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166 lines
4.5 KiB
166 lines
4.5 KiB
import paddle.v2 as paddle
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import paddle.v2.framework.layers as layers
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import paddle.v2.framework.core as core
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import paddle.v2.framework.optimizer as optimizer
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from paddle.v2.framework.framework import Program, g_program
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from paddle.v2.framework.executor import Executor
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import numpy as np
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init_program = Program()
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program = Program()
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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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word_dict = paddle.dataset.imikolov.build_dict()
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dict_size = len(word_dict)
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first_word = layers.data(
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name='firstw',
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shape=[1],
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data_type='int32',
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program=program,
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init_program=init_program)
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second_word = layers.data(
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name='secondw',
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shape=[1],
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data_type='int32',
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program=program,
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init_program=init_program)
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third_word = layers.data(
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name='thirdw',
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shape=[1],
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data_type='int32',
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program=program,
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init_program=init_program)
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forth_word = layers.data(
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name='forthw',
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shape=[1],
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data_type='int32',
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program=program,
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init_program=init_program)
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next_word = layers.data(
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name='nextw',
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shape=[1],
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data_type='int32',
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program=program,
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init_program=init_program)
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embed_param_attr_1 = {
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'name': 'shared_w',
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'init_attr': {
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'max': 1.0,
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'type': 'uniform_random',
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'min': -1.0
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}
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}
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embed_param_attr_2 = {'name': 'shared_w'}
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embed_first = layers.embedding(
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input=first_word,
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size=[dict_size, embed_size],
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data_type='float32',
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param_attr=embed_param_attr_1,
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program=program,
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init_program=init_program)
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embed_second = layers.embedding(
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input=second_word,
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size=[dict_size, embed_size],
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data_type='float32',
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param_attr=embed_param_attr_2,
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program=program,
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init_program=init_program)
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embed_third = layers.embedding(
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input=third_word,
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size=[dict_size, embed_size],
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data_type='float32',
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param_attr=embed_param_attr_2,
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program=program,
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init_program=init_program)
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embed_forth = layers.embedding(
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input=forth_word,
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size=[dict_size, embed_size],
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data_type='float32',
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param_attr=embed_param_attr_2,
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program=program,
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init_program=init_program)
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concat_embed = layers.concat(
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input=[embed_first, embed_second, embed_third, embed_forth],
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axis=1,
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program=program,
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init_program=init_program)
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hidden1 = layers.fc(input=concat_embed,
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size=hidden_size,
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act='sigmoid',
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program=program,
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init_program=init_program)
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predict_word = layers.fc(input=hidden1,
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size=dict_size,
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act='softmax',
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program=program,
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init_program=init_program)
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cost = layers.cross_entropy(
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input=predict_word,
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label=next_word,
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program=program,
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init_program=init_program)
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avg_cost = layers.mean(x=cost, program=program, init_program=init_program)
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sgd_optimizer = optimizer.SGDOptimizer(learning_rate=0.001)
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opts = 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 = core.CPUPlace()
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exe = Executor(place)
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exe.run(init_program, feed={}, fetch_list=[])
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PASS_NUM = 100
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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("int32"), input_data)
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input_data = map(lambda x: np.expand_dims(x, axis=1), input_data)
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first_data = input_data[0]
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first_tensor = core.LoDTensor()
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first_tensor.set(first_data, place)
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second_data = input_data[0]
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second_tensor = core.LoDTensor()
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second_tensor.set(second_data, place)
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third_data = input_data[0]
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third_tensor = core.LoDTensor()
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third_tensor.set(third_data, place)
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forth_data = input_data[0]
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forth_tensor = core.LoDTensor()
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forth_tensor.set(forth_data, place)
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next_data = input_data[0]
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next_tensor = core.LoDTensor()
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next_tensor.set(next_data, place)
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outs = exe.run(program,
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feed={
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'firstw': first_tensor,
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'secondw': second_tensor,
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'thirdw': third_tensor,
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'forthw': forth_tensor,
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'nextw': next_tensor
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},
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fetch_list=[avg_cost])
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out = np.array(outs[0])
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if out[0] < 10.0:
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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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