为什么我只放了一个，却有两个位置论点？

Question

以下代码用于在keras中使用tensorflow后端定义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.convo

class 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.convo



inputs = 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笔记本内执行时，将生成以下stacktrace：

^{pr2}$

跟踪的最后一行。。。在

TypeError: __init__() takes 1 positional argument but 2 were given

。。。讨论了将两个位置参数传递给第一个UpBlock（）调用，而我显然传递了一个-

d1 = DownBlock(s, num_chann = 16, drop_rate = 0.1)

另一个位置参数在哪里？如果没有，为什么我会得到这个错误？在

Answer 1

尽管错误源于对DownBlock构造函数的调用，但Python也注意到错误回溯是(most recent call last)。此错误是指将两个参数传递给Add构造函数。Python告诉您的是对Add()的调用有太多的参数。在

这里的诀窍是，虽然看起来您只向Add()提供一个列表参数，但Python类构造函数都会接收一个隐式self参数作为它们的第一个位置参数。见the Python docs。在

来自评论：

在Keras中使用函数API时，必须首先创建层对象，如a = Add()中所示，然后通过调用结果对象将层添加到计算图中，如下所示：

out = a([input1, input2, ...])

或者在你最初的例子中：

self.convo = Add()([self.prev_layer, self.convo])