keras学习记录——训练resnet模型对cifar10分类

 

目录

keras学习记录——训练resnet模型对cifar10分类

前言

一、数据准备

二、构建网络

三、网络训练

四、训练结果

总结

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前言

最近在对resnet中的add操作做量化处理，所以先将resnet对cifar10分类训练记录下来。

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提示：以下是本篇文章正文内容，下面案例可供参考

一、数据准备

直接调用keras中的数据库： from keras.datasets import cifar10 (data_train, label_train), (data_test, label_test) = cifar10.load_data() data_train = (data_train-127.5) / 128 data_test = (data_test - 127.5) /128

如果需要将便签转变为one-hot编码，可以使用如下：

label_train = keras.utils.to_categorical(label_train,10) label_test = keras.utils.to_categorical(label_test, 10)

 

如果调用时太慢，可以下载cifar-10-python.tar.gz，网上有很多资源，这里就不放链接了，之后将cifar10.load_data()函数中下述路径改为本地路径即可。

origin = 'https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz'

二、构建网络

resnet_block

代码如下：

def resnet_block(input, num_filters=16, kernel_size=3, strides=1, activation='relu'): # x = Conv2D(num_filters, kernel_size=kernel_size,strides=strides,padding='same')(input) x = Conv2D(num_filters, kernel_size=kernel_size, strides=strides, padding='same', kernel_initializer='he_normal', kernel_regularizer=l2(1e-4))(input) x = BatchNormalization()(x) x = Activation('relu')(x) x = Conv2D(num_filters, kernel_size=kernel_size, strides=1,padding='same')(x) x = BatchNormalization()(x) if strides==1: shortcut = input else: shortcut = Conv2D(num_filters, kernel_size=kernel_size,strides=strides,padding='same')(input) # x = myAdd(activation='relu')([x, shortcut]) x = keras.layers.add([x, shortcut]) x = Activation('relu')(x) return x

结构如下：

2. resnet

代码如下：

def resnet(input_shape): inputs = Input(shape=input_shape) # 1 x = Conv2D(16, activation='relu', kernel_size=3, strides=1,padding='same')(inputs) x = BatchNormalization()(x) print('layer1,xshape:', x.shape) # 2-7 for i in range(6): x = resnet(x, num_filters=16, kernel_size=3, strides=1, activation='relu') # out: 32*32*16 # 8-13 for i in range(6): if i == 0: x = resnet(x, num_filters=32, kernel_size=3, strides=2, activation='relu') else: x = resnet(x, num_filters=32, kernel_size=3, strides=1, activation='relu') # out: 16*16*32 # 14-19 for i in range(6): if i == 0: x = resnet(x, num_filters=64, kernel_size=3, strides=2, activation='relu') else: x = resnet(x, num_filters=64, kernel_size=3, strides=1, activation='relu') # out: 8*8*64 x = AveragePooling2D(pool_size=2)(x) # out: 4*4*64 y = Flatten()(x) # out:1024 outputs = Dense(10, kernel_initializer='he_normal')(y) # outputs = myDense(10, kernel_initializer='glorot_normal', use_batchnormal = False)(y) outputs = Activation('softmax')(outputs) model = Model(inputs=inputs, outputs=outputs) return model

三、网络训练

代码如下：

model = resnet((32,32,3)) model.compile(loss='sparse_categorical_crossentropy', optimizer=Adam(), metrics=['accuracy']) model.summary() plot_model(model, to_file='./model.png', show_shapes=True) # checkpoint = ModelCheckpoint(filepath='saved_models/'+config['mode']+'-'+str(config['epoch'])+'-'+str(config['batch_size'])+'-{val_accuracy:.4f}'+'.h5',monitor='val_accuracy', # verbose=1,save_best_only=True) csv_logger = CSVLogger('saved_models/'+config['mode']+'-'+str(config['epoch'])+'-'+str(config['batch_size'])+'.log', append=False) lr_reducer = ReduceLROnPlateau(monitor='val_accuracy', factor=0.2, patience=5, mode='max', min_lr=1e-3) # model.fit(data_train, label_train,batch_size=64,epochs=1, validation_data=(data_test,label_test), verbose=1, callbacks=callbacks) aug = ImageDataGenerator(width_shift_range=0.2,height_shift_range=0.2,horizontal_flip=True,zoom_range=0.2) aug.fit(data_train) gen = aug.flow(data_train, label_train, batch_size=config['batch_size']) model.fit_generator(generator=gen,epochs=config['epoch'],validation_data=(data_test,label_test), callbacks=[lr_reducer, csv_logger], verbose=1)

 

四、训练结果

由于时间关系，并未将网络训练完全。

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总结

学习到了关于keras中训练的小技巧，如：earlystop等，并且学会了如何构建残差神经网络。