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)

损失函数

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