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)
:param keep_prob: Dropout keep probability :return: RNN state """ # TODO: Implement Function lstm_cell = tf.contrib.rnn.BasicLSTMCell(rnn_size) lstm_cell = tf.contrib.rnn.DropoutWrapper(lstm_cell, output_keep_prob=keep_prob) lstm_mul = tf.contrib.rnn.MultiRNNCell([lstm_cell] * num_layers) _, final_state = tf.nn.dynamic_rnn(lstm_mul, rnn_inputs, dtype=tf.float32) return final_state """ DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE """ tests.test_encoding_layer(encoding_layer) # ### 解码 - 训练 # # 使用 [`tf.contrib.seq2seq.simple_decoder_fn_train()`](https://www.tensorflow.org/versions/r1.0/api_docs/python/tf/contrib/seq2seq/simple_decoder_fn_train) 和 [`tf.contrib.seq2seq.dynamic_rnn_decoder()`](https://www.tensorflow.org/versions/r1.0/api_docs/python/tf/contrib/seq2seq/dynamic_rnn_decoder) 创建训练分对数(training logits)。将 `output_fn` 应用到 [`tf.contrib.seq2seq.dynamic_rnn_decoder()`](https://www.tensorflow.org/versions/r1.0/api_docs/python/tf/contrib/seq2seq/dynamic_rnn_decoder) 输出上。 # In[10]: def decoding_layer_train(encoder_state, dec_cell, dec_embed_input, sequence_length, decoding_scope, output_fn, keep_prob): """ Create a decoding layer for training :param encoder_state: Encoder State :param dec_cell: Decoder RNN Cell