源代码链接（github地址）
https://github.com/dennybritz/cnn-text-classification-tf
my https://github.com/tddfly/cnn-text-classification-tf
参考博文： https://blog.csdn.net/github_38414650/article/details/74019595
数据集：https://github.com/cystanford/text_classification
包含训练集（四种类别）、测试集、停用词表三个文件夹
四个文件
data_helpers.py 数据预处理
train.py神经网络的训练过程
text_cnn.py 卷积神经网络结构
eval.py预测、评估
data_helpers.py文件
import numpy as np
import re
import os
import jieba
# 数据预处理
def clean_str(string):
"""
Tokenization/string cleaning for all datasets except for SST.
Original taken from https://github.com/yoonkim/CNN_sentence/blob/master/process_data.py
"""
string = re.sub(r"[^A-Za-z0-9(),!?\'\`]", " ", string) # 匹配所有大小写字母、数字、括号、逗号、感叹号、问号、引号等，不在这些字符之类的都用空格替代
#string = re.sub(r"[A-Za-z0-9(),!?\'\`]", " ", string) 注意这个去掉^ 以后，就是将所有中括号内的替换成空格
string = re.sub(r"\'s", " \'s", string) # 将“'s”用“ 's”代替，即family's变成family 's
string = re.sub(r"\'ve", " \'ve", string)
string = re.sub(r"n\'t", " n\'t", string) #将isn't变成 is n't
string = re.sub(r"\'re", " \'re", string) #将you're变成you 're
string = re.sub(r"\'d", " \'d", string) #将i'd变成 i 'd
string = re.sub(r"\'ll", " \'ll", string)
string = re.sub(r",", " , ", string)
string = re.sub(r"!", " ! ", string)
string = re.sub(r"\(", " \( ", string)
string = re.sub(r"\)", " \) ", string)
string = re.sub(r"\?", " \? ", string)
string = re.sub(r"\s{2,}", " ", string) # s是空格，将2个或者2个以上的空格用一个空格代替
return string.strip().lower()
def load_data_and_labels(positive_data_file, negative_data_file):
"""
Loads MR polarity data from files, splits the data into words and generates labels.
Returns split sentences and labels.
"""
# Load data from files
positive_examples = list(open(positive_data_file, "r", encoding='utf-8').readlines())
positive_examples = [s.strip() for s in positive_examples] # 去掉换行符
negative_examples = list(open(negative_data_file, "r", encoding='utf-8').readlines())
negative_examples = [s.strip() for s in negative_examples]
# Split by words
x_text = positive_examples + negative_examples # 将两个列表合并类似extend，将结果放到一个新的列表中；extend是在原有列表中相加
x_text = [clean_str(sent) for sent in x_text]
# Generate labels
positive_labels = [[0, 1] for _ in positive_examples]
negative_labels = [[1, 0] for _ in negative_examples]
y = np.concatenate([positive_labels, negative_labels], 0) # np.concatenate 数组拼接，传入的参数必须是一个多个数组的元组或者列表
return [x_text, y]
# batches = data_helpers.batch_iter(list(zip(x_train, y_train)), FLAGS.batch_size=64, FLAGS.num_epochs=200)
def batch_iter(data, batch_size, num_epochs, shuffle=True): # 每一轮 获得一个batch的数据
"""
定义一个函数，输出batch样本，参数为data（包括feature和label），batchsize，epoch
"""
data = np.array(data) # [(text0,label0),(test1,label1)..]
