TensorFlow使用CNN实现中文文本分类
TensorFlow使用CNN实现中文文本分类
读研期间使用过TensorFlow实现过简单的CNN情感分析（分类），固然这是比较low的二分类状况，后来进行多分类状况。但以前的学习基本上都是在英文词库上训练的。断断续续，想整理一下手头的项目资料，因而就拾起读研期间的文本分类的小项目，花了一点时间，把原来英文文本分类的项目，应用在中文文本分类，效果还不错，在THUCNews中文数据集上，准确率93.9%左右，老规矩，先上源码地址python
Github项目源码：https://github.com/PanJinquan/nlp-learning-tutorials/tree/master/THUCNews， 记得给个“Star”哈git
目录github
TensorFlow使用CNN实现中文文本分类ubuntu
1、项目介绍网络
1.1 目录结构session
2、CNN模型结构dom
3、文本预处理ide
1、项目介绍
1.1 目录结构
Github项目源码：https://github.com/PanJinquan/nlp-learning-tutorials/tree/master/THUCNews， 记得给个“Star”哈
其余资源地址：
- 1.THUCTC官方数据集，连接: http://thuctc.thunlp.org/message
- 2.THUCTC百度网盘，连接: https://pan.baidu.com/s/1DT5xY9m2yfu1YGaGxpWiBQ 提取码: bbpe
- 3.已经训练好的word2vec模型：连接: https://pan.baidu.com/s/1n4ZgiF0gbY0zsK0706wZiw 提取码: mtrj
- 4.使用词向量处理的THUCNews数据：连接: https://pan.baidu.com/s/12Hdf36QafQ3y6KgV_vLTsw 提取码: m9dx
1.2 THUCNews数据集
THUCNews是根据新浪新闻RSS订阅频道2005~2011年间的历史数据筛选过滤生成，包含74万篇新闻文档（2.19 GB），均为UTF-8纯文本格式。咱们在原始新浪新闻分类体系的基础上，从新整合划分出14个候选分类类别：财经、彩票、房产、股票、家居、教育、科技、社会、时尚、时政、体育、星座、游戏、娱乐。相关介绍，能够看这里http://thuctc.thunlp.org/
下载地址：
1.官方数据集下载连接: http://thuctc.thunlp.org/message
2.百度网盘下载连接: https://pan.baidu.com/s/1DT5xY9m2yfu1YGaGxpWiBQ 提取码: bbpe
2、CNN模型结构
CNN文本分类的网络结，以下：
简单分析一下：
（1）咱们假定输入CNN的数据是二维的，其中每一行表示一个样本（即一个字词），如图中“I”、“like”等。每个样本（字词）有d个维度，能够当作是词向量长度，即每一个字词的维度，程序中用embedding_dim表示。
（2）使用CNN的卷积对这个二维数据进行卷积：在图像的CNN卷积中，卷积核的大小通常是3*3，5*5等，但在NLP中就不就不能这么搞了，由于这里的输入数据每行是一个样本了！假设卷积核的大小为[filter_height,filter_width],那么卷积核的高度filter_height能够为1，2，3等任意值，而宽度filter_width只能是embedding_dim的大小，这样才能把完整的样本框进去！
下面是使用TensorFlow实现的CNN文本分类网络：TextCNN,
max_sentence_length = 300 # 最大句子长度，也就是说文本样本中字词的最大长度，不足补零，多余的截断
embedding_dim = 128 #词向量长度，即每一个字词的维度
filter_sizes = [3, 4, 5, 6] #卷积核大小
num_filters = 200 # Number of filters per filter size 卷价个数
base_lr=0.001 # 学习率
dropout_keep_prob = 0.5
l2_reg_lambda = 0.0 # "L2 regularization lambda (default: 0.0)
import tensorflow as tf
import numpy as np
class TextCNN(object):
'''
A CNN for text classification
Uses and embedding layer, followed by a convolutional, max-pooling and softmax layer.
