def run_tests():
import problem_unittests as t
t.test_decoding_layer(decoding_layer)
t.test_decoding_layer_infer(decoding_layer_infer)
t.test_decoding_layer_train(decoding_layer_train)
t.test_encoding_layer(encoding_layer)
t.test_model_inputs(model_inputs)
t.test_process_encoding_input(process_decoder_input)
t.test_sentence_to_seq(sentence_to_seq)
t.test_seq2seq_model(seq2seq_model)
t.test_text_to_ids(text_to_ids)
def run_all_tests():
tests.test_text_to_ids(text_to_ids)
check_tensorflow_gpu()
tests.test_model_inputs(model_inputs)
tests.test_process_encoding_input(process_decoder_input)
from imp import reload
reload(tests)
tests.test_encoding_layer(encoding_layer)
tests.test_decoding_layer_train(decoding_layer_train)
tests.test_decoding_layer_infer(decoding_layer_infer)
tests.test_decoding_layer(decoding_layer)
tests.test_seq2seq_model(seq2seq_model)
tests.test_sentence_to_seq(sentence_to_seq)
keep_prob)
with tf.variable_scope("decoding", reuse=True) as decoding_scope:
start_of_sequence_id = target_vocab_to_int['<GO>']
end_of_sequence_id = target_vocab_to_int['<EOS>']
inference_logits = decoding_layer_infer(
encoder_state, dec_cell, dec_embeddings, start_of_sequence_id,
end_of_sequence_id, sequence_length - 1, vocab_size,
decoding_scope, output_fn, keep_prob)
return train_logits, inference_logits
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_decoding_layer(decoding_layer)
# ### 构建神经网络
#
# 应用你在上方实现的函数，以：
#
# - 向编码器的输入数据应用嵌入。
# - 使用 `encoding_layer(rnn_inputs, rnn_size, num_layers, keep_prob)` 编码输入。
# - 使用 `process_decoding_input(target_data, target_vocab_to_int, batch_size)` 函数处理目标数据。
# - 向解码器的目标数据应用嵌入。
# - 使用 `decoding_layer(dec_embed_input, dec_embeddings, encoder_state, vocab_size, sequence_length, rnn_size, num_layers, target_vocab_to_int, keep_prob)` 解码编码的输入数据。
# In[13]:
def seq2seq_model(input_data, target_data, keep_prob, batch_size,