我是Keras的新手，我正在使用它来修改一些现有的模型，在背景中使用tensorflow gpu，通过将UpSampling2D图层添加到特定大小。我知道Upsampling2D给出了最接近的整数值的输出，我试图使用这个属性。
我能够获得所需的大小但得到一个AttributeError。
class myUnet(object):
def __init__(self, img_rows = 200, img_cols = 200):
self.img_rows = img_rows
self.img_cols = img_cols
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))
crop6=drop4[:,0:24,0:24,:] ### Line Added by Me
merge6 = merge([crop6,up6], mode = 'concat', concat_axis = 3)
conv6 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge6)
up7 = Conv2D(256, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv6))
conv6 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv6)
#### Lines Added by Me######
resize7 = UpSampling2D(size=(conv3.get_shape()[1:3][0].value/up7.get_shape()[1:3][0].value, conv3.get_shape()[1:3][1].value/up7.get_shape()[1:3][1].value))(up7)
merge7 = merge([conv3,resize7], mode = 'concat', concat_axis = 3)
#####END:Lines Added by Me#####
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))
merge8 = merge([conv2,up8], mode = 'concat', concat_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))
merge9 = merge([conv1,up9], mode = 'concat', concat_axis = 3)
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)
model = Model(input = inputs, output = conv10)
model.compile(optimizer = Adam(lr = 1e-6), loss = 'binary_crossentropy', metrics = ['accuracy'])
return model
但是一旦我添加了这些行，我就会得到一个AttributeError。
AttributeError: 'Tensor' object has no attribute '_keras_history'
详细的调用图是：
Traceback (most recent call last):
File "unet.py", line 209, in <module>
myunet.train()
File "unet.py", line 166, in train
model = self.get_unet()
File "unet.py", line 154, in get_unet
model = Model(input = inputs, output = conv10)
File "/data/unet/unet_env/lib/python3.6/site-packages/keras/legacy/interfaces.py", line 91, in wrapper
return func(*args, **kwargs)
File "/data/unet/unet_env/lib/python3.6/site-packages/keras/engine/topology.py", line 1732, in __init__
build_map_of_graph(x, finished_nodes, nodes_in_progress)
File "/data/unet/unet_env/lib/python3.6/site-packages/keras/engine/topology.py", line 1722, in build_map_of_graph
layer, node_index, tensor_index)
File "/data/unet/unet_env/lib/python3.6/site-packages/keras/engine/topology.py", line 1722, in build_map_of_graph
layer, node_index, tensor_index)
File "/data/unet/unet_env/lib/python3.6/site-packages/keras/engine/topology.py", line 1722, in build_map_of_graph
layer, node_index, tensor_index)
[Previous line repeated 17 more times]
File "/data/unet/unet_env/lib/python3.6/site-packages/keras/engine/topology.py", line 1693, in build_map_of_graph
layer, node_index, tensor_index = tensor._keras_history
AttributeError: 'Tensor' object has no attribute '_keras_history'