'''
def __init__(
self, sequence_length, num_classes,
embedding_size, filter_sizes, num_filters, l2_reg_lambda=0.0):
# Placeholders for input, output, dropout
self.input_x = tf.placeholder(tf.float32, [None, sequence_length, embedding_size], name = "input_x")
self.input_y = tf.placeholder(tf.float32, [None, num_classes], name = "input_y")
self.dropout_keep_prob = tf.placeholder(tf.float32, name = "dropout_keep_prob")
# Keeping track of l2 regularization loss (optional)
l2_loss = tf.constant(0.0)
# Embedding layer
# self.embedded_chars = [None(batch_size), sequence_size, embedding_size]
# self.embedded_chars = [None(batch_size), sequence_size, embedding_size, 1(num_channels)]
self.embedded_chars = self.input_x
self.embedded_chars_expended = tf.expand_dims(self.embedded_chars, -1)
# Create a convolution + maxpool layer for each filter size
pooled_outputs = []
for i, filter_size in enumerate(filter_sizes):# "filter_sizes", "3,4,5",
with tf.name_scope("conv-maxpool-%s" % filter_size):
# Convolution layer
filter_shape = [filter_size, embedding_size, 1, num_filters] # num_filters= 200
# filter_shape =[height, width, in_channels, output_channels]
W = tf.Variable(tf.truncated_normal(filter_shape, stddev=0.1), name="W")
b = tf.Variable(tf.constant(0.1, shape=[num_filters]), name="b")
conv = tf.nn.conv2d(self.embedded_chars_expended,
W,
strides=[1,1,1,1],
padding="VALID",
name="conv")
# Apply nonlinearity
h = tf.nn.relu(tf.nn.bias_add(conv, b), name = "relu")
# Maxpooling over the outputs
pooled = tf.nn.max_pool(
h,
ksize=[1, sequence_length - filter_size + 1, 1, 1],
strides=[1,1,1,1],
padding="VALID",
name="pool")
pooled_outputs.append(pooled)
# Combine all the pooled features
num_filters_total = num_filters * len(filter_sizes)
self.h_pool = tf.concat(pooled_outputs, 3)
# self.h_pool = tf.concat(3, pooled_outputs)
self.h_pool_flat = tf.reshape(self.h_pool, [-1, num_filters_total])
# Add dropout
with tf.name_scope("dropout"):
self.h_drop = tf.nn.dropout(self.h_pool_flat, self.dropout_keep_prob)
# Final (unnomalized) scores and predictions
with tf.name_scope("output"):
W = tf.get_variable(
"W",
shape = [num_filters_total, num_classes],
initializer = tf.contrib.layers.xavier_initializer())
b = tf.Variable(tf.constant(0.1, shape=[num_classes], name = "b"))
l2_loss += tf.nn.l2_loss(W)
l2_loss += tf.nn.l2_loss(b)
self.scores = tf.nn.xw_plus_b(self.h_drop, W, b, name = "scores")
self.predictions = tf.argmax(self.scores, 1, name = "predictions")
# Calculate Mean cross-entropy loss
with tf.name_scope("loss"):
losses = tf.nn.softmax_cross_entropy_with_logits(logits = self.scores, labels = self.input_y)
self.loss = tf.reduce_mean(losses) + l2_reg_lambda * l2_loss
# Accuracy
with tf.name_scope("accuracy"):
correct_predictions = tf.equal(self.predictions, tf.argmax(self.input_y, 1))
self.accuracy = tf.reduce_mean(tf.cast(correct_predictions, "float"), name = "accuracy")
3、文本预处理
本博客使用jieba工具进行中文分词，使用词进行训练会比使用字进行训练，效果更好。
这部分：已经在《使用gensim训练中文语料word2vec》https://blog.csdn.net/guyuealian/article/details/84072158，详解讲解，本身看吧！
一、jieba中文分词
