Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into refine_code
Yu Yang
7 years ago
commit
9d7279b953
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# 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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from . import core
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__all__ = ['Params', ]
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class Params(object):
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def __init__(self, path=None):
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self.scope = core.Scope()
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if path:
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self._load(path)
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def _load(self, path):
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# reference: load_persistables in io.py
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pass
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def save(self, path):
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# reference: save_persistables in io.py
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pass
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def add_params(self, scope):
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# take the keys from the scope,
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# if not already exists in self.scope,
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# add the key and value into self.scope.
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pass
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@ -0,0 +1,140 @@
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# 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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from __future__ import print_function
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import paddle
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import paddle.fluid as fluid
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from functools import partial
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CLASS_DIM = 2
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EMB_DIM = 128
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HID_DIM = 512
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STACKED_NUM = 3
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def stacked_lstm_net(data, input_dim, class_dim, emb_dim, hid_dim, stacked_num):
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assert stacked_num % 2 == 1
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emb = fluid.layers.embedding(
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input=data, size=[input_dim, emb_dim], is_sparse=True)
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fc1 = fluid.layers.fc(input=emb, size=hid_dim)
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lstm1, cell1 = fluid.layers.dynamic_lstm(input=fc1, size=hid_dim)
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inputs = [fc1, lstm1]
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for i in range(2, stacked_num + 1):
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fc = fluid.layers.fc(input=inputs, size=hid_dim)
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lstm, cell = fluid.layers.dynamic_lstm(
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input=fc, size=hid_dim, is_reverse=(i % 2) == 0)
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inputs = [fc, lstm]
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fc_last = fluid.layers.sequence_pool(input=inputs[0], pool_type='max')
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lstm_last = fluid.layers.sequence_pool(input=inputs[1], pool_type='max')
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prediction = fluid.layers.fc(input=[fc_last, lstm_last],
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size=class_dim,
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act='softmax')
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return prediction
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def inference_network(word_dict):
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data = fluid.layers.data(
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name="words", shape=[1], dtype="int64", lod_level=1)
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dict_dim = len(word_dict)
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net = stacked_lstm_net(data, dict_dim, CLASS_DIM, EMB_DIM, HID_DIM,
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STACKED_NUM)
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return net
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def train_network(word_dict):
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prediction = inference_network(word_dict)
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label = fluid.layers.data(name="label", shape=[1], dtype="int64")
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cost = fluid.layers.cross_entropy(input=prediction, label=label)
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avg_cost = fluid.layers.mean(cost)
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accuracy = fluid.layers.accuracy(input=prediction, label=label)
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return avg_cost, accuracy
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def train(use_cuda, save_path):
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BATCH_SIZE = 128
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EPOCH_NUM = 5
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word_dict = paddle.dataset.imdb.word_dict()
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train_data = paddle.batch(
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paddle.reader.shuffle(
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paddle.dataset.imdb.train(word_dict), buf_size=1000),
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batch_size=BATCH_SIZE)
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test_data = paddle.batch(
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paddle.dataset.imdb.test(word_dict), batch_size=BATCH_SIZE)
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def event_handler(event):
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if isinstance(event, fluid.EndIteration):
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if (event.batch_id % 10) == 0:
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avg_cost, accuracy = trainer.test(reader=test_data)
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print('BatchID {1:04}, Loss {2:2.2}, Acc {3:2.2}'.format(
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event.batch_id + 1, avg_cost, accuracy))
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if accuracy > 0.01: # Low threshold for speeding up CI
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trainer.params.save(save_path)
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return
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place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
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trainer = fluid.Trainer(
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partial(train_network, word_dict),
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optimizer=fluid.optimizer.Adagrad(learning_rate=0.002),
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place=place,
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event_handler=event_handler)
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trainer.train(train_data, EPOCH_NUM, event_handler=event_handler)
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def infer(use_cuda, save_path):
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params = fluid.Params(save_path)
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place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
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word_dict = paddle.dataset.imdb.word_dict()
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inferencer = fluid.Inferencer(
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partial(inference_network, word_dict), params, place=place)
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def create_random_lodtensor(lod, place, low, high):
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data = np.random.random_integers(low, high,
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[lod[-1], 1]).astype("int64")
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res = fluid.LoDTensor()
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res.set(data, place)
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res.set_lod([lod])
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return res
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lod = [0, 4, 10]
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tensor_words = create_random_lodtensor(
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lod, place, low=0, high=len(word_dict) - 1)
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results = inferencer.infer({'words': tensor_words})
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print("infer results: ", results)
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def main(use_cuda):
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if use_cuda and not fluid.core.is_compiled_with_cuda():
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return
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save_path = "understand_sentiment_stacked_lstm.inference.model"
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train(use_cuda, save_path)
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infer(use_cuda, save_path)
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if __name__ == '__main__':
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for use_cuda in (False, True):
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main(use_cuda=use_cuda)
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