keras基础--9.RNN应用

import numpy as np
from keras.datasets import mnist
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers import Dense
from keras.layers.recurrent import SimpleRNN#常用的RNN有这三个，根据需求导入SimpleRNN,LSTM,GRU
from keras.optimizers import Adam

#数据长度-一行有28个像素
input_size = 28
#序列长度-一共有28行
time_steps = 28
#隐藏层cell个数
cell_size = 50

#载入数据
(x_train,y_train),(x_test,y_test) = mnist.load_data()
#(60000,28,28)
x_train = x_train/255.0
x_test = x_test/255.0
#换one-hot格式
y_train = np_utils.to_categorical(y_train,num_classes=10)
y_test = np_utils.to_categorical(y_test,num_classes=10)

#创建模型
model = Sequential()

#循环神经网络
model.add(SimpleRNN(
    units = cell_size,#输出
    input_shape = (time_steps,input_size),#输入
))

#输出层
model.add(Dense(10,activation='softmax'))

#定义优化器
adam = Adam(lr=1e-4)

#定义优化器，loss_function,训练过程中计算准确率
model.compile(optimizer=adam,loss='categorical_crossentropy',metrics=['accuracy'])

#训练模型
model.fit(x_train,y_train,batch_size=64,epochs=10)

#评估模型
loss,accuracy = model.evaluate(x_test,y_test)

print('test loss',loss)
print('test accuracy',accuracy)