标签：tcl 会话 nump multi 卷积神经网络 nat scale optimizer rdp
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：
word2vec预训练词向量
textCNN 模型
charCNN 模型
Bi-LSTM 模型
Bi-LSTM + Attention 模型
Transformer 模型
ELMo 预训练模型
BERT 预训练模型
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
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Transformer模型的相关介绍可见这几篇文章：介绍 ，源码讲解。
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 | # Author:yifan #需要的所有导入包，存放留用，转换到jupyter后直接使用 # 1 配置训练参数 class TrainingConfig(object): epoches = 4 evaluateEvery = 100 checkpointEvery = 100 learningRate = 0.001 ? class ModelConfig(object): embeddingSize = 200 filters = 128 #内层一维卷积核的数量，外层卷积核的数量应该等于embeddingSize，因为要确保每个layer后的输出维度和输入维度是一致的。 numHeads = 8 # Attention 的头数 numBlocks = 1 # 设置transformer block的数量 epsilon = 1e-8 # LayerNorm 层中的最小除数 keepProp = 0.9 # multi head attention 中的dropout dropoutKeepProb = 0.5 # 全连接层的dropout l2RegLambda = 0.0 ? class Config(object): sequenceLength = 200 # 取了所有序列长度的均值 batchSize = 128 dataSource = "../data/preProcess/labeledTrain.csv" stopWordSource = "../data/english" numClasses = 1 # 二分类设置为1，多分类设置为类别的数目 rate = 0.8 # 训练集的比例 training = TrainingConfig() model = ModelConfig() ? # 实例化配置参数对象 config = Config() |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 | # Author:yifan import json from collections import Counter import gensim import pandas as pd import numpy as np import parameter_config ? ? # 2 数据预处理的类，生成训练集和测试集 class Dataset(object): def __init__(self, config): self.config = config self._dataSource = config.dataSource self._stopWordSource = config.stopWordSource self._sequenceLength = config.sequenceLength # 每条输入的序列处理为定长 self._embeddingSize = config.model.embeddingSize self._batchSize = config.batchSize self._rate = config.rate self._stopWordDict = {} self.trainReviews = [] self.trainLabels = [] self.evalReviews = [] self.evalLabels = [] self.wordEmbedding = None self.labelList = [] def _readData(self, filePath): """ 从csv文件中读取数据集,就本次测试的文件做记录 """ df = pd.read_csv(filePath) #读取文件，是三列的数据，第一列是review,第二列sentiment，第三列rate if self.config.numClasses == 1: labels = df["sentiment"].tolist() #读取sentiment列的数据， 显示输出01序列数组25000条 elif self.config.numClasses > 1: labels = df["rate"].tolist() #因为numClasses控制，本次取样没有取超过二分类 该处没有输出 review = df["review"].tolist() reviews = [line.strip().split() for line in review] #按空格语句切分 return reviews, labels def _labelToIndex(self, labels, label2idx): """ 将标签转换成索引表示 """ labelIds = [label2idx[label] for label in labels] #print(labels==labelIds) 结果显示为true,也就是两个一样 return labelIds def _wordToIndex(self, reviews, word2idx): """将词转换成索引""" reviewIds = [[word2idx.get(item, word2idx["UNK"]) for item in review] for review in reviews] # print(max(max(reviewIds))) # print(reviewIds) return reviewIds #返回25000个无序的数组 def _genTrainEvalData(self, x, y, word2idx, rate): """生成训练集和验证集 """ reviews = [] # print(self._sequenceLength) # print(len(x)) for review in x: #self._sequenceLength为200，表示长的切成200，短的补齐，x数据依旧是25000 if len(review) >= self._sequenceLength: reviews.append(review[:self._sequenceLength]) else: reviews.append(review + [word2idx["PAD"]] * (self._sequenceLength - len(review))) # print(len(review + [word2idx["PAD"]] * (self._sequenceLength - len(review)))) #以下是按照rate比例切分训练和测试数据： trainIndex = int(len(x) * rate) trainReviews = np.asarray(reviews[:trainIndex], dtype="int64") trainLabels = np.array(y[:trainIndex], dtype="float32") evalReviews = np.asarray(reviews[trainIndex:], dtype="int64") evalLabels = np.array(y[trainIndex:], dtype="float32") return trainReviews, trainLabels, evalReviews, evalLabels ? ? def _getWordEmbedding(self, words): """按照我们的数据集中的单词取出预训练好的word2vec中的词向量 反馈词和对应的向量（200维度），另外前面增加PAD对用0的数组，UNK对应随机数组。 """ wordVec = gensim.models.KeyedVectors.load_word2vec_format("../word2vec/word2Vec.bin", binary=True) vocab = [] wordEmbedding = [] # 添加 "pad" 和 "UNK", vocab.append("PAD") vocab.append("UNK") wordEmbedding.append(np.zeros(self._embeddingSize)) # _embeddingSize 本文定义的是200 wordEmbedding.append(np.random.randn(self._embeddingSize)) # print(wordEmbedding) for word in words: try: vector = wordVec.wv[word] vocab.append(word) wordEmbedding.append(vector) except: print(word + "不存在于词向量中") # print(vocab[:3],wordEmbedding[:3]) return vocab, np.array(wordEmbedding) def _genVocabulary(self, reviews, labels): """生成词向量和词汇-索引映射字典，可以用全数据集""" allWords = [word for review in reviews for word in review] #单词数量5738236 reviews是25000个观点句子【】 subWords = [word for word in allWords if word not in self.stopWordDict] # 去掉停用词 wordCount = Counter(subWords) # 统计词频 sortWordCount = sorted(wordCount.items(), key=lambda x: x[1], reverse=True) #返回键值对，并按照数量排序 # print(len(sortWordCount)) #161330 # print(sortWordCount[:4],sortWordCount[-4:]) # [(‘movie‘, 41104), (‘film‘, 36981), (‘one‘, 24966), (‘like‘, 19490)] [(‘daeseleires‘, 1), (‘nice310‘, 1), (‘shortsightedness‘, 1), (‘unfairness‘, 1)] words = [item[0] for item in sortWordCount if item[1] >= 5] # 去除低频词，低于5的 vocab, wordEmbedding = self._getWordEmbedding(words) self.wordEmbedding = wordEmbedding word2idx = dict(zip(vocab, list(range(len(vocab))))) #生成类似这种{‘I‘: 0, ‘love‘: 1, ‘yanzi‘: 2} uniqueLabel = list(set(labels)) #标签去重 最后就 0 1了 label2idx = dict(zip(uniqueLabel, list(range(len(uniqueLabel))))) #本文就 {0: 0, 1: 1} self.labelList = list(range(len(uniqueLabel))) # 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 with open("../data/wordJson/word2idx.json", "w", encoding="utf-8") as f: json.dump(word2idx, f) with open("../data/wordJson/label2idx.json", "w", encoding="utf-8") as f: json.dump(label2idx, f) return word2idx, label2idx ? ? def _readStopWord(self, stopWordPath): """ 读取停用词 """ with open(stopWordPath, "r") as f: stopWords = f.read() stopWordList = stopWords.splitlines() # 将停用词用列表的形式生成，之后查找停用词时会比较快 self.stopWordDict = dict(zip(stopWordList, list(range(len(stopWordList))))) ? ? def dataGen(self): """ 初始化训练集和验证集 """ # 初始化停用词 self._readStopWord(self._stopWordSource) # 初始化数据集 reviews, labels = self._readData(self._dataSource) # 初始化词汇-索引映射表和词向量矩阵 word2idx, label2idx = self._genVocabulary(reviews, labels) # 将标签和句子数值化 labelIds = self._labelToIndex(labels, label2idx) reviewIds = self._wordToIndex(reviews, word2idx) # 初始化训练集和测试集 trainReviews, trainLabels, evalReviews, evalLabels = self._genTrainEvalData(reviewIds, labelIds, word2idx, self._rate) self.trainReviews = trainReviews self.trainLabels = trainLabels ? ? self.evalReviews = evalReviews self.evalLabels = evalLabels ? ? #获取前些模块的数据 # config =parameter_config.Config() # data = Dataset(config) # data.dataGen() |
