It does some random squishing by default too.
Yes, in Colab use this function
from google.colab import files
uploaded = files.upload()
You can upload any filetypes also .obj for Pytorch3D http://www.bimhox.com/2020/03/15/pytorch3d-3d-deep-learning-in-architecture/
When this is available on Amazon, will there be a kindle version.
I’m happy to buy a physical book.
But with current state of the world, I think I have to wait a lot of time.
(Due to shipping issues around the world these days)
Yes I believe so.
Awesome. Thanks.
Further down from in chapter 4 notebook of lesson 3 in the SGD section there is an area which may give confusion.
def train_epoch(model, lr, params):
for xb,yb in dl:
calc_grad(xb, yb, model)
for p in params:
p.data -= p.grad*lr
p.grad.zero_()
it seems we have a reference to a global variable dl
in this method which may create confusion.
Perhaps put the method into a class initialised with dl etc
so the relationship is explicit and run the methods of that class.
Yes, the binary cross entropy loss is incorrect in the 06_multicat
notebook.
I believe the correct loss is:
def binary_cross_entropy(inputs, targets):
inputs = inputs.sigmoid()
return -torch.where(targets==0, 1-inputs, inputs).log().mean()
The change is in the last line. It was:
return -torch.where(targets==1, 1-inputs, inputs).log().mean()