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74 lines
3.0 KiB
74 lines
3.0 KiB
#edit-mode: -*- python -*-
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# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from paddle.trainer_config_helpers import *
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settings(batch_size=15, learning_rate=0)
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num_words = 5
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beam_flag = get_config_arg('beam_search', bool, False)
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sent_id = data_layer(name="sent_id", size=1)
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# This layer has no actual use, but only to decide batch_size in generation.
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# When generating, at least one Memory in RecurrentLayer MUST have a boot layer.
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dummy_data = data_layer(name="dummy_data_input", size=2)
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def outer_step(dummy_data):
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gen_inputs = [StaticInput(input=dummy_data, size=2, is_seq=True),
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GeneratedInput(size=num_words,
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embedding_name="wordvec",
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embedding_size=num_words)]
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def inner_step(dummy_memory, predict_word):
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# simplified RNN for testing
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with mixed_layer(size=num_words) as layer:
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layer += full_matrix_projection(input=predict_word,
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param_attr=ParamAttr(name="transtable"))
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with mixed_layer(size=num_words, act=ExpActivation()) as out:
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out += trans_full_matrix_projection(input=layer,
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param_attr=ParamAttr(name="wordvec"))
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return out
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beam_gen = beam_search(name="rnn_gen",
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step=inner_step,
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input=gen_inputs,
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bos_id=0,
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eos_id=num_words-1,
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beam_size=2 if beam_flag else 1,
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num_results_per_sample=2 if beam_flag else 1,
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max_length=10)
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return beam_gen
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beam_gen_concat = recurrent_group(name="rnn_gen_concat",
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step=outer_step,
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input=[SubsequenceInput(dummy_data)])
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seqtext_printer_evaluator(input=beam_gen_concat,
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id_input=sent_id,
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dict_file="./trainer/tests/test_gen_dict.txt",
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result_file="./trainer/tests/dump_text.test")
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#outputs(beam_gen_concat)
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# In this config, as dummy_data_input doesn't work on beam_gen (we can find dummy_memory
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# is read-only memory, and isn't used by other layers of step), we show the Inputs and Outputs
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# as follows. Note that "__beam_search_predict__" is the default output name of beam_search.
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Inputs("sent_id","dummy_data_input")
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Outputs("__beam_search_predict__")
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