1 import numpy as np
2 from keras.datasets import mnist
3 from keras.utils import np_utils
4 from keras.models import Sequential
5 from keras.layers import Dense,Dropout
6 from keras.optimizers import SGD
# 载入数据
(x_train,y_train),(x_test,y_test) = mnist.load_data()
# (60000,28,28)
print('x_shape:',x_train.shape)
# (60000)
print('y_shape:',y_train.shape)
# (60000,28,28)->(60000,784)
x_train = x_train.reshape(x_train.shape[0],-1)/255.0
x_test = x_test.reshape(x_test.shape[0],-1)/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([
Dense(units=200,input_dim=784,bias_initializer='one',activation='tanh'),
Dropout(0.4),
Dense(units=100,bias_initializer='one',activation='tanh'),
Dropout(0.4),
Dense(units=10,bias_initializer='one',activation='softmax')
])
# 定义优化器
sgd = SGD(lr=0.2)
# 定义优化器,loss function,训练过程中计算准确率
model.compile(
optimizer = sgd,
loss = 'categorical_crossentropy',
metrics=['accuracy'],
)
# 训练模型
model.fit(x_train,y_train,batch_size=32,epochs=10)
# 评估模型
loss,accuracy = model.evaluate(x_test,y_test)
print('\ntest loss',loss)
print('test accuracy',accuracy)
loss,accuracy = model.evaluate(x_train,y_train)
print('train loss',loss)
print('train accuracy',accuracy)
![](https://img2018.cnblogs.com/blog/1776985/201909/1776985-20190922102435644-212906596.jpg)