关于transformer模型的一些使用心得：
1）在这里选择固定的one-hot的position embedding比论文中提出的利用正弦余弦函数生成的position embedding的效果要好，可能的原因是论文中提出的position embedding是作为可训练的值传入的，
这样就增加了模型的复杂度，在小数据集（IMDB训练集大小：20000）上导致性能有所下降。
2）mask可能不需要，添加mask和去除mask对结果基本没啥影响，也许在其他的任务或者数据集上有作用，但论文也并没有提出一定要在encoder结构中加入mask，mask更多的是用在decoder。
3）transformer的层数，transformer的层数可以根据自己的数据集大小调整，在小数据集上基本上一层就够了。
4）在subLayers上加dropout正则化，主要是在multi-head attention层加，因为feed forward是用卷积实现的，不加dropout应该没关系，当然如果feed forward用全连接层实现，那也加上dropout。
5）在小数据集上transformer的效果并不一定比Bi-LSTM + Attention好，在IMDB上效果就更差。
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 | import os import datetime import numpy as np import tensorflow as tf import parameter_config import get_train_data import mode_structure ? ? #获取前些模块的数据 config =parameter_config.Config() data = get_train_data.Dataset(config) data.dataGen() ? ? #4生成batch数据集 def nextBatch(x, y, batchSize): # 生成batch数据集，用生成器的方式输出 perm = np.arange(len(x)) #返回[0 1 2 ... len(x)]的数组 np.random.shuffle(perm) #乱序 x = x[perm] y = y[perm] numBatches = len(x) // batchSize for i in range(numBatches): start = i * batchSize end = start + batchSize batchX = np.array(x[start: end], dtype="int64") batchY = np.array(y[start: end], dtype="float32") yield batchX, batchY ? ? # 5 定义计算metrics的函数 """ 定义各类性能指标 """ def mean(item: list) -> float: """ 计算列表中元素的平均值 :param item: 列表对象 :return: """ res = sum(item) / len(item) if len(item) > 0 else 0 return res ? ? def accuracy(pred_y, true_y): """ 计算二类和多类的准确率 :param pred_y: 预测结果 :param true_y: 真实结果 :return: """ if isinstance(pred_y[0], list): pred_y = [item[0] for item in pred_y] corr = 0 for i in range(len(pred_y)): if pred_y[i] == true_y[i]: corr += 1 acc = corr / len(pred_y) if len(pred_y) > 0 else 0 return acc ? ? def binary_precision(pred_y, true_y, positive=1): """ 二类的精确率计算 :param pred_y: 预测结果 :param true_y: 真实结果 :param positive: 正例的索引表示 :return: """ corr = 0 pred_corr = 0 for i in range(len(pred_y)): if pred_y[i] == positive: pred_corr += 1 if pred_y[i] == true_y[i]: corr += 1 ? ? prec = corr / pred_corr if pred_corr > 0 else 0 return prec ? ? def binary_recall(pred_y, true_y, positive=1): """ 二类的召回率 :param pred_y: 预测结果 :param true_y: 真实结果 :param positive: 正例的索引表示 :return: """ corr = 0 true_corr = 0 for i in range(len(pred_y)): if true_y[i] == positive: true_corr += 1 if pred_y[i] == true_y[i]: corr += 1 ? ? rec = corr / true_corr if true_corr > 0 else 0 return rec ? ? def binary_f_beta(pred_y, true_y, beta=1.0, positive=1): """ 二类的f beta值 :param pred_y: 预测结果 :param true_y: 真实结果 :param beta: beta值 :param positive: 正例的索引表示 :return: """ precision = binary_precision(pred_y, true_y, positive) recall = binary_recall(pred_y, true_y, positive) try: f_b = (1 + beta * beta) * precision * recall / (beta * beta * precision + recall) except: f_b = 0 return f_b ? ? def multi_precision(pred_y, true_y, labels): """ 多类的精确率 :param pred_y: 预测结果 :param true_y: 真实结果 :param labels: 标签列表 :return: """ if isinstance(pred_y[0], list): pred_y = [item[0] for item in pred_y] ? ? precisions = [binary_precision(pred_y, true_y, label) for label in labels] prec = mean(precisions) return prec ? ? def multi_recall(pred_y, true_y, labels): """ 