【文本分类-03】charCNN
目录
- 大纲概述
- 数据集合
- 数据处理
- 预训练word2vec模型
一、大纲概述
文本分类这个系列将会有8篇左右文章，从github直接下载代码，从百度云下载训练数据，在pycharm上导入即可使用，包括基于word2vec预训练的文本分类，与及基于近几年的预训练模型（ELMo，BERT等）的文本分类。总共有以下系列：
charCNN 模型结构
在charCNN论文Character-level Convolutional Networks for Text Classification中提出了6层卷积层 + 3层全连接层的结构，具体结构如下图：
针对不同大小的数据集提出了两种结构参数：
1）卷积层
2）全连接层
二、数据集合
数据集为IMDB 电影影评，总共有三个数据文件，在/data/rawData目录下，包括unlabeledTrainData.tsv，labeledTrainData.tsv，testData.tsv。在进行文本分类时需要有标签的数据（labeledTrainData），但是在训练word2vec词向量模型（无监督学习）时可以将无标签的数据一起用上。
训练数据地址：链接：https://pan.baidu.com/s/1-XEwx1ai8kkGsMagIFKX_g 提取码：rtz8
三、主要代码
3.1 配置训练参数：parameter_config.py
1 # Author:yifan
2 # 1、参数配置
3 class TrainingConfig(object):
4 epoches = 6
5 evaluateEvery = 100
6 checkpointEvery = 100
7 learningRate = 0.001
8
9 class ModelConfig(object):
10 # 该列表中子列表的三个元素分别:卷积核的数量，卷积核的高度，池化的尺寸
11 convLayers = [[256, 7, 4],
12 [256, 7, 4],
13 [256, 3, 4]]
14 fcLayers = [512]
15 dropoutKeepProb = 0.5
16 epsilon = 1e-3 # BN层中防止分母为0而加入的极小值
17 decay = 0.999 # BN层中用来计算滑动平均的值
18
19 class Config(object):
20 # 我们使用论文中提出的69个字符来表征输入数据
21 alphabet = "abcdefghijklmnopqrstuvwxyz0123456789-,;.!?:'\"/\\|_@#$%^&*~`+-=<>()[]{}"
22 # alphabet = "abcdefghijklmnopqrstuvwxyz0123456789"
23 sequenceLength = 1014 # 字符表示的序列长度
24 batchSize = 128
25 rate = 0.8 # 训练集的比例
26 dataSource = "../data/preProcess/labeledCharTrain.csv"
27 training = TrainingConfig()
28 model = ModelConfig()
29 config = Config()
3.2 获取训练数据：get_train_data.py
1） 加载数据，将所有的句子分割成字符表示
2） 构建字符-索引映射表，并保存成json的数据格式，方便在inference阶段加载使用
3）将字符转换成one-hot的嵌入形式，作为模型中embedding层的初始化值。
4） 将数据集分割成训练集和验证集
1 # Author:yifan
2 import json
3 import pandas as pd
4 import numpy as np
5 import parameter_config
6 # 2、 训练数据生成
7 # 1） 加载数据，将所有的句子分割成字符表示
8 # 2） 构建字符-索引映射表，并保存成json的数据格式，方便在inference阶段加载使用
9 # 3）将字符转换成one-hot的嵌入形式，作为模型中embedding层的初始化值。
10 # 4） 将数据集分割成训练集和验证集
11 # 数据预处理的类，生成训练集和测试集
12 class Dataset(object):
13 def __init__(self, config): #config.的部分都是从parameter.config.py中带出
14 self._dataSource = config.dataSource #路径
15 self._sequenceLength = config.sequenceLength # 字符表示的序列长度
16 self._rate = config.rate # 训练集的比例
17 self._alphabet = config.alphabet
18 self.trainReviews = []
19 self.trainLabels = []
20 self.evalReviews = []
21 self.evalLabels = []
22 self.charEmbedding = None
23 self._charToIndex = {}
24 self._indexToChar = {}
25
26 def _readData(self, filePath):
27 """
28 从csv文件中读取数据集
29 """
30 df = pd.read_csv(filePath)
31 labels = df["sentiment"].tolist()
32 review = df["review"].tolist()
33 reviews = [[char for char in line if char != " "] for line in review]
