多層感知機MLP的gluon版分類minist

 

MLP_Gluon

 

 

In [2]:

import gluonbook as gb
from mxnet import gluon, init
from mxnet.gluon import loss as gloss,nn

In [4]:

net = nn.Sequential()
net.add(nn.Dense(256,activation='relu'),nn.Dense(10))
net.initialize(init.Normal(sigma=0.01))

In [5]:

batch_size = 256
train_iter, test_iter = gb.load_data_fashion_mnist(batch_size)

 

損失函數

In [6]:

loss = gloss.SoftmaxCrossEntropyLoss()
trainer = gluon.Trainer(net.collect_params(),'sgd',{'learning_rate':0.5})
num_epochs = 5
gb.train_ch3(net,train_iter,test_iter,loss,num_epochs,batch_size,None,None,trainer)

 

epoch 1, loss 0.8074, train acc 0.700, test acc 0.829
epoch 2, loss 0.4819, train acc 0.823, test acc 0.852
epoch 3, loss 0.4306, train acc 0.840, test acc 0.855
epoch 4, loss 0.3935, train acc 0.856, test acc 0.856
epoch 5, loss 0.3714, train acc 0.863, test acc 0.865