 (Flatten)          (None, 2048)              0         \n",
            "_________________________________________________________________\n",
            "dense_17 (Dense)             (None, 10)                20490     \n",
            "=================================================================\n",
            "Total params: 309,290\n",
            "Trainable params: 308,394\n",
            "Non-trainable params: 896\n",
            "_________________________________________________________________\n",
            "Epoch 1/125\n",
            "781/781 [==============================] - 24s 31ms/step - loss: 1.8871 - acc: 0.4351 - val_loss: 1.3360 - val_acc: 0.5848\n",
            "Epoch 2/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 1.3057 - acc: 0.6002 - val_loss: 1.5015 - val_acc: 0.6157\n",
            "Epoch 3/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 1.1571 - acc: 0.6527 - val_loss: 1.0131 - val_acc: 0.7003\n",
            "Epoch 4/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 1.0617 - acc: 0.6863 - val_loss: 0.9398 - val_acc: 0.7323\n",
            "Epoch 5/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.9899 - acc: 0.7079 - val_loss: 1.0336 - val_acc: 0.7016\n",
            "Epoch 6/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.9275 - acc: 0.7260 - val_loss: 0.9115 - val_acc: 0.7345\n",
            "Epoch 7/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.8854 - acc: 0.7396 - val_loss: 0.7835 - val_acc: 0.7766\n",
            "Epoch 8/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.8414 - acc: 0.7538 - val_loss: 0.8006 - val_acc: 0.7712\n",
            "Epoch 9/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.8144 - acc: 0.7626 - val_loss: 0.7609 - val_acc: 0.7862\n",
            "Epoch 10/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.7915 - acc: 0.7679 - val_loss: 0.7427 - val_acc: 0.7927\n",
            "Epoch 11/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.7671 - acc: 0.7804 - val_loss: 0.7085 - val_acc: 0.8014\n",
            "Epoch 12/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.7481 - acc: 0.7844 - val_loss: 0.7196 - val_acc: 0.7998\n",
            "Epoch 13/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.7342 - acc: 0.7924 - val_loss: 0.7713 - val_acc: 0.7907\n",
            "Epoch 14/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.7259 - acc: 0.7956 - val_loss: 0.7470 - val_acc: 0.7967\n",
            "Epoch 15/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.7181 - acc: 0.7973 - val_loss: 0.6776 - val_acc: 0.8145\n",
            "Epoch 16/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.7036 - acc: 0.8025 - val_loss: 0.6551 - val_acc: 0.8254\n",
            "Epoch 17/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.6976 - acc: 0.8046 - val_loss: 0.6960 - val_acc: 0.8139\n",
            "Epoch 18/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.6846 - acc: 0.8125 - val_loss: 0.6689 - val_acc: 0.8241\n",
            "Epoch 19/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.6825 - acc: 0.8125 - val_loss: 0.6282 - val_acc: 0.8390\n",
            "Epoch 20/125\n",
            "781/781 [==============================] - 22s 29ms/step - loss: 0.6726 - acc: 0.8151 - val_loss: 0.6321 - val_acc: 0.8358\n",
            "Epoch 21/125\n",
            "781/781 [==============================] - 22s 29ms/step - loss: 0.6631 - acc: 0.8180 - val_loss: 0.7107 - val_acc: 0.8108\n",
            "Epoch 22/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.6644 - acc: 0.8191 - val_loss: 0.6974 - val_acc: 0.8145\n",
            "Epoch 23/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.6519 - acc: 0.8241 - val_loss: 0.6094 - val_acc: 0.8407\n",
            "Epoch 24/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.6505 - acc: 0.8244 - val_loss: 0.6949 - val_acc: 0.8218\n",
            "Epoch 25/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.6486 - acc: 0.8268 - val_loss: 0.6016 - val_acc: 0.8453\n",
