问题描述
ValueError: Layer model_101 expects 2 input(s),but it received 1 input tensors. Inputs received: [<tf.Tensor 'IteratorGetNext:0' shape=(None,1,64) dtype=float32>]
我正在关注 Jason brownlee 关于如何开发用于序列到序列预测的编码器-解码器模型的 guide。除了使用 LSTM 单元,我还希望能够使用 GRU 单元。我设法使用 LSTMa 和 GRU 使一切正常工作,我可以通过调整教程中的“define_models”函数来定义和训练模型,如下所示:
def define_models(n_input,n_output,hparams):
n_units = hparams["NUmunitS"]
Unit = tf.keras.layers.LSTM if hparams["UNIT"] =="LSTM" else tf.keras.layers.GRU
dropout = hparams["DROPOUT"]
# define training encoder
encoder_inputs = Input(shape=(None,n_input))
encoder = Unit(n_units,return_state=True)
if hparams["UNIT"] == "LSTM":
encoder_outputs,state_h,state_c = encoder(encoder_inputs)
encoder_states = [state_h,state_c]
else:
encoder_outputs,state_h = encoder(encoder_inputs)
encoder_states = [state_h]
# define training decoder
decoder_inputs = Input(shape=(None,n_output))
decoder_lstm = Unit(n_units,return_sequences=True,return_state=True)
if hparams["UNIT"] == "LSTM":
decoder_outputs,_,_ = decoder_lstm(decoder_inputs,initial_state=encoder_states)
else:
decoder_outputs,initial_state=encoder_states)
decoder_dense = Dense(n_output)
decoder_outputs = decoder_dense(decoder_outputs)
model = Model([encoder_inputs,decoder_inputs],decoder_outputs)
# define inference encoder
encoder_model = Model(encoder_inputs,encoder_states)
# define inference decoder
decoder_state_input_h = Input(shape=(n_units,))
if hparams["UNIT"] == "LSTM":
decoder_state_input_c = Input(shape=(n_units,))
decoder_states_inputs = [decoder_state_input_h,decoder_state_input_c]
decoder_outputs,state_c = decoder_lstm(decoder_inputs,initial_state=decoder_states_inputs)
decoder_states = [state_h,state_c]
else:
decoder_states_inputs = [decoder_state_input_h]
decoder_outputs,state_h = decoder_lstm(decoder_inputs,initial_state=decoder_states_inputs)
decoder_states = [state_h]
decoder_outputs = decoder_dense(decoder_outputs)
decoder_model = Model([decoder_inputs] + decoder_states_inputs,[decoder_outputs] + decoder_states)
# return all models
return model,encoder_model,decoder_model
我按照教程中的方法训练了 GRU-Encoder-Decoder 模型,当我想用它来使用这个调整后的 predict_sequence 函数预测序列时:
def predict_sequence(infenc,infdec,source,n_steps,cardinality,hparams):
unit = hparams['UNIT']
print(f'unit: {unit}')
state = infenc.predict(source)
target_seq = np.array([0.0 for _ in range(cardinality)]).reshape(1,cardinality)
output = list()
for t in range(n_steps):
if unit == "LSTM":
yhat,h,c = infdec.predict([target_seq] + state)
else:
yhat,h = infdec.predict([target_seq] + state)
output.append(yhat[0,:])
if unit == "LSTM":
state = [h,c]
else:
state = [h]
target_seq = yhat
return np.array(output)
但是在这个函数中的行
yhat,= infdec.predict([target_seq] + state)
产生错误。
作为如何调整代码以使其适用于 GRU 的参考,我使用了来自 Keras 的 guide。
解决方法
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