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  • 多层感知机MLP的gluon版分类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)
    
     

    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
    
     
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  • 原文地址:https://www.cnblogs.com/TreeDream/p/10021237.html
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