这个须要本身安装：pip install jieba 或者pip3 install jieba
二、gensim训练word2vec模型
这里使用jieba工具对THUCNews数据集进行分词，并利用gensim训练基于THUCNews的word2vec模型，这里提供已经训练好的word2vec模型：连接: https://pan.baidu.com/s/1n4ZgiF0gbY0zsK0706wZiw 提取码: mtrj
二、THUCNews数据处理
有了word2vec模型，我就能够用word2vec词向量处理THUCNews数据：先使用jieba工具将中文句子转为字词，再将字词根据word2vec模型转为embadding 的索引，有了索引就能够得到词向量embadding 。这里并把这些索引数据保存为npy文件。后续训练时，CNN网络只须要读取这些npy文件，并将索引转为embadding，就能够进行训练了。
处理好的THUCNews数据下载地址：连接: https://pan.baidu.com/s/12Hdf36QafQ3y6KgV_vLTsw 提取码: m9dx
下面的代码实现的功能：使用jieba工具将中文句子转为字词，再将字词根据word2vec模型转为embadding 的索引矩阵，而后把这些索引矩阵保存下来（*.npy文件）,源代码中batchSize=20000表示：将20000中文TXT文件处理成字词，转为索引矩阵并保存为一个*.npy文件，至关于将20000中文TXT文件保存为一个*.npy文件，主要是为了压缩数据，避免单个文件过大的状况。
# -*-coding: utf-8 -*-
"""
@Project: nlp-learning-tutorials
@File : create_word2vec.py
@Author : panjq
@E-mail : pan_jinquan@163.com
@Date : 2018-11-08 17:37:21
"""
from gensim.models import Word2Vec
import random
import numpy as np
import os
import math
from utils import files_processing,segment
def info_npy(file_list):
sizes=0
for file in file_list:
data = np.load(file)
print("data.shape:{}".format(data.shape))
size = data.shape[0]
sizes+=size
print("files nums:{}, data nums:{}".format(len(file_list), sizes))
return sizes
def save_multi_file(files_list,labels_list,word2vec_path,out_dir,prefix,batchSize,max_sentence_length,labels_set=None,shuffle=False):
'''
将文件内容映射为索引矩阵，而且将数据保存为多个文件
:param files_list:
:param labels_list:
:param word2vec_path: word2vec模型的位置
:param out_dir: 文件保存的目录
:param prefix: 保存文件的前缀名
:param batchSize: 将多个文件内容保存为一个文件
:param labels_set: labels集合
:return:
'''
if not os.path.exists(out_dir):
os.mkdir(out_dir)
# 把该目录下的全部文件都删除
files_processing.delete_dir_file(out_dir)
if shuffle:
random.seed(100)
random.shuffle(files_list)
random.seed(100)
random.shuffle(labels_list)
sample_num = len(files_list)
w2vModel=load_wordVectors(word2vec_path)
if labels_set is None:
labels_set= files_processing.get_labels_set(label_list)
labels_list, labels_set = files_processing.labels_encoding(labels_list, labels_set)
labels_list=labels_list.tolist()
batchNum = int(math.ceil(1.0 * sample_num / batchSize))
for i in range(batchNum):
start = i * batchSize
end = min((i + 1) * batchSize, sample_num)
batch_files = files_list[start:end]
batch_labels = labels_list[start:end]
# 读取文件内容，字词分割
batch_content = files_processing.read_files_list_to_segment(batch_files,
max_sentence_length,
padding_token='<PAD>',
segment_type='word')
# 将字词转为索引矩阵
batch_indexMat = word2indexMat(w2vModel, batch_content, max_sentence_length)
batch_labels=np.asarray(batch_labels)
batch_labels = batch_labels.reshape([len(batch_labels), 1])
# 保存*.npy文件
filename = os.path.join(out_dir,prefix + '{0}.npy'.format(i))
labels_indexMat = cat_labels_indexMat(batch_labels, batch_indexMat)
np.save(filename, labels_indexMat)
print('step:{}/{}, save:{}, data.shape{}'.format(i,batchNum,filename,labels_indexMat.shape))
def cat_labels_indexMat(labels,indexMat):
indexMat_labels = np.concatenate([labels,indexMat], axis=1)
return indexMat_labels
def split_labels_indexMat(indexMat_labels,label_index=0):
labels = indexMat_labels[:, 0:label_index+1] # 第一列是labels
indexMat = indexMat_labels[:, label_index+1:] # 其他是indexMat
return labels, indexMat
def load_wordVectors(word2vec_path):
w2vModel = Word2Vec.load(word2vec_path)
return w2vModel
def word2vector_lookup(w2vModel, sentences):
'''
将字词转换为词向量
:param w2vModel: word2vector模型
:param sentences: type->list[list[str]]
:return: sentences对应的词向量,type->list[list[ndarray[list]]
'''
all_vectors = []
embeddingDim = w2vModel.vector_size
embeddingUnknown = [0 for i in range(embeddingDim)]
for sentence in sentences:
this_vector = []
for word in sentence:
if word in w2vModel.wv.vocab:
v=w2vModel[word]
this_vector.append(v)
else:
this_vector.append(embeddingUnknown)
all_vectors.append(this_vector)
all_vectors=np.array(all_vectors)
return all_vectors
def word2indexMat(w2vModel, sentences, max_sentence_length):
'''
将字词word转为索引矩阵
:param w2vModel:
:param sentences:
:param max_sentence_length:
:return:
'''
nums_sample=len(sentences)
indexMat = np.zeros((nums_sample, max_sentence_length), dtype='int32')
rows = 0
for sentence in sentences:
indexCounter = 0
for word in sentence:
try:
index = w2vModel.wv.vocab[word].index # 得到单词word的下标
indexMat[rows][indexCounter] = index
except :
indexMat[rows][indexCounter] = 0 # Vector for unkown words
indexCounter = indexCounter + 1
if indexCounter >= max_sentence_length:
break
rows+=1
return indexMat
def indexMat2word(w2vModel, indexMat, max_sentence_length=None):
'''
将索引矩阵转为字词word
:param w2vModel:
:param indexMat:
:param max_sentence_length:
:return:
'''
if max_sentence_length is None:
row,col =indexMat.shape
max_sentence_length=col
sentences=[]
for Mat in indexMat:
indexCounter = 0
sentence=[]
for index in Mat:
try:
word = w2vModel.wv.index2word[index] # 得到单词word的下标
sentence+=[word]
except :
sentence+=['<PAD>']
indexCounter = indexCounter + 1
if indexCounter >= max_sentence_length:
break
sentences.append(sentence)
return sentences
def save_indexMat(indexMat,path):
np.save(path, indexMat)
def load_indexMat(path):
indexMat = np.load(path)
return indexMat
def indexMat2vector_lookup(w2vModel,indexMat):
'''
将索引矩阵转为词向量
:param w2vModel:
:param indexMat:
:return: 词向量
'''
all_vectors = w2vModel.wv.vectors[indexMat]
return all_vectors
def pos_neg_test():
positive_data_file = "./data/ham_5000.utf8"
negative_data_file = './data/spam_5000.utf8'
word2vec_path = 'out/trained_word2vec.model'
sentences, labels = files_processing.load_pos_neg_files(positive_data_file, negative_data_file)
# embedding_test(positive_data_file,negative_data_file)
sentences, max_document_length = segment.padding_sentences(sentences, '<PADDING>', padding_sentence_length=190)
# train_wordVectors(sentences,embedding_size=128,word2vec_path=word2vec_path) # 训练word2vec，并保存word2vec_path
w2vModel=load_wordVectors(word2vec_path) #加载训练好的word2vec模型
'''
转换词向量提供有两种方法：
[1]直接转换：根据字词直接映射到词向量：word2vector_lookup
[2]间接转换：先将字词转为索引矩阵，再由索引矩阵映射到词向量：word2indexMat->indexMat2vector_lookup
'''
# [1]根据字词直接映射到词向量
x1=word2vector_lookup(w2vModel, sentences)
# [2]先将字词转为索引矩阵，再由索引矩阵映射到词向量
indexMat_path = 'out/indexMat.npy'
