IMDB数据集

from keras.datasets import imdb

(train_data,train_labels),(test_data,test_labels)=imdb.load_data(num_words=10000)
print(train_data[0])
print(train_labels[0])

print(max([max(sequence) for sequence in train_data]))
word_index=imdb.get_word_index()

reverse_word_index=dict(
    [(value,key) for (key,value) in word_index.items()]
)
decoded_review=' '.join([reverse_word_index.get(i-3,'?') for i in train_data[0]])
print(decoded_review)

#将整数序列编码为二进制矩阵
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')

####模型定义#####
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'))

####模型编译####
model.compile(optimizer='rmsprop',loss='binary_crossentropy',metrics=['accuracy'])

#####配置优化器#####
from keras import optimizers

model.compile(optimizer=optimizers.RMSprop(lr=0.001),loss='binary_crossentropy',metrics=['accuracy'])

####使用自定义的损失和指标###
from keras import losses
from keras import metrics

model.compile(optimizer=optimizers.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))
history_dict=history.history
print(history_dict.keys())

#####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')  ###'bo'表示蓝色圆点
plt.plot(epochs,val_loss_values,'b',label='Validation loss')
plt.title('Training and 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()
acc=history_dict['acc']
val_acc=history_dict['val_acc']

plt.plot(epochs,acc,'bo',label='Training acc')  ###'bo'表示蓝色圆点
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()