An interactive book on deep learning. Much easy, so MXNet. Wow. [Straight Dope is growing up] ---> Much of this content has been incorporated into the new Dive into Deep Learning Book available at https://d2l.ai/.
the function evaluate_accuracy that appears in various examples in the tutorial is written in a suboptimal way, e.g. from deep conv nets:
def evaluate_accuracy(data_iterator, net):
acc = mx.metric.Accuracy()
for d, l in data_iterator:
data = d.as_in_context(ctx)
label = l.as_in_context(ctx) # This is unnecessary
output = net(data)
predictions = nd.argmax(output, axis=1)
acc.update(preds=predictions, labels=label)
return acc.get()[1]
Usually in the examples, ctx refers to mx.gpu(), however within the definition ofmx.metric.Accuracy
we see that both predictions and labels are transformed to numpy arrays (i.e. copies to mx.cpu() context). Therefore, if ctx = mx.gpu() this function definition has unnecessary copies of labels into gpu memory and drops performance.
Thank you for the awesome tutorial you've created!
Regards
Dear all,
the function
evaluate_accuracythat appears in various examples in the tutorial is written in a suboptimal way, e.g. from deep conv nets:Usually in the examples, ctx refers to
mx.gpu(), however within the definition ofmx.metric.Accuracywe see that both predictions and labels are transformed to numpy arrays (i.e. copies tomx.cpu()context). Therefore, ifctx = mx.gpu()this function definition has unnecessary copies of labels into gpu memory and drops performance.Thank you for the awesome tutorial you've created! Regards