indexMat=word2indexMat(w2vModel, sentences, max_sentence_length=190) # 将字词转为索引矩阵
save_indexMat(indexMat, indexMat_path)
x2=indexMat2vector_lookup(w2vModel, indexMat) # 索引矩阵映射到词向量
print("x.shape = {}".format(x2.shape))# shape=(10000, 190, 128)->(样本个数10000,每一个样本的字词个数190，每一个字词的向量长度128)
if __name__=='__main__':
# THUCNews_path='/home/ubuntu/project/tfTest/THUCNews/test'
# THUCNews_path='/home/ubuntu/project/tfTest/THUCNews/spam'
THUCNews_path='/home/ubuntu/project/tfTest/THUCNews/THUCNews'
# 读取全部文件列表
files_list, label_list = files_processing.gen_files_labels(THUCNews_path)
max_sentence_length=300
word2vec_path="../../word2vec/models/THUCNews_word2Vec/THUCNews_word2Vec_128.model"
# 得到标签集合，并保存在本地
# labels_set=['星座','财经','教育']
# labels_set = files_processing.get_labels_set(label_list)
labels_file='../data/THUCNews_labels.txt'
# files_processing.write_txt(labels_file, labels_set)
# 将数据划分为train val数据集
train_files, train_label, val_files, val_label= files_processing.split_train_val_list(files_list, label_list, facror=0.9, shuffle=True)
# contents, labels=files_processing.read_files_labels(files_list,label_list)
# word2vec_path = 'out/trained_word2vec.model'
train_out_dir='../data/train_data'
prefix='train_data'
batchSize=20000
labels_set=files_processing.read_txt(labels_file)
# labels_set2 = files_processing.read_txt(labels_file)
save_multi_file(files_list=train_files,
labels_list=train_label,
word2vec_path=word2vec_path,
out_dir=train_out_dir,
prefix=prefix,
batchSize=batchSize,
max_sentence_length=max_sentence_length,
labels_set=labels_set,
shuffle=True)
print("*******************************************************")
val_out_dir='../data/val_data'
prefix='val_data'
save_multi_file(files_list=val_files,
labels_list=val_label,
word2vec_path=word2vec_path,
out_dir=val_out_dir,
prefix=prefix,
batchSize=batchSize,
max_sentence_length=max_sentence_length,
labels_set=labels_set,
shuffle=True)
4、训练过程
训练代码以下，注意，Github上不能上传大文件，因此你须要把上面提供的文件都下载下来，并放在对应的文件目录，就能够训练了。
训练中须要读取训练数据，即*.npy文件，*.npy文件保存的是索引数据，所以须要转为CNN的embadding数据：这个过程由函数：indexMat2vector_lookup完成：train_batch_data = create_word2vec.indexMat2vector_lookup(w2vModel, train_batch_data)
#! /usr/bin/env python
# encoding: utf-8
import tensorflow as tf
import numpy as np
import os
from text_cnn import TextCNN
from utils import create_batch_data, create_word2vec, files_processing
def train(train_dir,val_dir,labels_file,word2vec_path,batch_size,max_steps,log_step,val_step,snapshot,out_dir):
'''
训练...
:param train_dir: 训练数据目录
:param val_dir: val数据目录
:param labels_file: labels文件目录
:param word2vec_path: 词向量模型文件
:param batch_size: batch size
:param max_steps: 最大迭代次数
:param log_step: log显示间隔
:param val_step: 测试间隔
:param snapshot: 保存模型间隔
:param out_dir: 模型ckpt和summaries输出的目录
:return:
'''
max_sentence_length = 300
embedding_dim = 128
filter_sizes = [3, 4, 5, 6]
num_filters = 200 # Number of filters per filter size
base_lr=0.001# 学习率
dropout_keep_prob = 0.5
l2_reg_lambda = 0.0 # "L2 regularization lambda (default: 0.0)
allow_soft_placement = True # 若是你指定的设备不存在，容许TF自动分配设备
log_device_placement = False # 是否打印设备分配日志
print("Loading data...")