            "Epoch 26/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.6474 - acc: 0.8270 - val_loss: 0.6059 - val_acc: 0.8453\n",
            "Epoch 27/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.6432 - acc: 0.8269 - val_loss: 0.6415 - val_acc: 0.8366\n",
            "Epoch 28/125\n",
            "781/781 [==============================] - 22s 29ms/step - loss: 0.6371 - acc: 0.8293 - val_loss: 0.7431 - val_acc: 0.8171\n",
            "Epoch 29/125\n",
            "781/781 [==============================] - 22s 29ms/step - loss: 0.6418 - acc: 0.8287 - val_loss: 0.6126 - val_acc: 0.8437\n",
            "Epoch 30/125\n",
            "781/781 [==============================] - 22s 29ms/step - loss: 0.6325 - acc: 0.8332 - val_loss: 0.6318 - val_acc: 0.8391\n",
            "Epoch 31/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.6335 - acc: 0.8336 - val_loss: 0.6341 - val_acc: 0.8427\n",
            "Epoch 32/125\n",
            "781/781 [==============================] - 22s 29ms/step - loss: 0.6279 - acc: 0.8328 - val_loss: 0.6054 - val_acc: 0.8471\n",
            "Epoch 33/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.6261 - acc: 0.8344 - val_loss: 0.6104 - val_acc: 0.8458\n",
            "Epoch 34/125\n",
            "781/781 [==============================] - 22s 29ms/step - loss: 0.6251 - acc: 0.8350 - val_loss: 0.6359 - val_acc: 0.8387\n",
            "Epoch 35/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.6213 - acc: 0.8379 - val_loss: 0.5929 - val_acc: 0.8577\n",
            "Epoch 36/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.6187 - acc: 0.8371 - val_loss: 0.6434 - val_acc: 0.8377\n",
            "Epoch 37/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.6211 - acc: 0.8371 - val_loss: 0.5786 - val_acc: 0.8593\n",
            "Epoch 38/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.6172 - acc: 0.8396 - val_loss: 0.6290 - val_acc: 0.8450\n",
            "Epoch 39/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.6131 - acc: 0.8416 - val_loss: 0.5915 - val_acc: 0.8561\n",
            "Epoch 40/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.6127 - acc: 0.8405 - val_loss: 0.5801 - val_acc: 0.8604\n",
            "Epoch 41/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.6082 - acc: 0.8391 - val_loss: 0.6135 - val_acc: 0.8485\n",
            "Epoch 42/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.6075 - acc: 0.8428 - val_loss: 0.5634 - val_acc: 0.8666\n",
            "Epoch 43/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.6056 - acc: 0.8431 - val_loss: 0.6246 - val_acc: 0.8484\n",
            "Epoch 44/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.6074 - acc: 0.8423 - val_loss: 0.5726 - val_acc: 0.8627\n",
            "Epoch 45/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.6021 - acc: 0.8433 - val_loss: 0.6716 - val_acc: 0.8392\n",
            "Epoch 46/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.5996 - acc: 0.8453 - val_loss: 0.6244 - val_acc: 0.8472\n",
            "Epoch 47/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.6015 - acc: 0.8453 - val_loss: 0.6340 - val_acc: 0.8464\n",
            "Epoch 48/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.5998 - acc: 0.8448 - val_loss: 0.6056 - val_acc: 0.8512\n",
            "Epoch 49/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.5935 - acc: 0.8480 - val_loss: 0.6166 - val_acc: 0.8509\n",
            "Epoch 50/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.5972 - acc: 0.8466 - val_loss: 0.6124 - val_acc: 0.8531\n",
            "Epoch 51/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.5990 - acc: 0.8469 - val_loss: 0.6102 - val_acc: 0.8494\n",
            "Epoch 52/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.5955 - acc: 0.8481 - val_loss: 0.6329 - val_acc: 0.8439\n",
            "Epoch 53/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.5967 - acc: 0.8475 - val_loss: 0.6223 - val_acc: 0.8461\n",
            "Epoch 54/125\n",
            "781/781 [==============================] - 22s 29ms/step - loss: 0.5989 - acc: 0.8453 - val_loss: 0.6108 - val_acc: 0.8512\n",
