mnist_basic.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "mnist_basic.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyO41KjxYKNfAMOpslaXMlWN",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/jeonggunlee/DeepLearningBasics/blob/master/mnist_basic.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0iYhpK_4REVY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "e1285e5b-64d1-42c5-b659-6a3a7f752970"
      },
      "source": [
        "pip install mnist"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: mnist in /usr/local/lib/python3.6/dist-packages (0.2.2)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from mnist) (1.18.5)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m2U9TW6xQ3od",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import mnist\n",
        "import keras\n",
        "\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "from keras.utils import to_categorical\n",
        "\n",
        "# The first time you run this might be a bit slow, since the\n",
        "# mnist package has to download and cache the data.\n",
        "train_images = mnist.train_images()\n",
        "train_labels = mnist.train_labels()\n",
        "test_images = mnist.test_images()\n",
        "test_labels = mnist.test_labels()"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rr42cou9TrVj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "72eedbb9-943b-43cf-e5d7-5575be83af23"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "# https://medium.com/@mrdatascience/how-to-plot-mnist-digits-using-matplotlib-65a2e0cc068\n",
        "\n",
        "# pick a sample to plot\n",
        "sample = 1\n",
        "image = train_images[sample]\n",
        "\n",
        "# plot the sample\n",
        "#fig = plt.figure\n",
        "plt.imshow(image, cmap='gray')\n",
        "plt.show()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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I826NkY1TiQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mhEbNlPrUNp6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "9ed1315e-a87c-4de3-dc35-427d8b252306"
      },
      "source": [
        "# Normalize the images.\n",
        "train_images = (train_images / 255) - 0.5\n",
        "test_images = (test_images / 255) - 0.5\n",
        "\n",
        "# Flatten the images.\n",
        "train_images = train_images.reshape((-1, 784))\n",
        "test_images = test_images.reshape((-1, 784))\n",
        "\n",
        "print(train_images.shape) # (60000, 784)\n",
        "print(test_images.shape)  # (10000, 784)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(60000, 784)\n",
            "(10000, 784)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "29SiS9luReM_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = Sequential([\n",
        "  Dense(64, activation='relu', input_shape=(784,)),\n",
        "  Dense(64, activation='relu'),\n",
        "  Dense(10, activation='softmax'),\n",
        "])"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xx3UBdkVRql_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.compile(\n",
        "  optimizer='adam',\n",
        "  loss='categorical_crossentropy',\n",
        "  metrics=['accuracy'],\n",
        ")"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4g-lEfMeRtNJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 209
        },
        "outputId": "fb13d1b9-bff6-41a3-8a42-fec7c494e03d"
      },
      "source": [
        "model.fit(\n",
        "  train_images,\n",
        "  to_categorical(train_labels),\n",
        "  epochs=5,\n",
        "  batch_size=32,\n",
        ")"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "60000/60000 [==============================] - 3s 54us/step - loss: 0.3625 - accuracy: 0.8924\n",
            "Epoch 2/5\n",
            "60000/60000 [==============================] - 3s 54us/step - loss: 0.1855 - accuracy: 0.9442\n",
            "Epoch 3/5\n",
            "60000/60000 [==============================] - 3s 53us/step - loss: 0.1442 - accuracy: 0.9559\n",
            "Epoch 4/5\n",
            "60000/60000 [==============================] - 3s 50us/step - loss: 0.1224 - accuracy: 0.9617\n",
            "Epoch 5/5\n",
            "60000/60000 [==============================] - 3s 52us/step - loss: 0.1079 - accuracy: 0.9661\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.callbacks.History at 0x7fa57668c390>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yvE8QbLVS8Mb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "b4dcb74e-143b-455f-de0c-573b154a74b6"
      },
      "source": [
        "model.evaluate(\n",
        "  test_images,\n",
        "  to_categorical(test_labels)\n",
        ")\n",
        "# return loss, accuracy"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10000/10000 [==============================] - 0s 25us/step\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.11181644864585251, 0.9670000076293945]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    }
  ]
}