keras LSTM实现imdb电影评论文本二分类

keras 实现IMDB电影评论情绪分类

        数据集来自IMDB的2.5万部电影评论，以情绪（正面/负面）标记。评论已被预处理，并且每个评论被编码为一系列词索引（整数）。为了方便起见，词汇通过数据集中的整体频率进行索引，例如整数“3”对数据中第三个最频繁的词进行编码。这允许快速过滤操作，例如：“只考虑前10000个最常用的词，但是排除前20个最常见的词”。作为惯例，“0”不代表特定的单词，而是用于编码任何未知的单词。

IMDB数据集下载超时，详见本篇博客提供下载和使用：

https://blog.csdn.net/sinat_41144773/article/details/89843688

 

代码实现（评价指标+画损失图）

from __future__ import print_function
import numpy as np
np.random.seed(1337)
from keras.preprocessing import sequence
from keras.models import Sequential
from keras.layers import Dense,Embedding
from keras.layers import LSTM
from keras.datasets import imdb
from keras.callbacks import Callback
from keras.optimizers import Adam
import matplotlib.pyplot as plt
from sklearn.metrics import confusion_matrix, f1_score, precision_score, recall_score,accuracy_score
# 写一个LossHistory类，保存loss和acc
class LossHistory(Callback):
    def on_train_begin(self, logs={}):
        self.losses = {'batch':[], 'epoch':[]}
        self.accuracy = {'batch':[], 'epoch':[]}
        self.val_loss = {'batch':[], 'epoch':[]}
        self.val_acc = {'batch':[], 'epoch':[]}

    def on_batch_end(self, batch, logs={}):
        self.losses['batch'].append(logs.get('loss'))
        self.accuracy['batch'].append(logs.get('acc'))
        self.val_loss['batch'].append(logs.get('val_loss'))
        self.val_acc['batch'].append(logs.get('val_acc'))

    def on_epoch_end(self, batch, logs={}):
        self.losses['epoch'].append(logs.get('loss'))
        self.accuracy['epoch'].append(logs.get('acc'))
        self.val_loss['epoch'].append(logs.get('val_loss'))
        self.val_acc['epoch'].append(logs.get('val_acc'))

    def loss_plot(self, loss_type):
        iters = range(len(self.losses[loss_type]))
        plt.figure()
        # acc
        plt.plot(iters, self.accuracy[loss_type], 'r', label='train acc')
        # loss
        plt.plot(iters, self.losses[loss_type], 'g', label='train loss')
        if loss_type == 'epoch':
            # val_acc
            plt.plot(iters, self.val_acc[loss_type], 'b', label='val acc')
            # val_loss
            plt.plot(iters, self.val_loss[loss_type], 'k', label='val loss')
        plt.grid(True)
        plt.xlabel(loss_type)
        plt.ylabel('acc-loss')
        plt.legend(loc="upper right")
        plt.savefig("imdb_keras.png")
        plt.show()

# 训练参数
learning_rate = 0.001
epochs = 3
batch_size = 128


(x_train,y_train),(x_test,y_test) = imdb.load_data(num_words= 5000)
print(len(x_train),'train sequences')
print(len(x_test),'test sequences')
x_train = sequence .pad_sequences(x_train ,maxlen= 80 )
x_test = sequence .pad_sequences(x_test ,maxlen= 80 )
print('x_train shape:',x_train .shape )
print('x_test shape:',x_test .shape )

print('Build model...')
model = Sequential()
model.add(Embedding (8000 ,64))#嵌入层将正整数下标转换为固定大小的向量。只能作为模型的第一层
# model.add(LSTM(units=16,return_sequences=True))
model.add(LSTM(units=16))
model.add(Dense(1,activation= 'sigmoid'))

model.summary()
model.compile(loss= 'binary_crossentropy',optimizer=Adam(lr=learning_rate),metrics= ['accuracy'])
history = LossHistory()
model.fit(x_train ,y_train ,batch_size= batch_size ,epochs= epochs,validation_data= (x_test ,y_test ),callbacks=[history])


y_predict = model.predict(x_test, batch_size=512, verbose=1)
y_predict = (y_predict > 0.5).astype(int)
y_true = np.reshape(y_test, [-1])
y_pred = np.reshape(y_predict, [-1])

# 评价指标
accuracy = accuracy_score(y_true, y_pred)
precision = precision_score(y_true, y_pred)
recall = recall_score(y_true, y_pred, average='binary')
f1score = f1_score(y_true, y_pred, average='binary')

print('accuracy:',accuracy)
print('precision:',precision)
print('recall:',recall)
print('f1score:',f1score)

#绘制训练的acc-loss曲线
history.loss_plot('epoch')

评价指标 

accuracy: 0.83404
precision: 0.865790626368813
recall: 0.79064
f1score: 0.8265105582270541

精度+损失函数图 

 

结束。