Lottery_Ticket_Hypothesis-TensorFlow_2
data-science-python
| Lottery_Ticket_Hypothesis-TensorFlow_2 | data-science-python | |
|---|---|---|
| 6 | 1 | |
| 33 | 0 | |
| - | - | |
| 4.1 | 5.0 | |
| about 1 month ago | 7 days ago | |
| Jupyter Notebook | Jupyter Notebook | |
| - | - |
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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.
data-science-python
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Tensorflow misclassification problem
Data source used in the project credit_data.h5.
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