# 注意：data是个列表，因为里面的元素的数据类型是 一致的，所以用数组np.array存储，节省内存
# type(data)返回data的数据类型，data.dtype返回数组中内容的数据类型。
data_size = len(data)
num_batches_per_epoch = int((len(data)-1)/batch_size) + 1 # 每次迭代训练所有数据，分成多少个batch
for epoch in range(num_epochs): # 在每一轮迭代过程中，都打乱数据
# Shuffle the data at each epoch
if shuffle:
shuffle_indices = np.random.permutation(np.arange(data_size)) #打乱的索引
# 注：shuffle 与 permutation的区别：都是对原来的数组进行重新洗牌，shuffle直接在原来的数组上进行操作，改变原来的元素顺序，无返回值；
# permutation不直接在原来的数组上进行操作，而是返回一个新的打乱顺序的数组，并不改变原来的数组。
shuffled_data = data[shuffle_indices] # 数据打乱
else:
shuffled_data = data
for batch_num in range(num_batches_per_epoch): # 对于每个batch的数据，获得batch内的起始与终止的位置
start_index = batch_num * batch_size
end_index = min((batch_num + 1) * batch_size, data_size)
yield shuffled_data[start_index:end_index]
# yield，在for循环执行时，每次返回一个batch的data，占用的内存为常数
#============分隔线==================
# 返回的数据格式，x_corpus为列表，里面每个元素是一个文本字符串
# y_label是个列表，对应文本的标签，ont-hot表示，如[ [1,0,0,0],[0,1,0,0]]里面是2个标签
def load_data_and_labels_3_27(corpus_path,stop_list_Path):
x_corpus = []
y_label = []
cate_dir = os.listdir(corpus_path) # 获取子类别目录
# [女性、体育、文学、校园]
for cate in cate_dir:
if cate == '女性':
label = [1,0,0,0]
elif cate =='体育':
label = [0, 1, 0, 0]
elif cate =='文学':
label = [0, 0, 1, 0]
else:
label = [0,0,0,1]
cate_complete_dir = corpus_path+'\\'+cate+"\\" # 获取子类别的完整路径
file_dir = os.listdir(cate_complete_dir)#获取每个类别下的文件
for file in file_dir:
file_complete_dir = cate_complete_dir+file # 获取每个类别下的文件的完整路径
content = readfile(file_complete_dir) # 返回一个文本
# 对文本进行处理,删除换行以及多余空格
content = content.replace("\n",'').strip()
content_seg = jieba.cut(content)
#创建停用词表
stopwords = stopwordsList(stop_list_Path)
outstr =''
for word in content_seg:
if word not in stopwords:
if word !='\t':
outstr+=word
outstr+=" "
text = ' '.join(outstr)
x_corpus.append(text)
y_label.append(label)
return [x_corpus,y_label]
def stopwordsList(stop_list_Path):
f = open(stop_list_Path,'r',encoding='utf-8')
stopwords = [line.strip() for line in f.readlines()]
return stopwords
def readfile(filepath):
f = open(filepath,'r',encoding='gb2312',errors='ignore')
content = f.read()
# read()返回的是字符串,读全文本的内容。readline()返回一行，是字符串类型。readlines()读取所有行，保存在列表中
f.close()
return content
# 这里返回整个文本，以便后续进行分词
text_cnn.py文件
import tensorflow as tf
import numpy as np
# 网络结构设计
# 一个embedding layer+一个convolution layer（Relu）+一个maxpooling层+softmax
class TextCNN(object):
"""
A CNN for text classification.
Uses an embedding layer, followed by a convolutional, max-pooling and softmax layer.
"""
def __init__(
self, sequence_length, num_classes, vocab_size,
embedding_size, filter_sizes, num_filters, l2_reg_lambda=0.0):
# sequence_length 最长句子长度，不足长度就填充
# num_classes：分类的类别数
# vocab_size:字典词汇大小
# embedding_size：词向量维度
# filter_sizes：卷积核尺寸，简写为3,4,5。实际上为3*embedding_size
# num_filters：每种尺寸的卷积核的个数
# l2_reg_lambda=0.0 ：L2正则化参数
# Placeholders for input, output and dropout
# 句子矩阵，长为句子数量（自适应、样本个数），宽为sequence_length（句子固定宽度）
self.input_x = tf.placeholder(tf.int32, [None, sequence_length], name="input_x") # 输入数据 维度为batch_size*sequence_length.一段文本是一个样本
# 存储对应分类结果 长度自适应、宽度为num_classes
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
with tf.device('/cpu:0'), tf.name_scope("embedding"):
# self.W可以理解为词向量词典，存储vocab_size个大小为embedding_size的词向量，随机初始化为-1带1之间的值
self.W = tf.Variable(
