MXNet中的LeNet net gluon.nn.Sequential() with net.name_scope(): net.add(gluon.nn.Conv2D(channels=20,kernel_size=5, activation='tanh')) net.add(gluon.nn.AvgPool2D(pool_size=2)) net.add(gluon.nn.Conv2D(channels=50,kernel_size=5, activation='tanh')) net.add(gluon.nn.AvgPool2D(pool_size=2)) ● net.add(gluon.nn.Flatten()) net.add(gluon.nn.Dense(500,activation='tanh')) ● net.add(gluon.nn.Dense(10)) loss gluon.loss.SoftmaxCrossEntropyLoss() .(size and shape inference is automatic) D2L.aiMXNet 中的 LeNet • net = gluon.nn.Sequential() • with net.name_scope(): • net.add(gluon.nn.Conv2D(channels=20, kernel_size=5, activation='tanh')) • net.add(gluon.nn.AvgPool2D(pool_size=2)) • net.add(gluon.nn.Conv2D(channels=50, kernel_size=5, activation='tanh')) • net.add(gluon.nn.AvgPool2D(pool_size=2)) • net.add(gluon.nn.Flatten()) • net.add(gluon.nn.Dense(500, activation='tanh')) • net.add(gluon.nn.Dense(10)) • loss = gluon.loss.SoftmaxCrossEntropyLoss() • (size and shape inference is automatic)