1 大綱概述
文本分類這個系列將會有十篇左右，包括基於word2vec預訓練的文本分類，與及基於最新的預訓練模型（ELMo，BERT等）的文本分類。總共有以下系列：
jupyter notebook代碼均在textClassifier倉庫中，python代碼在NLP-Project中的text_classfier中。
2 數據集
數據集爲IMDB 電影影評，總共有三個數據文件，在/data/rawData目錄下，包括unlabeledTrainData.tsv，labeledTrainData.tsv，testData.tsv。在進行文本分類時需要有標籤的數據（labeledTrainData），數據預處理如文本分類實戰（一）—— word2vec預訓練詞向量中一樣，預處理後的文件爲/data/preprocess/labeledTrain.csv。
3 Bi-LSTM + Attention 模型
Bi-LSTM + Attention模型來源於論文Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification。關於Attention的介紹見這篇。
Bi-LSTM + Attention 就是在Bi-LSTM的模型上加入Attention層，在Bi-LSTM中我們會用最後一個時序的輸出向量 作爲特徵向量，然後進行softmax分類。Attention是先計算每個時序的權重，然後將所有時序 的向量進行加權和作爲特徵向量，然後進行softmax分類。在實驗中，加上Attention確實對結果有所提升。其模型結構如下圖：
4 參數配置
import os
import csv
import time
import datetime
import random
import json
import warnings
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
warnings.filterwarnings("ignore")
# 配置參數
class TrainingConfig(object):
epoches = 4
evaluateEvery = 100
checkpointEvery = 100
learningRate = 0.001
class ModelConfig(object):
embeddingSize = 200
hiddenSizes = [256, 128] # LSTM結構的神經元個數
dropoutKeepProb = 0.5
l2RegLambda = 0.0
class Config(object):
sequenceLength = 200 # 取了所有序列長度的均值
batchSize = 128
dataSource = "../data/preProcess/labeledTrain.csv"
stopWordSource = "../data/english"
numClasses = 1 # 二分類設置爲1，多分類設置爲類別的數目
rate = 0.8 # 訓練集的比例
training = TrainingConfig()
model = ModelConfig()
# 實例化配置參數對象
config = Config()
5 生成訓練數據
1）將數據加載進來，將句子分割成詞表示，並去除低頻詞和停用詞。
2）將詞映射成索引表示，構建詞彙-索引映射表，並保存成json的數據格式，之後做inference時可以用到。（注意，有的詞可能不在word2vec的預訓練詞向量中，這種詞直接用UNK表示）
3）從預訓練的詞向量模型中讀取出詞向量，作爲初始化值輸入到模型中。
4）將數據集分割成訓練集和測試集
# 數據預處理的類，生成訓練集和測試集
class Dataset(object):
def __init__(self, config):
self.config = config
self._dataSource = config.dataSource
self._stopWordSource = config.stopWordSource
self._sequenceLength = config.sequenceLength # 每條輸入的序列處理爲定長
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):
"""
從csv文件中讀取數據集
"""
df = pd.read_csv(filePath)
if self.config.numClasses == 1:
labels = df["sentiment"].tolist()
elif self.config.numClasses > 1:
labels = df["rate"].tolist()
review = df["review"].tolist()
reviews = [line.strip().split() for line in review]
return reviews, labels
def _labelToIndex(self, labels, label2idx):
"""
將標籤轉換成索引表示
"""
labelIds = [label2idx[label] for label in labels]
return labelIds
def _wordToIndex(self, reviews, word2idx):
"""
將詞轉換成索引
"""
reviewIds = [[word2idx.get(item, word2idx["UNK"]) for item in review] for review in reviews]
return reviewIds
def _genTrainEvalData(self, x, y, word2idx, rate):
"""
生成訓練集和驗證集
"""
reviews = []
for review in x:
if len(review) >= self._sequenceLength:
reviews.append(review[:self._sequenceLength])
else:
reviews.append(review + [word2idx["PAD"]] * (self._sequenceLength - len(review)))
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 _genVocabulary(self, reviews, labels):
"""
生成詞向量和詞彙-索引映射字典，可以用全數據集
"""
allWords = [word for review in reviews for word in review]
# 去掉停用詞
subWords = [word for word in allWords if word not in self.stopWordDict]
wordCount = Counter(subWords) # 統計詞頻
sortWordCount = sorted(wordCount.items(), key=lambda x: x[1], reverse=True)
# 去除低頻詞
words = [item[0] for item in sortWordCount if item[1] >= 5]
vocab, wordEmbedding = self._getWordEmbedding(words)
self.wordEmbedding = wordEmbedding
word2idx = dict(zip(vocab, list(range(len(vocab)))))
uniqueLabel = list(set(labels))
label2idx = dict(zip(uniqueLabel, list(range(len(uniqueLabel)))))
