相关代码

如果有代码丢失的部分,可以要求更多。

def get_decoder_layers(char_list, encoder_state=None):
    input = layers.Input(shape=(None, len(char_list)))

    cells = []
    for hidden in [256, 256]:
        cells.append(layers.GRUCell(hidden))

    decoder = layers.RNN(cells, return_state=True, return_sequences=True)
    state = decoder(input, initial_state=encoder_state)
    output = state[0] # only get the output
    dense = layers.Dense(len(char_list), activation='linear')
    output = dense(output)

    return input, output, decoder, dense

def get_encoder_layers(char_list):
    input = layers.Input(shape=(None, len(char_list)))

    cells = []
    for hidden in [256, 256]:
        cells.append(layers.GRUCell(hidden))

    encoder = layers.RNN(cells, return_state=True)
    output = encoder(input)
    states = output[1:] # get rid of the ouput

    return input, states

decoder_input, decoder_output, decoder, dense = m.get_decoder_layers(french_chars)
encoder_input, encoder_states = m.get_encoder_layers(english_chars)

这里有错误:

encoder_inference_model, decoder_inference_model = m.get_inference_models(
        encoder_input=encoder_input,
        decoder_input=decoder_input,
        states=encoder_states,
        decoder=decoder,
        dense_layer=dense)

def get_inference_models(encoder_input=None, decoder_input=None, states=None, decoder=None, dense_layer=None):
    encoder_inference = keras.Model(encoder_input, states)

    decoder_state_input_a = layers.Input(shape=(256,)) # these have to be different variables
    decoder_state_input_b = layers.Input(shape=(256,))

    decoder_output_states = decoder(decoder_input, initial_state=states)

    output = decoder_output_states[0] # the first element is the output
    decoder_states = decoder_output_states[1:] # the rest are states

    output = dense_layer(output)

    return encoder_inference, keras.Model(inputs=[decoder_input, decoder_state_input_a, decoder_state_input_b], outputs=[output] + decoder_states) # error is at `keras.Model`

误差

Traceback (most recent call last):
  File "/Applications/PyCharm.app/Contents/helpers/pydev/pydevd.py", line 1664, in <module>
    main()
  File "/Applications/PyCharm.app/Contents/helpers/pydev/pydevd.py", line 1658, in main
    globals = debugger.run(setup['file'], None, None, is_module)
  File "/Applications/PyCharm.app/Contents/helpers/pydev/pydevd.py", line 1068, in run
    pydev_imports.execfile(file, globals, locals)  # execute the script
  File "/Users/zoe/Developer/CoreText/Lis/main.py", line 51, in <module>
    dense_layer=dense)
  File "/Users/zoe/Developer/CoreText/Lis/model.py", line 86, in get_decoder_inference
    return encoder_inference, keras.Model(inputs=[decoder_input, decoder_state_input_a, decoder_state_input_b], outputs=[output] + decoder_states)
  File "/Users/zoe/Developer/CoreText/Lis/venv/lib/python2.7/site-packages/keras/legacy/interfaces.py", line 91, in wrapper
    return func(*args, **kwargs)
  File "/Users/zoe/Developer/CoreText/Lis/venv/lib/python2.7/site-packages/keras/engine/network.py", line 93, in __init__
    self._init_graph_network(*args, **kwargs)
  File "/Users/zoe/Developer/CoreText/Lis/venv/lib/python2.7/site-packages/keras/engine/network.py", line 231, in _init_graph_network
    self.inputs, self.outputs)
  File "/Users/zoe/Developer/CoreText/Lis/venv/lib/python2.7/site-packages/keras/engine/network.py", line 1443, in _map_graph_network
    str(layers_with_complete_input))
ValueError: Graph disconnected: cannot obtain value for tensor Tensor("input_1:0", shape=(?, ?, 31), dtype=float32) at layer "input_1". The following previous layers were accessed without issue: []

该错误表示我的某些层已断开连接,但看起来所有层都已连接(除了 decoder_state_input_a 和 decoder_state_input_b 哪一个 认为 没关系。