DenseNet_keras.py 文件源码

def densenet(img_input,classes_num):

    def bn_relu(x):
        x = BatchNormalization()(x)
        x = Activation('relu')(x)
        return x

    def bottleneck(x):
        channels = growth_rate * 4
        x = bn_relu(x)
        x = Conv2D(channels,kernel_size=(1,1),strides=(1,1),padding='same',kernel_initializer=he_normal(),kernel_regularizer=regularizers.l2(weight_decay),use_bias=False)(x)
        x = bn_relu(x)
        x = Conv2D(growth_rate,kernel_size=(3,3),strides=(1,1),padding='same',kernel_initializer=he_normal(),kernel_regularizer=regularizers.l2(weight_decay),use_bias=False)(x)
        return x

    def single(x):
        x = bn_relu(x)
        x = Conv2D(growth_rate,kernel_size=(3,3),strides=(1,1),padding='same',kernel_initializer=he_normal(),kernel_regularizer=regularizers.l2(weight_decay),use_bias=False)(x)
        return x

    def transition(x, inchannels):
        outchannels = int(inchannels * compression)
        x = bn_relu(x)
        x = Conv2D(outchannels,kernel_size=(1,1),strides=(1,1),padding='same',kernel_initializer=he_normal(),kernel_regularizer=regularizers.l2(weight_decay),use_bias=False)(x)
        x = AveragePooling2D((2,2), strides=(2, 2))(x)
        return x, outchannels

    def dense_block(x,blocks,nchannels):
        concat = x
        for i in range(blocks):
            x = bottleneck(concat)
            concat = concatenate([x,concat], axis=-1)
            nchannels += growth_rate
        return concat, nchannels

    def dense_layer(x):
        return Dense(classes_num,activation='softmax',kernel_initializer=he_normal(),kernel_regularizer=regularizers.l2(weight_decay))(x)


    nblocks = (depth - 4) // 6 
    nchannels = growth_rate * 2

    x = Conv2D(nchannels,kernel_size=(3,3),strides=(1,1),padding='same',kernel_initializer=he_normal(),kernel_regularizer=regularizers.l2(weight_decay),use_bias=False)(img_input)

    x, nchannels = dense_block(x,nblocks,nchannels)
    x, nchannels = transition(x,nchannels)
    x, nchannels = dense_block(x,nblocks,nchannels)
    x, nchannels = transition(x,nchannels)
    x, nchannels = dense_block(x,nblocks,nchannels)
    x, nchannels = transition(x,nchannels)
    x = bn_relu(x)
    x = GlobalAveragePooling2D()(x)
    x = dense_layer(x)
    return x