This is a novel and intriguing method: training an Energy-Based Model (EBM) as a Generalised Additive Model (GAM) inside a huge CNN or Transformer architecture. While training classic EBMs usually involves end-to-end optimisation techniques, it is possible to modify them to operate within a broader neural network architecture and train them incrementally (batch-by-batch).

Here is a code example of how you can achieve this:

#import necessary libries
import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np

#define neural network architecture with an EBM layer
class CNNWithEBM(nn.Module):
def init(self):
super(CNNWithEBM, self).init()
self.cnn = nn.Sequential(
nn.Conv2d(in_channels=1, out_channels=32, kernel_size=3, stride=1, padding=1),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2)
)
self.ebm = nn.Linear(32 * 14 * 14, 1) # Example linear EBM layer

def forward(self, x):
    x = self.cnn(x)
    x = x.view(x.size(0), -1)  # Flatten the output
    energy = self.ebm(x)
    return energy

#obtain synthetic dataset and define training loop

Generate synthetic dataset

def generate_data(batch_size=32):
# Generate random data and labels
data = torch.randn(batch_size, 1, 28, 28) # MNIST-like data
labels = torch.randint(0, 2, (batch_size,))
return data, labels

Instantiate the model

model = CNNWithEBM()

Define loss function (energy-based loss)

criterion = nn.MSELoss()

Define optimizer

optimizer = optim.Adam(model.parameters(), lr=0.001)

Training loop

num_epochs = 10
batch_size = 32
for epoch in range(num_epochs):
total_loss = 0.0
for batch_idx in range(num_batches):
# Generate mini-batch data
data, labels = generate_data(batch_size)

    # Zero the gradients
    optimizer.zero_grad()

    # Forward pass
    energy = model(data)

    # Compute loss
    loss = criterion(energy.squeeze(), labels.float())  # Energy-based loss

    # Backward pass
    loss.backward()

    # Update parameters
    optimizer.step()

    # Accumulate total loss
    total_loss += loss.item()

# Print average loss for the epoch
print(f"Epoch {epoch + 1}, Avg. Loss: {total_loss / num_batches:.4f}")

I hope that this helps.
Thank you

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Dear Sunnycasmir,
Thank you for your reply!

This is a novel and intriguing method: training an Energy-Based Model (EBM) as a Generalised Additive Model (GAM) inside a huge CNN or Transformer architecture. While training classic EBMs usually involves end-to-end optimisation techniques, it is possible to modify them to operate within a broader neural network architecture and train them incrementally (batch-by-batch).

Here is a code example of how you can achieve this:

#import necessary libries import torch import torch.nn as nn import torch.optim as optim import numpy as np

#define neural network architecture with an EBM layer class CNNWithEBM(nn.Module): def init(self): super(CNNWithEBM, self).init() self.cnn = nn.Sequential( nn.Conv2d(in_channels=1, out_channels=32, kernel_size=3, stride=1, padding=1), nn.ReLU(), nn.MaxPool2d(kernel_size=2, stride=2) ) self.ebm = nn.Linear(32 * 14 * 14, 1) # Example linear EBM layer

def forward(self, x):
    x = self.cnn(x)
    x = x.view(x.size(0), -1)  # Flatten the output
    energy = self.ebm(x)
    return energy

#obtain synthetic dataset and define training loop

Generate synthetic dataset

def generate_data(batch_size=32): # Generate random data and labels data = torch.randn(batch_size, 1, 28, 28) # MNIST-like data labels = torch.randint(0, 2, (batch_size,)) return data, labels

Instantiate the model

model = CNNWithEBM()

Define loss function (energy-based loss)

criterion = nn.MSELoss()

Define optimizer

optimizer = optim.Adam(model.parameters(), lr=0.001)

Training loop

num_epochs = 10 batch_size = 32 for epoch in range(num_epochs): total_loss = 0.0 for batch_idx in range(num_batches): # Generate mini-batch data data, labels = generate_data(batch_size)

    # Zero the gradients
    optimizer.zero_grad()

    # Forward pass
    energy = model(data)

    # Compute loss
    loss = criterion(energy.squeeze(), labels.float())  # Energy-based loss

    # Backward pass
    loss.backward()

    # Update parameters
    optimizer.step()

    # Accumulate total loss
    total_loss += loss.item()

# Print average loss for the epoch
print(f"Epoch {epoch + 1}, Avg. Loss: {total_loss / num_batches:.4f}")

I hope that this helps. Thank you

Dear Sunnycasmir,
Thank you very much for your reply!
More specifically, I want to use EBM (explainable boosting machine) as the output layer of a large CNN/transformer. I considered using EBM as a custom layer of torch, but this would make EBM untrainable. So my question is how to train EBM incrementally (batch-by-batch) as a custom layer of torch? I think the example code didn't solve this question.

from interpret.

Is it possible to see the code you are working on to see how I can contribute more

from interpret.