34 return reviews, labels
35
36 def _reviewProcess(self, review, sequenceLength, charToIndex):
37 """
38 将数据集中的每条评论用index表示
39 wordToIndex中“pad”对应的index为0
40 """
41 reviewVec = np.zeros((sequenceLength))
42 sequenceLen = sequenceLength
43 # 判断当前的序列是否小于定义的固定序列长度
44 if len(review) < sequenceLength:
45 sequenceLen = len(review)
46 for i in range(sequenceLen):
47 if review[i] in charToIndex:
48 reviewVec[i] = charToIndex[review[i]]
49 else:
50 reviewVec[i] = charToIndex["UNK"]
51 return reviewVec
52
53 def _genTrainEvalData(self, x, y, rate):
54 """
55 生成训练集和验证集,最后生成的一行表示一个句子，包含单词数为sequenceLength = 1014。每个单词用index表示
56 """
57 reviews = []
58 labels = []
59 # 遍历所有的文本，将文本中的词转换成index表示
60 for i in range(len(x)):
61 reviewVec = self._reviewProcess(x[i], self._sequenceLength, self._charToIndex)
62 reviews.append(reviewVec)
63 labels.append([y[i]])
64 trainIndex = int(len(x) * rate)
65 trainReviews = np.asarray(reviews[:trainIndex], dtype="int64")
66 trainLabels = np.array(labels[:trainIndex], dtype="float32")
67 evalReviews = np.asarray(reviews[trainIndex:], dtype="int64")
68 evalLabels = np.array(labels[trainIndex:], dtype="float32")
69 return trainReviews, trainLabels, evalReviews, evalLabels
70
71 def _getCharEmbedding(self, chars):
72 """
73 按照one的形式将字符映射成向量
74 字母pad表示【0，0，0...】,UNK是【1，0，0...】，a表示【0，1，0...】等等
75 """
76 alphabet = ["UNK"] + [char for char in self._alphabet]
77 vocab = ["pad"] + alphabet
78 charEmbedding = []
79 charEmbedding.append(np.zeros(len(alphabet), dtype="float32"))
80
81 for i, alpha in enumerate(alphabet):
82 onehot = np.zeros(len(alphabet), dtype="float32")
83 # 生成每个字符对应的向量
84 onehot[i] = 1
85 # 生成字符嵌入的向量矩阵
86 charEmbedding.append(onehot)
87 return vocab, np.array(charEmbedding)
88
89 def _genVocabulary(self, reviews):
90 """
91 生成字符向量和字符-索引映射字典
92 """
93 chars = [char for char in self._alphabet]
94 vocab, charEmbedding = self._getCharEmbedding(chars)
95 self.charEmbedding = charEmbedding
96
97 self._charToIndex = dict(zip(vocab, list(range(len(vocab)))))
98 self._indexToChar = dict(zip(list(range(len(vocab))), vocab))
99
100 # 将词汇-索引映射表保存为json数据，之后做inference时直接加载来处理数据
101 with open("../data/charJson/charToIndex.json", "w", encoding="utf-8") as f:
102 json.dump(self._charToIndex, f)
103 with open("../data/charJson/indexToChar.json", "w", encoding="utf-8") as f:
104 json.dump(self._indexToChar, f)
105
106 def dataGen(self):
107 """
108 初始化训练集和验证集
109 """
110 # 初始化数据集
111 # reviews: [['"', 'w', 'i', 't', 'h', 'a', 'l', 'l', 't', 'h', 'i', 's', 's', 't', 'u', 'f', 'f
112 #labels:[1, ...
