Things on this page are fragmentary and immature notes/thoughts of the author. Please read with your own judgement!
optimizer.zero_grad()
output = model(data)
loss = F.nll_loss(output, target)
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), args.clip)
optimizer.step()
torch.nn.utils.clips_grad_norm_ (which is in-place)
instead …Transformations in torchvision.transforms work on images, tensors (representing images)
and possibly on numpy arrays (representing images).
However,
a transformation (e.g., ToTensor) might work differently on different input types.
So you'd be clear about what exactly a transformation function does.
A good practice is to always convert your non-tensor input data to tensors using the transformation ToTensor
Things on this page are fragmentary and immature notes/thoughts of the author. Please read with your own judgement!
Ansible is a better alternative to Fabric. It is suggested that you use Ansible instead.
Docstring will be displayed when you type the command fab -l.
Invoke is for local use …
Things on this page are fragmentary and immature notes/thoughts of the author. Please read with your own judgement!
http://en.tldp.org/LDP/solrhe/Securing-Optimizing-Linux-RH-Edition-v1.3/x4733.html
https://stackoverflow.com/questions/14471564/what-does-ulimit-s-unlimited-do
https://access.redhat.com/solutions/384633
The attribute pandas.Series.str can only be used with Series of str values.
You will either encounter an AttributionError
(Can only use .str accessor with string values, which use np.object_ dtype in pandas)
or find it to yield a Series of NaN's if you invoke it on a Series of non-string values.
If you have control of the DataFrame,
the preferred way is to cast the type the column to str