d1 = Conv2DTranspose(64, (3, 3))(a5)
a6 = Activation('relu')(d1)
m1 = add([a4, a6])
a7 = Activation('relu')(m1)
d2 = Conv2DTranspose(64, (3, 3))(a7)
a8 = Activation('relu')(d2)
m2 = add([a3, a8])
a9 = Activation('relu')(m2)
答案 0 :(得分:0)
此代码段来自keras中的ResNet实现。跳过连接在这里使用layers.add
函数。
def identity_block(input_tensor, kernel_size, filters, stage, block):
"""The identity block is the block that has no conv layer at shortcut.
# Arguments
input_tensor: input tensor
kernel_size: default 3, the kernel size of middle conv layer at main path
filters: list of integers, the filters of 3 conv layer at main path
stage: integer, current stage label, used for generating layer names
block: 'a','b'..., current block label, used for generating layer names
# Returns
Output tensor for the block.
"""
filters1, filters2, filters3 = filters
if K.image_data_format() == 'channels_last':
bn_axis = 3
else:
bn_axis = 1
conv_name_base = 'res' + str(stage) + block + '_branch'
bn_name_base = 'bn' + str(stage) + block + '_branch'
x = Conv2D(filters1, (1, 1), name=conv_name_base + '2a')(input_tensor)
x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x)
x = Activation('relu')(x)
x = Conv2D(filters2, kernel_size,
padding='same', name=conv_name_base + '2b')(x)
x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x)
x = Activation('relu')(x)
x = Conv2D(filters3, (1, 1), name=conv_name_base + '2c')(x)
x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x)
x = layers.add([x, input_tensor])
x = Activation('relu')(x)
return x
如文档中所述,
It takes as input a list of tensors, all of the same shape, and returns a single tensor (also of the same shape).
如果您想添加x1
和x2
以形成一个新层(在ResNets中完成),则可以执行以下操作
added = keras.layers.Add()([x1, x2])
或
added = keras.layers.add([x1, x2])
然后,您可以将added
视为图层输出,并在其上面构建其他图层。
希望这会有所帮助!