113 reviews, labels = self._readData(self._dataSource)
114 # 初始化词汇-索引映射表和词向量矩阵
115 self._genVocabulary(reviews)
116 # 初始化训练集和测试集 训练集20000，测试集5000
117 trainReviews, trainLabels, evalReviews, evalLabels = self._genTrainEvalData(reviews, labels, self._rate)
118 self.trainReviews = trainReviews
119 self.trainLabels = trainLabels
120 self.evalReviews = evalReviews
121 self.evalLabels = evalLabels
122 # print(trainReviews)
123 # print("++++")
124 # print(trainLabels)
125 # print(len(trainReviews[0]))
126 # print(len(trainReviews[2]))
127 # print(len(evalLabels))
128 #test
129 # config =parameter_config.Config()
130 # data = Dataset(config)
131 # data.dataGen()
3.3 模型构建：mode_structure.py
1 # Author:yifan
2 import tensorflow as tf
3 import math
4 import parameter_config
5
6 # 构建模型 3 textCNN 模型
7 # 定义char-CNN分类器
8 class CharCNN(object):
9 """
10 char-CNN用于文本分类
11 在charCNN 模型中我们引入了BN层，但是效果并不明显，甚至存在一些收敛问题，待之后去探讨。
12 """
13 def __init__(self, config, charEmbedding):
14 # placeholders for input, output and dropuot
15 self.inputX = tf.placeholder(tf.int32, [None, config.sequenceLength], name="inputX")
16 self.inputY = tf.placeholder(tf.float32, [None, 1], name="inputY")
17 self.dropoutKeepProb = tf.placeholder(tf.float32, name="dropoutKeepProb")
18 self.isTraining = tf.placeholder(tf.bool, name="isTraining")
19 self.epsilon = config.model.epsilon
20 self.decay = config.model.decay
21
22 # 字符嵌入
23 with tf.name_scope("embedding"):
24 # 利用one-hot的字符向量作为初始化词嵌入矩阵
25 self.W = tf.Variable(tf.cast(charEmbedding, dtype=tf.float32, name="charEmbedding"), name="W")
26 # 获得字符嵌入
27 self.embededChars = tf.nn.embedding_lookup(self.W, self.inputX)
28 # 添加一个通道维度
29 self.embededCharsExpand = tf.expand_dims(self.embededChars, -1)
30
31 for i, cl in enumerate(config.model.convLayers):
32 print("开始第" + str(i + 1) + "卷积层的处理")
33 # 利用命名空间name_scope来实现变量名复用
34 with tf.name_scope("convLayer-%s" % (i + 1)):
35 # 获取字符的向量长度
36 filterWidth = self.embededCharsExpand.get_shape()[2].value
37 # filterShape = [height, width, in_channels, out_channels]
38 filterShape = [cl[1], filterWidth, 1, cl[0]]
39 stdv = 1 / math.sqrt(cl[0] * cl[1])
40
41 # 初始化w和b的值
42 wConv = tf.Variable(tf.random_uniform(filterShape, minval=-stdv, maxval=stdv),
43 dtype='float32', name='w')
44 bConv = tf.Variable(tf.random_uniform(shape=[cl[0]], minval=-stdv, maxval=stdv), name='b')
45
46 # w_conv = tf.Variable(tf.truncated_normal(filter_shape, stddev=0.05), name="w")
47 # b_conv = tf.Variable(tf.constant(0.1, shape=[cl[0]]), name="b")
48 # 构建卷积层，可以直接将卷积核的初始化方法传入（w_conv）
49 conv = tf.nn.conv2d(self.embededCharsExpand, wConv, strides=[1, 1, 1, 1], padding="VALID", name="conv")
50 # 加上偏差
51 hConv = tf.nn.bias_add(conv, bConv)
52 # 可以直接加上relu函数，因为tf.nn.conv2d事实上是做了一个卷积运算，然后在这个运算结果上加上偏差，再导入到relu函数中
53 hConv = tf.nn.relu(hConv)
54
55 # with tf.name_scope("batchNormalization"):
56 # hConvBN = self._batchNorm(hConv)
57
58 if cl[-1] is not None:
59 ksizeShape = [1, cl[2], 1, 1]