tf.random_uniform([vocab_size, embedding_size], -1.0, 1.0),name="W") # 随机初始化embedding矩阵
# input_x是词的id序列
self.embedded_chars = tf.nn.embedding_lookup(self.W, self.input_x) # 查找一个张量里面 索引对应的元素
# 第一个参数为一个张量或者索引、第二个参数为索引
# embedded_chars是输入input_x对应的词向量表示，维度为[句子数量,sequence_length,embedding_size]
self.embedded_chars_expanded = tf.expand_dims(self.embedded_chars, -1)
# tf.expand_dims(input,dim，name=None) 增加维度,主要是卷积2d输入是四维，这里是将词向量扩充一个维度，
# 维度变为[句子数量, sequence_length, embedding_size, 1]，方便卷积
# Create a convolution + maxpool layer for each filter size
pooled_outputs = [] # 五维向量，对于每种尺寸的卷积核，得到的结果是四维向量。
for i, filter_size in enumerate(filter_sizes): # 迭代索引及对应的尺寸
with tf.name_scope("conv-maxpool-%s" % filter_size): # 对于每种尺寸的卷积核，创建一个命名空间
# 输入：batch_size（句子数） * sequence_length （句子定长）* embedding_size （对应宽度）*1（输入通道数）
# 卷积尺寸 ： filter_size（卷积核高度），embedding_size（卷积核宽度、词向量维度），1（图像通道数）， num_filters（输出通道数）
# 卷积输出 ：batch_size * （sequence_length-filter_size+1） * 1 * num_filters
# 池化尺寸 ： （sequence_length-filter_size+1） * 1
# 池化输出 ：batch_size * 1 * 1 * num_filters 三维矩阵
# Convolution Layer
# num_filters个（输出通道数）大小为filter_size*embedding_size的卷积核，输入通道数为1
filter_shape = [filter_size, embedding_size, 1, num_filters]
# 卷积核宽、高、输入通道数、输出通道数—深度（卷积核的个数）
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_expanded, # 输入、卷积参数、步长、填充方式-不填0
W,
strides=[1, 1, 1, 1],
padding="VALID",# VALID窄卷积，SAME为等长卷积
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 为 batch_size * 1 * num_filters。其中1个pool值为一个列表,比如说batch_size=3，num_filters = 2
# pooled:[ [[1],[2]], [[3],[4]], [[5],[6]] ] # 最里面的元素表示一个filter得到的一个特征值
# [[3,4,5],[1,2,0]],每个元素为一个batch的num_filters个pool值
pooled_outputs.append(pooled) # 每个样本pooled_outputs中含有num_filters个数量的特征
# pooled_outputs为五维矩阵[ ]
#[len(filter_sizes), batch, height, width, channels = 1] # 对width，即词的角度串联
# Combine all the pooled features
num_filters_total = num_filters * len(filter_sizes)
self.h_pool = tf.concat(pooled_outputs, 3)
# 将不同核尺寸的对应特征进行拼接，比如说[[3]]与[[4]]拼接后就是[[34]]
self.h_pool_flat = tf.reshape(self.h_pool, [-1, num_filters_total])
# 扁平化，压成一维，维度为batch_size * 卷积核总数
# Add dropout
with tf.name_scope("dropout"): # dropout层，对池化后的结果做dropout
self.h_drop = tf.nn.dropout(self.h_pool_flat, self.dropout_keep_prob)
# Final (unnormalized) 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") # matmul(x, weights) + biases.
self.predictions = tf.argmax(self.scores, 1, name="predictions") # 返回索引
# 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)
#losses = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=self.scores,labels=tf.argmax(self.input_y))
# 交叉熵
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")
train.py文件
#! /usr/bin/env python
# 网络训练
import tensorflow as tf
import numpy as np
import os
import time
import datetime
import data_helpers
from text_cnn import TextCNN
from tensorflow.contrib import learn
# 命令行参数
# ==================================================
# Vocabulary Size: 3658
# Train/Dev split: 2976/330
# Data loading params
tf.flags.DEFINE_float("dev_sample_percentage", 0.1, "Percentage of the training data to use for validation")
tf.flags.DEFINE_string("positive_data_file", "./data/rt-polaritydata/rt-polarity.pos", "Data source for the positive data.")
tf.flags.DEFINE_string("negative_data_file", "./data/rt-polaritydata/rt-polarity.neg", "Data source for the negative data.")
