tensorflow58 《TensorFlow技术解析与实战》07 Tensorflow的高级框架-keras

'''Trains a simple convnet on the MNIST dataset.Gets to 99.25% test accuracy after 12 epochs(there is still a lot of margin for parameter tuning).16 seconds per epoch on a GRID K520 GPU.'''# 《TensorFlow技术解析与实战》07 Tensorflow的高级框架# win10 Tensorflow-gpu1.2.0 python3.5.3# CUDA v8.0 cudnn-8.0-windows10-x64-v5.1# filename:nntf07.02.py Keras# git clone https://github.com/fchollet/keras.git# https://github.com/fchollet/keras/blob/master/examples/mnist_cnn.pyfrom __future__ import print_functionimport kerasfrom keras.datasets import mnistfrom keras.models import Sequentialfrom keras.layers import Dense, Dropout, Flattenfrom keras.layers import Conv2D, MaxPooling2Dfrom keras import backend as Kbatch_size = 128num_classes = 10epochs = 12# input image dimensionsimg_rows, img_cols = 28, 28# the data, shuffled and split between train and test sets(x_train, y_train), (x_test, y_test) = mnist.load_data()if K.image_data_format() == 'channels_first':    x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)    x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)    input_shape = (1, img_rows, img_cols)else:    x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)    x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)    input_shape = (img_rows, img_cols, 1)x_train = x_train.astype('float32')x_test = x_test.astype('float32')x_train /= 255x_test /= 255print('x_train shape:', x_train.shape)print(x_train.shape[0], 'train samples')print(x_test.shape[0], 'test samples')# convert class vectors to binary class matricesy_train = keras.utils.to_categorical(y_train, num_classes)y_test = keras.utils.to_categorical(y_test, num_classes)model = Sequential()model.add(Conv2D(32, kernel_size=(3, 3),                 activation='relu',                 input_shape=input_shape))model.add(Conv2D(64, (3, 3), activation='relu'))model.add(MaxPooling2D(pool_size=(2, 2)))model.add(Dropout(0.25))model.add(Flatten())model.add(Dense(128, activation='relu'))model.add(Dropout(0.5))model.add(Dense(num_classes, activation='softmax'))model.compile(loss=keras.losses.categorical_crossentropy,              optimizer=keras.optimizers.Adadelta(),              metrics=['accuracy'])model.fit(x_train, y_train,          batch_size=batch_size,          epochs=epochs,          verbose=1,          validation_data=(x_test, y_test))score = model.evaluate(x_test, y_test, verbose=0)print('Test loss:', score[0])print('Test accuracy:', score[1])'''x_train shape: (60000, 28, 28, 1)60000 train samples10000 test samplesTrain on 60000 samples, validate on 10000 samplesEpoch 1/12128/60000 [..............................] - ETA: 4310s - loss: 2.3242 - acc: 0.0312384/60000 [..............................] - ETA: 1441s - loss: 2.2603 - acc: 0.1432...59776/60000 [============================>.] - ETA: 0s - loss: 0.0383 - acc: 0.988860000/60000 [==============================] - 17s - loss: 0.0382 - acc: 0.9889 - val_loss: 0.0274 - val_acc: 0.9915Test loss: 0.0274153048534Test accuracy: 0.9915'''