How can I test my pytorch model on validation data during training?
I know that there is the function myNet.eval()
which apparantly switches of any dropout layers, but is it also preventing the gradients from being accumulated?
Also how would I undo the myNet.eval()
command in order to continue with the training?
If anyone has some code snippet / toy example I would be grateful!
How can I test my pytorch model on validation data during training?
There are plenty examples where there are train and test steps for every epoch during training. An easy one would be the official MNIST example. Since pytorch does not offer any high-level training, validation or scoring framework you have to write it yourself. Commonly this consists of
torch.utils.dataloader.Dataloader
)train()
function that uses training data to optimize the modeltest()
or valid()
function to measure the effectiveness of the model given validation data and a metricThis is also what you will find in the linked example.
Alternatively you can use a framework that provides basic looping and validation facilities so you don't have to implement everything by yourself all the time.
Also how would I undo the
myNet.eval()
command in order to continue with the training?
myNet.train()
or, alternatively, supply a boolean to switch between eval and training: myNet.train(True)
for train mode.
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