IMDB-二分类问题

from keras.datasets import imdb

from keras.utils.np_utils import to_categorical

import numpy as np

from keras import models

from keras import layers

import matplotlib.pyplot as plt

#one-hot编码

def vectorize_sequences(sequences,dimension = 10000):

    results = np.zeros((len(sequences),dimension))

    for i,sequence in enumerate(sequences):

        results[i,sequence] = 1

    return results

#imdb是一个二分类问题

#一共有5w条数据，2.5w用于训练，2.5w用于测试

#每条数据是一个list,list里保存的是英文单词对应的排序

#num_words=10000表示保留前1w个常出现的单词

(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=10000)

#下面的代码用来解码第一条数据的内容

data = x_train[0]

#word_index是一个dict,保存的是英文单词:单词排序位置

word_index = imdb.get_word_index()

index_word = dict((index,word) for (word,index) in word_index.items())

#i-3是because 0, 1 and 2 are reserved indices for "padding", "start of sequence", and "unknown".

data = ''.join(index_word.get(i-3,'?') for i in data)

######################################################

#神经网络的输入得是一个张量，使用one-hot编码处理数据

x_train = vectorize_sequences(x_train)

x_test = vectorize_sequences(x_test)

#keras的输入数据要转换为float类型，y是int类型，做一个类型转换

#构建神经网络

network = models.Sequential()

network.add(layers.Dense(16,activation='relu'))

network.add(layers.Dense(16,activation='relu'))

network.add(layers.Dense(1,activation='sigmoid'))

#选择优化器、损失函数、评估准则

network.compile('rmsprop',loss='binary_crossentropy',metrics=['accuracy'])

#训练模型

history = network.fit(x_train,y_train,epochs=5,batch_size=512,validation_split=0.2)

history_dict = history.history

loss = history_dict['loss']

val_loss = history_dict['val_loss']

acc = history_dict['acc']

val_acc = history_dict['val_acc']

epochs = range(1,6)

#loss的图

plt.subplot(121)

plt.plot(epochs,loss,'g',label = 'Training loss')

plt.plot(epochs,val_loss,'b',label = 'Validation loss')

plt.xlabel('Epochs')

plt.ylabel('Loss')

#显示图例

plt.legend()

plt.subplot(122)

plt.plot(epochs,acc,'g',label = 'Training accuracy')

plt.plot(epochs,val_acc,'b',label = 'Validation accuracy')

plt.xlabel('Epochs')

plt.ylabel('accuracy')

plt.legend()

plt.show()

pre = network.predict(x_test)

print(pre)

print(y_test)