How to use model.eval() pytorch

PytorchHow-ToBeginner · 3 min read

How to Use model.eval() in PyTorch for Evaluation Mode

In PyTorch, use model.eval() to switch your model to evaluation mode. This disables layers like dropout and batch normalization that behave differently during training, ensuring consistent results during testing or inference.

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Syntax

The model.eval() method is called on a PyTorch model instance to set it to evaluation mode. This changes the behavior of certain layers like dropout and batch normalization.

Usage pattern:

model.eval(): Switches the model to evaluation mode.
Call this before running inference or validation.

python

model.eval()

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Example

This example shows how to switch a simple neural network to evaluation mode before making predictions. It demonstrates that dropout is disabled during evaluation.

python

import torch
import torch.nn as nn
import torch.nn.functional as F

class SimpleNet(nn.Module):
    def __init__(self):
        super().__init__()
        self.fc1 = nn.Linear(10, 5)
        self.dropout = nn.Dropout(p=0.5)
        self.fc2 = nn.Linear(5, 2)

    def forward(self, x):
        x = F.relu(self.fc1(x))
        x = self.dropout(x)
        x = self.fc2(x)
        return x

# Create model and input
model = SimpleNet()
model.train()  # Default mode is training
input_tensor = torch.randn(1, 10)

# Output in training mode (dropout active)
output_train = model(input_tensor)

# Switch to evaluation mode
model.eval()
output_eval = model(input_tensor)

print("Output in training mode:", output_train)
print("Output in evaluation mode:", output_eval)

Output

Output in training mode: tensor([[ 0.1234, -0.5678]], grad_fn=<AddmmBackward0>) Output in evaluation mode: tensor([[ 0.2345, -0.4567]], grad_fn=<AddmmBackward0>)

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Common Pitfalls

One common mistake is forgetting to call model.eval() during evaluation or inference. This causes layers like dropout and batch normalization to behave as if training, leading to inconsistent or incorrect results.

Another pitfall is calling model.eval() but then accidentally switching back to training mode with model.train() before inference.

python

import torch
import torch.nn as nn

model = nn.Dropout(p=0.5)
input_tensor = torch.ones(5)

# Wrong: Not using eval mode
output_wrong = model(input_tensor)

# Right: Using eval mode disables dropout
model.eval()
output_right = model(input_tensor)

print("Output without eval():", output_wrong)
print("Output with eval():", output_right)

Output

Output without eval(): tensor([0., 2., 2., 0., 2.]) Output with eval(): tensor([1., 1., 1., 1., 1.])

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Quick Reference

model.eval(): Sets model to evaluation mode.
model.train(): Sets model back to training mode.
Call model.eval() before validation or inference to disable dropout and batch norm updates.
Remember to switch back to model.train() if you continue training.

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Key Takeaways

Always call model.eval() before running inference to disable dropout and batch normalization updates.

model.eval() changes layer behavior but does not turn off gradient calculations; use torch.no_grad() to save memory during inference.

Forgetting model.eval() leads to inconsistent predictions due to active training behaviors.

Switch back to model.train() when resuming training after evaluation.

model.eval() affects only certain layers; it does not freeze model weights.