如何导入pytorch训练好的模型或者权重文件？
Love the book! Ran into this display issue when viewing it on Chrome's PDF viewer:
This issue is to create the TabNet model and add it to the basic model zoo. TabNet is a good example of a deep learning model that will work with the tabular modality. Then, it can be trained or tested with an implementation of the CsvDataset such as AirfoilRandomAccess or AmesRandomAccess.
Right now, qml.operation.expand_matrix is often called in a code-block like:
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if wire_order is None or self.wires == Wires(wire_order):
return canonical_matrix
expand_matrix(canonical_matrix, wires=self.wires, wire_order=wire_order)
see [pennylane/operation.py Line 587](https://github.com/PennyLaneAI/pennylane/blob/b6fc5380abea6215661704ebe2f5cb8e7a599635/pennylane/operation.p
Issue to track tutorial requests:
I recently submitted a PR and got a really nice and friendly welcome from this guy: https://github.com/apps/welcome
We should add similar bots to easen the workflow by triaging issues, PRs, and generally greet people nicely :)
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 approachWe 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
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The current implementation of Zero Redundancy optimizer has its own implementation of object broadcasting.
We should replace it with c10d [broadcast_object_list](https://pytorch.org/docs/stable/distributed.html#torch.distributed.broadcast_object_lis