self.labelList = list(range(len(uniqueLabel)))
# 將詞彙-索引映射表保存爲json數據，之後做inference時直接加載來處理數據
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 _getWordEmbedding(self, words):
"""
按照我們的數據集中的單詞取出預訓練好的word2vec中的詞向量
"""
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))
wordEmbedding.append(np.random.randn(self._embeddingSize))
for word in words:
try:
vector = wordVec.wv[word]
vocab.append(word)
wordEmbedding.append(vector)
except:
print(word + "不存在於詞向量中")
return vocab, np.array(wordEmbedding)
def _readStopWord(self, stopWordPath):
"""
讀取停用詞
"""
with open(stopWordPath, "r") as f:
stopWords = f.read()
stopWordList = stopWords.splitlines()
# 將停用詞用列表的形式生成，之後查找停用詞時會比較快
self.stopWordDict = dict(zip(stopWordList, list(range(len(stopWordList)))))
def dataGen(self):
"""
初始化訓練集和驗證集
"""
# 初始化停用詞
self._readStopWord(self._stopWordSource)
# 初始化數據集
reviews, labels = self._readData(self._dataSource)
# 初始化詞彙-索引映射表和詞向量矩陣
word2idx, label2idx = self._genVocabulary(reviews, labels)
# 將標籤和句子數值化
labelIds = self._labelToIndex(labels, label2idx)
reviewIds = self._wordToIndex(reviews, word2idx)
# 初始化訓練集和測試集
trainReviews, trainLabels, evalReviews, evalLabels = self._genTrainEvalData(reviewIds, labelIds, word2idx, self._rate)
self.trainReviews = trainReviews
self.trainLabels = trainLabels
self.evalReviews = evalReviews
self.evalLabels = evalLabels
data = Dataset(config)
data.dataGen()
6 生成batch數據集
採用生成器的形式向模型輸入batch數據集，（生成器可以避免將所有的數據加入到內存中）
# 輸出batch數據集
def nextBatch(x, y, batchSize):
"""
生成batch數據集，用生成器的方式輸出
"""
perm = np.arange(len(x))
np.random.shuffle(perm)
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
7 Bi-LSTM + Attention模型
# 構建模型
class BiLSTMAttention(object):
"""
Text CNN 用於文本分類
"""
def __init__(self, config, wordEmbedding):
# 定義模型的輸入
self.inputX = tf.placeholder(tf.int32, [None, config.sequenceLength], name="inputX")
self.inputY = tf.placeholder(tf.int32, [None], name="inputY")
self.dropoutKeepProb = tf.placeholder(tf.float32, name="dropoutKeepProb")
# 定義l2損失
l2Loss = tf.constant(0.0)
# 詞嵌入層
with tf.name_scope("embedding"):
# 利用預訓練的詞向量初始化詞嵌入矩陣
self.W = tf.Variable(tf.cast(wordEmbedding, dtype=tf.float32, name="word2vec") ,name="W")
# 利用詞嵌入矩陣將輸入的數據中的詞轉換成詞向量，維度[batch_size, sequence_length, embedding_size]
self.embeddedWords = tf.nn.embedding_lookup(self.W, self.inputX)
# 定義兩層雙向LSTM的模型結構
with tf.name_scope("Bi-LSTM"):
for idx, hiddenSize in enumerate(config.model.hiddenSizes):
with tf.name_scope("Bi-LSTM" + str(idx)):
# 定義前向LSTM結構
lstmFwCell = tf.nn.rnn_cell.DropoutWrapper(tf.nn.rnn_cell.LSTMCell(num_units=hiddenSize, state_is_tuple=True),
output_keep_prob=self.dropoutKeepProb)
# 定義反向LSTM結構
lstmBwCell = tf.nn.rnn_cell.DropoutWrapper(tf.nn.rnn_cell.LSTMCell(num_units=hiddenSize, state_is_tuple=True),
output_keep_prob=self.dropoutKeepProb)
# 採用動態rnn，可以動態的輸入序列的長度，若沒有輸入，則取序列的全長
# outputs是一個元祖(output_fw, output_bw)，其中兩個元素的維度都是[batch_size, max_time, hidden_size],fw和bw的hidden_size一樣
# self.current_state 是最終的狀態，二元組(state_fw, state_bw)，state_fw=[batch_size, s]，s是一個元祖(h, c)
outputs_, self.current_state = tf.nn.bidirectional_dynamic_rnn(lstmFwCell, lstmBwCell,
self.embeddedWords, dtype=tf.float32,
scope="bi-lstm" + str(idx))
# 對outputs中的fw和bw的結果拼接 [batch_size, time_step, hidden_size * 2], 傳入到下一層Bi-LSTM中
self.embeddedWords = tf.concat(outputs_, 2)
# 將最後一層Bi-LSTM輸出的結果分割成前向和後向的輸出
outputs = tf.split(self.embeddedWords, 2, -1)
# 在Bi-LSTM+Attention的論文中，將前向和後向的輸出相加
with tf.name_scope("Attention"):