60 hPool = tf.nn.max_pool(hConv, ksize=ksizeShape, strides=ksizeShape, padding="VALID", name="pool")
61 else:
62 hPool = hConv
63
64 print(hPool.shape)
65
66 # 对维度进行转换，转换成卷积层的输入维度
67 self.embededCharsExpand = tf.transpose(hPool, [0, 1, 3, 2], name="transpose")
68 print(self.embededCharsExpand)
69 with tf.name_scope("reshape"):
70 fcDim = self.embededCharsExpand.get_shape()[1].value * self.embededCharsExpand.get_shape()[2].value
71 self.inputReshape = tf.reshape(self.embededCharsExpand, [-1, fcDim])
72
73 weights = [fcDim] + config.model.fcLayers
74
75 for i, fl in enumerate(config.model.fcLayers): #fcLayers = [512]
76 with tf.name_scope("fcLayer-%s" % (i + 1)):
77 print("开始第" + str(i + 1) + "全连接层的处理")
78 stdv = 1 / math.sqrt(weights[i])
79 # 定义全连接层的初始化方法，均匀分布初始化w和b的值
80 wFc = tf.Variable(tf.random_uniform([weights[i], fl], minval=-stdv, maxval=stdv), dtype="float32",
81 name="w")
82 bFc = tf.Variable(tf.random_uniform(shape=[fl], minval=-stdv, maxval=stdv), dtype="float32", name="b")
83
84 # w_fc = tf.Variable(tf.truncated_normal([weights[i], fl], stddev=0.05), name="W")
85 # b_fc = tf.Variable(tf.constant(0.1, shape=[fl]), name="b")
86
87 self.fcInput = tf.nn.relu(tf.matmul(self.inputReshape, wFc) + bFc)
88 with tf.name_scope("dropOut"):
89 self.fcInputDrop = tf.nn.dropout(self.fcInput, self.dropoutKeepProb)
90 self.inputReshape = self.fcInputDrop
91
92 with tf.name_scope("outputLayer"):
93 stdv = 1 / math.sqrt(weights[-1])
94 # 定义隐层到输出层的权重系数和偏差的初始化方法
95 # w_out = tf.Variable(tf.truncated_normal([fc_layers[-1], num_classes], stddev=0.1), name="W")
96 # b_out = tf.Variable(tf.constant(0.1, shape=[num_classes]), name="b")
97
98 wOut = tf.Variable(tf.random_uniform([config.model.fcLayers[-1], 1], minval=-stdv, maxval=stdv),
99 dtype="float32", name="w")
100 bOut = tf.Variable(tf.random_uniform(shape=[1], minval=-stdv, maxval=stdv), name="b")
101 # tf.nn.xw_plus_b就是x和w的乘积加上b
102 self.predictions = tf.nn.xw_plus_b(self.inputReshape, wOut, bOut, name="predictions")
103 # 进行二元分类
104 self.binaryPreds = tf.cast(tf.greater_equal(self.predictions, 0.0), tf.float32, name="binaryPreds")
105
106 with tf.name_scope("loss"):
107 # 定义损失函数，对预测值进行softmax，再求交叉熵。
108 losses = tf.nn.sigmoid_cross_entropy_with_logits(logits=self.predictions, labels=self.inputY)
109 self.loss = tf.reduce_mean(losses)
110
111 def _batchNorm(self, x):
112 # BN层代码实现
113 gamma = tf.Variable(tf.ones([x.get_shape()[3].value]))
114 beta = tf.Variable(tf.zeros([x.get_shape()[3].value]))
115 self.popMean = tf.Variable(tf.zeros([x.get_shape()[3].value]), trainable=False, name="popMean")
116 self.popVariance = tf.Variable(tf.ones([x.get_shape()[3].value]), trainable=False, name="popVariance")
117
118 def batchNormTraining():
119 # 一定要使用正确的维度确保计算的是每个特征图上的平均值和方差而不是整个网络节点上的统计分布值
120 batchMean, batchVariance = tf.nn.moments(x, [0, 1, 2], keep_dims=False)
121 decay = 0.99
122 trainMean = tf.assign(self.popMean, self.popMean * self.decay + batchMean * (1 - self.decay))
