使用keras實現深度殘差網路
阿新 • • 發佈:2018-11-05
from keras.models import Model from keras.layers import Input, Dense, Dropout, BatchNormalization, Conv2D, MaxPooling2D, AveragePooling2D, concatenate, \ Activation, ZeroPadding2D from keras.layers import add, Flatten # from keras.layers.convolutional import Conv2D,MaxPooling2D,AveragePooling2D import numpy as np seed = 7 np.random.seed(seed) def Conv2d_BN(x, nb_filter, kernel_size, strides=(1, 1), padding='same', name=None): if name is not None: bn_name = name + '_bn' conv_name = name + '_conv' else: bn_name = None conv_name = None x = Conv2D(nb_filter, kernel_size, padding=padding, strides=strides, activation='relu', name=conv_name)(x) x = BatchNormalization(axis=3, name=bn_name)(x) return x def Conv_Block(inpt, nb_filter, kernel_size, strides=(1, 1), with_conv_shortcut=False): x = Conv2d_BN(inpt, nb_filter=nb_filter, kernel_size=kernel_size, strides=strides, padding='same') x = Conv2d_BN(x, nb_filter=nb_filter, kernel_size=kernel_size, padding='same') if with_conv_shortcut: shortcut = Conv2d_BN(inpt, nb_filter=nb_filter, strides=strides, kernel_size=kernel_size) x = add([x, shortcut]) return x else: x = add([x, inpt]) return x inpt = Input(shape=(224, 224, 3)) x = ZeroPadding2D((3, 3))(inpt) x = Conv2d_BN(x, nb_filter=64, kernel_size=(7, 7), strides=(2, 2), padding='valid') x = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='same')(x) # (56,56,64) x = Conv_Block(x, nb_filter=64, kernel_size=(3, 3)) x = Conv_Block(x, nb_filter=64, kernel_size=(3, 3)) x = Conv_Block(x, nb_filter=64, kernel_size=(3, 3)) # (28,28,128) x = Conv_Block(x, nb_filter=128, kernel_size=(3, 3), strides=(2, 2), with_conv_shortcut=True) x = Conv_Block(x, nb_filter=128, kernel_size=(3, 3)) x = Conv_Block(x, nb_filter=128, kernel_size=(3, 3)) x = Conv_Block(x, nb_filter=128, kernel_size=(3, 3)) # (14,14,256) x = Conv_Block(x, nb_filter=256, kernel_size=(3, 3), strides=(2, 2), with_conv_shortcut=True) x = Conv_Block(x, nb_filter=256, kernel_size=(3, 3)) x = Conv_Block(x, nb_filter=256, kernel_size=(3, 3)) x = Conv_Block(x, nb_filter=256, kernel_size=(3, 3)) x = Conv_Block(x, nb_filter=256, kernel_size=(3, 3)) x = Conv_Block(x, nb_filter=256, kernel_size=(3, 3)) # (7,7,512) x = Conv_Block(x, nb_filter=512, kernel_size=(3, 3), strides=(2, 2), with_conv_shortcut=True) x = Conv_Block(x, nb_filter=512, kernel_size=(3, 3)) x = Conv_Block(x, nb_filter=512, kernel_size=(3, 3)) x = AveragePooling2D(pool_size=(7, 7))(x) x = Flatten()(x) x = Dense(1000, activation='softmax')(x) model = Model(inputs=inpt, outputs=x) model.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy']) model.summary()