ResNet 残差网络学习

ResNet 残差网络学习

最近学习ResNet 残差网络，思路大致是首先构建出残差块，然后在添加网络层。先构建最简单的残差块。

残差块结构 2 x (3 x 3) Conv2D-BN-ReLU

def res_block_v1(x, input_filter, output_filter): res_x = Conv2D(kernel_size=(3,3), filters=output_filter, strides=1, padding='same')(x) res_x = BatchNormalization()(res_x) res_x = Activation('relu')(res_x) res_x = Conv2D(kernel_size=(3,3), filters=output_filter, strides=1, padding='same')(res_x) res_x = BatchNormalization()(res_x) if input_filter == output_filter: identity = x else: identity = Conv2D(kernel_size=(1,1), filters=output_filter, strides=1, padding='same')(x) x = keras.layers.add([identity, res_x]) output = Activation('relu')(x) return output

然后搭建网络层，以自己实际情况搭建，一般50层以内可采用上面的残差块结构，这里就简易搭建几层

def resnet_v1(x): # x为tensor x = Conv2D(kernel_size=(3,3), filters=16, strides=1, padding='same', activation='relu')(x) x = res_block_v1(x, 16, 16) x = res_block_v1(x, 16, 32) x = Flatten()(x) outputs = Dense(10, activation='softmax', kernel_initializer='he_normal')(x) return outputs

导入训练数据`

import keras from keras.datasets import cifar10 from keras.layers import Conv2D,Input,BatchNormalization,Activation,Dense,Flatten,MaxPooling2D,Dropout from keras.optimizers import Optimizer,Adam from keras.preprocessing.image import ImageDataGenerator from keras.callbacks import ModelCheckpoint,ReduceLROnPlateau,EarlyStopping from keras.utils.np_utils import to_categorical (x_train_src,y_train_src),(x_test_src,y_test_src) = cifar10.load_data() num_classes = 10 x_train = x_train_src.astype('float32') / 255 x_test = x_test_src.astype('float32') / 255 y_train = to_categorical(y_train_src, num_classes) y_test = to_categorical(y_test_src, num_classes) input_shape = x_train.shape[1:] x = Input(shape=input_shape) model = Model(x,resnet_v1(x)) model.compile(loss = 'categorical_crossentropy',optimizer=Adam(lr=0.01),metrics=['accuracy']) model.summary batch_size = 128 epochs = 200 history = model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, verbose=1, validation_data=(x_test, y_test))