July 2018
Beginner to intermediate
312 pages
8h 31m
English
Content preview from Hands-On Natural Language Processing with Python
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Sequence-to-sequence model
We will now combine the encoder and decoder to create the sequence-to-sequence model:
def seq2seq_model(input_data, target_en_data, dropout_prob, fr_len, en_len, max_en_len, v_size, rnn_cell_size, n_layers, word2int_en, batch_size): input_word_embeddings = tf.Variable(fr_embeddings_matrix, name="input_word_embeddings") encoding_embed_input = tf.nn.embedding_lookup(input_word_embeddings, input_data) encoding_op, encoding_st = encoding_layer(rnn_cell_size, fr_len, n_layers, encoding_embed_input, dropout_prob) decoding_input = process_encoding_input(target_en_data, word2int_en, batch_size) decoding_embed_input = tf.nn.embedding_lookup(en_embeddings_matrix, decoding_input) tr_logits, inf_logits = decoding_layer(decoding_embed_input, ...
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