diff --git a/python/paddle/v2/fluid/tests/book/test_word2vec.py b/python/paddle/v2/fluid/tests/book/test_word2vec.py index 8cf54846fe5dba2742ce69e34e0788e124a1a85d..cdfa910fcd302642a1bdec300df55d1cf13f5bea 100644 --- a/python/paddle/v2/fluid/tests/book/test_word2vec.py +++ b/python/paddle/v2/fluid/tests/book/test_word2vec.py @@ -12,76 +12,104 @@ # See the License for the specific language governing permissions and # limitations under the License. -import numpy as np import paddle.v2 as paddle import paddle.v2.fluid as fluid +import unittest -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.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: - exit(0) # if avg cost less than 10.0, we think our code is good. -exit(1) + +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()