[Suggestion] Improving TorchSharp experience in Notebook with Torchsharp.Summary
Nobody has claimed this yet.
Assessment
- Difficulty
- 5/5
- Estimated time
- Over a week
- Newbie friendliness
- 25/100
- Issue type
- Feature
- Clarity
- Mostly clear
- Activity status
- Stale
- Tech stack
- jupyter-notebook
- Domain
- machine-learning
Research direction
Start by reviewing the TorchSharp notebook examples and the linked torch-summary reference to determine the intended integration point. Done should be a TorchSharp.Summary experience in a notebook that produces the demonstrated layer, shape, parameter, and size information.
Written by the indexing model from the issue text.
Description
Is there interest to port
torch-summary to TorchSharp.Summary
from torchsummary import summary
model = ConvNet()
summary(model, (1, 28, 28))
So in TorchSharp notebook, we get:
==========================================================================================
Layer (type:depth-idx) Output Shape Param #
==========================================================================================
├─Conv2d: 1-1 [-1, 10, 24, 24] 260
├─Conv2d: 1-2 [-1, 20, 8, 8] 5,020
├─Dropout2d: 1-3 [-1, 20, 8, 8] --
├─Linear: 1-4 [-1, 50] 16,050
├─Linear: 1-5 [-1, 10] 510
==========================================================================================
Total params: 21,840
Trainable params: 21,840
Non-trainable params: 0
==========================================================================================
Input size (MB): 0.00
Forward/backward pass size (MB): 0.05
Params size (MB): 0.08
Estimated Total Size (MB): 0.14
==========================================================================================
- Dominant language
- Jupyter Notebook
- Stars
- 175
- Forks
- 61
- PR merge metrics
- No merged PRs in 30d
Contributor guide
No contributing guide indexed for this repository
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
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