Plotting validation loss along with training loss

Part 2 2022/23
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How can we plot validation loss along with training loss when using minai/miniai/miniminiai Learner?

1 Like
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To plot validation loss along with training loss when using a minai/miniai/miniminiai Learner, you can follow these steps: Tell Culvers

  1. Modify Training Code: Ensure your training code includes validation and test splits in addition to the training split of the dataset. This allows you to compute both training and validation loss values during the learning process.
  2. Store Loss Values: Modify your training code to store the computed training and validation loss values, as well as the trained model weights.
  3. Plot Loss Curves: Use a plotting library (e.g., Matplotlib in Python) to plot the stored training and validation loss values over the number of epochs.

Here’s a simple example in Python using Matplotlib:

import matplotlib.pyplot as plt

# Assuming you have lists of training and validation loss values
train_loss = [0.5, 0.4, 0.3, 0.2, 0.1]
val_loss = [0.6, 0.5, 0.4, 0.3, 0.2]

epochs = range(1, len(train_loss) + 1)

plt.plot(epochs, train_loss, 'b-o', label='Training Loss')
plt.plot(epochs, val_loss, 'r-o', label='Validation Loss')
plt.title('Training and Validation Loss Curves')
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.legend()
plt.show()

This code will generate a plot showing both the training and validation loss curves, helping you diagnose any issues with learning performance and make informed decisions about your model.

Does this help?

4

Hello,
To plot validation loss along with training loss using a minai/miniai/miniminiai Learner, you can use the following approach: MyWakeID

  1. Track Losses During Training: Ensure your training loop records both training and validation losses at each epoch.
  2. Plot the Losses: Use a plotting library like Matplotlib to visualize the recorded losses.

Here’s a sample code snippet:

python

import matplotlib.pyplot as plt

# Assuming you have lists of training and validation losses
training_losses = [...]
validation_losses = [...]

plt.plot(training_losses, label='Training Loss')
plt.plot(validation_losses, label='Validation Loss')
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.legend()
plt.show()

Best regards,
James Henry