labml
Lottery_Ticket_Hypothesis-TensorFlow_2
| labml | Lottery_Ticket_Hypothesis-TensorFlow_2 | |
|---|---|---|
| 23 | 6 | |
| 1,889 | 33 | |
| 1.2% | - | |
| 9.8 | 4.1 | |
| 9 days ago | 2 months ago | |
| Jupyter Notebook | Jupyter Notebook | |
| MIT License | - |
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labml
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Creating stickers using SD with img2img
Used the PromptArt app by labml.ai to generate a sticker of an image I took from my iPhone. The results are amazing.
- [D] Why doesn’t your team use an experiment tracking tool?
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Probe PyTorch models
💻 Github
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[P] Probe PyTorch models
🧑🏫 Demo that extracts attention maps of BERT
- Show HN: Probe PyTorch Models
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- Machine Learning Best Practices
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[D] Machine Learning Best Practices
from github
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[P] Annotated deep learning paper implementations
labmlai/labml is a set of tools (tracking experiments, configurations, a bunch of helpers) we coded to ease our ML work (which later improved and open sourced). So we use it in all our projects because it makes things easier for us.
- React's UI State Model vs. Vanilla JavaScript
Lottery_Ticket_Hypothesis-TensorFlow_2
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Freeze certain weights - TensorFlow 2
I have already implemented "The Lottery Ticket Hypothesis" by Frankle et al. using TensorFlow 2. You can refer to the code here. Here, a binary mask (0, 1) is used for element-wise multiplication to keep the number of pruned parameters constant because by default, when you apply gradient descent algorithm, then using the weight update rule, all of the weights are updated.
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[R] Remove pruned connections
Some of my recent experiments in GitHub can be referred: Lottery Ticket Hypothesis implementation and Neural Network Pruning.
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TensorFlow Lite: RuntimeError
I am using TensorFlow version: 2.3.0 and Python3. I am experimenting in Quantizing a pruned and trained Conv-2 CNN model. The model architecture is: conv -> conv -> max pool -> dense -> dense -> output for CIFAR-10. You can see the Jupyter-notebook here.
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Iterative Pruning: LeNet-300-100 - PyTorch
The code can be accessed here
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Neural Network Compression - Implementation benefits
here
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ValueError: TensorFlow2 Input 0 is incompatible with layer model
True, removing he_normal initialization does increase the accuracy. For most of my previous experiments I have usually used the kernel initialization as mentioned in the different author's paper(s). Therefore for ResNet, I thought of using Kaiming He initialization as he is the author of the research paper. However, the default kernel initialization in TF2 is 'glorot_uniform' which leads to 60.04% val_accuracy.
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text-to-text-transfer-transformer - Code for the paper "Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer"