{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "mnist-cnn.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4kyB8MUKn7Vh"
      },
      "source": [
        "# MNIST with Convolutional Neural Networks"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YyFM4icCoAaX"
      },
      "source": [
        "## Prerequisites\n",
        "\n",
        "Install the following packages"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u63awVMPoD7Y",
        "outputId": "9d63b7c2-f9f5-46a5-816b-e9a8efaa6bed"
      },
      "source": [
        "! pip3 install cloudmesh-installer\n",
        "! pip3 install cloudmesh-common"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting cloudmesh-installer\n",
            "  Downloading https://files.pythonhosted.org/packages/97/bc/25b2c79e608e948999f96a1609c3c91a32483f530b519a327ef6420a06e4/cloudmesh_installer-4.4.19-py2.py3-none-any.whl\n",
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            "Collecting colorama\n",
            "  Downloading https://files.pythonhosted.org/packages/44/98/5b86278fbbf250d239ae0ecb724f8572af1c91f4a11edf4d36a206189440/colorama-0.4.4-py2.py3-none-any.whl\n",
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            "Collecting cloudmesh-common\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/84/c3/b8c750f5c6edb5b590fdd3b5730fa16b3a19cb239e00272d1347e8aff4b8/cloudmesh_common-4.3.43-py2.py3-none-any.whl (78kB)\n",
            "\u001b[K     |████████████████████████████████| 81kB 3.3MB/s \n",
            "\u001b[?25hCollecting bump2version==1.0.0\n",
            "  Downloading https://files.pythonhosted.org/packages/61/10/560509d9bfe8300e03d268dadec74fac7ae04a430f62e2d06d11d9e4e704/bump2version-1.0.0-py2.py3-none-any.whl\n",
            "Collecting pipdeptree\n",
            "  Downloading https://files.pythonhosted.org/packages/fa/22/8f1350b55e4297670813815142425b58829036197f0b4a0fc8f543928717/pipdeptree-2.0.0-py3-none-any.whl\n",
            "Collecting pytest-cov\n",
            "  Downloading https://files.pythonhosted.org/packages/e3/1a/6affecd2344efee7f2487fac82242474cbac09f9e04929da5944907baf11/pytest_cov-2.11.1-py2.py3-none-any.whl\n",
            "Requirement already satisfied: humanize in /usr/local/lib/python3.6/dist-packages (from cloudmesh-installer) (0.5.1)\n",
            "Collecting flake8\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/d4/ca/3971802ee6251da1abead1a22831d7f4743781e2f743bd266bdd2f46c19b/flake8-3.8.4-py2.py3-none-any.whl (72kB)\n",
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            "Collecting ordered-set\n",
            "  Downloading https://files.pythonhosted.org/packages/f5/ab/8252360bfe965bba31ec05112b3067bd129ce4800d89e0b85613bc6044f6/ordered-set-4.0.2.tar.gz\n",
            "Collecting python-hostlist\n",
            "  Downloading https://files.pythonhosted.org/packages/2b/4f/f31dd4b4bf1a57a5c29599e1165d0df70dbdddcfa59a7c1d04ee2ff4ccbd/python-hostlist-1.21.tar.gz\n",
            "Collecting oyaml\n",
            "  Downloading https://files.pythonhosted.org/packages/37/aa/111610d8bf5b1bb7a295a048fc648cec346347a8b0be5881defd2d1b4a52/oyaml-1.0-py2.py3-none-any.whl\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from cloudmesh-installer) (2.23.0)\n",
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            "Collecting pyfiglet\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/33/07/fcfdd7a2872f5b348953de35acce1544dab0c1e8368dca54279b1cde5c15/pyfiglet-0.8.post1-py2.py3-none-any.whl (865kB)\n",
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            "Collecting simplejson\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/73/96/1e6b19045375890068d7342cbe280dd64ae73fd90b9735b5efb8d1e044a1/simplejson-3.17.2-cp36-cp36m-manylinux2010_x86_64.whl (127kB)\n",
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            "\u001b[?25hRequirement already satisfied: pip>=6.0.0 in /usr/local/lib/python3.6/dist-packages (from pipdeptree->cloudmesh-installer) (19.3.1)\n",
            "Collecting mccabe<0.7.0,>=0.6.0\n",
            "  Downloading https://files.pythonhosted.org/packages/87/89/479dc97e18549e21354893e4ee4ef36db1d237534982482c3681ee6e7b57/mccabe-0.6.1-py2.py3-none-any.whl\n",
            "Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from flake8->cloudmesh-installer) (3.4.0)\n",
            "Collecting pyflakes<2.3.0,>=2.2.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/69/5b/fd01b0c696f2f9a6d2c839883b642493b431f28fa32b29abc465ef675473/pyflakes-2.2.0-py2.py3-none-any.whl (66kB)\n",
            "\u001b[K     |████████████████████████████████| 71kB 6.0MB/s \n",
            "\u001b[?25hCollecting pycodestyle<2.7.0,>=2.6.0a1\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/10/5b/88879fb861ab79aef45c7e199cae3ef7af487b5603dcb363517a50602dd7/pycodestyle-2.6.0-py2.py3-none-any.whl (41kB)\n",
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            "\u001b[?25hRequirement already satisfied: click>=7 in /usr/local/lib/python3.6/dist-packages (from pip-tools->cloudmesh-installer) (7.1.2)\n",
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            "Requirement already satisfied: typing-extensions>=3.6.4; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from importlib-metadata; python_version < \"3.8\"->flake8->cloudmesh-installer) (3.7.4.3)\n",
            "Building wheels for collected packages: ordered-set, python-hostlist\n",
            "  Building wheel for ordered-set (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for ordered-set: filename=ordered_set-4.0.2-py2.py3-none-any.whl size=8210 sha256=122dc0bb00726d7f94b83ab4c455182e02cf6f78c8c878393f530e7bf1d4d59a\n",
            "  Stored in directory: /root/.cache/pip/wheels/e1/c6/9b/651d8a21d59b51a75ab9c070838f9231b8126421bc0569af47\n",
            "  Building wheel for python-hostlist (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for python-hostlist: filename=python_hostlist-1.21-cp36-none-any.whl size=38931 sha256=7f5cdc41de29b7b3a31f9376b6523865dca048c2e246362dfc674a25bd3653b6\n",
            "  Stored in directory: /root/.cache/pip/wheels/0b/5b/55/ddcf52288f0b10f4564ca1b2531594ff7ccc65f487ba8dc437\n",
            "Successfully built ordered-set python-hostlist\n",
            "\u001b[31mERROR: pytest-cov 2.11.1 has requirement coverage>=5.2.1, but you'll have coverage 3.7.1 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: pytest-cov 2.11.1 has requirement pytest>=4.6, but you'll have pytest 3.6.4 which is incompatible.\u001b[0m\n",
            "Installing collected packages: colorama, pyfiglet, simplejson, python-hostlist, oyaml, cloudmesh-common, bump2version, pipdeptree, pytest-cov, mccabe, pyflakes, pycodestyle, flake8, ordered-set, cloudmesh-installer\n",
            "Successfully installed bump2version-1.0.0 cloudmesh-common-4.3.43 cloudmesh-installer-4.4.19 colorama-0.4.4 flake8-3.8.4 mccabe-0.6.1 ordered-set-4.0.2 oyaml-1.0 pipdeptree-2.0.0 pycodestyle-2.6.0 pyfiglet-0.8.post1 pyflakes-2.2.0 pytest-cov-2.11.1 python-hostlist-1.21 simplejson-3.17.2\n",
            "Requirement already satisfied: cloudmesh-common in /usr/local/lib/python3.6/dist-packages (4.3.43)\n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RlBJ-XuWopPh"
      },
      "source": [
        "## Import Libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RylyDeO2oC4m"
      },
      "source": [
        "from __future__ import absolute_import\n",
        "from __future__ import division\n",
        "from __future__ import print_function\n",
        "\n",
        "import numpy as np\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Activation, Dense, Dropout\n",
        "from keras.layers import Conv2D, MaxPooling2D, Flatten, AveragePooling2D\n",
        "from keras.utils import to_categorical, plot_model\n",
        "from keras.datasets import mnist"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e2lUNBJxore4"
      },
      "source": [
        "## Download Data and Pre-Process"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3X9sLwfUocZ6",
        "outputId": "c7856a83-0d33-44c5-a71e-ee9364dad122"
      },
      "source": [
        "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
        "\n",
        "num_labels = len(np.unique(y_train))\n",
        "\n",
        "y_train = to_categorical(y_train)\n",
        "y_test = to_categorical(y_test)\n",
        "\n",
        "image_size = x_train.shape[1]\n",
        "x_train = np.reshape(x_train,[-1, image_size, image_size, 1])\n",
        "x_test = np.reshape(x_test,[-1, image_size, image_size, 1])\n",
        "x_train = x_train.astype('float32') / 255\n",
        "x_test = x_test.astype('float32') / 255\n",
        "\n",
        "input_shape = (image_size, image_size, 1)\n",
        "print(input_shape)\n",
        "batch_size = 128\n",
        "kernel_size = 3\n",
        "pool_size = 2\n",
        "filters = 64\n",
        "dropout = 0.2"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "11493376/11490434 [==============================] - 0s 0us/step\n",
            "(28, 28, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O8DwuhcaowYQ"
      },
      "source": [
        "## Define Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "O4tidxRFofEj",
        "outputId": "325d5686-f256-4620-f2f6-61c8e49e447c"
      },
      "source": [
        "model = Sequential()\n",
        "model.add(Conv2D(filters=filters,\n",
        "                 kernel_size=kernel_size,\n",
        "                 activation='relu',\n",
        "                 input_shape=input_shape,\n",
        "                 padding='same'))\n",
        "model.add(MaxPooling2D(pool_size))\n",
        "model.add(Conv2D(filters=filters,\n",
        "                 kernel_size=kernel_size,\n",
        "                 activation='relu',\n",
        "                 input_shape=input_shape,\n",
        "                 padding='same'))\n",
        "model.add(MaxPooling2D(pool_size))\n",
        "model.add(Conv2D(filters=filters,\n",
        "                 kernel_size=kernel_size,\n",
        "                 activation='relu',\n",
        "                 padding='same'))\n",
        "model.add(MaxPooling2D(pool_size))\n",
        "model.add(Conv2D(filters=filters,\n",
        "                 kernel_size=kernel_size,\n",
        "                 activation='relu'))\n",
        "model.add(Flatten())\n",
        "model.add(Dropout(dropout))\n",
        "model.add(Dense(num_labels))\n",
        "model.add(Activation('softmax'))\n",
        "model.summary()\n",
        "plot_model(model, to_file='cnn-mnist.png', show_shapes=True)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_4 (Conv2D)            (None, 28, 28, 64)        640       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2 (None, 14, 14, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_5 (Conv2D)            (None, 14, 14, 64)        36928     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_4 (MaxPooling2 (None, 7, 7, 64)          0         \n",
            "_________________________________________________________________\n",
            "conv2d_6 (Conv2D)            (None, 7, 7, 64)          36928     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_5 (MaxPooling2 (None, 3, 3, 64)          0         \n",
            "_________________________________________________________________\n",
            "conv2d_7 (Conv2D)            (None, 1, 1, 64)          36928     \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 64)                0         \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 64)                0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 10)                650       \n",
            "_________________________________________________________________\n",
            "activation_1 (Activation)    (None, 10)                0         \n",
            "=================================================================\n",
            "Total params: 112,074\n",
            "Trainable params: 112,074\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZxW3sG8WoyYC"
      },
      "source": [
        "# Train"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SjT5wffFoiEg",
        "outputId": "ad0eba1c-cc81-481c-fcd8-45a89be382c2"
      },
      "source": [
        "model.compile(loss='categorical_crossentropy',\n",
        "              optimizer='adam',\n",
        "              metrics=['accuracy'])\n",
        "# train the network\n",
        "model.fit(x_train, y_train, epochs=10, batch_size=batch_size)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "469/469 [==============================] - 125s 266ms/step - loss: 0.6794 - accuracy: 0.7783\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f35d4b104e0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E3jjqPJqoljt"
      },
      "source": [
        "## Test"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2B1-bFcvokdd",
        "outputId": "c9703915-ae16-415a-ef16-5382747b6b3b"
      },
      "source": [
        "loss, acc = model.evaluate(x_test, y_test, batch_size=batch_size)\n",
        "print(\"\\nTest accuracy: %.1f%%\" % (100.0 * acc))"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "79/79 [==============================] - 6s 68ms/step - loss: 0.0608 - accuracy: 0.9813\n",
            "\n",
            "Test accuracy: 98.1%\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}