03_tf_train_mnist_cnn.ipynb

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  "metadata": {
    "colab": {
      "name": "03-tf-train-mnist-cnn.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyNqcTcanIzmsbuOR746okpP",
      "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/changsin/DeepLearning-101/blob/master/03_tf_train_mnist_cnn.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lIT_6IAwnay_"
      },
      "source": [
        "Import necessary libraries."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2x3DgRsQm8oT"
      },
      "source": [
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import Sequential\n",
        "from tensorflow.keras.datasets import mnist\n",
        "from tensorflow.keras.layers import Conv2D, Dense, Dropout, Flatten, MaxPooling2D\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "from tensorflow.keras import backend as K\n",
        "from matplotlib import pyplot as plt\n"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ywzagULZnVil"
      },
      "source": [
        "Define hyperparameters first."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ha1Wm3vEnSt5"
      },
      "source": [
        "NUM_OF_CLASSES = 10\n",
        "BATCH_SIZE = 128\n",
        "EPOCHS = 10\n",
        "# input image dimensions\n",
        "IMG_ROWS, IMG_COLS = 28, 28"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PNatYOI1oA4V"
      },
      "source": [
        "Load the dataset. [MNIST](https://en.wikipedia.org/wiki/MNIST_database) is a built-in dataset in TensorFlow."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MjMiieL_n3oq"
      },
      "source": [
        "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
        "x_train, x_test = x_train / 255.0, x_test / 255.0"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mIKZqU3NrmHs",
        "outputId": "333e4f63-4401-4fcd-fb69-ded279f01125",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "print(x_train.shape)\n",
        "print(y_train.shape)\n",
        "print(x_test.shape)\n",
        "print(y_test.shape)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(60000, 28, 28)\n",
            "(60000,)\n",
            "(10000, 28, 28)\n",
            "(10000,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "28UlznRhranR",
        "outputId": "f412057f-05b8-4430-f5dc-f051f3b75316",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "y_train = to_categorical(y_train, NUM_OF_CLASSES)\n",
        "y_test = to_categorical(y_test, NUM_OF_CLASSES)\n",
        "\n",
        "print(x_train.shape)\n",
        "print(y_train.shape)\n",
        "print(x_test.shape)\n",
        "print(y_test.shape)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(60000, 28, 28)\n",
            "(60000, 10)\n",
            "(10000, 28, 28)\n",
            "(10000, 10)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3ML-SvlLr1rN",
        "outputId": "afee6c8e-6053-4c24-ba81-d7dde2c6a8ae",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "data_format = K.image_data_format()\n",
        "print(data_format)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "channels_last\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "07wpDyewr85K"
      },
      "source": [
        "if data_format == 'channels_first':\n",
        "    x_train = x_train.reshape(x_train.shape[0], 1, IMG_ROWS, IMG_COLS)\n",
        "    x_test = x_test.reshape(x_test.shape[0], 1, IMG_ROWS, IMG_COLS)\n",
        "    input_shape = (1, IMG_ROWS, IMG_COLS)\n",
        "else:\n",
        "    x_train = x_train.reshape(x_train.shape[0], IMG_ROWS, IMG_COLS, 1)\n",
        "    x_test = x_test.reshape(x_test.shape[0], IMG_ROWS, IMG_COLS, 1)\n",
        "    input_shape = (IMG_ROWS, IMG_COLS, 1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MipXAb--ovCH"
      },
      "source": [
        "Define the model using Sequential."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qcQXU7NwoUEj",
        "outputId": "9c732c66-49f5-4937-8aab-7a44ff564bcc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "model = Sequential([\n",
        "    Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=input_shape),\n",
        "    Conv2D(64, (3, 3), activation='relu'),\n",
        "    MaxPooling2D(pool_size=(2, 2)),\n",
        "    Dropout(0.25),\n",
        "    Flatten(),\n",
        "    Dense(128, activation='relu'),\n",
        "    Dropout(0.5),\n",
        "    Dense(NUM_OF_CLASSES, activation='softmax')\n",
        "])\n",
        "model.summary()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d (Conv2D)              (None, 26, 26, 32)        320       \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 24, 24, 64)        18496     \n",
            "_________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D) (None, 12, 12, 64)        0         \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 12, 12, 64)        0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 9216)              0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 128)               1179776   \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 10)                1290      \n",
            "=================================================================\n",
            "Total params: 1,199,882\n",
            "Trainable params: 1,199,882\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YnsphvDWo5Z8"
      },
      "source": [
        "Specify the optimizer for backpropagation. Note that the loss (cost) funciton is cross entropy that tends to go hand-in-hand with softmax activation function."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HtaD_mpQogYh"
      },
      "source": [
        "optimizer = keras.optimizers.Adadelta() \n",