            "Epoch 55/125\n",
            "781/781 [==============================] - 22s 28ms/step - loss: 0.5915 - acc: 0.8498 - val_loss: 0.5875 - val_acc: 0.8571\n",
            "Epoch 56/125\n",
            "781/781 [==============================] - 22s 28ms/step - loss: 0.5927 - acc: 0.8488 - val_loss: 0.6001 - val_acc: 0.8556\n",
            "Epoch 57/125\n",
            "781/781 [==============================] - 22s 28ms/step - loss: 0.5946 - acc: 0.8463 - val_loss: 0.5873 - val_acc: 0.8562\n",
            "Epoch 58/125\n",
            "781/781 [==============================] - 23s 30ms/step - loss: 0.5935 - acc: 0.8485 - val_loss: 0.6470 - val_acc: 0.8443\n",
            "Epoch 59/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.5917 - acc: 0.8494 - val_loss: 0.5457 - val_acc: 0.8722\n",
            "Epoch 60/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.5879 - acc: 0.8514 - val_loss: 0.6385 - val_acc: 0.8419\n",
            "Epoch 61/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.5858 - acc: 0.8500 - val_loss: 0.5867 - val_acc: 0.8585\n",
            "Epoch 62/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.5872 - acc: 0.8514 - val_loss: 0.6040 - val_acc: 0.8550\n",
            "Epoch 63/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.5877 - acc: 0.8513 - val_loss: 0.6168 - val_acc: 0.8537\n",
            "Epoch 64/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.5878 - acc: 0.8503 - val_loss: 0.6574 - val_acc: 0.8426\n",
            "Epoch 65/125\n",
            "781/781 [==============================] - 23s 30ms/step - loss: 0.5798 - acc: 0.8543 - val_loss: 0.5954 - val_acc: 0.8563\n",
            "Epoch 66/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.5791 - acc: 0.8524 - val_loss: 0.5747 - val_acc: 0.8597\n",
            "Epoch 67/125\n",
            "781/781 [==============================] - 23s 30ms/step - loss: 0.5798 - acc: 0.8543 - val_loss: 0.5684 - val_acc: 0.8634\n",
            "Epoch 68/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.5806 - acc: 0.8529 - val_loss: 0.6285 - val_acc: 0.8472\n",
            "Epoch 69/125\n",
            "781/781 [==============================] - 23s 30ms/step - loss: 0.5793 - acc: 0.8531 - val_loss: 0.6297 - val_acc: 0.8493\n",
            "Epoch 70/125\n",
            "781/781 [==============================] - 23s 30ms/step - loss: 0.5830 - acc: 0.8527 - val_loss: 0.6176 - val_acc: 0.8504\n",
            "Epoch 71/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.5800 - acc: 0.8525 - val_loss: 0.5723 - val_acc: 0.8618\n",
            "Epoch 72/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.5753 - acc: 0.8561 - val_loss: 0.6222 - val_acc: 0.8523\n",
            "Epoch 73/125\n",
            "781/781 [==============================] - 23s 30ms/step - loss: 0.5734 - acc: 0.8561 - val_loss: 0.6831 - val_acc: 0.8332\n",
            "Epoch 74/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.5770 - acc: 0.8534 - val_loss: 0.5302 - val_acc: 0.8778\n",
            "Epoch 75/125\n",
            "781/781 [==============================] - 23s 30ms/step - loss: 0.5728 - acc: 0.8528 - val_loss: 0.5712 - val_acc: 0.8637\n",
            "Epoch 76/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.5715 - acc: 0.8574 - val_loss: 0.6234 - val_acc: 0.8494\n",
            "Epoch 77/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.5307 - acc: 0.8692 - val_loss: 0.5323 - val_acc: 0.8775\n",
            "Epoch 78/125\n",
            "781/781 [==============================] - 23s 30ms/step - loss: 0.5218 - acc: 0.8705 - val_loss: 0.5268 - val_acc: 0.8769\n",
            "Epoch 79/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.5032 - acc: 0.8768 - val_loss: 0.4985 - val_acc: 0.8842\n",
            "Epoch 80/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.4962 - acc: 0.8780 - val_loss: 0.5493 - val_acc: 0.8692\n",
            "Epoch 81/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.4996 - acc: 0.8757 - val_loss: 0.5150 - val_acc: 0.8781\n",
