神经网络：文本分类II

作者: zqh_zy | 来源:发表于2017-04-20 11:52 被阅读546次

文本分类；数据增强；模型微调 2020-02-25
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tensorflow

本篇文章主要记录对之前用神经网络做文本识别的初步优化，进一步将准确率由原来的65%提高到80%，这里优化的几个方面包括：

随机打乱训练数据
增加隐层，和验证集
正则化
对原数据进行PCA预处理
调节训练参数（迭代次数，batch大小等）

随机化训练数据

观察训练数据集，发现训练集是按类别存储，读进内存后在仍然是按类别顺序存放。这样顺序取一部分作为验证集，很大程度上会减少一个类别的训练样本数，对该类别的预测准确率会有所下降。所以首先考虑打乱训练数据。
在已经向量化的训练数据的基础上打乱数据，首先合并data和label，打乱后再将数据和标签分离为trian.txt和train_label.txt。这里可以直接使用shell命令：

1、将labels加到trian.txt的第一列
paste -d" " train_labels.txt train.txt > train_to_shuf.txt

2、随机打乱文件行

shuf train_to_shuf.txt -o train.txt

3、提取打乱后文件的第一列，保存到train_labels.txt
cat train.txt | awk '{print $1}' > train_labels.txt

4、删除第一列label.
awk '{$1="";print $0}' train.txt

这样再次以相同方式训练，准确率由65%上升到75% 。

改变网络结构，增加隐层

之前的网络直接对输入数据做softmax回归，这里考虑增加隐层，数量并加入验证集观察准确率的变化情况。这里加入一个隐层，隐层节点数为500，激励函数使用Relu。替换原来的网络结构，准确率进一步上升。

正则化，改善过拟合

观察模型对训练集的拟合程度到90%+，而通过上步对训练数据的准确率为76%，一定程度上出现了过拟合的现象，这里在原有cost function中上加入正则项，希望减轻过拟合的现象。这里使用L2正则。连同上步部分的代码如下：

#!/usr/bin/python
#-*-coding:utf-8-*-

LAYER_NODE1 = 500 # layer1 node num
INPUT_NODE = 5000
OUTPUT_NODE = 10
REG_RATE = 0.01

import tensorflow as tf
from datasets import datasets

def interface(inputs, w1, b1, w2,b2):
    """
        compute forword progration result
    """
    lay1 = tf.nn.relu(tf.matmul(inputs, w1) + b1)
    return tf.nn.softmax(tf.matmul(lay1, w2) + b2) # need softmax??

data_sets = datasets()
data_sets.read_train_data(".", True)

sess = tf.InteractiveSession()

x = tf.placeholder(tf.float32, [None, INPUT_NODE], name="x-input")
y_ = tf.placeholder(tf.float32, [None, OUTPUT_NODE], name="y-input")

w1 = tf.Variable(tf.truncated_normal([INPUT_NODE, LAYER_NODE1], stddev=0.1))
b1 = tf.Variable(tf.constant(0.0, shape=[LAYER_NODE1]))

w2 = tf.Variable(tf.truncated_normal([LAYER_NODE1, OUTPUT_NODE], stddev=0.1))
b2 = tf.Variable(tf.constant(0.0, shape=[OUTPUT_NODE]))

y = interface(x, w1, b1, w2, b2)

cross_entropy = -tf.reduce_sum(y_ * tf.log(y + 1e-10))
regularizer = tf.contrib.layers.l2_regularizer(REG_RATE)
regularization = regularizer(w1) + regularizer(w2)
loss = cross_entropy + regularization


train_step = tf.train.GradientDescentOptimizer(0.01).minimize(loss)

#training
tf.global_variables_initializer().run()
saver = tf.train.Saver()

cv_feed = {x: data_sets.cv.text, y_: data_sets.cv.label}
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
acc = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

for i in range(5000):
    if i % 200 == 0:
        cv_acc = sess.run(acc, feed_dict=cv_feed)
        print "train steps: %d, cv accuracy is %g " % (i, cv_acc)
    batch_xs, batch_ys = data_sets.train.next_batch(100)
    train_step.run({x: batch_xs, y_: batch_ys})

path = saver.save(sess, "./model4/model.md")

PCA处理

一方面对文本向量集是严重稀疏的矩阵，而且维度较大，一方面影响训练速度，一方面消耗内存。这里考虑对数据进行PCA处理。该部分希望保存99%的差异率，得到相应的k，即对应的维度。

#!/usr/bin/python
#-*-coding:utf-8-*-
"""
    PCA for datasets
    
"""
import os
import sys
import commands
import numpy
from contextlib import nested
from datasets import datasets

ORIGIN_DIM = 5000

def pca(origin_mat):
    """
        gen matrix using pca
        row of origin_mat is one sample of dataset
        col of origin_mat is one feature
        return matrix  U, s and  V 
    """
    # mean,normaliza1on
    avg = numpy.mean(origin_mat, axis=0)
    # covariance matrix
    cov = numpy.cov(origin_mat-avg,rowvar=0)
    #Singular Value Decomposition
    U, s, V = numpy.linalg.svd(cov, full_matrices=True)

    k = 1;
    sigma_s = numpy.sum(s)
    # chose smallest k for 99% of variance retained 
    for k in range(1, ORIGIN_DIM+1):
        variance = numpy.sum(s[0:k]) / sigma_s
        print "k = %d, variance is %f" % (k, variance)
        if variance >= 0.99:
            break

    if k == ORIGIN_DIM:
        print "some thing unexpected , k is same as ORIGIN_DIM"
        exit(1)

    return U[:, 0:k], k

if __name__ == '__main__':
    """
        main, read train.txt, and do pca
        save file to train_pca.txt
    """
    data_sets = datasets()
    train_text, _ = data_sets.read_from_disk(".", "train", one_hot=False)

    U, k = pca(train_text)
    print "U shpae: ", U.shape
    print "k is : ", k

    text_pca = numpy.dot(train_text, U)
    text_num = text_pca.shape[0]
    print "text_num in pca is ", text_num

    with open("./train_pca.txt", "a+") as f:
        for i in range(0, text_num):
            f.write(" ".join(map(str, text_pca[i,:])) + "\n")

最终得到k=2583。该部分准确率有所提高但影响不大。