* no of categories for efficientnet
* need layer_init_uniforn
* merge fail
* merge fail
* batchnorms
* needs work
* needs work how determine training
* pow
* needs work
* reshape was needed
* sum with axis
* sum with axis and tests
* broken
* works again
* clean up
* Update test_ops.py
* using sum
* don't always update running_stats
* space
* self
* default return running_stats
* passes test
* need to use mean
* merge
* testing
* fixing pow
* test_ops had a line dropped
* undo pow
* rebase
* to make it work locally
* definitely not working
* Conv2D GPU passes some of the tests
* Conv2D GPU passes more of the tests
* passes some tests and mnist
* removed unecessary code
* Conv2D Backpass works
* wrong test_ops.py
* white space + test backward
* ereased useless code
* removed default argument
* long lines
* works also with 4 channel .png files
* commenting out
* track
* pygame is fine, cv2 can also do the trick
* retimg and copy constructor not needed
* shape is missing without copy constructor
* retimg put back
* addressing capture buffering
from https://github.com/lukemelas/EfficientNet-PyTorch/blob/master/efficientnet_pytorch/utils.py
```
blocks_args = [
'r1_k3_s11_e1_i32_o16_se0.25',
'r2_k3_s22_e6_i16_o24_se0.25',
'r2_k5_s22_e6_i24_o40_se0.25',
'r3_k3_s22_e6_i40_o80_se0.25',
'r3_k5_s11_e6_i80_o112_se0.25',
'r4_k5_s22_e6_i112_o192_se0.25',
'r1_k3_s11_e6_i192_o320_se0.25',
]
```
now it's a persian cat.
* streamlined numerical_jacobian
* Got rid of the g loop in Conv2D.forward
* ereased stupid line
* nothing
* no loops in Conv2D forward
* Conv2D backprop improved
* stupid things in examples
* alternative to einsum
* Conv2D backward einsum alternative
* tidying up
* tidied up
* no ravel
* got rid of print
* Update efficientnet.py
* Update efficientnet.py
* Update efficientnet.py
* only tensordot
* 255.0
* whitespace
* aspect ratio error in efficientnet
* noprint
Co-authored-by: Marcel Bischoff <marcel@Marcels-iMac.local>
