Weight Auto-encoder

[csmangum]

Take neural network weights and encode them through another neural network to a N-component transformation (similar to PCA). I also want the model to be able to go from an N-component to full weights (reversing the encoding)

[csmangum]

Here's a high-level overview of how you could approach this:

1. Design the Autoencoder Architecture:

   • Your autoencoder will consist of an encoder network and a decoder network.
   • The encoder network will take the full weights of the neural network as input and output the encoded representation (N components).
   • The decoder network will take the encoded representation and reconstruct the full weights.

2. Training Data:

   • You'll need a dataset of neural network weights to train your autoencoder.
   • You can generate this dataset by training your neural network on various tasks and then extracting and saving its weights.

3. Training:

   • Train the autoencoder using the dataset of neural network weights.
   • The objective will be to minimize the reconstruction error between the original weights and the reconstructed weights.

4. Encoder and Decoder Implementation:

   • After training, you can use the encoder part of the autoencoder to encode the full weights into the N component representation.
   • You can then use the decoder part of the autoencoder to decode the N component representation back into the full weights.

5. Evaluation:

   • Evaluate the performance of your autoencoder in terms of how accurately it can reconstruct the original weights from the encoded representation.

import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np

# Define the Autoencoder class
class Autoencoder(nn.Module):
    def __init__(self, input_dim, encoding_dim):
        super(Autoencoder, self).__init__()
        self.encoder = nn.Sequential(
            nn.Linear(input_dim, 128),
            nn.ReLU(True),
            nn.Linear(128, encoding_dim),
            nn.ReLU(True)
        )
        self.decoder = nn.Sequential(
            nn.Linear(encoding_dim, 128),
            nn.ReLU(True),
            nn.Linear(128, input_dim),
            nn.Sigmoid()
        )

    def forward(self, x):
        x = self.encoder(x)
        x = self.decoder(x)
        return x

# Example usage
input_dim = 1000  # Assuming your original weights have 1000 dimensions
encoding_dim = 50  # Desired number of components

# Instantiate the autoencoder
autoencoder = Autoencoder(input_dim, encoding_dim)

# Define loss function and optimizer
criterion = nn.MSELoss()
optimizer = optim.Adam(autoencoder.parameters(), lr=0.001)

# Generate some dummy data
num_samples = 1000
original_weights = np.random.rand(num_samples, input_dim)
original_weights = torch.tensor(original_weights, dtype=torch.float)

# Training loop
num_epochs = 100
for epoch in range(num_epochs):
    # Forward pass
    outputs = autoencoder(original_weights)
    loss = criterion(outputs, original_weights)

    # Backward pass and optimization
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

    # Print progress
    if (epoch+1) % 10 == 0:
        print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}')

# Encode and decode
encoded_weights = autoencoder.encoder(original_weights)
reconstructed_weights = autoencoder.decoder(encoded_weights)

# Convert to numpy arrays
encoded_weights = encoded_weights.detach().numpy()
reconstructed_weights = reconstructed_weights.detach().numpy()

In this example:

• We define the Autoencoder class which inherits from nn.Module and includes encoder and decoder layers.
• We use the mean squared error loss and the Adam optimizer for training.
• We generate some dummy data for demonstration purposes.
• We train the autoencoder for a number of epochs and print the loss periodically.
• Finally, we encode and decode the original weights to obtain the reconstructed weights.