123 trainVariance = tf.assign(self.popVariance,
124 self.popVariance * self.decay + batchVariance * (1 - self.decay))
125
126 with tf.control_dependencies([trainMean, trainVariance]):
127 return tf.nn.batch_normalization(x, batchMean, batchVariance, beta, gamma, self.epsilon)
128
129 def batchNormInference():
130 return tf.nn.batch_normalization(x, self.popMean, self.popVariance, beta, gamma, self.epsilon)
131 batchNormalizedOutput = tf.cond(self.isTraining, batchNormTraining, batchNormInference)
132 return tf.nn.relu(batchNormalizedOutput)
3.4 模型训练：mode_trainning.py
1 # Author:yifan
2 import os
3 import datetime
4 import warnings
5 import numpy as np
6 import tensorflow as tf
7 from sklearn.metrics import roc_auc_score, accuracy_score, precision_score, recall_score
8 warnings.filterwarnings("ignore")
9 import parameter_config
10 import get_train_data
11 import mode_structure
12
13 #获取前些模块的数据
14 config =parameter_config.Config()
15 data = get_train_data.Dataset(config)
16 data.dataGen()
17
18 #4生成batch数据集
19 def nextBatch(x, y, batchSize):
20 # 生成batch数据集，用生成器的方式输出
21 perm = np.arange(len(x))
22 np.random.shuffle(perm)
23 x = x[perm]
24 y = y[perm]
25 # print("++++++++++++++")
26 # print(x)
27 numBatches = len(x) // batchSize
28
29 for i in range(numBatches):
30 start = i * batchSize
31 end = start + batchSize
32 batchX = np.array(x[start: end], dtype="int64")
33 batchY = np.array(y[start: end], dtype="float32")
34 yield batchX, batchY
35
36 # 5 定义计算metrics的函数
37 """
38 定义各类性能指标
39 """
40 def mean(item):
41 return sum(item) / len(item)
42
43 def genMetrics(trueY, predY, binaryPredY):
44 """
45 生成acc和auc值
46 """
47 auc = roc_auc_score(trueY, predY)
48 accuracy = accuracy_score(trueY, binaryPredY)
49 precision = precision_score(trueY, binaryPredY, average='macro')
50 recall = recall_score(trueY, binaryPredY, average='macro')
51 return round(accuracy, 4), round(auc, 4), round(precision, 4), round(recall, 4)
52
53 # 6 训练模型
54 # 生成训练集和验证集
55 trainReviews = data.trainReviews
56 trainLabels = data.trainLabels
57 evalReviews = data.evalReviews
58 evalLabels = data.evalLabels
59 charEmbedding = data.charEmbedding
60
61 # 定义计算图
62 with tf.Graph().as_default():
63 session_conf = tf.ConfigProto(allow_soft_placement=True, log_device_placement=False)
64 session_conf.gpu_options.allow_growth = True
65 session_conf.gpu_options.per_process_gpu_memory_fraction = 0.9 # 配置gpu占用率
66 sess = tf.Session(config=session_conf)
67
68 # 定义会话
69 with sess.as_default():
70 cnn = mode_structure.CharCNN(config, charEmbedding)
71 globalStep = tf.Variable(0, name="globalStep", trainable=False)
72 # 定义优化函数，传入学习速率参数
73 optimizer = tf.train.RMSPropOptimizer(config.training.learningRate)
74 # 计算梯度,得到梯度和变量
75 gradsAndVars = optimizer.compute_gradients(cnn.loss)
76 # 将梯度应用到变量下，生成训练器
77 trainOp = optimizer.apply_gradients(gradsAndVars, global_step=globalStep)
78
79 # 用summary绘制tensorBoard
