[英]Error: 'NoneType' object has no attribute '_inbound_nodes'
[在此處輸入圖片描述] [1]我正在嘗試建立一個並行的ANN網絡。 我打算 :
def conv_net():
input_shape = [120,120,1]
inp=Input(shape=input_shape)
print(type(inp))
print(inp.shape)
row_layers = []
col_layers = []
# fn = lambda x: self.conv(x)
for i in range(0, 120, 40):
row_layers = []
for j in range(0, 120, 40):
# out = (self.conv(inp[:,i:i+39,j:j+39]))
inputs = inp[:, i:i + 40, j:j + 40]
x = Dense(64, activation='relu')(inputs)
out = Dense(64, activation='relu')(x)
print(out.shape)
row_layers.append(out)
col_layers.append(keras.layers.concatenate(row_layers, axis=2))
print((len(col_layers)))
merged = keras.layers.concatenate(col_layers, axis=1)
print(merged.shape)
con = Conv2D(1, kernel_size=5, strides=2, padding='same', activation='relu')(merged)
print(con.shape)
output = Flatten()(con)
output = Dense(1)(output)
print(output.shape)
model = Model(inputs=inp, outputs=output)
# plot_model(model,to_file='model.png')
return model
我收到一個錯誤NoneType
對象沒有屬性_inbound_nodes
。
我調試了一下。 錯誤是因為這條線。
inputs = inp[:,i:i+40,j:j+40]
錯誤:
Traceback (most recent call last):
File "C:/Users/Todd Letcher/machine_learning_examples/unsupervised_class3/slicing_img.py", line 83, in <module>
conv_net()
File "C:/Users/Todd Letcher/machine_learning_examples/unsupervised_class3/slicing_img.py", line 80, in conv_net
model = Model(inputs=inp, outputs = output)
File "C:\Users\Todd Letcher\AppData\Local\Programs\Python\Python36\lib\site-packages\keras\legacy\interfaces.py", line 91, in wrapper
return func(*args, **kwargs)
File "C:\Users\Todd Letcher\AppData\Local\Programs\Python\Python36\lib\site-packages\keras\engine\network.py", line 91, in __init__
self._init_graph_network(*args, **kwargs)
File "C:\Users\Todd Letcher\AppData\Local\Programs\Python\Python36\lib\site-packages\keras\engine\network.py", line 235, in _init_graph_network
self.inputs, self.outputs)
File "C:\Users\Todd Letcher\AppData\Local\Programs\Python\Python36\lib\site-packages\keras\engine\network.py", line 1406, in _map_graph_network
tensor_index=tensor_index)
File "C:\Users\Todd Letcher\AppData\Local\Programs\Python\Python36\lib\site-packages\keras\engine\network.py", line 1393, in build_map
node_index, tensor_index)
File "C:\Users\Todd Letcher\AppData\Local\Programs\Python\Python36\lib\site-packages\keras\engine\network.py", line 1393, in build_map
node_index, tensor_index)
File "C:\Users\Todd Letcher\AppData\Local\Programs\Python\Python36\lib\site-packages\keras\engine\network.py", line 1393, in build_map
node_index, tensor_index)
File "C:\Users\Todd Letcher\AppData\Local\Programs\Python\Python36\lib\site-packages\keras\engine\network.py", line 1365, in build_map
node = layer._inbound_nodes[node_index]
AttributeError: 'NoneType' object has no attribute '_inbound_nodes'
幫助表示贊賞。 謝謝
PS:我刪除了切片線inp[:,i:i+39,j:j+39]
,它運行正常。
該圖顯示了我打算做什么。 唯一的區別是我想將圖像分成9個圖塊。 在此,將相同的圖像饋送到所有並行的Conv網絡。
[1]: https://i.stack.imgur.com/Z7nt0.png
終於得出了答案。 盡管我仍然想知道為什么以前的代碼會出錯,但是我只是添加了lambda層進行拆分。
def conv_net(self): # Add dropout if Overfiting
input_shape = [120,120,1]
inp=Input(shape=input_shape)
col_layers = []
def sliced(x,i,j):
return x[:,i:i+40,j:j+40]
for i in range(0,120,40):
row_layers = []
for j in range(0,120,40):
#out = (self.conv(inp[:,i:i+39,j:j+39]))
inputs = Lambda(sliced,arguments={'i':i,'j':j})(inp)
#inputs = Input(shape=input_shape_small)
out = (self.conv(inputs))
print(out.shape)
row_layers.append(out)
col_layers.append(keras.layers.concatenate(row_layers, axis=2))
print((len(col_layers)))
merged = keras.layers.concatenate(col_layers,axis=1)
print(merged.shape)
#merged = Reshape((3,3,1))(merged)
print(merged.shape)
con = Conv2D(1,kernel_size=5,strides=2,padding='same',activation='relu')(merged)
con = (BatchNormalization(momentum=0.8))(con)
print(con.shape)
#con = Conv2D(1,kernel_size=5,strides=2,padding='same',activation='relu')(inp)
output = Flatten()(con)
output = Dense(1)(output)
print(output.shape)
model = Model(inputs=inp, outputs = output)
#plot_model(model,to_file='model.png')
print(model.summary())
plot_model(model, to_file='model_plot.png', show_shapes=True, show_layer_names=True)
return model
這沒有錯誤。
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