嗨，我一直在努力解决这个问题，尽管我无法真正弄清楚。对于我的特殊情况，我将不胜感激。非常感谢你！我的网络结构如下：
def get_unet(self):
inputs = Input((self.img_rows, self.img_cols, 1))
conv1 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(inputs)
conv1 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv1)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
conv2 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool1)
conv2 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv2)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
conv3 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool2)
conv3 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv3)
pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
conv4 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool3)
conv4 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv4)
drop4 = Dropout(0.5)(conv4)
pool4 = MaxPooling2D(pool_size=(2, 2))(drop4)
conv5 = Conv2D(1024, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool4)
conv5 = Conv2D(1024, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv5)
drop5 = Dropout(0.5)(conv5)
up6 = Conv2D(512, 2, activation='relu', padding='same', kernel_initializer='he_normal')(
UpSampling2D(size=(2, 2))(drop5))
print("drop4 shape",type(drop4),drop4.shape)
print("up6 shape",type(up6),up6.shape)
merge6=tf.concat([drop4, up6], axis=3)
print(merge6.shape)
conv6 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge6)
conv6 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv6)
up7 = Conv2D(256, 2, activation='relu', padding='same', kernel_initializer='he_normal')(
UpSampling2D(size=(2, 2))(conv6))
print("conv3,up7",conv3.shape,up7.shape)
merge7 =tf.concat([conv3, up7],axis=3)
conv7 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge7)
conv7 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv7)
up8 = Conv2D(128, 2, activation='relu', padding='same', kernel_initializer='he_normal')(
UpSampling2D(size=(2, 2))(conv7))
print("conv2,up8",conv2.shape,up8.shape)
merge8 = tf.concat([conv2, up8],axis=3)
conv8 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge8)
conv8 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv8)
up9 = Conv2D(64, 2, activation='relu', padding='same', kernel_initializer='he_normal')(
UpSampling2D(size=(2, 2))(conv8))
print("conv1,up9",conv1.shape,up9.shape)
merge9 = tf.concat([conv1, up9], axis=3)
print("merge9 shape",merge9.shape)
conv9 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge9)
conv9 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv9)
conv9 = Conv2D(2, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv9)
conv10 = Conv2D(1, 1, activation='sigmoid')(conv9)
print("conv10 shape",conv10.shape)
print("inputs shape1,outputs conv10 shape2",inputs.shape,conv10.shape)
model = Model(inputs=inputs, outputs=conv10)
model.compile(optimizer=Adam(lr=1e-4), loss='binary_crossentropy', metrics=['accuracy'])
print('model compile')
return model
这是错误：
model = Model(inputs=inputs, outputs=conv10)
File "/usr/local/lib/python3.5/dist-packages/keras/legacy/interfaces.py", line 91, in wrapper
return func(*args, **kwargs)
File "/usr/local/lib/python3.5/dist-packages/keras/engine/network.py", line 91, in __init__
self._init_graph_network(*args, **kwargs)
File "/usr/local/lib/python3.5/dist-packages/keras/engine/network.py", line 235, in _init_graph_network
self.inputs, self.outputs)
File "/usr/local/lib/python3.5/dist-packages/keras/engine/network.py", line 1412, in _map_graph_network
tensor_index=tensor_index)
File "/usr/local/lib/python3.5/dist-packages/keras/engine/network.py", line 1399, in build_map
node_index, tensor_index)
File "/usr/local/lib/python3.5/dist-packages/keras/engine/network.py", line 1399, in build_map
node_index, tensor_index)
File "/usr/local/lib/python3.5/dist-packages/keras/engine/network.py", line 1399, in build_map
node_index, tensor_index)
File "/usr/local/lib/python3.5/dist-packages/keras/engine/network.py", line 1399, in build_map
node_index, tensor_index)
File "/usr/local/lib/python3.5/dist-packages/keras/engine/network.py", line 1371, in build_map
node = layer._inbound_nodes[node_index]
AttributeError: 'NoneType' object has no attribute '_inbound_nodes'
答案 0 :(得分：1)
将您所有的tf.concat()替换为keras.layers.concatenate()。这就是问题所在。另外，如果还没有更新，请更新您的keras。