            "Epoch 82/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.4910 - acc: 0.8763 - val_loss: 0.5440 - val_acc: 0.8689\n",
            "Epoch 83/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.4856 - acc: 0.8783 - val_loss: 0.5373 - val_acc: 0.8679\n",
            "Epoch 84/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.4826 - acc: 0.8793 - val_loss: 0.4920 - val_acc: 0.8865\n",
            "Epoch 85/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.4814 - acc: 0.8788 - val_loss: 0.5045 - val_acc: 0.8797\n",
            "Epoch 86/125\n",
            "781/781 [==============================] - 23s 30ms/step - loss: 0.4799 - acc: 0.8784 - val_loss: 0.4804 - val_acc: 0.8862\n",
            "Epoch 87/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.4727 - acc: 0.8804 - val_loss: 0.4853 - val_acc: 0.8825\n",
            "Epoch 88/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.4794 - acc: 0.8771 - val_loss: 0.4813 - val_acc: 0.8881\n",
            "Epoch 89/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.4682 - acc: 0.8817 - val_loss: 0.4724 - val_acc: 0.8882\n",
            "Epoch 90/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.4697 - acc: 0.8801 - val_loss: 0.5211 - val_acc: 0.8719\n",
            "Epoch 91/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.4680 - acc: 0.8810 - val_loss: 0.4884 - val_acc: 0.8821\n",
            "Epoch 92/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.4696 - acc: 0.8807 - val_loss: 0.5901 - val_acc: 0.8561\n",
            "Epoch 93/125\n",
            "781/781 [==============================] - 23s 30ms/step - loss: 0.4674 - acc: 0.8815 - val_loss: 0.5063 - val_acc: 0.8755\n",
            "Epoch 94/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.4623 - acc: 0.8824 - val_loss: 0.4965 - val_acc: 0.8801\n",
            "Epoch 95/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.4594 - acc: 0.8833 - val_loss: 0.4861 - val_acc: 0.8819\n",
            "Epoch 96/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.4605 - acc: 0.8829 - val_loss: 0.5108 - val_acc: 0.8743\n",
            "Epoch 97/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.4588 - acc: 0.8819 - val_loss: 0.4723 - val_acc: 0.8857\n",
            "Epoch 98/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.4644 - acc: 0.8806 - val_loss: 0.4604 - val_acc: 0.8899\n",
            "Epoch 99/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.4556 - acc: 0.8823 - val_loss: 0.5005 - val_acc: 0.8801\n",
            "Epoch 100/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.4543 - acc: 0.8829 - val_loss: 0.4789 - val_acc: 0.8799\n",
            "Epoch 101/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.4583 - acc: 0.8821 - val_loss: 0.5117 - val_acc: 0.8738\n",
            "Epoch 102/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.4545 - acc: 0.8833 - val_loss: 0.5116 - val_acc: 0.8741\n",
            "Epoch 103/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.4529 - acc: 0.8835 - val_loss: 0.5008 - val_acc: 0.8782\n",
            "Epoch 104/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.4515 - acc: 0.8842 - val_loss: 0.5913 - val_acc: 0.8493\n",
            "Epoch 105/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.4544 - acc: 0.8833 - val_loss: 0.5289 - val_acc: 0.8709\n",
            "Epoch 106/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.4500 - acc: 0.8821 - val_loss: 0.5038 - val_acc: 0.8784\n",
            "Epoch 107/125\n",
            "781/781 [==============================] - 23s 30ms/step - loss: 0.4457 - acc: 0.8847 - val_loss: 0.5001 - val_acc: 0.8800\n",
            "Epoch 108/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.4506 - acc: 0.8827 - val_loss: 0.5149 - val_acc: 0.8751\n",
            "Epoch 109/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.4460 - acc: 0.8838 - val_loss: 0.5205 - val_acc: 0.8731\n",
            "Epoch 110/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.4478 - acc: 0.8854 - val_loss: 0.4851 - val_acc: 0.8833\n",
