Wide_ResNet_keras.py 文件源码

def wide_residual_network(img_input,classes_num,depth,k):

    print('Wide-Resnet %dx%d' %(depth, k))
    n_filters  = [16, 16*k, 32*k, 64*k]
    n_stack    = (depth - 4) / 6
    in_filters = 16

    def conv3x3(x,filters):
        return Conv2D(filters=filters, kernel_size=(3,3), strides=(1,1), padding='same',
        kernel_initializer=he_normal(),
        kernel_regularizer=regularizers.l2(weight_decay))(x)

    def residual_block(x,out_filters,increase_filter=False):
        if increase_filter:
            first_stride = (2,2)
        else:
            first_stride = (1,1)
        pre_bn   = BatchNormalization()(x)
        pre_relu = Activation('relu')(pre_bn)
        conv_1 = Conv2D(out_filters,kernel_size=(3,3),strides=first_stride,padding='same',kernel_initializer=he_normal(),kernel_regularizer=regularizers.l2(weight_decay))(pre_relu)
        bn_1   = BatchNormalization()(conv_1)
        relu1  = Activation('relu')(bn_1)
        conv_2 = Conv2D(out_filters, kernel_size=(3,3), strides=(1,1), padding='same', kernel_initializer=he_normal(),kernel_regularizer=regularizers.l2(weight_decay))(relu1)
        if increase_filter or in_filters != out_filters:
            projection = Conv2D(out_filters,kernel_size=(1,1),strides=first_stride,padding='same',kernel_initializer=he_normal(),kernel_regularizer=regularizers.l2(weight_decay))(x)
            block = add([conv_2, projection])
        else:
            block = add([conv_2,x])
        return block

    def wide_residual_layer(x,out_filters,increase_filter=False):
        x = residual_block(x,out_filters,increase_filter)
        in_filters = out_filters
        for _ in range(1,int(n_stack)):
            x = residual_block(x,out_filters)
        return x


    x = conv3x3(img_input,n_filters[0])
    x = wide_residual_layer(x,n_filters[1])
    x = wide_residual_layer(x,n_filters[2],increase_filter=True)
    x = wide_residual_layer(x,n_filters[3],increase_filter=True)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)
    x = GlobalAveragePooling2D()(x)
    x = Dense(classes_num,activation='softmax',kernel_initializer=he_normal(),kernel_regularizer=regularizers.l2(weight_decay))(x)
    return x