test_word2vec.py

#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import paddle.v2 as paddle
import paddle.v2.fluid as fluid
import unittest


def main_impl(use_cuda):
    if use_cuda and not fluid.core.is_compiled_with_cuda():
        return

    PASS_NUM = 100
    EMBED_SIZE = 32
    HIDDEN_SIZE = 256
    N = 5
    BATCH_SIZE = 32
    IS_SPARSE = True

    word_dict = paddle.dataset.imikolov.build_dict()
    dict_size = len(word_dict)

    first_word = fluid.layers.data(name='firstw', shape=[1], dtype='int64')
    second_word = fluid.layers.data(name='secondw', shape=[1], dtype='int64')
    third_word = fluid.layers.data(name='thirdw', shape=[1], dtype='int64')
    forth_word = fluid.layers.data(name='forthw', shape=[1], dtype='int64')
    next_word = fluid.layers.data(name='nextw', shape=[1], dtype='int64')

    embed_first = fluid.layers.embedding(
        input=first_word,
        size=[dict_size, EMBED_SIZE],
        dtype='float32',
        is_sparse=IS_SPARSE,
        param_attr='shared_w')
    embed_second = fluid.layers.embedding(
        input=second_word,
        size=[dict_size, EMBED_SIZE],
        dtype='float32',
        is_sparse=IS_SPARSE,
        param_attr='shared_w')
    embed_third = fluid.layers.embedding(
        input=third_word,
        size=[dict_size, EMBED_SIZE],
        dtype='float32',
        is_sparse=IS_SPARSE,
        param_attr='shared_w')
    embed_forth = fluid.layers.embedding(
        input=forth_word,
        size=[dict_size, EMBED_SIZE],
        dtype='float32',
        is_sparse=IS_SPARSE,
        param_attr='shared_w')

    concat_embed = fluid.layers.concat(
        input=[embed_first, embed_second, embed_third, embed_forth], axis=1)
    hidden1 = fluid.layers.fc(input=concat_embed,
                              size=HIDDEN_SIZE,
                              act='sigmoid')
    predict_word = fluid.layers.fc(input=hidden1, size=dict_size, act='softmax')
    cost = fluid.layers.cross_entropy(input=predict_word, label=next_word)
    avg_cost = fluid.layers.mean(x=cost)
    sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001)
    sgd_optimizer.minimize(avg_cost)

    train_reader = paddle.batch(
        paddle.dataset.imikolov.train(word_dict, N), BATCH_SIZE)

    place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
    exe = fluid.Executor(place)
    feeder = fluid.DataFeeder(
        feed_list=[first_word, second_word, third_word, forth_word, next_word],
        place=place)

    exe.run(fluid.default_startup_program())

    for pass_id in range(PASS_NUM):
        for data in train_reader():
            avg_cost_np = exe.run(fluid.default_main_program(),
                                  feed=feeder.feed(data),
                                  fetch_list=[avg_cost])
            if avg_cost_np[0] < 5.0:
                return
    raise AssertionError("Cost is too large {0:2.2}".format(avg_cost_np[0]))


def main(*args, **kwargs):
    prog = fluid.Program()
    startup_prog = fluid.Program()
    scope = fluid.core.Scope()
    with fluid.scope_guard(scope):
        with fluid.program_guard(prog, startup_prog):
            main_impl(*args, **kwargs)


class W2VTest(unittest.TestCase):
    def test_cpu_normal(self):
        main(use_cuda=False)

    def test_gpu_normal(self):
        main(use_cuda=True)


if __name__ == '__main__':
    unittest.main()