Object_Classifier_CNN.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Object Classifier CNN.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sA9VMTBJ8pSa",
        "colab_type": "text"
      },
      "source": [
        "#Object Classifier CNN\n",
        "This simple convolutional neural network will handle recognizing various common objects found in the world. Particularly, the following:\n",
        "  * Airplane\n",
        "  * Automobile\n",
        "  * Bird\n",
        "  * Cat\n",
        "  * Deer\n",
        "  * Dog\n",
        "  * Frog\n",
        "  * Horse\n",
        "  * Ship\n",
        "  * Truck "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "neXhKor_8aad",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "a8f6ad90-cb6e-4237-99d2-c05bd409c0c3"
      },
      "source": [
        "# Get required imports\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras import datasets, layers, models\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from keras.preprocessing import image\n",
        "from keras.preprocessing.image import ImageDataGenerator"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4AUXQZRH9QBS",
        "colab_type": "text"
      },
      "source": [
        "## Loading and setting up the data\n",
        "There is a dataset that comes with the Keras library that would suit our purposes very well here that includes the following class names.\n",
        "\n",
        "Data processing for this dataset is very minimal."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_zNPflu79nc3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "8d299b77-705c-4577-b0db-c1b76fa7300c"
      },
      "source": [
        "#  Extract the trainining and test data\n",
        "(train_images, train_labels), (test_images, test_labels) = datasets.cifar10.load_data()\n",
        "\n",
        "# Convert the grayscale images from 0-255 to 0-1\n",
        "train_images, test_images = train_images / 255.0, test_images / 255.0\n",
        "\n",
        "CLASS_NAMES = ['airplane', 'automobile', 'bird', 'cat', 'deer',\n",
        "               'dog', 'frog', 'horse', 'ship', 'truck']"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\n",
            "170500096/170498071 [==============================] - 12s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9rl0U6rB91Hn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "outputId": "ad155def-22f5-4c01-e37d-6eb1fce16d11"
      },
      "source": [
        "# Examine an image for reference\n",
        "plt.imshow(train_images[1], cmap=plt.cm.binary)\n",
        "plt.xlabel(CLASS_NAMES[train_labels[1][0]]) # It's a truck!\n",
        "plt.show()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XIlf9M6Rk75Z",
        "colab_type": "text"
      },
      "source": [
        "## Creating the CNN Model\n",
        "We will add a stack of Conv2D and MaxPooling2D layers first.\n",
        "These will then be flattened and connected to dense layers to extract the overall object type from the image.\n",
        "\n",
        "First, the Conv layers will filter through the image to look for any potential patterns.\n",
        "\n",
        "Then, this will be flattened and passed onto dense layers to overall piece together what object the image represents."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8g_MukBulR4l",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439
        },
        "outputId": "d914464f-725c-4a47-a24f-f407404790d9"
      },
      "source": [
        "# Create the CNN model\n",
        "model = models.Sequential()\n",
        "# Input of 32 by 32 images with a depth of 3 with 32 filters of 3x3\n",
        "model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)))\n",
        "# Pool with a stride of 2 and a size of 2 x 2\n",
        "model.add(layers.MaxPooling2D((2, 2)))\n",
        "# Double the filters as there is a smaller image now, so more computation available\n",
        "model.add(layers.Conv2D(64, (3, 3), activation='relu'))\n",
        "model.add(layers.MaxPooling2D((2, 2)))\n",
        "model.add(layers.Conv2D(64, (3, 3), activation='relu'))\n",
        "# Now, add the flattening and the dense layers\n",
        "# Note: with flattening the output shape is increased to 1024 outputs (32*32 pixels)\n",
        "model.add(layers.Flatten())\n",
        "model.add(layers.Dense(64, activation='relu'))\n",
        "# The output dense layer with a output size of the number of classes (10 here)\n",
        "model.add(layers.Dense(len(CLASS_NAMES)))\n",
        "model.summary()"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d (Conv2D)              (None, 30, 30, 32)        896       \n",
            "_________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D) (None, 15, 15, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 13, 13, 64)        18496     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 6, 6, 64)          0         \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 4, 4, 64)          36928     \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 1024)              0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 64)                65600     \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 10)                650       \n",
            "=================================================================\n",
            "Total params: 122,570\n",
            "Trainable params: 122,570\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qN6IHZIpmgOH",
        "colab_type": "text"
      },
      "source": [
        "## Model training\n",
        "We will be using the *adam* optimizer (for this simple neural network, it's perforamnce and memory efficiency are very good for the results it provides).\n",
        "\n",
        "Similarly, the *SparseCategoricalCrossentropy* is also a computationally efficient algorithm and uses up less memory as opposed to its vector-based variants.\n",
        "\n",
        "Currently, 4 epochs will be used for the model to find the right balance between underfitting and overfitting."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PTkK-nums6sB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 385
        },
        "outputId": "4a501399-146a-4a88-92cf-1a4d8e310d34"
      },
      "source": [
        "model.compile(optimizer='adam',\n",
