It seems the current implementation will cut the tensor to min or max according to their values, which might be a problem when debugging logical errors or typos. Maybe it would be better to just throw an exception? Also it would be nice to note that in the documentation.

>>> import torch
>>> print(torch.__version__)
1.6.0
>>> a

Description

Implement a YOLO model and add it to the DJL model zoo

References

• https://arxiv.org/abs/1506.02640

Issue to track tutorial requests:

• Deep Learning with PyTorch: A 60 Minute Blitz - #69

In the LSNNLayer constructor we permit positional arguments, but not key-value arguments. To improve useability, we should allow both as in: https://pytorch.org/docs/stable/_modules/torch/nn/modules/rnn.html#RNN

• asarray : converts all array-likes to a numpy array; no copy for arrays
• astensor converts all array-likes to a mygrad tensor; returns tensors unchanged
• assert_almost_equal with various options to include checks of gradients and to include checks ac