手写识别

import numpy as np
import matplotlib.pyplot as plt
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

(x_train, y_train), (x_test, y_test) = mnist.load_data()

x_train = x_train.reshape(x_train.shape[0], x_train.shape[1]*x_train.shape[1])/255.0

x_test = x_test.reshape(x_test.shape[0], x_test.shape[1]*x_test.shape[1])/255.0

#10个数字 10个类
y_train = np_utils.to_categorical(y_train, num_classes=10)

y_test = np_utils.to_categorical(y_test, num_classes=10)
#结果

#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')
])

sgd = SGD(lr=0.2)

model.compile(
    optimizer=sgd,
    loss='mse',
    metrics=['accuracy']
)
#epochs 迭代周期
#训练
model.fit(x_train,y_train,batch_size=32,epochs=10)

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

print('\ntest loss',loss)
print('accuracy',accuracy)