batch_size = 16
train_data = gdata.DataLoader(cifar_train.transform_first(transform_train),
batch_size=batch_size,
shuffle=True)
transform_test = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize([0.4914, 0.4822, 0.4465], [0.2023, 0.1994, 0.2010])
])
val_data = gluon.data.DataLoader(cifar_test.transform_first(transform_test),
batch_size=batch_size,
shuffle=False)
if __name__ == '__main__':
ctx = mx.gpu()
net = get_model('cifar_resnet20_v1', classes=10, pretrained=True)
net.collect_params().reset_ctx(ctx)
net.initialize(ctx=ctx)
model = Classification(net=net, ctx=ctx)
model.summary()
history = model.fit(train_data, 1, val_data)
history.plot()
plt.legend()
plt.show()