H = outputs[0] + outputs[1]
# 得到Attention的輸出
output = self.attention(H)
outputSize = config.model.hiddenSizes[-1]
# 全連接層的輸出
with tf.name_scope("output"):
outputW = tf.get_variable(
"outputW",
shape=[outputSize, config.numClasses],
initializer=tf.contrib.layers.xavier_initializer())
outputB= tf.Variable(tf.constant(0.1, shape=[config.numClasses]), name="outputB")
l2Loss += tf.nn.l2_loss(outputW)
l2Loss += tf.nn.l2_loss(outputB)
self.logits = tf.nn.xw_plus_b(output, outputW, outputB, name="logits")
if config.numClasses == 1:
self.predictions = tf.cast(tf.greater_equal(self.logits, 0.0), tf.float32, name="predictions")
elif config.numClasses > 1:
self.predictions = tf.argmax(self.logits, axis=-1, name="predictions")
# 計算二元交叉熵損失
with tf.name_scope("loss"):
if config.numClasses == 1:
losses = tf.nn.sigmoid_cross_entropy_with_logits(logits=self.logits, labels=tf.cast(tf.reshape(self.inputY, [-1, 1]),
dtype=tf.float32))
elif config.numClasses > 1:
losses = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=self.logits, labels=self.inputY)
self.loss = tf.reduce_mean(losses) + config.model.l2RegLambda * l2Loss
def attention(self, H):
"""
利用Attention機制得到句子的向量表示
"""
# 獲得最後一層LSTM的神經元數量
hiddenSize = config.model.hiddenSizes[-1]
# 初始化一個權重向量，是可訓練的參數
W = tf.Variable(tf.random_normal([hiddenSize], stddev=0.1))
# 對Bi-LSTM的輸出用激活函數做非線性轉換
M = tf.tanh(H)
# 對W和M做矩陣運算，W=[batch_size, time_step, hidden_size]，計算前做維度轉換成[batch_size * time_step, hidden_size]
# newM = [batch_size, time_step, 1]，每一個時間步的輸出由向量轉換成一個數字
newM = tf.matmul(tf.reshape(M, [-1, hiddenSize]), tf.reshape(W, [-1, 1]))
# 對newM做維度轉換成[batch_size, time_step]
restoreM = tf.reshape(newM, [-1, config.sequenceLength])
# 用softmax做歸一化處理[batch_size, time_step]
self.alpha = tf.nn.softmax(restoreM)
# 利用求得的alpha的值對H進行加權求和，用矩陣運算直接操作
r = tf.matmul(tf.transpose(H, [0, 2, 1]), tf.reshape(self.alpha, [-1, config.sequenceLength, 1]))
# 將三維壓縮成二維sequeezeR=[batch_size, hidden_size]
sequeezeR = tf.reshape(r, [-1, hiddenSize])
sentenceRepren = tf.tanh(sequeezeR)
# 對Attention的輸出可以做dropout處理
output = tf.nn.dropout(sentenceRepren, self.dropoutKeepProb)
return output
8 定義計算metrics的函數
"""
定義各類性能指標
"""
def mean(item: list) -> float:
"""
計算列表中元素的平均值
:param item: 列表對象
:return:
"""
res = sum(item) / len(item) if len(item) > 0 else 0
return res
def accuracy(pred_y, true_y):
"""
計算二類和多類的準確率
:param pred_y: 預測結果
:param true_y: 真實結果
: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):
"""
二類的精確率計算
:param pred_y: 預測結果
:param true_y: 真實結果
: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):
"""
二類的召回率
:param pred_y: 預測結果
:param true_y: 真實結果
: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):
"""
二類的f beta值
:param pred_y: 預測結果
:param true_y: 真實結果
: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):
"""
多類的精確率
:param pred_y: 預測結果
:param true_y: 真實結果
:param labels: 標籤列表
: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):
"""
多類的召回率
:param pred_y: 預測結果
:param true_y: 真實結果
:param labels: 標籤列表
: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):
"""
多類的f beta值
:param pred_y: 預測結果
:param true_y: 真實結果
:param labels: 標籤列表
: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):
"""
得到二分類的性能指標
: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):
"""
得到多分類的性能指標
: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
9 訓練模型
在訓練時，我們定義了tensorBoard的輸出，並定義了兩種模型保存的方法。
# 訓練模型
# 生成訓練集和驗證集
trainReviews = data.trainReviews
trainLabels = data.trainLabels
evalReviews = data.evalReviews
evalLabels = data.evalLabels
wordEmbedding = data.wordEmbedding
labelList = data.labelList
# 定義計算圖
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佔用率
sess = tf.Session(config=session_conf)
# 定義會話
with sess.as_default():
lstm = BiLSTMAttention(config, wordEmbedding)
globalStep = tf.Variable(0, name="globalStep", trainable=False)
# 定義優化函數，傳入學習速率參數
optimizer = tf.train.AdamOptimizer(config.training.learningRate)
# 計算梯度,得到梯度和變量
gradsAndVars = optimizer.compute_gradients(lstm.loss)
# 將梯度應用到變量下，生成訓練器
trainOp = optimizer.apply_gradients(gradsAndVars, global_step=globalStep)
# 用summary繪製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", lstm.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)
# 初始化所有變量
saver = tf.train.Saver(tf.global_variables(), max_to_keep=5)
# 保存模型的一種方式，保存爲pb文件
savedModelPath = "../model/bilstm-atten/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):
"""
訓練函數
"""
feed_dict = {
lstm.inputX: batchX,
lstm.inputY: batchY,
lstm.dropoutKeepProb: config.model.dropoutKeepProb
}
_, summary, step, loss, predictions = sess.run(
[trainOp, summaryOp, globalStep, lstm.loss, lstm.predictions],
feed_dict)
timeStr = datetime.datetime.now().isoformat()
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):
"""
驗證函數
"""
feed_dict = {
lstm.inputX: batchX,
lstm.inputY: batchY,
lstm.dropoutKeepProb: 1.0
}
summary, step, loss, predictions = sess.run(
[summaryOp, globalStep, lstm.loss, lstm.predictions],
feed_dict)
if config.numClasses == 1:
acc, precision, recall, f_beta = get_binary_metrics(pred_y=predictions, true_y=batchY)
elif config.numClasses > 1:
acc, precision, recall, f_beta = get_multi_metrics(pred_y=predictions, true_y=batchY, labels=labelList)
evalSummaryWriter.add_summary(summary, step)
return loss, acc, precision, recall, f_beta
for i in range(config.training.epoches):
# 訓練模型
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:
# 保存模型的另一種方法，保存checkpoint文件
path = saver.save(sess, "../model/Bi-LSTM-atten/model/my-model", global_step=currentStep)
print("Saved model checkpoint to {}\n".format(path))
inputs = {"inputX": tf.saved_model.utils.build_tensor_info(lstm.inputX),
"keepProb": tf.saved_model.utils.build_tensor_info(lstm.dropoutKeepProb)}
outputs = {"predictions": tf.saved_model.utils.build_tensor_info(lstm.binaryPreds)}
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()
10 預測代碼
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"
# 注：下面兩個詞典要保證和當前加載的模型對應的詞典是一致的
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.load(f)
idx2label = {value: key for key, value in label2idx.items()}
xIds = [word2idx.get(item, word2idx["UNK"]) for item in x.split(" ")]
if len(xIds) >= config.sequenceLength:
xIds = xIds[:config.sequenceLength]
else:
xIds = xIds + [word2idx["PAD"]] * (config.sequenceLength - len(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():
checkpoint_file = tf.train.latest_checkpoint("../model/Bi-LSTM-atten/model/")
saver = tf.train.import_meta_graph("{}.meta".format(checkpoint_file))
saver.restore(sess, checkpoint_file)
# 獲得需要餵給模型的參數，輸出的結果依賴的輸入值
inputX = graph.get_operation_by_name("inputX").outputs[0]
dropoutKeepProb = graph.get_operation_by_name("dropoutKeepProb").outputs[0]
# 獲得輸出的結果
predictions = graph.get_tensor_by_name("output/predictions:0")
pred = sess.run(predictions, feed_dict={inputX: [xIds], dropoutKeepProb: 1.0})[0]
pred = [idx2label[item] for item in pred]
print(pred)