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81 lines
2.8 KiB
81 lines
2.8 KiB
import numpy as np
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import paddle.v2 as paddle
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import paddle.v2.fluid as fluid
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PASS_NUM = 100
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EMBED_SIZE = 32
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HIDDEN_SIZE = 256
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N = 5
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BATCH_SIZE = 32
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IS_SPARSE = True
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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 = fluid.layers.data(name='firstw', shape=[1], dtype='int64')
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second_word = fluid.layers.data(name='secondw', shape=[1], dtype='int64')
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third_word = fluid.layers.data(name='thirdw', shape=[1], dtype='int64')
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forth_word = fluid.layers.data(name='forthw', shape=[1], dtype='int64')
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next_word = fluid.layers.data(name='nextw', shape=[1], dtype='int64')
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embed_first = fluid.layers.embedding(
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input=first_word,
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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='shared_w')
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embed_second = fluid.layers.embedding(
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input=second_word,
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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='shared_w')
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embed_third = fluid.layers.embedding(
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input=third_word,
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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='shared_w')
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embed_forth = fluid.layers.embedding(
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input=forth_word,
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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='shared_w')
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concat_embed = fluid.layers.concat(
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input=[embed_first, embed_second, embed_third, embed_forth], axis=1)
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hidden1 = fluid.layers.fc(input=concat_embed, size=HIDDEN_SIZE, act='sigmoid')
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predict_word = fluid.layers.fc(input=hidden1, size=dict_size, act='softmax')
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cost = fluid.layers.cross_entropy(input=predict_word, label=next_word)
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avg_cost = fluid.layers.mean(x=cost)
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sgd_optimizer = fluid.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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place = fluid.CPUPlace()
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exe = fluid.Executor(place)
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exe.run(fluid.default_startup_program())
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for pass_id in range(PASS_NUM):
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for data in train_reader():
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input_data = [[data_idx[idx] for data_idx in data] for idx in xrange(5)]
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input_data = map(lambda x: np.array(x).astype("int64"), input_data)
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input_data = map(lambda x: np.expand_dims(x, axis=1), input_data)
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avg_cost_np = exe.run(fluid.default_main_program(),
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feed={
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'firstw': input_data[0],
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'secondw': input_data[1],
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'thirdw': input_data[2],
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'forthw': input_data[3],
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'nextw': input_data[4]
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},
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fetch_list=[avg_cost])
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if avg_cost_np[0] < 5.0:
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exit(0) # if avg cost less than 10.0, we think our code is good.
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exit(1)
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