Keras mlp 手写数字识别示例

#基于mnist数据集的手写数字识别

#构造了三层全连接层组成的多层感知机，最后一层为输出层

#基于Keras 2.1.1 Tensorflow 1.4.0

代码：

import keras
from keras.datasets import mnist 
from keras.models import Sequential 
from keras.layers import Dense,Dropout
from keras.optimizers import RMSprop

(x_train,y_train),(x_test,y_test) = mnist.load_data()
#载入数据，第一次运行时会从外部网络下载数据集到对应目录下
print(x_train.shape,y_train.shape)
print(x_test.shape,y_test.shape)

# import matplotlib.pyplot as plt
# im = plt.imshow(x_train[0],cmap='gray')
# plt.show()
# im2 = plt.imshow(x_train[1],cmap='gray')
# plt.show()
x_train = x_train.reshape(60000,784)
x_test = x_test.reshape(10000,784)
x_train = x_train.astype('float32')
x_train = x_train.astype('float32')
print(x_train.shape)
x_train = x_train/255
x_test = x_test/255
y_train = keras.utils.to_categorical(y_train,10)
y_test = keras.utils.to_categorical(y_test,10)

model = Sequential()
model.add(Dense(512,activation='relu',input_shape=(784,)))
model.add(Dropout(0.2))
model.add(Dense(512,activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(10,activation='softmax'))
model.summary()
model.compile(loss='categorical_crossentropy',optimizer=RMSprop(),metrics=['accuracy'])
model.fit(x_train,y_train,batch_size=64,epochs=2,verbose=1,validation_data=(x_test,y_test))
score = model.evaluate(x_test,y_test,verbose=1)
print('Test loss:',score[0])
print('Test accuracy',score[1])

结果：

Test loss: 0.123420921481
Test accuracy 0.9682