0

我有一个编码器-解码器模型，其结构与machinelearningmastery.com上的模型相同，带有num_encoder_tokens = 1949、 num_decoder_tokens = 1944和latent_dim = 2048.

我想通过加载已经训练好的模型来构建编码器和解码器模型并尝试解码一些样本，但我得到了错误"Graph disconnected: cannot obtain value for tensor Tensor("input_1_1:0", shape=(?,?, 1949), dtype=float32) at layer "input_1". The following previous layers were accessed without issue: []。

我的部分代码如下：

encoder_inputs = Input(shape=(None, num_encoder_tokens))
encoder = LSTM(latent_dim, return_state=True)
encoder_outputs, state_h, state_c = encoder(encoder_inputs)
encoder_states = [state_h, state_c]

decoder_inputs = Input(shape=(None, num_decoder_tokens))
decoder_lstm = LSTM(latent_dim, return_sequences=True, return_state=True)
decoder_outputs, _, _ = decoder_lstm(decoder_inputs,
                                     initial_state=encoder_states)
decoder_dense = Dense(num_decoder_tokens, activation='softmax')
decoder_outputs = decoder_dense(decoder_outputs)

model = Model([encoder_inputs, decoder_inputs], decoder_outputs)

model.compile(optimizer='rmsprop', loss='categorical_crossentropy')
model.fit([encoder_input_data, decoder_input_data], decoder_target_data,
          batch_size=batch_size,
          epochs=epochs,
          validation_split=0.2)
model.save('modelname.h5')

# ...from here different python file for inference...

encoder = LSTM(latent_dim, return_state=True)
model = load_model('modelname.h5')
encoder_model = Model(model.output, encoder(model.output)) # I get the error here

我想在这里做的是：

encoder_inputs = Input(shape=(None, 1949))
encoder = LSTM(2048, return_state=True)
encoder_outputs, state_h, state_c = encoder(encoder_inputs)
encoder_states = [state_h, state_c]
encoder_model = Model(encoder_inputs, encoder_states)

如果有人可以帮助我，我将不胜感激。

4

1 回答 1

0

看看 Robert Sim 在堆栈溢出中对这篇文章的回答：Restore keras seq2seq model

对于 github 中的这篇文章：https ://github.com/keras-team/keras/pull/9119 。

他还在：https ://github.com/simra/keras/blob/simra/s2srestore/examples/lstm_seq2seq_restore.py 中提供了一个示例，您可以在其中查看模型的加载方式。以下代码取自该示例。

# Restore the model and construct the encoder and decoder.
model = load_model('s2s.h5')

encoder_inputs = model.input[0]   # input_1
encoder_outputs, state_h_enc, state_c_enc = model.layers[2].output   # lstm_1
encoder_states = [state_h_enc, state_c_enc]
encoder_model = Model(encoder_inputs, encoder_states)

decoder_inputs = model.input[1]   # input_2
decoder_state_input_h = Input(shape=(latent_dim,), name='input_3')
decoder_state_input_c = Input(shape=(latent_dim,), name='input_4')
decoder_states_inputs = [decoder_state_input_h, decoder_state_input_c]
decoder_lstm = model.layers[3]
decoder_outputs, state_h_dec, state_c_dec = decoder_lstm(
    decoder_inputs, initial_state=decoder_states_inputs)
decoder_states = [state_h_dec, state_c_dec]
decoder_dense = model.layers[4]
decoder_outputs = decoder_dense(decoder_outputs)
decoder_model = Model(
    [decoder_inputs] + decoder_states_inputs,
    [decoder_outputs] + decoder_states)
于 2019-06-04T16:55:25.277 回答