示例#1
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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)
示例#2
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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)
示例#3
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    :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