I am doing making simple NN using MXnet , but having some problem in step() method
x1.shape=(64, 1, 1000)
y1.shape=(64, 1, 10)
net =nm.Sequential()
net.add(nn.Dense(H,activation='relu'),nn.Dense(90,activation='relu'),nn.Dense(D_out))
for t in range(500):
#y_pred = net(x1)
#loss = loss_fn(y_pred, y)
#for i in range(len(x1)):
with autograd.record():
output=net(x1)
loss =loss_fn(output,y1)
loss.backward()
trainer.step(64)
if t % 100 == 99:
print(t, loss)
#optimizer.zero_grad()
UserWarning: Gradient of Parameter
dense30_weight
on context cpu(0) has not been updated by backward since laststep
. This could mean a bug in your model that made it only use a subset of the Parameters (Blocks) for this iteration. If you are intentionally only using a subset, call step with ignore_stale_grad=True to suppress this warning and skip updating of Parameters with stale gradient