#Keras Residual-Block+U-net
將residual-block加在U-net上，增加U-net的深度

# batchnormalization 後啟用
def BatchActivate(x):
    x = BatchNormalization()(x)
    x = Activation('relu')(x)
    return x
# 卷積block
def convolution_block(x, filters, size, strides=(1,1), padding='same', activation=True):
    x = Conv2D(
 
filters, size, strides=strides, padding=padding)(x)
    if activation == True:
        x = BatchActivate(x)
    return x
# residual_block
def residual_block(blockInput, num_filters=16, batch_activate = False):
    x = BatchActivate(blockInput)
    x = convolution_block(x, num_filters, (3,3) )
    x =
 
 convolution_block(x, num_filters, (3,3), activation=False)
    x = Add()([x, blockInput])
    if batch_activate:
        x = BatchActivate(x)
    return x

定義residual block

def residual_block(blockInput, num_filters=16, batch_activate = False):
    x = BatchActivate(blockInput)
    x = convolution_block(
 
x, num_filters, (3,3) )
    x = convolution_block(x, num_filters, (3,3), activation=False)
    x = Add()([x, blockInput])
    if batch_activate:
        x = BatchActivate(x)
    return x

使用residual block

# Build model
def build_model(input_layer, start_neurons, DropoutRatio = 0.5):
    # 101 -> 50
    conv1 = Conv2D(start_neurons * 1, (3, 3), activation=None, padding="same")(input_layer)
    conv1 = residual_block(conv1,start_neurons * 1)
    conv1 = residual_block(conv1,start_neurons * 1, True)
    pool1 = MaxPooling2D((2, 2))(conv1)
    pool1 = Dropout(DropoutRatio/2)(pool1)

    # 50 -> 25
    conv2 = Conv2D(start_neurons * 2, (3, 3), activation=None, padding="same")(pool1)
    conv2 = residual_block(conv2,start_neurons * 2)
    conv2 = residual_block(conv2,start_neurons * 2, True)
    pool2 = MaxPooling2D((2, 2))(conv2)
    pool2 = Dropout(DropoutRatio)(pool2)

    # 25 -> 12
    conv3 = Conv2D(start_neurons * 4, (3, 3), activation=None, padding="same")(pool2)
    conv3 = residual_block(conv3,start_neurons * 4)
    conv3 = residual_block(conv3,start_neurons * 4, True)
    pool3 = MaxPooling2D((2, 2))(conv3)
    pool3 = Dropout(DropoutRatio)(pool3)

    # 12 -> 6
    conv4 = Conv2D(start_neurons * 8, (3, 3), activation=None, padding="same")(pool3)
    conv4 = residual_block(conv4,start_neurons * 8)
    conv4 = residual_block(conv4,start_neurons * 8, True)
    pool4 = MaxPooling2D((2, 2))(conv4)
    pool4 = Dropout(DropoutRatio)(pool4)

    # Middle
    convm = Conv2D(start_neurons * 16, (3, 3), activation=None, padding="same")(pool4)
    convm = residual_block(convm,start_neurons * 16)
    convm = residual_block(convm,start_neurons * 16, True)
    
    # 6 -> 12
    deconv4 = Conv2DTranspose(start_neurons * 8, (3, 3), strides=(2, 2), padding="same")(convm)
    uconv4 = concatenate([deconv4, conv4])
    uconv4 = Dropout(DropoutRatio)(uconv4)
    
    uconv4 = Conv2D(start_neurons * 8, (3, 3), activation=None, padding="same")(uconv4)
    uconv4 = residual_block(uconv4,start_neurons * 8)
    uconv4 = residual_block(uconv4,start_neurons * 8, True)
    
    # 12 -> 25
    #deconv3 = Conv2DTranspose(start_neurons * 4, (3, 3), strides=(2, 2), padding="same")(uconv4)
    deconv3 = Conv2DTranspose(start_neurons * 4, (3, 3), strides=(2, 2), padding="valid")(uconv4)
    uconv3 = concatenate([deconv3, conv3])    
    uconv3 = Dropout(DropoutRatio)(uconv3)
    
    uconv3 = Conv2D(start_neurons * 4, (3, 3), activation=None, padding="same")(uconv3)
    uconv3 = residual_block(uconv3,start_neurons * 4)
    uconv3 = residual_block(uconv3,start_neurons * 4, True)

    # 25 -> 50
    deconv2 = Conv2DTranspose(start_neurons * 2, (3, 3), strides=(2, 2), padding="same")(uconv3)
    uconv2 = concatenate([deconv2, conv2])
        
    uconv2 = Dropout(DropoutRatio)(uconv2)
    uconv2 = Conv2D(start_neurons * 2, (3, 3), activation=None, padding="same")(uconv2)
    uconv2 = residual_block(uconv2,start_neurons * 2)
    uconv2 = residual_block(uconv2,start_neurons * 2, True)
    
    # 50 -> 101
    #deconv1 = Conv2DTranspose(start_neurons * 1, (3, 3), strides=(2, 2), padding="same")(uconv2)
    deconv1 = Conv2DTranspose(start_neurons * 1, (3, 3), strides=(2, 2), padding="valid")(uconv2)
    uconv1 = concatenate([deconv1, conv1])
    
    uconv1 = Dropout(DropoutRatio)(uconv1)
    uconv1 = Conv2D(start_neurons * 1, (3, 3), activation=None, padding="same")(uconv1)
    uconv1 = residual_block(uconv1,start_neurons * 1)
    uconv1 = residual_block(uconv1,start_neurons * 1, True)
    
    #uconv1 = Dropout(DropoutRatio/2)(uconv1)
    #output_layer = Conv2D(1, (1,1), padding="same", activation="sigmoid")(uconv1)
    output_layer_noActi = Conv2D(1, (1,1), padding="same", activation=None)(uconv1)
    output_layer =  Activation('sigmoid')(output_layer_noActi)
    
    return output_layer

參考：
https://www.kaggle.com/shaojiaxin/u-net-with-simple-resnet-blocks-v2-new-loss