基于Keras的IMDB数据集电影评论情感二分类

原创不易，如果有转载需要的话，请在首行附上本文地址，谢谢。

第一步加载IMDB数据集，如若加载不成功，这里提供一种解决方法：点开IMDB数据下载链接（喜欢的话给个小星星和follow一下），fork到自己的仓库中下载imdb.pnz，或者直接下载imdb.pnz。下载好后将imdb.pnz放入你的keras数据库中如：~/.keras/datasets/，即可正常运行。

具体代码如下（放到编译器中可直接运行）：

部分代码有注释，便于读者理解

from keras.datasets import imdb
(train_data,train_labels),(test_data,test_labels)=imdb.load_data(num_words=10000)

import numpy as np
def vectorize_sequences(sequences,dimension=10000):
    results=np.zeros((len(sequences),dimension))
    for i,sequence in enumerate(sequences):
        results[i,sequence]=1
    return results

x_train=vectorize_sequences(train_data)
x_test=vectorize_sequences(test_data)
print(x_train[0])


y_train=np.asarray(train_labels).astype('float32')
y_test=np.asarray(test_labels).astype('float32')

        
from keras import models
from keras import layers

model= models.Sequential()
model.add(layers.Dense(16,activation='relu',input_shape=(10000,)))
model.add(layers.Dense(16,activation='relu'))
model.add(layers.Dense(1,activation='sigmoid'))
# =============================================================================
# #编译模型
# model.compile(optimizer='rmsprop',loss='binary_crossentropy',metrics=['accuracy'])
# =============================================================================

# =============================================================================
##配置优化器、损失函数
# from keras import optimezers
# from keras import losses
# from keras import metrics
# model.compile(optimezers=optimezers.RMSprop(lr=0.001),loss=losses.binary_crossentropy,metrics=[metrics.binary_accuracy])
# =============================================================================

#留出验证集
x_val=x_train[:10000]
partial_x_train=x_train[10000:]
y_val=y_train[:10000]
partial_y_train=y_train[10000:]

#训练模型
model.compile(optimizer='rmsprop',loss='binary_crossentropy',metrics=['acc'])
history=model.fit(partial_x_train,partial_y_train,epochs=20,batch_size=512,validation_data=(x_val,y_val))

#绘制训练损失和验证损失
import matplotlib.pyplot as plt
history_dict=history.history
loss_values=history_dict['loss']
val_loss_values=history_dict['val_loss']
epochs=range(1,len(loss_values)+1)
plt.plot(epochs,loss_values,'bo',label='Training loss')
plt.plot(epochs,val_loss_values,'b',label='Validation loss')
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.legend()
plt.show()

#绘制训练精度和验证精度
plt.clf()  #清空图像
acc=history_dict['acc']
val_acc=history_dict['val_acc']
plt.plot(epochs,acc,'bo',label='Training acc')
plt.plot(epochs,val_acc,'b',label='Validation acc')
plt.title('Training and validation accuracy')
plt.xlabel('Epochs')
plt.ylabel('Accuracy')
plt.legend()
plt.show()

# =============================================================================
# #重头开始训练，epoch改为4
# model.compile(optimizer='rmsprop',loss='binary_crossentropy',metrics=['accuracy'])
# model.fit(x_train,y_train,epochs=4,batch_size=512)
# results=model.evaluate(x_test,y_test)
# =============================================================================

# =============================================================================
# #使用训练好的网络在新数据上生成预测结果
# model.predict(x_test)
# =============================================================================