tf.flags.DEFINE_string("corpus_train_path",r'I:\代码\tensorflow\cnn-text-classification-tf-master\cnn-text-classification-tf-master\data\text_classification_data\train',"训练集路径")
tf.flags.DEFINE_string("stop_list_Path",r'I:\代码\tensorflow\cnn-text-classification-tf-master\cnn-text-classification-tf-master\data\text_classification_data\stop\stopword.txt','停用词路径')
# Model Hyperparameters
tf.flags.DEFINE_integer("embedding_dim", 128, "Dimensionality of character embedding (default: 128)")
tf.flags.DEFINE_string("filter_sizes", "3,4,5", "Comma-separated filter sizes (default: '3,4,5')")
tf.flags.DEFINE_integer("num_filters", 128, "Number of filters per filter size (default: 128)")
tf.flags.DEFINE_float("dropout_keep_prob", 0.5, "Dropout keep probability (default: 0.5)")
tf.flags.DEFINE_float("l2_reg_lambda", 0.0, "L2 regularization lambda (default: 0.0)")
# Training parameters
tf.flags.DEFINE_integer("batch_size", 64, "Batch Size (default: 64)")
tf.flags.DEFINE_integer("num_epochs", 200, "Number of training epochs (default: 200)")
tf.flags.DEFINE_integer("evaluate_every", 100, "Evaluate model on dev set after this many steps (default: 100)")
# 每100个batch后在验证集上评估一次
tf.flags.DEFINE_integer("checkpoint_every", 100, "Save model after this many steps (default: 100)")
# 每100个batch后次保存checkpoint，即模型参数
tf.flags.DEFINE_integer("num_checkpoints", 5, "Number of checkpoints to store (default: 5)")
# Misc Parameters
tf.flags.DEFINE_boolean("allow_soft_placement", True, "Allow device soft device placement")
tf.flags.DEFINE_boolean("log_device_placement", False, "Log placement of ops on devices")
FLAGS = tf.flags.FLAGS
# FLAGS._parse_flags()
#FLAGS.flag_values_dict()
# print("\nParameters:")
# for attr, value in sorted(FLAGS.__flags.items()):
# print("{}={}".format(attr.upper(), value))
# print("")
# 数据预处理，包括：构建词典，将文本变成词id序列，随机打乱数据、划分数据集（训练集、验证集、可用交叉验证)
def preprocess():
# Data Preparation
# ==================================================
# Load data
print("Loading data...")
x_text, y = data_helpers.load_data_and_labels_3_27(FLAGS.corpus_train_path,FLAGS.stop_list_Path)
# x_text 为训练集，以列表的形式存储，每一个元素为字符串，处理过的文本
# y为每个文本对应的标签，以列表存储，每个元素为一个列表（one_hot向量表示）
# Build vocabulary 词典的id是从1开始的，0是留给那些未出现的词以及作填充长度用的
max_document_length = max([len(x.split(" ")) for x in x_text]) # 统计每个文本的长度，并获得文本最大的长度
vocab_processor = learn.preprocessing.VocabularyProcessor(max_document_length)
# 构建词典，文本中每个词分配个id
x = np.array(list(vocab_processor.fit_transform(x_text)))
# 将文本表示成词id的形式。 x为numpy.ndarray 类似[[1 2 3 0] [4 5 0 0]]这种形式
# Randomly shuffle data
np.random.seed(10)
shuffle_indices = np.random.permutation(np.arange(len(y)))
x_shuffled = x[shuffle_indices]
y_shuffled = np.array(y)[shuffle_indices]
# Split train/test set
# TODO: This is very crude, should use cross-validation
# 这里只是简单的划分，对于小数据来说，应该使用交叉验证
dev_sample_index = -1 * int(FLAGS.dev_sample_percentage * float(len(y)))
x_train, x_dev = x_shuffled[:dev_sample_index], x_shuffled[dev_sample_index:]
y_train, y_dev = y_shuffled[:dev_sample_index], y_shuffled[dev_sample_index:]
del x, y, x_shuffled, y_shuffled
# 获取词典的大小以及训练集、验证集的比例
print("Vocabulary Size: {:d}".format(len(vocab_processor.vocabulary_)))
print("Train/Dev split: {:d}/{:d}".format(len(y_train), len(y_dev)))
return x_train, y_train, vocab_processor, x_dev, y_dev
def train(x_train, y_train, vocab_processor, x_dev, y_dev):
# Training
# ==================================================
with tf.Graph().as_default():
# tf.ConfigProto用在创建session的时候，用来对session进行参数配置
session_conf = tf.ConfigProto(
allow_soft_placement=FLAGS.allow_soft_placement, # 记录设备指派情况
log_device_placement=FLAGS.log_device_placement) # 自动选择运行设备
sess = tf.Session(config=session_conf)
with sess.as_default():
cnn = TextCNN(
sequence_length=x_train.shape[1],
num_classes=y_train.shape[1],
vocab_size=len(vocab_processor.vocabulary_),
embedding_size=FLAGS.embedding_dim,
filter_sizes=list(map(int, FLAGS.filter_sizes.split(","))),
num_filters=FLAGS.num_filters,
l2_reg_lambda=FLAGS.l2_reg_lambda)
# Define Training procedure
global_step = tf.Variable(0, name="global_step", trainable=False)
# 初始化为0，每次自动加1
optimizer = tf.train.AdamOptimizer(1e-3)
# 计算梯度，返回元组列表[ (gradient,variable)..]
grads_and_vars = optimizer.compute_gradients(cnn.loss)
# 根据梯度更新变量
train_op = optimizer.apply_gradients(grads_and_vars, global_step=global_step)
