{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "simple.ipynb", "provenance": [], "collapsed_sections": [] }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "accelerator": "GPU" }, "cells": [ { "cell_type": "code", "metadata": { "id": "BeibK12dAiot", "colab_type": "code", "colab": {} }, "source": [ "from tensorflow.keras.datasets import mnist\n", "from tensorflow.keras.models import Sequential\n", "from tensorflow.keras.layers import Dense, Dropout, Flatten\n", "from tensorflow.keras.layers import Conv2D, MaxPooling2D\n", "from tensorflow.keras.utils import to_categorical" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "VAUkfCwTA_Km", "colab_type": "code", "outputId": "8dabe82c-7999-49d6-d675-20979c006f10", "colab": { "base_uri": "https://localhost:8080/", "height": 51 } }, "source": [ "(x_train, y_train), (x_test, y_test) = mnist.load_data()" ], "execution_count": 0, "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" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "apDIDKb-BEjA", "colab_type": "code", "outputId": "6c25331a-2276-4d3d-9739-3acfb601c0dc", "colab": { "base_uri": "https://localhost:8080/", "height": 51 } }, "source": [ "# Reshaping for channels_last (tensorflow) with one channel\n", "size = 28\n", "print(x_train.shape, x_test.shape)\n", "x_train = x_train.reshape(len(x_train), size, size, 1).astype('float32')\n", "x_test = x_test.reshape(len(x_test), size, size, 1).astype('float32')\n", "print(x_train.shape, x_test.shape)" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "(60000, 28, 28) (10000, 28, 28)\n", "(60000, 28, 28, 1) (10000, 28, 28, 1)\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "RwTWdxVbB6Wq", "colab_type": "code", "outputId": "1a75bb86-8e7b-425f-c064-2f12c8615d8c", "colab": { "base_uri": "https://localhost:8080/", "height": 34 } }, "source": [ "# Normalize\n", "upper = max(x_train.max(), x_test.max())\n", "lower = min(x_train.min(), x_test.min())\n", "print(f'Max: {upper} Min: {lower}')\n", "x_train /= upper\n", "x_test /= upper" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "Max: 255.0 Min: 0.0\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "lYQVq00XBacf", "colab_type": "code", "colab": {} }, "source": [ "total_classes = 10\n", "y_train = to_categorical(y_train, total_classes)\n", "y_test = to_categorical(y_test, total_classes)" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "Ad2UK8LkBhKe", "colab_type": "code", "colab": {} }, "source": [ "# Make the model\n", "model = Sequential()\n", "model.add(Conv2D(64, (3, 3), activation='relu', input_shape=(size,size, 1), data_format='channels_last'))\n", "model.add(Conv2D(32, (3, 3), activation='relu'))\n", "model.add(MaxPooling2D(pool_size=(2, 2)))\n", "model.add(Dropout(0.25))\n", "model.add(Flatten())\n", "model.add(Dense(128, activation='relu'))\n", "model.add(Dropout(0.5))\n", "model.add(Dense(total_classes, activation='softmax'))\n", "\n", "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "v4Vb1_CfEVIl", "colab_type": "code", "outputId": "f00919d7-56b6-4577-9c97-283a48cbf804", "colab": { "base_uri": "https://localhost:8080/", "height": 459 } }, "source": [ "# Train\n", "model.fit(x_train, y_train,\n", " batch_size=32,\n", " epochs=12,\n", " verbose=True)" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "Train on 60000 samples\n", "Epoch 1/12\n", "60000/60000 [==============================] - 15s 256us/sample - loss: 0.2029 - acc: 0.9382\n", "Epoch 2/12\n", "60000/60000 [==============================] - 8s 133us/sample - loss: 0.0827 - acc: 0.9750\n", "Epoch 3/12\n", "60000/60000 [==============================] - 8s 134us/sample - loss: 0.0650 - acc: 0.9800\n", "Epoch 4/12\n", "60000/60000 [==============================] - 7s 123us/sample - loss: 0.0541 - acc: 0.9839\n", "Epoch 5/12\n", "60000/60000 [==============================] - 7s 123us/sample - loss: 0.0476 - acc: 0.9850\n", "Epoch 6/12\n", "60000/60000 [==============================] - 8s 130us/sample - loss: 0.0391 - acc: 0.9883\n", "Epoch 7/12\n", "60000/60000 [==============================] - 8s 128us/sample - loss: 0.0354 - acc: 0.9889\n", "Epoch 8/12\n", "60000/60000 [==============================] - 8s 128us/sample - loss: 0.0330 - acc: 0.9895\n", "Epoch 9/12\n", "60000/60000 [==============================] - 7s 122us/sample - loss: 0.0283 - acc: 0.9909\n", "Epoch 10/12\n", "60000/60000 [==============================] - 7s 122us/sample - loss: 0.0275 - acc: 0.9909\n", "Epoch 11/12\n", "60000/60000 [==============================] - 7s 122us/sample - loss: 0.0265 - acc: 0.9918\n", "Epoch 12/12\n", "60000/60000 [==============================] - 7s 123us/sample - loss: 0.0239 - acc: 0.9922\n" ], "name": "stdout" }, { "output_type": "execute_result", "data": { "text/plain": [ "" ] }, "metadata": { "tags": [] }, "execution_count": 11 } ] }, { "cell_type": "code", "metadata": { "id": "BybcqHVqEaQD", "colab_type": "code", "outputId": "9b5a41a0-9420-4387-8a01-5462c40878bd", "colab": { "base_uri": "https://localhost:8080/", "height": 51 } }, "source": [ "score = model.evaluate(x_test, y_test, verbose=0)\n", "print('Test loss:', score[0])\n", "print('Test accuracy:', score[1])" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "Test loss: 0.028924251638433304\n", "Test accuracy: 0.9922\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "NNnMBuoURyaX", "colab_type": "code", "colab": {} }, "source": [ "# Save for keras\n", "model.save(\"model.h5\")" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "Ia7WzlH9_UxY", "colab_type": "code", "outputId": "1d61e90a-f2a8-4ef8-8df2-301d61a89c8d", "colab": { "base_uri": "https://localhost:8080/", "height": 513 } }, "source": [ "!pip install tensorflowjs\n", "import tensorflowjs as tfjs\n", "\n", "# Save for the web\n", "tfjs.converters.save_keras_model(model, './js')" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "Requirement already satisfied: tensorflowjs in /usr/local/lib/python3.6/dist-packages (1.4.0)\n", "Requirement already satisfied: six==1.11.0 in /usr/local/lib/python3.6/dist-packages (from tensorflowjs) (1.11.0)\n", "Requirement already satisfied: tensorflow==1.15.0 in /usr/local/lib/python3.6/dist-packages (from tensorflowjs) (1.15.0)\n", "Requirement already satisfied: h5py==2.8.0 in /usr/local/lib/python3.6/dist-packages (from tensorflowjs) (2.8.0)\n", "Requirement already 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