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: Inference Logits
"""
# TODO: Implement Function
infer_decoder_fn = tf.contrib.seq2seq.simple_decoder_fn_inference(
output_fn, encoder_state, dec_embeddings, start_of_sequence_id,
end_of_sequence_id, maximum_length, vocab_size)
infer_logits, _, _ = tf.contrib.seq2seq.dynamic_rnn_decoder(
dec_cell, infer_decoder_fn, scope=decoding_scope)
return infer_logits
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_decoding_layer_infer(decoding_layer_infer)
# ### 构建解码层级
#
# 实现 `decoding_layer()` 以创建解码器 RNN 层级。
#
# - 使用 `rnn_size` 和 `num_layers` 创建解码 RNN 单元。
# - 使用 [`lambda`](https://docs.python.org/3/tutorial/controlflow.html#lambda-expressions) 创建输出函数，将输入，也就是分对数转换为类分对数（class logits）。
# - 使用 `decoding_layer_train(encoder_state, dec_cell, dec_embed_input, sequence_length, decoding_scope, output_fn, keep_prob)` 函数获取训练分对数。
# - 使用 `decoding_layer_infer(encoder_state, dec_cell, dec_embeddings, start_of_sequence_id, end_of_sequence_id, maximum_length, vocab_size, decoding_scope, output_fn, keep_prob)` 函数获取推论分对数。
#
# 注意：你将需要使用 [tf.variable_scope](https://www.tensorflow.org/api_docs/python/tf/variable_scope) 在训练和推论分对数间分享变量。
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