多类的召回率 :param pred_y: 预测结果 :param true_y: 真实结果 :param labels: 标签列表 :return: """ if isinstance(pred_y[0], list): pred_y = [item[0] for item in pred_y] ? ? recalls = [binary_recall(pred_y, true_y, label) for label in labels] rec = mean(recalls) return rec ? ? def multi_f_beta(pred_y, true_y, labels, beta=1.0): """ 多类的f beta值 :param pred_y: 预测结果 :param true_y: 真实结果 :param labels: 标签列表 :param beta: beta值 :return: """ if isinstance(pred_y[0], list): pred_y = [item[0] for item in pred_y] ? ? f_betas = [binary_f_beta(pred_y, true_y, beta, label) for label in labels] f_beta = mean(f_betas) return f_beta ? ? def get_binary_metrics(pred_y, true_y, f_beta=1.0): """ 得到二分类的性能指标 :param pred_y: :param true_y: :param f_beta: :return: """ acc = accuracy(pred_y, true_y) recall = binary_recall(pred_y, true_y) precision = binary_precision(pred_y, true_y) f_beta = binary_f_beta(pred_y, true_y, f_beta) return acc, recall, precision, f_beta ? ? def get_multi_metrics(pred_y, true_y, labels, f_beta=1.0): """ 得到多分类的性能指标 :param pred_y: :param true_y: :param labels: :param f_beta: :return: """ acc = accuracy(pred_y, true_y) recall = multi_recall(pred_y, true_y, labels) precision = multi_precision(pred_y, true_y, labels) f_beta = multi_f_beta(pred_y, true_y, labels, f_beta) return acc, recall, precision, f_beta ? ? # 6 训练模型 # 生成训练集和验证集 trainReviews = data.trainReviews trainLabels = data.trainLabels evalReviews = data.evalReviews evalLabels = data.evalLabels ? ? wordEmbedding = data.wordEmbedding labelList = data.labelList embeddedPosition = mode_structure.fixedPositionEmbedding(config.batchSize, config.sequenceLength) #使用的是one-hot形式 ? ? # 训练模型 # 定义计算图 with tf.Graph().as_default(): session_conf = tf.ConfigProto(allow_soft_placement=True, log_device_placement=False) session_conf.gpu_options.allow_growth=True session_conf.gpu_options.per_process_gpu_memory_fraction = 0.9 # 配置gpu占用率 sess = tf.Session(config=session_conf) ? # 定义会话 with sess.as_default(): transformer = mode_structure.Transformer(config, wordEmbedding) globalStep = tf.Variable(0, name="globalStep", trainable=False) # 定义优化函数，传入学习速率参数 optimizer = tf.train.AdamOptimizer(config.training.learningRate) # 计算梯度,得到梯度和变量 gradsAndVars = optimizer.compute_gradients(transformer.loss) # 将梯度应用到变量下，生成训练器 trainOp = optimizer.apply_gradients(gradsAndVars, global_step=globalStep) ? # 用summary绘制tensorBoard gradSummaries = [] for g, v in gradsAndVars: if g is not None: tf.summary.histogram("{}/grad/hist".format(v.name), g) tf.summary.scalar("{}/grad/sparsity".format(v.name), tf.nn.zero_fraction(g)) ? outDir = os.path.abspath(os.path.join(os.path.curdir, "summarys")) print("Writing to {}\n".format(outDir)) ? lossSummary = tf.summary.scalar("loss", transformer.loss) summaryOp = tf.summary.merge_all() ? trainSummaryDir = os.path.join(outDir, "train") trainSummaryWriter = tf.summary.FileWriter(trainSummaryDir, sess.graph) evalSummaryDir = os.path.join(outDir, "eval") evalSummaryWriter = tf.summary.FileWriter(evalSummaryDir, sess.graph) ? ? # 初始化所有变量 saver = tf.train.Saver(tf.global_variables(), max_to_keep=5) ? # 保存模型的一种方式，保存为pb文件 savedModelPath = "../model/transformer/savedModel" if os.path.exists(savedModelPath): os.rmdir(savedModelPath) builder = tf.saved_model.builder.SavedModelBuilder(savedModelPath) ? sess.run(tf.global_variables_initializer()) ? ? def trainStep(batchX, batchY): """ 训练函数 """ feed_dict = { transformer.inputX: batchX, transformer.inputY: batchY, transformer.dropoutKeepProb: config.model.dropoutKeepProb, transformer.embeddedPosition: embeddedPosition } _, summary, step, loss, predictions = sess.run( [trainOp, summaryOp, globalStep, transformer.loss, transformer.predictions], feed_dict) ? if config.numClasses == 1: acc, recall, prec, f_beta = get_binary_metrics(pred_y=predictions, true_y=batchY) elif config.numClasses > 1: acc, recall, prec, f_beta = get_multi_metrics(pred_y=predictions, true_y=batchY, labels=labelList) ? trainSummaryWriter.add_summary(summary, step) return loss, acc, prec, recall, f_beta ? ? def devStep(batchX, batchY): """ 验证函数 """ feed_dict = { transformer.inputX: batchX, transformer.inputY: batchY, transformer.dropoutKeepProb: 1.0, transformer.embeddedPosition: embeddedPosition } summary, step, loss, predictions = sess.run( [summaryOp, globalStep, transformer.loss, transformer.predictions], feed_dict) ? if config.numClasses == 1: acc, recall, prec, f_beta = get_binary_metrics(pred_y=predictions, true_y=batchY) ? ? ? elif config.numClasses > 1: acc, recall, prec, f_beta = get_multi_metrics(pred_y=predictions, true_y=batchY, labels=labelList) ? trainSummaryWriter.add_summary(summary, step) ? return loss, acc, prec, recall, f_beta ? for i in range(config.training.epoches): # 训练模型 print("start training model") for batchTrain in nextBatch(trainReviews, trainLabels, config.batchSize): loss, acc, prec, recall, f_beta = trainStep(batchTrain[0], batchTrain[1]) ? currentStep = tf.train.global_step(sess, globalStep) print("train: step: {}, loss: {}, acc: {}, recall: {}, precision: {}, f_beta: {}".format( currentStep, loss, acc, recall, prec, f_beta)) if currentStep % config.training.evaluateEvery == 0: print("\nEvaluation:") ? losses = [] accs = [] f_betas = [] precisions = [] recalls = [] ? for batchEval in nextBatch(evalReviews, evalLabels, config.batchSize): loss, acc, precision, recall, f_beta = devStep(batchEval[0], batchEval[1]) losses.append(loss) accs.append(acc) f_betas.append(f_beta) precisions.append(precision) recalls.append(recall) ? time_str = datetime.datetime.now().isoformat() print("{}, step: {}, loss: {}, acc: {},precision: {}, recall: {}, f_beta: {}".format(time_str, currentStep, mean(losses), mean(accs), mean(precisions), mean(recalls), mean(f_betas))) ? if currentStep % config.training.checkpointEvery == 0: # 保存模型的另一种方法，保存checkpoint文件 path = saver.save(sess, "../model/Transformer/model/my-model", global_step=currentStep) print("Saved model checkpoint to {}\n".format(path)) ? inputs = {"inputX": tf.saved_model.utils.build_tensor_info(transformer.inputX), "keepProb": tf.saved_model.utils.build_tensor_info(transformer.dropoutKeepProb)} ? ? outputs = {"predictions": tf.saved_model.utils.build_tensor_info(transformer.predictions)} ? ? prediction_signature = tf.saved_model.signature_def_utils.build_signature_def(inputs=inputs, outputs=outputs, method_name=tf.saved_model.signature_constants.PREDICT_METHOD_NAME) legacy_init_op = tf.group(tf.tables_initializer(), name="legacy_init_op") builder.add_meta_graph_and_variables(sess, [tf.saved_model.tag_constants.SERVING], signature_def_map={"predict": prediction_signature}, legacy_init_op=legacy_init_op) ? ? builder.save() |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 | # Author:yifan import os import csv import time import datetime import random import json from collections import Counter from math import sqrt import gensim import pandas as pd import numpy as np import tensorflow as tf from sklearn.metrics import roc_auc_score, accuracy_score, precision_score, recall_score import parameter_config config =parameter_config.Config() import mode_structure embeddedPositions = mode_structure.fixedPositionEmbedding(config.batchSize, config.sequenceLength)[0] #使用的是one-hot形式 # print(type(embeddedPositions)) # print(embeddedPositions.shape) #7预测代码 # 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" # x = "his movie is the same as the third level movie. There‘s no place to look good" x = "This film is not good" #最终反馈为1 感觉不准 # x = "This film is bad" #最终反馈为0 ? ? # 注：下面两个词典要保证和当前加载的模型对应的词典是一致的 with open("../data/wordJson/word2idx.json", "r", encoding="utf-8") as f: word2idx = json.load(f) with open("../data/wordJson/label2idx.json", "r", encoding="utf-8") as f: #label2idx.json内容{"0": 0, "1": 1} label2idx = json.load(f) idx2label = {value: key for key, value in label2idx.items()} ? ? #x 的处理，变成模型能识别的向量xIds xIds = [word2idx.get(item, word2idx["UNK"]) for item in x.split(" ")] #返回x对应的向量 if len(xIds) >= config.sequenceLength: #xIds 句子单词个数是否超过了sequenceLength（200） xIds = xIds[:config.sequenceLength] print("ddd",xIds) else: xIds = xIds + [word2idx["PAD"]] * (config.sequenceLength - len(xIds)) print("xxx", xIds) ? ? graph = tf.Graph() with graph.as_default(): gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.333) session_conf = tf.ConfigProto(allow_soft_placement=True, log_device_placement=False, gpu_options=gpu_options) sess = tf.Session(config=session_conf) ? ? with sess.as_default(): # 恢复模型 checkpoint_file = tf.train.latest_checkpoint("../model/transformer/model/") saver = tf.train.import_meta_graph("{}.meta".format(checkpoint_file)) saver.restore(sess, checkpoint_file) ? ? # 获得需要喂给模型的参数，输出的结果依赖的输入值 inputX = graph.get_operation_by_name("inputX").outputs[0] dropoutKeepProb = graph.get_operation_by_name("dropoutKeepProb").outputs[0] embeddedPosition = graph.get_operation_by_name("embeddedPosition").outputs[0] # inputX = tf.placeholder(tf.int32, [None, config.sequenceLength], name="inputX") # dropoutKeepProb = tf.placeholder(tf.float32, name="dropoutKeepProb") # embeddedPosition = tf.placeholder(tf.float32, [None, config.sequenceLength, config.sequenceLength], # name="embeddedPosition") #这种方式不行 ? ? # 获得输出的结果 predictions = graph.get_tensor_by_name("output/predictions:0") pred = sess.run(predictions, feed_dict={inputX: [xIds], dropoutKeepProb: 1.0, embeddedPosition: [embeddedPositions]})[0] ? ? # print(pred) pred = [idx2label[item] for item in pred] print(pred) |
相关代码可见：https://github.com/yifanhunter/NLP_textClassifier
【1】 https://home.cnblogs.com/u/jiangxinyang/
标签：tcl 会话 nump multi 卷积神经网络 nat scale optimizer rdp
原文地址：https://www.cnblogs.com/yifanrensheng/p/13363419.html