80 gradSummaries = []
81 for g, v in gradsAndVars:
82 if g is not None:
83 tf.summary.histogram("{}/grad/hist".format(v.name), g)
84 tf.summary.scalar("{}/grad/sparsity".format(v.name), tf.nn.zero_fraction(g))
85 outDir = os.path.abspath(os.path.join(os.path.curdir, "summarys"))
86 print("Writing to {}\n".format(outDir))
87 lossSummary = tf.summary.scalar("trainLoss", cnn.loss)
88
89 summaryOp = tf.summary.merge_all()
90
91 trainSummaryDir = os.path.join(outDir, "train")
92 trainSummaryWriter = tf.summary.FileWriter(trainSummaryDir, sess.graph)
93 evalSummaryDir = os.path.join(outDir, "eval")
94 evalSummaryWriter = tf.summary.FileWriter(evalSummaryDir, sess.graph)
95
96 # 初始化所有变量
97 saver = tf.train.Saver(tf.global_variables(), max_to_keep=5)
98
99 # 保存模型的一种方式，保存为pb文件
100 builder = tf.saved_model.builder.SavedModelBuilder("../model/charCNN/savedModel")
101 sess.run(tf.global_variables_initializer())
102
103 def trainStep(batchX, batchY):
104 """
105 训练函数
106 """
107 feed_dict = {
108 cnn.inputX: batchX,
109 cnn.inputY: batchY,
110 cnn.dropoutKeepProb: config.model.dropoutKeepProb,
111 cnn.isTraining: True
112 }
113 _, summary, step, loss, predictions, binaryPreds = sess.run(
114 [trainOp, summaryOp, globalStep, cnn.loss, cnn.predictions, cnn.binaryPreds],
115 feed_dict)
116 timeStr = datetime.datetime.now().isoformat()
117 acc, auc, precision, recall = genMetrics(batchY, predictions, binaryPreds)
118 print("{}, step: {}, loss: {}, acc: {}, auc: {}, precision: {}, recall: {}".format(timeStr, step, loss,
119 acc, auc, precision,
120 recall))
121 trainSummaryWriter.add_summary(summary, step)
122
123 def devStep(batchX, batchY):
124 """
125 验证函数
126 """
127 feed_dict = {
128 cnn.inputX: batchX,
129 cnn.inputY: batchY,
130 cnn.dropoutKeepProb: 1.0,
131 cnn.isTraining: False
132 }
133 summary, step, loss, predictions, binaryPreds = sess.run(
134 [summaryOp, globalStep, cnn.loss, cnn.predictions, cnn.binaryPreds],
135 feed_dict)
136
137 acc, auc, precision, recall = genMetrics(batchY, predictions, binaryPreds)
138
139 evalSummaryWriter.add_summary(summary, step)
140
141 return loss, acc, auc, precision, recall
142
143 for i in range(config.training.epoches):
144 # 训练模型
145 print("start training model")
146 for batchTrain in nextBatch(trainReviews, trainLabels, config.batchSize):
147 trainStep(batchTrain[0], batchTrain[1])
148
149 currentStep = tf.train.global_step(sess, globalStep)
150 if currentStep % config.training.evaluateEvery == 0:
151 print("\nEvaluation:")
152
153 losses = []
154 accs = []
155 aucs = []
156 precisions = []
157 recalls = []
158
159 for batchEval in nextBatch(evalReviews, evalLabels, config.batchSize):
160 loss, acc, auc, precision, recall = devStep(batchEval[0], batchEval[1])
161 losses.append(loss)
162 accs.append(acc)
163 aucs.append(auc)
164 precisions.append(precision)
165 recalls.append(recall)
166
167 time_str = datetime.datetime.now().isoformat()
