new algo

[jjaskirat-ssingh]

Description

• Introduced a new Restricted Boltzmann Machine (RBM) model in PyTorch with forward pass, Gibbs sampling, and sampling functionalities.
• Added training and evaluation routines for the RBM, including a method to visualize generated samples.
• Created a requirements file to manage project dependencies.

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Changes walkthrough

	Relevant files
Enhancement	main.py Implementation of Restricted Boltzmann Machine in PyTorch            rbm/main.py Implemented a Restricted Boltzmann Machine (RBM) model class with methods for forward pass, Gibbs sampling, and sampling from the model. Added a training function that includes a naive approach for initializing from a random distribution every gradient step. Included an evaluation function based on reconstruction error and a method for displaying generated samples as images. Created a main function to parse arguments and run the training, evaluation, and image generation processes. +147/-0
Configuration changes	requirements.txt Requirements for RBM Project                                                                      rbm/requirements.txt Specified dependencies for the RBM project including torch, torchvision, and matplotlib. +3/-0

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🔍 Anti-patterns Detected:

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rbm/main.py

Issue	Lines
If possible, it is better to rely on automatic pinning in PyTorch to avoid undefined behavior and for efficiency	129-129

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[codeant-ai]

Things to consider

1. Random Initialization in Training Loop: The training function initializes v as a random Bernoulli distribution in every iteration. This is not typical for RBM training, as it should ideally use the visible units from the previous step (Contrastive Divergence - CD) or persist the chain across different batches (Persistent Contrastive Divergence - PCD). The current implementation may not allow the model to learn the data distribution effectively.

2. Evaluation Based on Reconstruction Error: The evaluation function uses reconstruction error to assess the model. However, for generative models like RBMs, reconstruction error may not be the best metric to evaluate the quality of the generated samples. The model might have a low reconstruction error but still generate poor samples. A better approach might be to use a metric that assesses the quality of generated samples directly, such as the Free Energy gap.

3. Binary Data Transformation: The transformation of MNIST data into binary form (torch.where(data > 1, torch.tensor(1), torch.tensor(0))) assumes that any pixel with a value greater than 1 should be considered 'on' (1). This might not be the best way to binarize the data, as it could lead to loss of information. A more common approach is to normalize the data and then threshold, which is not seen here. This could affect the model's ability to learn the underlying distribution of the data.

[codeant-ai]

If possible, it is better to rely on automatic pinning in PyTorch to avoid undefined behavior and for efficiency

[jjaskirat-ssingh]

@CodeAnt-AI ask: Can you provide the correct implementation of Random Initialization in Training Loop.

[codeant-ai]

Question: Can you provide the correct implementation of Random Initialization in Training Loop.

Answer:
The current implementation of random initialization in the training loop is as follows:

for i in range(epochs + 1): for batch in train_loader: v = torch.bernoulli(torch.rand(batch_size,784)).to(device) b = batch.view(batch_size, -1).to(device) loss, _ = model(b, v) optimizer.zero_grad() loss.backward() optimizer.step()

In this snippet, v is initialized as a random binary tensor with the same shape as the visible layer of the RBM (batch_size, 784). This is done by first generating a tensor with random values between 0 and 1 using torch.rand(batch_size, 784) and then converting it to a binary tensor using torch.bernoulli, which treats the input values as probabilities for generating a 1 (with the probability of 0 being the complement).

This approach is correct for initializing the visible units v randomly at each gradient step, which is a common practice in training RBMs. However, it's important to note that this is a naive initialization and does not take into account the previous state of the system. For more advanced training techniques like Contrastive Divergence (CD) or Persistent Contrastive Divergence (PCD), the initialization would be different.

For CD, you would initialize v with the data b at the beginning of each gradient step:

for i in range(epochs + 1): for batch in train_loader: b = batch.view(batch_size, -1).to(device) loss, _ = model(b, b) # For CD, initialize v with b optimizer.zero_grad() loss.backward() optimizer.step()

For PCD, you would maintain the state of v outside the loop and update it with the sample generated from the model at the previous step:

v = torch.bernoulli(torch.rand(batch_size, 784)).to(device) # Initialize v once before the loop for i in range(epochs + 1): for batch in train_loader: b = batch.view(batch_size, -1).to(device) loss, v = model(b, v) # For PCD, use the persistent v optimizer.zero_grad() loss.backward() optimizer.step()

In summary, the current implementation of random initialization in the training loop is correct for a naive approach. For more sophisticated methods like CD or PCD, the initialization would be adapted as shown above.

[jjaskirat-ssingh]

@CodeAnt-AI ask: Also, can you suggest provide the code for the suggested approach for Binary Data Transformation?