# 注意：每次更新参数都会加1次，这里global_step=global_step这个操作会自动加1.
#train_op = optimizer.minimize(cnn.loss，global_step=global_step) 上面两个步骤等价于这一步，分开方便对梯度进行约束
# Keep track of gradient values and sparsity (optional) 可视化
# 查看一个张量在训练过程中值的分布情况时，可通过tf.summary.histogram()将其分布情况以直方图的形式在TensorBoard直方图仪表板上显示
grad_summaries = []
for g, v in grads_and_vars: # [ (gradient,variable)..] g:梯度，v:变量
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
timestamp = str(int(time.time()))
out_dir = os.path.abspath(os.path.join(os.path.curdir, "runs", timestamp))
print("Writing to {}\n".format(out_dir))
# tf.summary()的各类方法，能够保存训练过程以及参数分布图并在tensorboard显示。
# 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=FLAGS.num_checkpoints)
# Write vocabulary
vocab_processor.save(os.path.join(out_dir, "vocab"))
# Initialize all variables
sess.run(tf.global_variables_initializer())
# 定义了一个函数，输入为1个batch
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: FLAGS.dropout_keep_prob
}
_, step, summaries, loss, accuracy = sess.run(
[train_op, global_step, train_summary_op, cnn.loss, cnn.accuracy],
feed_dict)
# #梯度更新（更新模型），步骤加一，存储数据，计算一个batch的损失，计算一个batch的准确率
time_str = datetime.datetime.now().isoformat()
print("{}: step {}, loss {:g}, acc {:g}".format(time_str, step, loss, accuracy))
# 注意：这里的step是第几次更新参数，即一个batch更新一次参数，并不是num_epochs次数
# step总次数的计算过程为 每一轮含有的batch数目（文本总数量/一个batch含有的文本的数量）*总轮数（200）
# (2976/64)=46.40 = 47, 47 * 200 =9400
train_summary_writer.add_summary(summaries, step)
# 定义了一个函数，用于验证集，输入为一个batch
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)
time_str = datetime.datetime.now().isoformat()
print("{}: step {}, loss {:g}, acc {:g}".format(time_str, step, loss, accuracy))
if writer:
writer.add_summary(summaries, step)
# Generate batches（生成器），得到一个generator，每一次返回一个batch，没有构成list[batch1,batch2,batch3,...]
batches = data_helpers.batch_iter(
list(zip(x_train, y_train)), FLAGS.batch_size, FLAGS.num_epochs)
# 这里控制迭代次数
# Training loop. For each batch...
for batch in batches:
x_batch, y_batch = zip(*batch)
train_step(x_batch, y_batch)
current_step = tf.train.global_step(sess, global_step)
if current_step % FLAGS.evaluate_every == 0:
print("\nEvaluation:")
dev_step(x_dev, y_dev, writer=dev_summary_writer)
print("")
if current_step % FLAGS.checkpoint_every == 0:
path = saver.save(sess, checkpoint_prefix, global_step=current_step)
print("Saved model checkpoint to {}\n".format(path))
def main(argv=None):
x_train, y_train, vocab_processor, x_dev, y_dev = preprocess()
train(x_train, y_train, vocab_processor, x_dev, y_dev)
if __name__ == '__main__':
tf.app.run()
eval.py文件
#! /usr/bin/env python
# 预测与评估
import tensorflow as tf
import numpy as np
import os
import time
import datetime
import data_helpers
from text_cnn import TextCNN
from tensorflow.contrib import learn
import csv
# Parameters
# ==================================================
# Data Parameters
tf.flags.DEFINE_string("positive_data_file", "./data/rt-polaritydata/rt-polarity.pos", "Data source for the positive data.")
tf.flags.DEFINE_string("negative_data_file", "./data/rt-polaritydata/rt-polarity.neg", "Data source for the negative data.")