w2vModel = create_word2vec.load_wordVectors(word2vec_path)
labels_set = files_processing.read_txt(labels_file)
labels_nums = len(labels_set)
train_file_list = create_batch_data.get_file_list(file_dir=train_dir, postfix='*.npy')
train_batch = create_batch_data.get_data_batch(train_file_list, labels_nums=labels_nums, batch_size=batch_size,
shuffle=False, one_hot=True)
val_file_list = create_batch_data.get_file_list(file_dir=val_dir, postfix='*.npy')
val_batch = create_batch_data.get_data_batch(val_file_list, labels_nums=labels_nums, batch_size=batch_size,
shuffle=False, one_hot=True)
print("train data info *****************************")
train_nums=create_word2vec.info_npy(train_file_list)
print("val data info *****************************")
val_nums = create_word2vec.info_npy(val_file_list)
print("labels_set info *****************************")
files_processing.info_labels_set(labels_set)
# Training
with tf.Graph().as_default():
session_conf = tf.ConfigProto(allow_soft_placement = allow_soft_placement,log_device_placement = log_device_placement)
sess = tf.Session(config = session_conf)
with sess.as_default():
cnn = TextCNN(sequence_length = max_sentence_length,
num_classes = labels_nums,
embedding_size = embedding_dim,
filter_sizes = filter_sizes,
num_filters = num_filters,
l2_reg_lambda = l2_reg_lambda)
# Define Training procedure
global_step = tf.Variable(0, name="global_step", trainable=False)
optimizer = tf.train.AdamOptimizer(learning_rate=base_lr)
# optimizer = tf.train.MomentumOptimizer(learning_rate=0.01, momentum=0.9)
grads_and_vars = optimizer.compute_gradients(cnn.loss)
train_op = optimizer.apply_gradients(grads_and_vars, global_step=global_step)
# Keep track of gradient values and sparsity (optional)
grad_summaries = []
for g, v in grads_and_vars:
if g is not None:
grad_hist_summary = tf.summary.histogram("{}/grad/hist".format(v.name), g)
sparsity_summary = tf.summary.scalar("{}/grad/sparsity".format(v.name), tf.nn.zero_fraction(g))
grad_summaries.append(grad_hist_summary)
grad_summaries.append(sparsity_summary)
grad_summaries_merged = tf.summary.merge(grad_summaries)
# Output directory for models and summaries
print("Writing to {}\n".format(out_dir))
# Summaries for loss and accuracy
loss_summary = tf.summary.scalar("loss", cnn.loss)
acc_summary = tf.summary.scalar("accuracy", cnn.accuracy)
# Train Summaries
train_summary_op = tf.summary.merge([loss_summary, acc_summary, grad_summaries_merged])
train_summary_dir = os.path.join(out_dir, "summaries", "train")
train_summary_writer = tf.summary.FileWriter(train_summary_dir, sess.graph)
# Dev summaries
dev_summary_op = tf.summary.merge([loss_summary, acc_summary])
dev_summary_dir = os.path.join(out_dir, "summaries", "dev")
dev_summary_writer = tf.summary.FileWriter(dev_summary_dir, sess.graph)
# Checkpoint directory. Tensorflow assumes this directory already exists so we need to create it
checkpoint_dir = os.path.abspath(os.path.join(out_dir, "checkpoints"))
checkpoint_prefix = os.path.join(checkpoint_dir, "model")
if not os.path.exists(checkpoint_dir):
os.makedirs(checkpoint_dir)
saver = tf.train.Saver(tf.global_variables(), max_to_keep=5)
# Initialize all variables
sess.run(tf.global_variables_initializer())
def train_step(x_batch, y_batch):
"""
A single training step
"""
feed_dict = {
cnn.input_x: x_batch,
cnn.input_y: y_batch,
cnn.dropout_keep_prob: dropout_keep_prob
}
_, step, summaries, loss, accuracy = sess.run(
[train_op, global_step, train_summary_op, cnn.loss, cnn.accuracy],
feed_dict)
if step % log_step==0:
print("training: step {}, loss {:g}, acc {:g}".format(step, loss, accuracy))
train_summary_writer.add_summary(summaries, step)
def dev_step(x_batch, y_batch, writer=None):
"""
Evaluates model on a dev set
"""
feed_dict = {
cnn.input_x: x_batch,
cnn.input_y: y_batch,
cnn.dropout_keep_prob: 1.0
}
step, summaries, loss, accuracy = sess.run(
[global_step, dev_summary_op, cnn.loss, cnn.accuracy],
feed_dict)
if writer:
writer.add_summary(summaries, step)
return loss, accuracy
for i in range(max_steps):
train_batch_data, train_batch_label = create_batch_data.get_next_batch(train_batch)
