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301 lines
11 KiB
Plaintext
301 lines
11 KiB
Plaintext
{
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"nbformat": 4,
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"nbformat_minor": 0,
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"metadata": {
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"colab": {
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"name": "keras-tf-sample.ipynb",
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"version": "0.3.2",
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"provenance": [],
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"include_colab_link": true
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},
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3"
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}
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},
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "view-in-github",
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"colab_type": "text"
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},
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"source": [
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"<a href=\"https://colab.research.google.com/github/hrkt/Plugins/blob/master/keras_tf_sample.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "zy5ichg1VA97",
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"colab_type": "text"
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},
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"source": [
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"以下のサイトを参考にしました。\n",
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"- 「Keras / Tensorflowで始めるディープラーニング入門」 https://qiita.com/yampy/items/706d44417c433e68db0d"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "7jRl9looUSZ9",
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"colab_type": "code",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 34
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},
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"outputId": "b0c3cd80-32ac-413d-d585-b103ddadcda7"
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},
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"source": [
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"import keras\n",
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"from keras.datasets import mnist\n",
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"from keras.models import Sequential\n",
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"from keras.layers import Dense, Dropout, Flatten\n",
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"from keras.layers import Conv2D, MaxPooling2D\n",
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"from keras import backend as K"
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],
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"execution_count": 1,
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"outputs": [
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{
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"output_type": "stream",
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"text": [
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"Using TensorFlow backend.\n"
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],
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"name": "stderr"
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}
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "7tTXTQn0UyGi",
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"colab_type": "code",
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"colab": {}
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},
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"source": [
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"batch_size = 128\n",
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"num_classes = 10\n",
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"epochs = 3\n",
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"\n"
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],
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"execution_count": 0,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "trCXc0XGU1xt",
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"colab_type": "code",
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"colab": {}
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},
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"source": [
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"img_rows, img_cols = 28, 28\n",
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"\n",
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"(x_train, y_train), (x_test, y_test) = mnist.load_data()"
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],
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"execution_count": 0,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "9z32qSQqU9Px",
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"colab_type": "code",
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"colab": {}
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},
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"source": [
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"#Kerasのバックエンドで動くTensorFlowとTheanoでは入力チャンネルの順番が違うので場合分けして書いています\n",
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"if K.image_data_format() == 'channels_first':\n",
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" x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)\n",
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" x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)\n",
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" input_shape = (1, img_rows, img_cols)\n",
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"else:\n",
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" x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)\n",
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" x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)\n",
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" input_shape = (img_rows, img_cols, 1)\n"
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],
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"execution_count": 0,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "du1XCGbBVECT",
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"colab_type": "code",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 68
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},
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"outputId": "aadeacb2-91e6-4e05-d707-b9ece60eaef0"
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},
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"source": [
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"x_train = x_train.astype('float32')\n",
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"x_test = x_test.astype('float32')\n",
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"x_train /= 255\n",
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"x_test /= 255\n",
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"print('x_train shape:', x_train.shape)\n",
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"print(x_train.shape[0], 'train samples')\n",
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"print(x_test.shape[0], 'test samples')"
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],
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"execution_count": 5,
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"outputs": [
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{
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"output_type": "stream",
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"text": [
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"x_train shape: (60000, 28, 28, 1)\n",
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"60000 train samples\n",
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"10000 test samples\n"
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],
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"name": "stdout"
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}
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "AqnRhJyoVrjO",
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"colab_type": "code",
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"colab": {}
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},
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"source": [
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"y_train = y_train.astype('int32')\n",
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"y_test = y_test.astype('int32')\n",
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"y_train = keras.utils.np_utils.to_categorical(y_train, num_classes)\n",
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"y_test = keras.utils.np_utils.to_categorical(y_test, num_classes)\n"
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],
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"execution_count": 0,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "CSklWGNjVvOR",
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"colab_type": "code",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 315
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},
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"outputId": "ce8a8390-6d52-4e43-b0aa-6ab33ce0470c"
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},
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"source": [
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"model = Sequential()\n",
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"model.add(Conv2D(32, kernel_size=(3, 3),\n",
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" activation='relu',\n",
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" input_shape=input_shape))\n",
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"model.add(Conv2D(64, (3, 3), activation='relu'))\n",
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"model.add(MaxPooling2D(pool_size=(2, 2)))\n",
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"model.add(Dropout(0.25))\n",
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"model.add(Flatten())\n",
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"model.add(Dense(128, activation='relu'))\n",
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"model.add(Dropout(0.5))\n",
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"model.add(Dense(num_classes, activation='softmax'))"
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],
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"execution_count": 7,
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"outputs": [
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{
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"output_type": "stream",
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"text": [
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"WARNING: Logging before flag parsing goes to stderr.\n",
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"W0815 22:39:48.780859 139626640222080 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
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"\n",
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"W0815 22:39:48.803259 139626640222080 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
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"\n",
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"W0815 22:39:48.809604 139626640222080 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
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"\n",
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"W0815 22:39:48.843924 139626640222080 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3976: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
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"\n",
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"W0815 22:39:48.847448 139626640222080 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:133: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n",
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"\n",
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"W0815 22:39:48.860003 139626640222080 deprecation.py:506] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
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"Instructions for updating:\n",
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"Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n"
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],
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"name": "stderr"
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}
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "j_d10YewWISw",
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"colab_type": "code",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 105
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},
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"outputId": "a3d42703-5820-47ca-e5c1-737362237107"
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},
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"source": [
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"model.compile(loss=keras.losses.categorical_crossentropy,\n",
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" optimizer=keras.optimizers.Adadelta(),\n",
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" metrics=['accuracy'])"
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],
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"execution_count": 8,
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"outputs": [
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{
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"output_type": "stream",
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"text": [
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"W0815 22:39:48.964365 139626640222080 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
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"\n",
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"W0815 22:39:48.975083 139626640222080 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3295: The name tf.log is deprecated. Please use tf.math.log instead.\n",
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"\n"
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],
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"name": "stderr"
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}
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "D_Yuht6EWMpx",
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"colab_type": "code",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 1000
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},
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"outputId": "b422c382-6e64-4e24-d720-5528a99dd734"
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},
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"source": [
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"model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs,\n",
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" verbose=1, validation_data=(x_test, y_test))"
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],
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"execution_count": 9,
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"outputs": [
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{
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"output_type": "stream",
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"text": [
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"W0815 22:39:49.110717 139626640222080 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
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"Instructions for updating:\n",
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"Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
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],
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"name": "stderr"
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},
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{
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"output_type": "stream",
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"text": [
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"Train on 60000 samples, validate on 10000 samples\n",
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"Epoch 1/3\n",
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"60000/60000 [==============================] - 158s 3ms/step - loss: 0.2604 - acc: 0.9192 - val_loss: 0.0632 - val_acc: 0.9808\n",
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"Epoch 2/3\n",
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"60000/60000 [==============================] - 157s 3ms/step - loss: 0.0910 - acc: 0.9732 - val_loss: 0.0436 - val_acc: 0.9855\n",
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"Epoch 3/3\n",
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"60000/60000 [==============================] - 157s 3ms/step - loss: 0.0650 - acc: 0.9805 - val_loss: 0.0370 - val_acc: 0.9870\n"
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],
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"name": "stdout"
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},
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"<keras.callbacks.History at 0x7efd2228df98>"
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]
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},
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"metadata": {
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"tags": []
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},
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"execution_count": 9
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}
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]
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}
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]
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} |