tf.flags.DEFINE_string("Test_corpus",r"I:\代码\tensorflow\cnn-text-classification-tf-master\cnn-text-classification-tf-master\data\text_classification_data\test","测试语料集")
tf.flags.DEFINE_string("stop_list_Path",r'I:\代码\tensorflow\文本分类\分类数据\text_classification\stop\stopword.txt','停用词路径')
# Eval Parameters
tf.flags.DEFINE_integer("batch_size", 64, "Batch Size (default: 64)")
tf.flags.DEFINE_string("checkpoint_dir", r"I:\代码\tensorflow\cnn-text-classification-tf-master\cnn-text-classification-tf-master\runs\1553692717\checkpoints", "Checkpoint directory from training run")
tf.flags.DEFINE_boolean("eval_train", True, "Evaluate on all training data")
# Misc Parameters
tf.flags.DEFINE_boolean("allow_soft_placement", True, "Allow device soft device placement")
tf.flags.DEFINE_boolean("log_device_placement", False, "Log placement of ops on devices")
FLAGS = tf.flags.FLAGS
#FLAGS._parse_flags()
# FLAGS.flag_values_dict()
# print("\nParameters:")
# for attr, value in sorted(FLAGS.__flags.items()):
# print("{}={}".format(attr.upper(), value))
# print("")
# CHANGE THIS: Load data. Load your own data here
# if里面放的自己的测试集 eval_train = True时
if FLAGS.eval_train:
#x_raw, y_test = data_helpers.load_data_and_labels(FLAGS.positive_data_file, FLAGS.negative_data_file)
x_raw, y_test = data_helpers.load_data_and_labels_3_27(FLAGS.Test_corpus,FLAGS.stop_list_Path)
y_test = np.argmax(y_test, axis=1) # 类别变成0,1,2,3。分别对应女性、体育、文学、校园、
else:
x_raw = ["a masterpiece four years in the making", "everything is off."]
y_test = [1, 0]
#y_test = [2, 0]
# Map data into vocabulary
vocab_path = os.path.join(FLAGS.checkpoint_dir, "..", "vocab") # ..表示上一级
vocab_processor = learn.preprocessing.VocabularyProcessor.restore(vocab_path)
x_test = np.array(list(vocab_processor.transform(x_raw)))
print("\nEvaluating...\n")
# Evaluation
# ==================================================
checkpoint_file = tf.train.latest_checkpoint(FLAGS.checkpoint_dir)
graph = tf.Graph()
with graph.as_default():
session_conf = tf.ConfigProto(
allow_soft_placement=FLAGS.allow_soft_placement,
log_device_placement=FLAGS.log_device_placement)
sess = tf.Session(config=session_conf)
with sess.as_default():
# Load the saved meta graph and restore variables
saver = tf.train.import_meta_graph("{}.meta".format(checkpoint_file))
saver.restore(sess, checkpoint_file)
# Get the placeholders from the graph by name
input_x = graph.get_operation_by_name("input_x").outputs[0]
# input_y = graph.get_operation_by_name("input_y").outputs[0]
dropout_keep_prob = graph.get_operation_by_name("dropout_keep_prob").outputs[0]
# Tensors we want to evaluate
predictions = graph.get_operation_by_name("output/predictions").outputs[0]
# Generate batches for one epoch
batches = data_helpers.batch_iter(list(x_test), FLAGS.batch_size, 1, shuffle=False)
# Collect the predictions here
all_predictions = []
for x_test_batch in batches:
batch_predictions = sess.run(predictions, {input_x: x_test_batch, dropout_keep_prob: 1.0})
all_predictions = np.concatenate([all_predictions, batch_predictions])
# Print accuracy if y_test is defined
if y_test is not None:
correct_predictions = float(sum(all_predictions == y_test))
print("Total number of test examples: {}".format(len(y_test)))
print("Accuracy: {:g}".format(correct_predictions/float(len(y_test))))
# Save the evaluation to a csv
# 待预测的已经分词的文本，预测的类别，正确的类别，bo（预测正确返回True，否则返回False)
bo = []
for i in range(len(y_test)):
if all_predictions[i]==y_test[i]:
bo.append("True")
#bo.append(" ")
else :
bo.append(False)
#predictions_human_readable = np.column_stack((np.array(x_raw), all_predictions))
predictions_human_readable = np.column_stack((np.array(x_raw), all_predictions,y_test,bo))
out_path = os.path.join(FLAGS.checkpoint_dir, "..", "prediction.csv")
print("Saving evaluation to {0}".format(out_path))
with open(out_path, 'w',encoding='utf-8') as f:
csv.writer(f).writerows(predictions_human_readable)