import d2l from mxnet import gluon, init, npx from mxnet.gluon import nn npx.set_np() train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size=256)
Model and initialization
net = nn.Sequential() net.add(nn.Dense(10)) net.initialize(init.Normal(sigma=0.01))
Loss function, optimization algorithm and training
loss = gluon.loss.SoftmaxCrossEntropyLoss() trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': 0.1}) d2l.train_ch3(net, train_iter, test_iter, loss, 10, trainer)