train_batch_data = create_word2vec.indexMat2vector_lookup(w2vModel, train_batch_data)
train_step(train_batch_data, train_batch_label)
current_step = tf.train.global_step(sess, global_step)
if current_step % val_step == 0:
val_losses = []
val_accs = []
# for k in range(int(val_nums/batch_size)):
for k in range(100):
val_batch_data, val_batch_label = create_batch_data.get_next_batch(val_batch)
val_batch_data = create_word2vec.indexMat2vector_lookup(w2vModel, val_batch_data)
val_loss, val_acc=dev_step(val_batch_data, val_batch_label, writer=dev_summary_writer)
val_losses.append(val_loss)
val_accs.append(val_acc)
mean_loss = np.array(val_losses, dtype=np.float32).mean()
mean_acc = np.array(val_accs, dtype=np.float32).mean()
print("--------Evaluation:step {}, loss {:g}, acc {:g}".format(current_step, mean_loss, mean_acc))
if current_step % snapshot == 0:
path = saver.save(sess, checkpoint_prefix, global_step=current_step)
print("Saved model checkpoint to {}\n".format(path))
def main():
# Data preprocess
labels_file = 'data/THUCNews_labels.txt'
word2vec_path = "../word2vec/models/THUCNews_word2Vec/THUCNews_word2Vec_128.model"
max_steps = 100000 # 迭代次数
batch_size = 128
out_dir = "./models" # 模型ckpt和summaries输出的目录
train_dir = './data/train_data'
val_dir = './data/val_data'
train(train_dir=train_dir,
val_dir=val_dir,
labels_file=labels_file,
word2vec_path=word2vec_path,
batch_size=batch_size,
max_steps=max_steps,
log_step=50,
val_step=500,
snapshot=1000,
out_dir=out_dir)
if __name__=="__main__":
main()
5、测试过程
这里提供两种测试方法：
（1）：text_predict(files_list, labels_file, models_path, word2vec_path, batch_size)
该方法，能够直接测试待分类的中文文本
（2）：batch_predict(val_dir,labels_file,models_path,word2vec_path,batch_size)
该方法，用于批量测试，val_dir目录保存的是测试数据的npy文件，这些文件都是上面用word2vec词向量处理THUCNews数据文件。
#! /usr/bin/env python
# encoding: utf-8
import tensorflow as tf
import numpy as np
import os
from text_cnn import TextCNN
from utils import create_batch_data, create_word2vec, files_processing
import math
def text_predict(files_list, labels_file, models_path, word2vec_path, batch_size):
'''
预测...
:param val_dir: val数据目录
:param labels_file: labels文件目录
:param models_path: 模型文件
:param word2vec_path: 词向量模型文件
:param batch_size: batch size
:return:
'''
max_sentence_length = 300
embedding_dim = 128
filter_sizes = [3, 4, 5, 6]
num_filters = 200 # Number of filters per filter size
l2_reg_lambda = 0.0 # "L2 regularization lambda (default: 0.0)
print("Loading data...")
w2vModel = create_word2vec.load_wordVectors(word2vec_path)
labels_set = files_processing.read_txt(labels_file)
labels_nums = len(labels_set)
sample_num=len(files_list)
labels_list=[-1]
labels_list=labels_list*sample_num
with tf.Graph().as_default():
sess = tf.Session()
with sess.as_default():
cnn = TextCNN(sequence_length = max_sentence_length,
num_classes = labels_nums,
embedding_size = embedding_dim,
filter_sizes = filter_sizes,
num_filters = num_filters,
l2_reg_lambda = l2_reg_lambda)
# Initialize all variables
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver()
saver.restore(sess, models_path)
def pred_step(x_batch):
"""
predictions model on a dev set
"""
feed_dict = {
cnn.input_x: x_batch,
cnn.dropout_keep_prob: 1.0
}
pred = sess.run([cnn.predictions],feed_dict)
return pred
batchNum = int(math.ceil(1.0 * sample_num / batch_size))
for i in range(batchNum):
start = i * batch_size
end = min((i + 1) * batch_size, sample_num)
batch_files = files_list[start:end]
# 读取文件内容，字词分割
batch_content= files_processing.read_files_list_to_segment(batch_files,
max_sentence_length,
padding_token='<PAD>')
# [1]将字词转为索引矩阵,再映射为词向量
batch_indexMat = create_word2vec.word2indexMat(w2vModel, batch_content, max_sentence_length)
val_batch_data = create_word2vec.indexMat2vector_lookup(w2vModel, batch_indexMat)
# [2]直接将字词映射为词向量
# val_batch_data = create_word2vec.word2vector_lookup(w2vModel,batch_content)
pred=pred_step(val_batch_data)
pred=pred[0].tolist()
pred=files_processing.labels_decoding(pred,labels_set)
for k,file in enumerate(batch_files):
print("{}, pred:{}".format(file,pred[k]))
def batch_predict(val_dir,labels_file,models_path,word2vec_path,batch_size):
'''
预测...
