Tensorflow keras Двунаправленный LSTM для обобщения текста - PullRequest
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Tensorflow keras Двунаправленный LSTM для обобщения текста

0 голосов
/ 16 марта 2020

Я пытаюсь реализовать двунаправленный LSTM для суммирования текста. У меня проблема с разделом вывода. Размер не совпадает. Это моя модель: 

latent_dim = 300
embedding_dim=100

# Encoder
encoder_inputs = Input(shape=(max_news_len,))

#embedding layer
enc_emb =  Embedding(x_voc, embedding_dim,trainable=True)(encoder_inputs)

#encoder lstm 1
encoder_bi_lstm1 = Bidirectional(LSTM(latent_dim,
                                   return_sequences=True,
                                   return_state=True,
                                   dropout=0.4,
                                   recurrent_dropout=0.4), 
                                 merge_mode="concat")
encoder_output1, forward_state_h1, forward_state_c1, backward_state_h1, backward_state_c1 = encoder_bi_lstm1(enc_emb)
encoder_states1 = [forward_state_h1, forward_state_c1, backward_state_h1, backward_state_c1]

# Set up the decoder, using `encoder_states` as initial state.
decoder_inputs = Input(shape=(None,))

#embedding layer
dec_emb_layer = Embedding(y_voc, embedding_dim,trainable=True)
dec_emb = dec_emb_layer(decoder_inputs)

#decoder_lstm = LSTM(latent_dim, return_sequences=True, return_state=True,dropout=0.4,recurrent_dropout=0.2)
#decoder_outputs,decoder_fwd_state, decoder_back_state = decoder_lstm(dec_emb,initial_state=[state_h, state_c])

decoder_bi_lstm = Bidirectional(LSTM(latent_dim, 
                                  return_sequences=True, 
                                  return_state=True,
                                  dropout=0.4,
                                  recurrent_dropout=0.2),
                             merge_mode="concat")
decoder_outputs, decoder_fwd_state_h1, decoder_fwd_state_c1, decoder_back_state_h1, decoder_back_state_c1 = decoder_bi_lstm(dec_emb,initial_state=encoder_states1)
decoder_states = [decoder_fwd_state_h1, decoder_fwd_state_c1, decoder_back_state_h1, decoder_back_state_c1]

# Attention layer
attn_layer = AttentionLayer(name='attention_layer')
attn_out, attn_states = attn_layer([encoder_output1, decoder_outputs])

# Concat attention input and decoder LSTM output
decoder_concat_input = Concatenate(axis=-1, name='concat_layer')([decoder_outputs, attn_out])

#dense layer
decoder_dense =  TimeDistributed(Dense(y_voc, activation='softmax'))
decoder_outputs = decoder_dense(decoder_concat_input)

# Define the model 
model = Model([encoder_inputs, decoder_inputs], decoder_outputs)

model.summary()

Это моя логическая установка: 

# Encode the input sequence to get the feature vector
encoder_model = Model(inputs=encoder_inputs,outputs=encoder_states1)

# Decoder setup
# Below tensors will hold the states of the previous time step
decoder_state_input_h = Input(shape=(latent_dim,))
decoder_state_input_c = Input(shape=(latent_dim,))
decoder_hidden_state_input = Input(shape=(max_news_len,latent_dim))

# Get the embeddings of the decoder sequence
dec_emb2= dec_emb_layer(decoder_inputs) 
# To predict the next word in the sequence, set the initial states to the states from the previous time step
decoder_outputs2, decoder_fwd_state_h2, decoder_fwd_state_c2, decoder_back_state_h2, decoder_back_state_c2 = decoder_bi_lstm(dec_emb2, initial_state=decoder_states)
decoder_states2 = [decoder_fwd_state_h2, decoder_fwd_state_c2, decoder_back_state_h2, decoder_back_state_c2]

#attention inference
attn_out_inf, attn_states_inf = attn_layer([decoder_hidden_state_input, decoder_outputs2])
decoder_inf_concat = Concatenate(axis=-1, name='concat')([decoder_outputs2, attn_out_inf])

# A dense softmax layer to generate prob dist. over the target vocabulary
decoder_outputs2 = decoder_dense(decoder_inf_concat) 

# Final decoder model
decoder_model = Model(
    [decoder_inputs] + [decoder_hidden_state_input,decoder_state_input_h, decoder_state_input_c],
    [decoder_outputs2] + [decoder_fwd_state_h2, decoder_fwd_state_c2, decoder_back_state_h2, decoder_back_state_c2])

Ошибка: Размеры должны быть равны, но 300 и 600 для 'Внимание_layer_6 / MatMul' ( op: 'MatMul') с входными формами: [?, 300], [600 600].

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