多层感知机

1.mxnet实现

import d2lzh as d2l
from mxnet import nd
from mxnet.gluon import loss as gloss
#读取数据集
batch_size=256
train_iter,test_iter=d2l.load_data_fashion_mnist(batch_size)
#定义模型参数
num_inputs,num_outputs,num_hiddens=784,10,256
W1=nd.random.normal(scale=0.01,shape=(num_inputs,num_hiddens))
b1=nd.zeros(num_hiddens)
W2=nd.random.normal(scale=0.01,shape=(num_hiddens,num_outputs))
b2=nd.zeros(num_outputs)
params=[W1,b1,W2,b2]

for param in params:
    param.attach_grad()
#定义激活函数
def relu(X):
    return nd.maximum(X,0)
#定义模型
def net(X):
    X=X.reshape((-1,num_inputs))
    H=relu(nd.dot(X,W1)+b1)
    return nd.dot(H,W2)+b2
#定义损失函数
loss=gloss.SoftmaxCrossEntropyLoss()
#训练模型
num_epochs,lr=5,0.5
d2l.train_ch3(net,train_iter,test_iter,loss,num_epochs,batch_size,params,lr)

2.Gluon实现

import d2lzh as d2l
from mxnet import gluon,init
from mxnet.gluon import loss as gloss,nn
#定义模型
net=nn.Sequential()
net.add(nn.Dense(256,activation='relu'),nn.Dense(10))
net.initialize(init.Normal(sigma=0.01))
#训练模型
batch_size=256
train_iter,test_iter=d2l.load_data_fashion_mnist(batch_size)
loss=gloss.SoftmaxCrossEntropyLoss()
trainer=gluon.Trainer(net.collect_params(),'sgd',{'learning_rate':0.5})
num_epochs=5
d2l.train_ch3(net,train_iter,test_iter,loss,num_epochs,batch_size,None,None,trainer)