autograd

🚀 The feature, motivation and pitch

After the revert of pytorch/pytorch@7cf9b94 we've identified a need to add a lint that checks file names to ensure that they're compatible with Windows machines.

Observed error: (from example commit)

Error: error: invalid path 'test/test_ops_gradients.py '

A simple check on chang

环境

1.系统环境：

• Ubuntu 20.04.2 LTS x86_64
• GLIBCXX: 3.4.28 (libstdc++.so.6.0.28)
• GPU:
  

2.MegEngine版本：1.6.0rc1
3.python版本：Python 3.8.10

The program stuck at net.load when I was trying to use the MegFlow. I wait for more than 10min and there is no sign of finishing it.

Love the book! Ran into this display issue when viewing it on Chrome's PDF viewer:

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
• Sentence Classification - #79

Feature details

Due to the similarity, it is easy to confuse qml.X and qml.PauliX, especially since other methods of specifying circuits, e.g., QASM, use x for PauliX. But if a user uses qml.X in their circuit on a qubit device, nothing happens to inform them that the incorrect operation is being used:

@qml.qnode(dev)
def circ():
    qml.PauliX(wires=0)
    qml.Hada

Add a function and module that permits spike-time decoding, as suggested by @schmitts https://twitter.com/sbstnschmtthd/status/1432343373072019461

I think it would be very useful to have learning rate schedulers

• lr_cyclic() (https://arxiv.org/abs/1506.01186, Python source at https://pytorch.org/docs/stable/_modules/torch/optim/lr_scheduler.html#CyclicLR), and
• lr_cosine_annealing_warm_restarts() (https://arxiv.org/abs/1608.03983, Python source at https://pytorch.org/docs/stable/_modules/torch/optim/lr_scheduler.html#CosineAnnealin

The init module has been deprecated, and the recommend approach for generating initial weights is to use the Template.shape method:

>>> from pennylane.templates import StronglyEntanglingLayers
>>> qml.init.strong_ent_layers_normal(n_layers=3, n_wires=2) # deprecated
>>> np.random.random(StronglyEntanglingLayers.shape(n_layers=3, n_wires=2))  # new approach

We should upd

Okay, so this might not exactly be a "good first issue" - it is a little more advanced, but is still very much accessible to newcomers.

Similar to the mygrad.nnet.max_pool function, I would like there to be a mean-pooling layer. That is, a convolution-style windows is strided over the input, an