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I have made some performance improvements to the model, and testing them now.  If results turn out good, I will submit a separate PR.

Using python2 style '/' will convert int type to float in python3 which will cause the following error when creating FloatTensor:

TypeError: torch.FloatTensor constructor received an invalid combination of arguments - got (float, int, int, int), but expected one of:
 * no arguments
 * (int ...)
      didn't match because some of the arguments have invalid types: (float, int, int, int)

* clipping values, and replacing transposes with permute

Without clipping, any values larger than 255 will be replaced with
int(v mod 256) by byte(), which results in high freq noise in image.

* replaced clip with clamp

This reverts commit 2f579d58.

Without clipping, any values larger than 255 will be replaced with
int(v mod 256) by byte(), which results in high freq noise in image.

is already downloaded* for CIFAR and STL10

* Running sanity checks on cropped parts of scipy.misc.ascent

Doing this to ensure that the image is of the size 100 x 212 which is being followed throughout the sanity checks

* Update sanity_checks.ipynb

Doing this to ensure that the image is of the size 100 x 212 which is being followed throughout the sanity checks

In python2, calling `make_grid` won't display the last images if the number of images doesn't divide `nrow`.
Int division...

This should fix that!

* fix for to_tensor when input is np.ndarray of shape [H,W,C]. Issue #48 pytorch/vision

* update cifar datasets to transpose images from CHW -> HWC

* fix flake8 issue on test_transforms.py