        "loss = keras.losses.categorical_crossentropy\n",
        "\n",
        "model.compile(optimizer=optimizer, loss=loss, metrics=['accuracy'])"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kUcnG4_2piKv"
      },
      "source": [
        "Ready for training finally. Let's roll."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CkDGQo_fok06",
        "outputId": "c5331228-a509-401b-9c8d-983810beb9da",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "history = model.fit(x_train, y_train,\n",
        "                    batch_size=BATCH_SIZE,\n",
        "                    epochs=EPOCHS,\n",
        "                    verbose=1,\n",
        "                    validation_data=(x_test, y_test))"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "469/469 [==============================] - 142s 303ms/step - loss: 2.2876 - accuracy: 0.1482 - val_loss: 2.2608 - val_accuracy: 0.3184\n",
            "Epoch 2/10\n",
            "469/469 [==============================] - 142s 303ms/step - loss: 2.2455 - accuracy: 0.2467 - val_loss: 2.2091 - val_accuracy: 0.4601\n",
            "Epoch 3/10\n",
            "469/469 [==============================] - 141s 301ms/step - loss: 2.1903 - accuracy: 0.3422 - val_loss: 2.1371 - val_accuracy: 0.5685\n",
            "Epoch 4/10\n",
            "469/469 [==============================] - 146s 312ms/step - loss: 2.1133 - accuracy: 0.4258 - val_loss: 2.0383 - val_accuracy: 0.6348\n",
            "Epoch 5/10\n",
            "469/469 [==============================] - 141s 302ms/step - loss: 2.0093 - accuracy: 0.4940 - val_loss: 1.9056 - val_accuracy: 0.6875\n",
            "Epoch 6/10\n",
            "469/469 [==============================] - 143s 304ms/step - loss: 1.8723 - accuracy: 0.5512 - val_loss: 1.7322 - val_accuracy: 0.7319\n",
            "Epoch 7/10\n",
            "469/469 [==============================] - 142s 303ms/step - loss: 1.7026 - accuracy: 0.5969 - val_loss: 1.5233 - val_accuracy: 0.7649\n",
            "Epoch 8/10\n",
            "469/469 [==============================] - 142s 303ms/step - loss: 1.5181 - accuracy: 0.6278 - val_loss: 1.3039 - val_accuracy: 0.7923\n",
            "Epoch 9/10\n",
            "469/469 [==============================] - 142s 303ms/step - loss: 1.3424 - accuracy: 0.6565 - val_loss: 1.1065 - val_accuracy: 0.8123\n",
            "Epoch 10/10\n",
            "469/469 [==============================] - 143s 304ms/step - loss: 1.1928 - accuracy: 0.6805 - val_loss: 0.9484 - val_accuracy: 0.8272\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "utbqrG_Xpfs_"
      },
      "source": [
        "Let's evaluate the model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZalTREJpon5s",
        "outputId": "ffece77b-3d4c-4680-ab26-69db496fb3f4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "model.evaluate(x_test, y_test)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "313/313 [==============================] - 7s 21ms/step - loss: 0.9484 - accuracy: 0.8272\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.9484146237373352, 0.8271999955177307]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5cVg3kO7prnW"
      },
      "source": [
        "Predict means inferencing."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3lXlNsPGpe0N",
        "outputId": "190dce85-91bb-4fb8-92b8-1e9e7833d857",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "CLASS_NAMES = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])\n",
        "print(CLASS_NAMES)\n",
        "\n",
        "preds = model.predict(x_test)\n",
        "\n",
        "preds_single = CLASS_NAMES[np.argmax(preds, axis = -1)]\n",
        "actual_single = CLASS_NAMES[np.argmax(y_test, axis = -1)]"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0 1 2 3 4 5 6 7 8 9]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RUTQTQVNp_zU"
      },
      "source": [
        "Show the training loss and accuracy."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6E9z4Up4p1ws",
        "outputId": "a8389cf3-4f8e-4741-dc2e-b638029c1713",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        }
      },
      "source": [
        "# plot loss during training\n",
        "plt.subplot(211)\n",
        "plt.title('Loss')\n",
        "plt.plot(history.history['loss'], label='train')\n",
        "plt.legend()\n",
        "\n",
        "# plot mse during training\n",
        "plt.subplot(212)\n",
        "plt.title('Accuracy')\n",
        "plt.plot(history.history['accuracy'], label='train')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m2SWFNEup70d"
      },
      "source": [
        "Show the results in UI."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EHlVwIsxp6T5",
        "outputId": "3dbe2ec5-f6ca-4847-ea1f-f8c2b9758b6f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 138
        }
      },
      "source": [
        "n_to_show = 10\n",
        "indices = np.random.choice(range(len(x_test)), n_to_show)\n",
        "\n",
        "fig = plt.figure(figsize=(15, 3))\n",
        "fig.subplots_adjust(hspace=0.4, wspace=0.4)\n",
        "\n",
        "for i, idx in enumerate(indices):\n",
        "    img = x_test[idx]\n",
        "    ax = fig.add_subplot(1, n_to_show, i+1)\n",
        "    ax.axis('off')\n",
        "    ax.text(0.5, -0.35, 'pred = ' + str(preds_single[idx]), fontsize=10, ha='center', transform=ax.transAxes) \n",
        "    ax.text(0.5, -0.7, 'act = ' + str(actual_single[idx]), fontsize=10, ha='center', transform=ax.transAxes)\n",
        "    ax.imshow(img)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x216 with 10 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}