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code example for loss function to train resnet18 with constraints

Envious Echidna answered on June 19, 2023 Popularity 1/10 Helpfulness 1/10

answer code example for loss function to train resnet18 with constraints

code example for loss function to train resnet18 with constraints

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import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.models as models

# Define the ResNet-18 model
model = models.resnet18(pretrained=False)

# Define the constraint on the L2 norm of the model's weights
weight_constraint = 0.5

# Define the loss function with the weight constraint
criterion = nn.CrossEntropyLoss()

# Define the optimizer
optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)

# Function to calculate the L2 norm of the model's weights
def get_model_weights_norm(model):
    weights = torch.cat([param.view(-1) for param in model.parameters()])
    return torch.norm(weights, p=2)

# Training loop
for epoch in range(num_epochs):
    running_loss = 0.0
    for inputs, labels in train_dataloader:
        # Zero the parameter gradients
        optimizer.zero_grad()

        # Forward pass
        outputs = model(inputs)
        loss = criterion(outputs, labels)

        # Calculate the L2 norm of the model's weights
        weights_norm = get_model_weights_norm(model)

        # Add the weight constraint term to the loss function
        loss += weight_constraint * (weights_norm ** 2)

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

        # Update running loss
        running_loss += loss.item()

    # Print the average loss for the epoch
    epoch_loss = running_loss / len(train_dataloader)
    print(f"Epoch {epoch+1} Loss: {epoch_loss:.4f}")

Popularity 1/10 Helpfulness 1/10 Language python

Source: chat.openai.com

Tags: constraints loss-function python

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Contributed on Jun 19 2023

Envious Echidna

0 Answers Avg Quality 2/10

code example for loss function to train resnet18 with constraints

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