168 print("{}, step: {}, loss: {}, acc: {}, auc: {}, precision: {}, recall: {}".format(time_str,
169 currentStep,
170 mean(losses),
171 mean(accs),
172 mean(aucs),
173 mean(
174 precisions),
175 mean(
176 recalls)))
177
178 if currentStep % config.training.checkpointEvery == 0:
179 # 保存模型的另一种方法，保存checkpoint文件
180 path = saver.save(sess, "../model/charCNN/model/my-model", global_step=currentStep)
181 print("Saved model checkpoint to {}\n".format(path))
182
183 inputs = {"inputX": tf.saved_model.utils.build_tensor_info(cnn.inputX),
184 "keepProb": tf.saved_model.utils.build_tensor_info(cnn.dropoutKeepProb)}
185
186 outputs = {"binaryPreds": tf.saved_model.utils.build_tensor_info(cnn.binaryPreds)}
187
188 prediction_signature = tf.saved_model.signature_def_utils.build_signature_def(inputs=inputs,
189 outputs=outputs,
190 method_name=tf.saved_model.signature_constants.PREDICT_METHOD_NAME)
191 legacy_init_op = tf.group(tf.tables_initializer(), name="legacy_init_op")
192 builder.add_meta_graph_and_variables(sess, [tf.saved_model.tag_constants.SERVING],
193 signature_def_map={"predict": prediction_signature},
194 legacy_init_op=legacy_init_op)
195
196 builder.save()
3.5 预测：predict.py
1 # Author:yifan
2 import tensorflow as tf
3 import parameter_config
4 import get_train_data
5 config =parameter_config.Config()
6 data = get_train_data.Dataset(config)
7
8 #7预测代码
9 x = "this movie is full of references like mad max ii the wild one and many others the ladybug´s face it´s a clear reference or tribute to peter lorre this movie is a masterpiece we´ll talk much more about in the future"
10 # x = "This film is not good" #最终反馈为1
11 # x = "This film is bad" #最终反馈为0
12 # x = "This film is good" #最终反馈为1
13
14 # 根据前面get_train_data获取，变成可以用来训练的向量。
15 y = list(x)
16 data._genVocabulary(y)
17 print(x)
18 reviewVec = data._reviewProcess(y, config.sequenceLength, data._charToIndex)
19 print(reviewVec)
20
21 graph = tf.Graph()
22 with graph.as_default():
23 gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.333)
24 session_conf = tf.ConfigProto(allow_soft_placement=True, log_device_placement=False, gpu_options=gpu_options)
25 sess = tf.Session(config=session_conf)
26 with sess.as_default():
27 # 恢复模型
28 checkpoint_file = tf.train.latest_checkpoint("../model/charCNN/model/")
29 saver = tf.train.import_meta_graph("{}.meta".format(checkpoint_file))
30 saver.restore(sess, checkpoint_file)
31
32 # 获得需要喂给模型的参数，输出的结果依赖的输入值
33 inputX = graph.get_operation_by_name("inputX").outputs[0]
34 dropoutKeepProb = graph.get_operation_by_name("dropoutKeepProb").outputs[0]
35
36 # 获得输出的结果
37 predictions = graph.get_tensor_by_name("outputLayer/binaryPreds:0")
38 pred = sess.run(predictions, feed_dict={inputX: [reviewVec], dropoutKeepProb: 1.0,})[0]
39
40 # pred = [idx2label[item] for item in pred]
41 print(pred)
结果
相关代码可见：https://github.com/yifanhunter/NLP_textClassifier
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