MNIST手写体分类.py

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.optimizers import SGD

#载入数据
(x_train,y_train),(x_test,y_test) = mnist.load_data('F:\\keras_project\\mnist.npz')
#打印数据格式
print('x_shape: ',x_train.shape)#6000-28-28
print('y_shape: ',y_train.shape)#60000
#60000-28-28->60000-784，并归一化处理
x_train = x_train.reshape(x_train.shape[0],-1)/255.0
x_test = x_test.reshape(x_test.shape[0],-1)/255.0
#转换label成one hot形式
y_train = np_utils.to_categorical(y_train,num_classes=10)#keras函数，10个分类
y_test = np_utils.to_categorical(y_test,num_classes=10)
#创建模型，输入784个神经元，输出10个神经元
model = Sequential()
#偏置值1；激活函数softmax-输出转成概率值
model.add(Dense(input_dim=784,units=10,bias_initializer='one',activation='softmax'))
#定义优化器
sgd = SGD(lr=0.2)
#定义优化器和loss function,设置训练过程中计算准确率
model.compile(optimizer=sgd,loss='mse',metrics=['accuracy'])#loss均方差
#fit方法训练模型
model.fit(x_train,y_train,batch_size=64,epochs=10)
#评估模型
loss,accuracy = model.evaluate(x_test,y_test)
print('\ntest loss: ',loss)
print('accuracy: ',accuracy)