3.Minst数据集分类

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()
 # (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，输入784个神经元，输出10个神经元
 model = Sequential([
         Dense(units=10,input_dim=784,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 = 'mse',
     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('accuracy',accuracy)