keras基础--6.正则化应用_keras 正则化代码示例-优快云博客

本文介绍了一个使用Keras框架实现的手写数字识别系统。该系统通过加载MNIST数据集进行训练，并采用三层全连接神经网络模型。模型利用了tanh激活函数和softmax输出层，同时应用了L2正则化防止过拟合。通过SGD优化器进行训练，最终在测试集上达到了较高的识别精度。

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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
from keras.regularizers import l2

# 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)
# -1的意思是可以取任何值，自动判断。/255.0是做归一化
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, ='one',activation='tanh',kernel_regularizer=l2(0.0003)),
    Dense(units=100,bias_initializer='one',activation='tanh',kernel_regularizer=l2(0.0003)),
    Dense(units=10,bias_initializer='one',activation='softmax',kernel_regularizer=l2(0.0003)) 
])

#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('accuracy',accuracy)

loss,accuracy = model.evaluate(x_train,y_train)
print('train loss',loss)
print('train accuracy',accuracy)