词向量

1.网络结构

样本为一个词 和一个词的前k个词和后k个词，目标是推算样本词

2.代码实现

 

引入环境

from __future__ import print_function

import paddle
import paddle.fluid as fluid
import six
import numpy
import math

EMBED_SIZE = 32      # embedding维度
HIDDEN_SIZE = 256    # 隐层大小
N = 5                # ngram大小，这里固定取5
BATCH_SIZE = 100     # batch大小
PASS_NUM = 100       # 训练轮数

use_cuda = False  # 如果用GPU训练，则设置为True

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

定义网络，首先映射词到词向量，然后做连接，结果若干个隐层后映射为一个word_dict的向量，然后用softmax来计算结果的概率，最后优化此结果

 

def inference_program(words, is_sparse):

    embed_first = fluid.embedding(
        input=words[0],
        size=[dict_size, EMBED_SIZE],
        dtype='float32',
        is_sparse=is_sparse,
        param_attr='shared_w')
    embed_second = fluid.embedding(
        input=words[1],
        size=[dict_size, EMBED_SIZE],
        dtype='float32',
        is_sparse=is_sparse,
        param_attr='shared_w')
    embed_third = fluid.embedding(
        input=words[2],
        size=[dict_size, EMBED_SIZE],
        dtype='float32',
        is_sparse=is_sparse,
        param_attr='shared_w')
    embed_fourth = fluid.embedding(
        input=words[3],
        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_fourth], 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')
    return predict_word

def train_program(predict_word):
    # 'next_word'的定义必须要在inference_program的声明之后，
    # 否则train program输入数据的顺序就变成了[next_word, firstw, secondw,
    # thirdw, fourthw], 这是不正确的.
    next_word = fluid.data(name='nextw', shape=[None, 1], dtype='int64')
    cost = fluid.layers.cross_entropy(input=predict_word, label=next_word)
    avg_cost = fluid.layers.mean(cost)
    return avg_cost

def optimizer_func():
    return fluid.optimizer.AdagradOptimizer(
        learning_rate=3e-3,
        regularization=fluid.regularizer.L2DecayRegularizer(8e-4))

 

训练数据

 

def train(if_use_cuda, params_dirname, is_sparse=True):
    place = fluid.CUDAPlace(0) if if_use_cuda else fluid.CPUPlace()

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

    first_word = fluid.data(name='firstw', shape=[None, 1], dtype='int64')
    second_word = fluid.data(name='secondw', shape=[None, 1], dtype='int64')
    third_word = fluid.data(name='thirdw', shape=[None, 1], dtype='int64')
    forth_word = fluid.data(name='fourthw', shape=[None, 1], dtype='int64')
    next_word = fluid.data(name='nextw', shape=[None, 1], dtype='int64')

    word_list = [first_word, second_word, third_word, forth_word, next_word]
    feed_order = ['firstw', 'secondw', 'thirdw', 'fourthw', 'nextw']

    main_program = fluid.default_main_program()
    star_program = fluid.default_startup_program()

    predict_word = inference_program(word_list, is_sparse)
    avg_cost = train_program(predict_word)
    test_program = main_program.clone(for_test=True)

    optimizer = optimizer_func()
    optimizer.minimize(avg_cost)

    exe = fluid.Executor(place)

    def train_test(program, reader):
        count = 0
        feed_var_list = [
            program.global_block().var(var_name) for var_name in feed_order
        ]
        feeder_test = fluid.DataFeeder(feed_list=feed_var_list, place=place)
        test_exe = fluid.Executor(place)
        accumulated = len([avg_cost]) * [0]
        for test_data in reader():
            avg_cost_np = test_exe.run(
                program=program,
                feed=feeder_test.feed(test_data),
                fetch_list=[avg_cost])
            accumulated = [
                x[0] + x[1][0] for x in zip(accumulated, avg_cost_np)
            ]
            count += 1
        return [x / count for x in accumulated]

    def train_loop():
        step = 0
        feed_var_list_loop = [
            main_program.global_block().var(var_name) for var_name in feed_order
        ]
        feeder = fluid.DataFeeder(feed_list=feed_var_list_loop, place=place)
        exe.run(star_program)
        for pass_id in range(PASS_NUM):
            for data in train_reader():
                avg_cost_np = exe.run(
                    main_program, feed=feeder.feed(data), fetch_list=[avg_cost])

                if step % 10 == 0:
                    outs = train_test(test_program, test_reader)

                    print("Step %d: Average Cost %f" % (step, outs[0]))

                    # 整个训练过程要花费几个小时，如果平均损失低于5.8，
                    # 我们就认为模型已经达到很好的效果可以停止训练了。
                    # 注意5.8是一个相对较高的值，为了获取更好的模型，可以将
                    # 这里的阈值设为3.5，但训练时间也会更长。
                    if outs[0] < 5.8:
                        if params_dirname is not None:
                            fluid.io.save_inference_model(params_dirname, [
                                'firstw', 'secondw', 'thirdw', 'fourthw'
                            ], [predict_word], exe)
                        return
                step += 1
                if math.isnan(float(avg_cost_np[0])):
                    sys.exit("got NaN loss, training failed.")

        raise AssertionError("Cost is too large {0:2.2}".format(avg_cost_np[0]))

    train_loop()