以下代码片段用于在TensorFlow后端的Keras中定义一个CNN架构：
class DownBlock(object): def __init__(self, prev_layer, num_chann = 16, depthwise_initializer = 'glorot_uniform', kernel_initializer = 'glorot_uniform', bias_initializer = 'zeros', drop_rate = None, spdrop_rate = None, activation = 'relu', pool = True): self.prev_layer = prev_layer if pool == True: self.prev_layer = MaxPooling2D((2, 2)) (self.prev_layer) self.prev_layer = Conv2D(num_chann, (1, 1), kernel_initializer = kernel_initializer, bias_initializer = bias_initializer) (self.prev_layer) self.convo = Activation(activation) (self.prev_layer) self.convo = BatchNormalization() (self.convo) if not spdrop_rate == None: self.convo = SpatialDropout2D(spdrop_rate) (self.convo) if not drop_rate == None: self.convo = Dropout(drop_rate) (self.convo) self.convo = Conv2D(num_chann, (1, 1), kernel_initializer = kernel_initializer, bias_initializer = bias_initializer) (self.convo) self.convo = DepthwiseConv2D((3, 3), depthwise_initializer = depthwise_initializer, bias_initializer = bias_initializer, padding = 'same') (self.convo) self.convo = Conv2D(num_chann, (1, 1), kernel_initializer = kernel_initializer, bias_initializer = bias_initializer) (self.convo) self.convo = Activation(activation) (self.convo) self.convo = BatchNormalization() (self.convo) if not spdrop_rate == None: self.convo = SpatialDropout2D(spdrop_rate) (self.convo) if not drop_rate == None: self.convo = Dropout(drop_rate) (self.convo) self.convo = DepthwiseConv2D((3, 3), depthwise_initializer = depthwise_initializer, bias_initializer = bias_initializer, padding = 'same') (self.convo) self.convo = Conv2D(num_chann, (1, 1), kernel_initializer = kernel_initializer, bias_initializer = bias_initializer) (self.convo) self.convo = Add([self.prev_layer, self.convo]) def get(self): return self.convoclass UpBlock(object): def __init__(self, prev_layer, bridge_layer, num_chann = 16, depthwise_initializer = 'glorot_uniform', kernel_initializer = 'glorot_uniform', bias_initializer = 'zeros', drop_rate = None, spdrop_rate = None, activation = 'relu', up = True): self.prev_layer = prev_layer self.bridge_layer = bridge_layer self.convo = Activation(activation) (self.prev_layer) self.convo = BatchNormalization() (self.convo) if not spdrop_rate == None: self.convo = SpatialDropout2D(spdrop_rate) (self.convo) if not drop_rate == None: self.convo = Dropout(drop_rate) (self.convo) self.convo = Conv2D(num_chann, (1, 1), kernel_initializer = kernel_initializer, bias_initializer = bias_initializer) (self.convo) self.convo = DepthwiseConv2D((3, 3), depthwise_initializer = depthwise_initializer, bias_initializer = bias_initializer, padding = 'same') (self.convo) self.convo = Conv2D(num_chann, (1, 1), kernel_initializer = kernel_initializer, bias_initializer = bias_initializer) (self.convo) self.convo = Activation(activation) (self.convo) self.convo = BatchNormalization() (self.convo) if not spdrop_rate == None: self.convo = SpatialDropout2D(spdrop_rate) (self.convo) if not drop_rate == None: self.convo = Dropout(drop_rate) (self.convo) self.convo = DepthwiseConv2D((3, 3), depthwise_initializer = depthwise_initializer, bias_initializer = bias_initializer, padding = 'same') (self.convo) self.convo = Conv2D(num_chann, (1, 1), kernel_initializer = kernel_initializer, bias_initializer = bias_initializer) (self.convo) self.convo = Add([self.prev_layer, self.convo]) if up == True: self.convo = Conv2D(num_chann/2, (1, 1), kernel_initializer = kernel_initializer, bias_initializer = bias_initializer) (self.convo) self.convo = Conv2DTranspose(num_chann/2, (2, 2), strides = (2, 2), kernel_initializer = kernel_initializer, bias_initializer = bias_initializer, padding = 'same') (self.convo) self.convo = Add([self.bridge_layer, self.convo]) def get(self): return self.convoinputs = Input((IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS))s = Lambda(lambda x: x / 255) (inputs)s = Conv2D(8, (1, 1)) (s)d1 = DownBlock(s, num_chann = 16, drop_rate = 0.1)d2 = DownBlock(d1.get(), num_chann = 32, drop_rate = 0.1)d3 = DownBlock(d2.get(), num_chann = 64, drop_rate = 0.1)d4 = DownBlock(d3.get(), num_chann = 128, drop_rate = 0.1)d5 = DownBlock(d4.get(), num_chann = 256, drop_rate = 0.1)m = DownBlock(d5.get(), num_chann = 512, drop_rate = 0.1)u5 = UpBlock(m.get(), d4.get(), num_chann = 256, drop_rate = 0.1)u4 = UpBlock(u5.get(), d3.get(), num_chann = 128, drop_rate = 0.1)u3 = UpBlock(u4.get(), d2.get(), num_chann = 64, drop_rate = 0.1)u2 = UpBlock(u3.get(), d1.get(), num_chann = 32, drop_rate = 0.1)u1 = UpBlock(u2.get(), s, num_chann = 16, drop_rate = 0.1)final = Conv2D(1, (1, 1)) (u1.get())# final = SpatialDropout2D(0.1) (final)final = Dropout(0.1) (final)final = BatchNormalization() (final)outputs = Activation("sigmoid") (final)model = Model(inputs = [inputs], outputs = [outputs])在Jupyter笔记本中执行时，会生成以下堆栈跟踪：
TypeError Traceback (most recent call last)<ipython-input-31-f23b70d0be6d> in <module>() 79 s = Conv2D(8, (1, 1)) (s) 80 ---> 81 d1 = DownBlock(s, num_chann = 16, drop_rate = 0.1) 82 83 d2 = DownBlock(d1.get(), num_chann = 32, drop_rate = 0.1)<ipython-input-31-f23b70d0be6d> in __init__(self, prev_layer, num_chann, depthwise_initializer, kernel_initializer, bias_initializer, drop_rate, spdrop_rate, activation, pool) 29 self.convo = Conv2D(num_chann, (1, 1), kernel_initializer = kernel_initializer, bias_initializer = bias_initializer) (self.convo) 30 ---> 31 self.convo = Add([self.prev_layer, self.convo]) 32 33 def get(self):TypeError: __init__() takes 1 positional argument but 2 were given堆栈跟踪中的最后一行…
TypeError: __init__() takes 1 positional argument but 2 were given…提到向第一个UpBlock()调用传递了两个位置参数，而我明明只传递了一个-
d1 = DownBlock(s, num_chann = 16, drop_rate = 0.1)另一个位置参数在哪里？如果没有第二个位置参数，为什么我会收到这个错误？
回答：
虽然错误源自于对DownBlock构造函数的调用，但Python也指出错误回溯是(most recent call last)。此错误指的是向Add构造函数传递了过多的参数。Python告诉您的是，您对Add()的调用参数过多。
这里的技巧是，虽然看起来您只向Add()提供了一个列表参数，但Python类构造函数都会隐式地接收self作为其第一个位置参数。请参阅Python文档。
来自评论：
在使用Keras的函数式API时，必须先创建层对象，如a = Add()，然后通过调用生成的对象将其添加到计算图中，如下所示：
out = a([input1, input2, ...])
或者在您的原始示例中：
self.convo = Add()([self.prev_layer, self.convo])
