python - 如何在不指定固定解码器长度的情况下创建 seq2seq?
问题描述
基于此答案中提供的模型:
def create_seq2seq(features_num,latent_dim,decoder_length):
##
encoder_inputs = Input(shape=(None, features_num))
encoded = LSTM(latent_dim, return_state=False ,return_sequences=True)(encoder_inputs)
encoded = LSTM(latent_dim, return_state=False ,return_sequences=True)(encoded)
encoded = LSTM(latent_dim, return_state=False ,return_sequences=True)(encoded)
encoded = LSTM(latent_dim, return_state=True)(encoded)
encoder = Model (input=encoder_inputs, output=encoded)
##
encoder_outputs, state_h, state_c = encoder(encoder_inputs)
encoder_states = [state_h, state_c]
decoder_inputs=Input(shape=(1, features_num))
decoder_lstm_1 = LSTM(latent_dim, return_sequences=True, return_state=True)
decoder_lstm_2 = LSTM(latent_dim, return_sequences=True, return_state=True)
decoder_lstm_3 = LSTM(latent_dim, return_sequences=True, return_state=True)
decoder_lstm_4 = LSTM(latent_dim, return_sequences=True, return_state=True)
decoder_dense = Dense(features_num)
all_outputs = []
inputs = decoder_inputs
states_1=encoder_states
# Placeholder values:
states_2=states_1; states_3=states_1; states_4=states_1
###
for _ in range(1):
# Run the decoder on the first timestep
outputs_1, state_h_1, state_c_1 = decoder_lstm_1(inputs, initial_state=states_1)
outputs_2, state_h_2, state_c_2 = decoder_lstm_2(outputs_1)
outputs_3, state_h_3, state_c_3 = decoder_lstm_3(outputs_2)
outputs_4, state_h_4, state_c_4 = decoder_lstm_4(outputs_3)
# Store the current prediction (we will concatenate all predictions later)
outputs = decoder_dense(outputs_4)
all_outputs.append(outputs)
# Reinject the outputs as inputs for the next loop iteration
# as well as update the states
inputs = outputs
states_1 = [state_h_1, state_c_1]
states_2 = [state_h_2, state_c_2]
states_3 = [state_h_3, state_c_3]
states_4 = [state_h_4, state_c_4]
for _ in range(decoder_length):
# Run the decoder on each timestep
outputs_1, state_h_1, state_c_1 = decoder_lstm_1(inputs, initial_state=states_1)
outputs_2, state_h_2, state_c_2 = decoder_lstm_2(outputs_1, initial_state=states_2)
outputs_3, state_h_3, state_c_3 = decoder_lstm_3(outputs_2, initial_state=states_3)
outputs_4, state_h_4, state_c_4 = decoder_lstm_4(outputs_3, initial_state=states_4)
# Store the current prediction (we will concatenate all predictions later)
outputs = decoder_dense(outputs_4)
all_outputs.append(outputs)
# Reinject the outputs as inputs for the next loop iteration
# as well as update the states
inputs = outputs
states_1 = [state_h_1, state_c_1]
states_2 = [state_h_2, state_c_2]
states_3 = [state_h_3, state_c_3]
states_4 = [state_h_4, state_c_4]
# Concatenate all predictions
decoder_outputs = Lambda(lambda x: K.concatenate(x, axis=1))(all_outputs)
model = Model([encoder_inputs, decoder_inputs], decoder_outputs)
#model = load_model('pre_model.h5')
print(model.summary()
return (model)
可以创建一个 Keras seq2seq 模型,其中每个解码步骤的预测作为下一步的输入。
但是,虽然此代码定义的模型的输入序列长度不受限制,但它仅限于预定义的输出序列长度。
这对于演示使用此代码的玩具问题可能很好,也就是说,只是演示了预测给定正弦序列的连续性的能力,但是对于我正在尝试的 NLP 问题,长度不可知的解决方案将是更可取的处理。
如何在 Keras 中定义这样的模型?
解决方案
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