wehub-resource-sync
154 lines
4.9 KiB
Python
154 lines
4.9 KiB
Python
# Copyright (c) 2018 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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import os
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from test_dist_base import TestDistRunnerBase, runtime_main
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import paddle
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from paddle import base
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IS_SPARSE = True
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EMBED_SIZE = 32
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HIDDEN_SIZE = 256
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N = 5
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# Fix seed for test
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paddle.seed(1)
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class TestDistWord2vec2x2(TestDistRunnerBase):
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def get_model(self, batch_size=2):
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BATCH_SIZE = batch_size
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def __network__(words):
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embed_first = paddle.static.nn.embedding(
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input=words[0],
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size=[dict_size, EMBED_SIZE],
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dtype='float32',
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is_sparse=IS_SPARSE,
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param_attr=base.ParamAttr(
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name='shared_w',
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initializer=paddle.nn.initializer.Constant(value=0.1),
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),
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)
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embed_second = paddle.static.nn.embedding(
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input=words[1],
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size=[dict_size, EMBED_SIZE],
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dtype='float32',
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is_sparse=IS_SPARSE,
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param_attr=base.ParamAttr(
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name='shared_w',
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initializer=paddle.nn.initializer.Constant(value=0.1),
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),
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)
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embed_third = paddle.static.nn.embedding(
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input=words[2],
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size=[dict_size, EMBED_SIZE],
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dtype='float32',
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is_sparse=IS_SPARSE,
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param_attr=base.ParamAttr(
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name='shared_w',
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initializer=paddle.nn.initializer.Constant(value=0.1),
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),
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)
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embed_forth = paddle.static.nn.embedding(
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input=words[3],
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size=[dict_size, EMBED_SIZE],
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dtype='float32',
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is_sparse=IS_SPARSE,
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param_attr=base.ParamAttr(
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name='shared_w',
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initializer=paddle.nn.initializer.Constant(value=0.1),
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),
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)
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concat_embed = paddle.concat(
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[embed_first, embed_second, embed_third, embed_forth],
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axis=1,
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)
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hidden1 = paddle.static.nn.fc(
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x=concat_embed,
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size=HIDDEN_SIZE,
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activation='sigmoid',
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weight_attr=base.ParamAttr(
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initializer=paddle.nn.initializer.Constant(value=0.1)
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),
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)
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predict_word = paddle.static.nn.fc(
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x=hidden1,
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size=dict_size,
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activation='softmax',
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weight_attr=base.ParamAttr(
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initializer=paddle.nn.initializer.Constant(value=0.1)
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),
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)
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cost = paddle.nn.functional.cross_entropy(
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input=predict_word,
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label=words[4],
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reduction='none',
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use_softmax=False,
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)
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avg_cost = paddle.mean(cost)
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return avg_cost, predict_word
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word_dict = paddle.dataset.imikolov.build_dict()
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dict_size = len(word_dict)
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first_word = paddle.static.data(
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name='firstw', shape=[-1, 1], dtype='int64'
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)
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second_word = paddle.static.data(
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name='secondw', shape=[-1, 1], dtype='int64'
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)
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third_word = paddle.static.data(
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name='thirdw', shape=[-1, 1], dtype='int64'
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)
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forth_word = paddle.static.data(
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name='forthw', shape=[-1, 1], dtype='int64'
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)
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next_word = paddle.static.data(
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name='nextw', shape=[-1, 1], dtype='int64'
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)
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avg_cost, predict_word = __network__(
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[first_word, second_word, third_word, forth_word, next_word]
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)
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inference_program = paddle.base.default_main_program().clone()
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sgd_optimizer = paddle.optimizer.SGD(learning_rate=0.001)
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sgd_optimizer.minimize(avg_cost)
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train_reader = paddle.batch(
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paddle.dataset.imikolov.train(word_dict, N), BATCH_SIZE
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)
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test_reader = paddle.batch(
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paddle.dataset.imikolov.test(word_dict, N), BATCH_SIZE
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)
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return (
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inference_program,
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avg_cost,
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train_reader,
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test_reader,
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None,
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predict_word,
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)
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if __name__ == "__main__":
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os.environ['CPU_NUM'] = '1'
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os.environ['USE_CUDA'] = "FALSE"
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runtime_main(TestDistWord2vec2x2)
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