:param val_dir: val数据目录
:param labels_file: labels文件目录
:param models_path: 模型文件
:param word2vec_path: 词向量模型文件
:param batch_size: batch size
:return:
'''
max_sentence_length = 300
embedding_dim = 128
filter_sizes = [3, 4, 5, 6]
num_filters = 200 # Number of filters per filter size
l2_reg_lambda = 0.0 # "L2 regularization lambda (default: 0.0)
print("Loading data...")
w2vModel = create_word2vec.load_wordVectors(word2vec_path)
labels_set = files_processing.read_txt(labels_file)
labels_nums = len(labels_set)
val_file_list = create_batch_data.get_file_list(file_dir=val_dir, postfix='*.npy')
val_batch = create_batch_data.get_data_batch(val_file_list, labels_nums=labels_nums, batch_size=batch_size,
shuffle=False, one_hot=True)
print("val data info *****************************")
val_nums = create_word2vec.info_npy(val_file_list)
print("labels_set info *****************************")
files_processing.info_labels_set(labels_set)
# Training
with tf.Graph().as_default():
sess = tf.Session()
with sess.as_default():
cnn = TextCNN(sequence_length = max_sentence_length,
num_classes = labels_nums,
embedding_size = embedding_dim,
filter_sizes = filter_sizes,
num_filters = num_filters,
l2_reg_lambda = l2_reg_lambda)
# Initialize all variables
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver()
saver.restore(sess, models_path)
def dev_step(x_batch, y_batch):
"""
Evaluates model on a dev set
"""
feed_dict = {
cnn.input_x: x_batch,
cnn.input_y: y_batch,
cnn.dropout_keep_prob: 1.0
}
loss, accuracy = sess.run(
[cnn.loss, cnn.accuracy],
feed_dict)
return loss, accuracy
val_losses = []
val_accs = []
for k in range(int(val_nums/batch_size)):
# for k in range(int(10)):
val_batch_data, val_batch_label = create_batch_data.get_next_batch(val_batch)
val_batch_data = create_word2vec.indexMat2vector_lookup(w2vModel, val_batch_data)
val_loss, val_acc=dev_step(val_batch_data, val_batch_label)
val_losses.append(val_loss)
val_accs.append(val_acc)
print("--------Evaluation:step {}, loss {:g}, acc {:g}".format(k, val_loss, val_acc))
mean_loss = np.array(val_losses, dtype=np.float32).mean()
mean_acc = np.array(val_accs, dtype=np.float32).mean()
print("--------Evaluation:step {}, mean loss {:g}, mean acc {:g}".format(k, mean_loss, mean_acc))
def main():
# Data preprocess
labels_file = 'data/THUCNews_labels.txt'
# word2vec_path = 'word2vec/THUCNews_word2vec300.model'
word2vec_path = "../word2vec/models/THUCNews_word2Vec/THUCNews_word2Vec_128.model"
models_path='models/checkpoints/model-30000'
batch_size = 128
val_dir = './data/val_data'
batch_predict(val_dir=val_dir,
labels_file=labels_file,
models_path=models_path,
word2vec_path=word2vec_path,
batch_size=batch_size)
test_path='/home/ubuntu/project/tfTest/THUCNews/my_test'
files_list = files_processing.get_files_list(test_path,postfix='*.txt')
text_predict(files_list, labels_file, models_path, word2vec_path, batch_size)
if __name__=="__main__":
main()