        "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "history = model.fit(train_images, train_labels, epochs=4, \n",
        "                    validation_data=(test_images, test_labels))"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/4\n",
            "  34/1563 [..............................] - ETA: 59s - loss: 2.2749 - accuracy: 0.1241 "
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-35-9fb30562dcb9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m history = model.fit(train_images, train_labels, epochs=4, \n\u001b[0;32m----> 6\u001b[0;31m                     validation_data=(test_images, test_labels))\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36m_method_wrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     64\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_method_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_in_multi_worker_mode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 66\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     67\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     68\u001b[0m     \u001b[0;31m# Running inside `run_distribute_coordinator` already.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m    846\u001b[0m                 batch_size=batch_size):\n\u001b[1;32m    847\u001b[0m               \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 848\u001b[0;31m               \u001b[0mtmp_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    849\u001b[0m               \u001b[0;31m# Catch OutOfRangeError for Datasets of unknown size.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    850\u001b[0m               \u001b[0;31m# This blocks until the batch has finished executing.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    578\u001b[0m         \u001b[0mxla_context\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mExit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    579\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 580\u001b[0;31m       \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    581\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    582\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mtracing_count\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    609\u001b[0m       \u001b[0;31m# In this case we have created variables on the first call, so we run the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    610\u001b[0m       \u001b[0;31m# defunned version which is guaranteed to never create variables.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 611\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateless_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=not-callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    612\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    613\u001b[0m       \u001b[0;31m# Release the lock early so that multiple threads can perform the call\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2418\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2419\u001b[0m       \u001b[0mgraph_function\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_define_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2420\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_filtered_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2421\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2422\u001b[0m   \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_filtered_call\u001b[0;34m(self, args, kwargs)\u001b[0m\n\u001b[1;32m   1663\u001b[0m          if isinstance(t, (ops.Tensor,\n\u001b[1;32m   1664\u001b[0m                            resource_variable_ops.BaseResourceVariable))),\n\u001b[0;32m-> 1665\u001b[0;31m         self.captured_inputs)\n\u001b[0m\u001b[1;32m   1666\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1667\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_call_flat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcaptured_inputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcancellation_manager\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1744\u001b[0m       \u001b[0;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1745\u001b[0m       return self._build_call_outputs(self._inference_function.call(\n\u001b[0;32m-> 1746\u001b[0;31m           ctx, args, cancellation_manager=cancellation_manager))\n\u001b[0m\u001b[1;32m   1747\u001b[0m     forward_backward = self._select_forward_and_backward_functions(\n\u001b[1;32m   1748\u001b[0m         \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m    596\u001b[0m               \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    597\u001b[0m               \u001b[0mattrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattrs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 598\u001b[0;31m               ctx=ctx)\n\u001b[0m\u001b[1;32m    599\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    600\u001b[0m           outputs = execute.execute_with_cancellation(\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     58\u001b[0m     \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0;32m---> 60\u001b[0;31m                                         inputs, attrs, num_outputs)\n\u001b[0m\u001b[1;32m     61\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0fwJZInZC5kS",
        "colab_type": "text"
      },
      "source": [
        "## Evaluation\n",
        "Looking at the neural network, it can be seen that we achieve an accuracy of around 67 to 69%. This can be expected at this stage of the development. Thus, in order to further optimize the test accuracy, we are going to employ some optimizations."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O99Zj9PJwDp3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Evaluate the model on the test set\n",
        "test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)\n",
        "print(\"Accuracy on test set: \", test_acc)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "34ok5ZIcC37f",
        "colab_type": "text"
      },
      "source": [
        "## Improving the algorithm\n",
        "\n",
        "Currently only 50,000 examples are used. Image data augmentation can be used through Keras's ImageDataGenerator library to drastically increase the dataset.\n",
        "Such a small dataset is the main problem for the low accuracy. However, by altering the image (rotation, flip, translation, etc.), much more data can be generated."
      ]
    }
  ]
}