Tensorflow 2.3 - "关键字参数不理解："，'input'

Question

我正在尝试使用Keras的API功能来为我打算用于分割任务的神经网络中的跳过连接建模，我得到了前面提到的错误-

这是我的代码：

def unet_model(input_size = (256,256,1)):
input_ = keras.layers.Input(shape=input_size)
conv1 = keras.layers.Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(input_)
conv1 = keras.layers.Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv1)
pool1 = keras.layers.MaxPooling2D(pool_size=(2, 2))(conv1)
conv2 = keras.layers.Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool1)
conv2 = keras.layers.Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv2)
pool2 = keras.layers.MaxPooling2D(pool_size=(2, 2))(conv2)
conv3 = keras.layers.Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool2)
conv3 = keras.layers.Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv3)
pool3 = keras.layers.MaxPooling2D(pool_size=(2, 2))(conv3)
conv4 = keras.layers.Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool3)
conv4 = keras.layers.Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv4)
drop4 = keras.layers.Dropout(0.5)(conv4)
pool4 = keras.layers.MaxPooling2D(pool_size=(2, 2))(drop4)
conv5 = keras.layers.Conv2D(1024, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool4)
conv5 = keras.layers.Conv2D(1024, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv5)
drop5 = keras.layers.Dropout(0.5)(conv5)
up6 = keras.layers.Conv2D(512, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(keras.layers.UpSampling2D(size = (2,2))(drop5))
merge6 = keras.layers.Concatenate([drop4,up6], axis = 3)
conv6 = keras.layers.Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge6)
conv6 = keras.layers.Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv6)
up7 = keras.layers.Conv2D(256, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(keras.layers.UpSampling2D(size = (2,2))(conv6))
merge7 = keras.layers.Concatenate([conv3,up7], axis = 3)
conv7 = keras.layers.Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge7)
conv7 = keras.layers.Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv7)
up8 = keras.layers.Conv2D(128, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(keras.layers.UpSampling2D(size = (2,2))(conv7))
merge8 = keras.layers.Concatenate([conv2,up8], axis = 3)
conv8 = keras.layers.Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge8)
conv8 = keras.layers.Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv8)
up9 = keras.layers.Conv2D(64, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(keras.layers.UpSampling2D(size = (2,2))(conv8))
merge9 = keras.layers.Concatenate([conv1,up9], axis = 3)
conv9 = keras.layers.Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge9)
conv9 = keras.layers.Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv9)
conv9 = keras.layers.Conv2D(2, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv9)
conv10 = keras.layers.Conv2D(1, 1, activation = 'softmax')(conv9)
model = keras.Model(inputs = [input_], outputs = [conv10])
model.compile(optimizer = Adam(lr = 1e-4), loss = 'binary_crossentropy', metrics = ['accuracy'])
return model

当我这样做时，我会得到错误：

from unet_model import unet_model
model = unet_model()

怎么了？施工似乎与文件一致。请帮帮我

更新：在阅读此答案后，我用连接替换了串联，现在我有一个不同的错误：

24 
25         up6 = keras.layers.Conv2D(512, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(keras.layers.UpSampling2D(size = (2,2))(drop5))
---> 26         merge6 = keras.layers.concatenate([drop4,up6], axis = 3)
27         conv6 = keras.layers.Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge6)
28         conv6 = keras.layers.Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv6)
TypeError: __init__() got multiple values for argument 'axis'

Answer 1

**当我遇到类似的问题时，我正在克隆这个存储库。https://github.com/zhixuhao/unet

我遵循这些解决方案来解决这个问题。TypeError:('不理解关键字参数：'，'输入'(

更改：

input --> inputs 
output --> outputs 

至于级联，Keras连接类型错误：init((为参数"axis"获取了多个值

Concatenation --> concatenation