#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Sun Sep 30 18:00:30 2018 這是用keras搭建的簡單的cnn 網絡 @author: lg """ ##import keras from keras.datasets import cifar10 from keras.models import Sequential from keras.layers import Dense, Dropout, Activation, Flatten from keras.layers import Conv2D, MaxPooling2Dfrom matplotlib import pyplot as pltnum_classes = 10 model_name = 'cifar10.h5'# The data, shuffled and split between train and test sets: (x_train, y_train), (x_test, y_test) = cifar10.load_data()plt.imshow(x_train[0]) plt.show()x_train = x_train.astype('float32')/255 x_test = x_test.astype('float32')/255# Convert class vectors to binary class matrices. y_train = keras.utils.to_categorical(y_train, num_classes) y_test = keras.utils.to_categorical(y_test, num_classes)model = Sequential()#第一個 卷積層 的卷積核的數目是32 ，卷積核的大小是3*3，stride沒寫，默認應該是1*1 #對于stride=1*1,并且padding ='same',這種情況卷積后的圖像shape與卷積前相同，本層后shape還是32*32 model.add(Conv2D(32, (3, 3), padding='same',strides=(1,1) ,input_shape=x_train.shape[1:])) model.add(Activation('relu'))#keras Pool層有個奇怪的地方，stride,默認是(2*2),padding 默認是valid，在寫代碼是這些參數還是最好都加上 model.add( MaxPooling2D(pool_size=(2, 2),strides=(2,2),padding='same') )model.add(Dropout(0.25))model.add(Conv2D(64, (3, 3), padding='same')) model.add(Activation('relu'))model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.25))model.add(Flatten())model.add(Dense(512)) model.add(Activation('relu')) model.add(Dropout(0.5))model.add(Dense(num_classes)) model.add(Activation('softmax'))model.summary()# initiate RMSprop optimizer opt = keras.optimizers.rmsprop(lr=0.001, decay=1e-6)# train the model using RMSprop model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy'])hist = model.fit(x_train, y_train, epochs=40, shuffle=True) model.save(model_name)# evaluate loss, accuracy = model.evaluate(x_test, y_test) print (loss, accuracy)

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