IMDB-全连接神经网络
作者:互联网
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)
标签:loss,plt,word,index,神经网络,IMDB,dict,连接,history 来源: https://www.cnblogs.com/vshen999/p/10457180.html