            "Epoch 111/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.4464 - acc: 0.8836 - val_loss: 0.4775 - val_acc: 0.8836\n",
            "Epoch 112/125\n",
            "781/781 [==============================] - 22s 29ms/step - loss: 0.4475 - acc: 0.8843 - val_loss: 0.5379 - val_acc: 0.8700\n",
            "Epoch 113/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.4443 - acc: 0.8846 - val_loss: 0.4763 - val_acc: 0.8831\n",
            "Epoch 114/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.4501 - acc: 0.8835 - val_loss: 0.4799 - val_acc: 0.8818\n",
            "Epoch 115/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.4413 - acc: 0.8851 - val_loss: 0.5181 - val_acc: 0.8738\n",
            "Epoch 116/125\n",
            "781/781 [==============================] - 22s 29ms/step - loss: 0.4479 - acc: 0.8845 - val_loss: 0.4659 - val_acc: 0.8870\n",
            "Epoch 117/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.4471 - acc: 0.8844 - val_loss: 0.4878 - val_acc: 0.8780\n",
            "Epoch 118/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.4478 - acc: 0.8833 - val_loss: 0.4752 - val_acc: 0.8817\n",
            "Epoch 119/125\n",
            "781/781 [==============================] - 23s 30ms/step - loss: 0.4438 - acc: 0.8834 - val_loss: 0.4708 - val_acc: 0.8861\n",
            "Epoch 120/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.4380 - acc: 0.8859 - val_loss: 0.4738 - val_acc: 0.8801\n",
            "Epoch 121/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.4430 - acc: 0.8854 - val_loss: 0.4799 - val_acc: 0.8820\n",
            "Epoch 122/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.4451 - acc: 0.8847 - val_loss: 0.4864 - val_acc: 0.8768\n",
            "Epoch 123/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.4465 - acc: 0.8844 - val_loss: 0.5315 - val_acc: 0.8707\n",
            "Epoch 124/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.4446 - acc: 0.8839 - val_loss: 0.4663 - val_acc: 0.8862\n",
            "Epoch 125/125\n",
            "781/781 [==============================] - 23s 29ms/step - loss: 0.4378 - acc: 0.8864 - val_loss: 0.4657 - val_acc: 0.8855\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oUOx-D4MiYHy",
        "colab_type": "text"
      },
      "source": [
        "### Deep CNN Model Evaluation: \n",
        "\n",
        "#### We were successful in surpassing the industry standard of ~75% by creating a deeply multilayered CNN that utilized Batch Normalization, Data Augmentation, and RMSProp Optimization. Our final evaluation indicates that our model will generalize to new images of data in the 10 given classes and label them correctly with ~88% accuracy. Most importantly, our model will not overfit with a loss score of only 0.47."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UpCVANssVn7Z",
        "colab_type": "code",
        "outputId": "7ff0c74b-67c1-44d9-eb9e-766f340402d1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        }
      },
      "source": [
        "plot_curves(history)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Q6lNLfP5qM5VQVQwzn4Gg6Kaf9MWdsOkDuHNzfS/2nQuhIAXu2Fz/BWjpX2H5U3D3dpOL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\nxKcU4iIiIj6lEBcREfGp/wJiZBvRXXF9XgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8PRQKNQ1VuA9",
        "colab_type": "code",
        "outputId": "47831e06-d390-4156-f125-3a51fa9e9b24",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        }
      },
      "source": [
        "plot_losses(history)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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jgOWBKIhCXEREgpHfauLGmMnABCDFGLMFuBsIA7DWPm2tXW6M+QRYBNQAz1lrm5yO5k/q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\nrkJggg==\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "93S6nN3njQxq",
        "colab_type": "text"
      },
      "source": [
        "### Final Score: 88.55%"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mMVKfMXiVyja",
        "colab_type": "code",
