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Rewrite book chapter8 machine translation documentation and train.py #552
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daming-lu
approved these changes
Jun 21, 2018
| from functools import partial | ||
| import os | ||
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| dict_size = 30000 |
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Please add the old comments here : # dict dim
| bias_attr=False, | ||
| input=backward_first) | ||
| def encoder(is_sparse): | ||
| # encoder |
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we do not need this comment
| 4. Define the name for the decoder and the first two input for `gru_decoder_with_attention`. Note that `StaticInput` is used for the two inputs. Please refer to [StaticInput Document](/~https://github.com/PaddlePaddle/Paddle/blob/develop/doc/v2/howto/rnn/recurrent_group_en.md#input) for more details. | ||
| ```python | ||
| def train_decoder(context, is_sparse): | ||
| # decoder |
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| Print sequence and its `beam_size` generated translation results based on the dictionary. | ||
| We initialize ids and scores and create tensors for input. This test we are using first record data from `wmt14.test` for inference. At the end we get src dict and target dict for printing out results later. |
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In this test, we are using the first record data ...
| ```python | ||
| init_ids_data = np.array([1 for _ in range(batch_size)], dtype='int64') | ||
| init_scores_data = np.array( | ||
| [1. for _ in range(batch_size)], dtype='float32') |
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they could simply do this [1.0] * batch_size
| from functools import partial | ||
| import os | ||
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| dict_size = 30000 |
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add the old comments here
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