        "outputId": "77a0c35c-ba11-4a9d-cd98-8de7bd1b4bcd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "scores = model.evaluate(x_test, y_test, batch_size=128, verbose=1)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10000/10000 [==============================] - 1s 60us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TQ3K5DrnWELL",
        "colab_type": "code",
        "outputId": "eb747c74-faa2-4b9d-8fa1-c838c1560338",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "print(model.metrics_names)\n",
        "print(scores)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['loss', 'acc']\n",
            "[0.46566411571502686, 0.8855]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "leODat-Qr0Hh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "predictions = np.round(model.predict(x_test),0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xYnh_3kLsAMd",
        "colab_type": "code",
        "outputId": "b087ea6a-8216-48e0-d335-5bc3cde733a8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "print(\"rounded test labels\", predictions)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "rounded test labels [[0. 0. 0. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 1. 0.]\n",
            " [0. 0. 0. ... 0. 1. 0.]\n",
            " ...\n",
            " [0. 0. 0. ... 0. 0. 0.]\n",
            " [0. 1. 0. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 1. 0. 0.]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8z_V93kHsScE",
        "colab_type": "code",
        "outputId": "0f84689b-22d5-4b6e-ac31-bc2380b27930",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        }
      },
      "source": [
        "from sklearn import metrics\n",
        "cifar_classes = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
        "classification_metrics = metrics.classification_report(y_test, predictions, target_names=cifar_classes)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in samples with no predicted labels.\n",
            "  'precision', 'predicted', average, warn_for)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_N9Vt1PEtMN1",
        "colab_type": "text"
      },
      "source": [
        "### Classification Metrics of our Final Model:\n",
        "\n",
        "#### Interestingly, our f1-scores for the given objects were the strongest in \"man-made\" objects as opposed to objects found in nature, such as animals or pets. Automobiles and Trucks recieved the highest scores, while Cats and Dogs recieved the lowest f1-scores. Perhaps the shapes of animals are more difficult to learn than cars and trucks. This must be because the image vectors are more defined in box-like shapes as opposed to shapes of animals that have tails. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0Zqd0DUMs0m_",
        "colab_type": "code",
        "outputId": "ca56b565-b06a-4eaf-fe01-2010c8cb2e8b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        }
      },
      "source": [
        "print(classification_metrics)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "    airplane       0.93      0.87      0.90      1000\n",
            "  automobile       0.95      0.95      0.95      1000\n",
            "        bird       0.92      0.80      0.86      1000\n",
            "         cat       0.85      0.73      0.79      1000\n",
            "        deer       0.86      0.92      0.89      1000\n",
            "         dog       0.96      0.67      0.79      1000\n",
            "        frog       0.88      0.94      0.91      1000\n",
            "       horse       0.95      0.89      0.92      1000\n",
            "        ship       0.95      0.93      0.94      1000\n",
            "       truck       0.89      0.96      0.92      1000\n",
            "\n",
            "   micro avg       0.91      0.87      0.89     10000\n",
            "   macro avg       0.91      0.87      0.89     10000\n",
            "weighted avg       0.91      0.87      0.89     10000\n",
            " samples avg       0.87      0.87      0.87     10000\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}