Epoch vs Iteration when training neural networks

Epoch vs Iteration: Understanding the Fundamentals of Neural Network Training

Training a neural network involves feeding it data and adjusting its internal parameters (weights and biases) to minimize prediction errors. This process involves two key concepts: epochs and iterations. While they might seem interchangeable, they have distinct meanings and play crucial roles in optimizing neural network performance.

Epoch

An epoch represents one complete pass through the entire training dataset. In each epoch, the model processes all the training examples once, updating its parameters based on the accumulated errors.

• Example: If you have a training dataset of 10,000 images, one epoch would involve the model processing all 10,000 images.

Iteration

An iteration refers to a single update of the model’s parameters based on a specific batch of data. A batch is a subset of the training dataset used for a single parameter update. The number of iterations per epoch depends on the batch size.

• Example: If you have a batch size of 100, each epoch would consist of 100 iterations (10,000 images / 100 images per batch = 100 iterations).

The Relationship Between Epochs and Iterations

Epochs and iterations are closely related and work together in the training process:

• Epochs control the overall training process: They determine how many times the model sees the entire training dataset.
• Iterations are the individual steps within an epoch: They involve parameter updates based on smaller batches of data.

Illustrative Example

Imagine training a neural network on a dataset of 100 images. With a batch size of 10:

• Epoch 1:
  • Iteration 1: Processes images 1-10 and updates parameters.
  • Iteration 2: Processes images 11-20 and updates parameters.
  • …
  • Iteration 10: Processes images 91-100 and updates parameters.
• Epoch 2: The same process is repeated for all 100 images, starting again from image 1.

Practical Considerations

The choice of epochs and iterations significantly affects training time and model performance.

Batch Size

• Larger batch sizes lead to faster training but might result in less accurate models.
• Smaller batch sizes can lead to more accurate models but take longer to train.

Number of Epochs

• More epochs generally lead to better model performance but also increase training time.
• Early stopping techniques can help prevent overfitting by stopping training when performance on a validation set plateaus.

Code Example

import tensorflow as tf

# Define model, optimizer, and loss function
model = tf.keras.models.Sequential(...)
optimizer = tf.keras.optimizers.Adam(...)
loss_fn = tf.keras.losses.CategoricalCrossentropy(...)

# Training loop
epochs = 10
batch_size = 32

for epoch in range(epochs):
  for batch in range(num_batches):
    # Get a batch of data
    inputs, targets = get_batch(batch_size)

    # Train the model on the batch
    with tf.GradientTape() as tape:
      predictions = model(inputs)
      loss = loss_fn(targets, predictions)
    gradients = tape.gradient(loss, model.trainable_variables)
    optimizer.apply_gradients(zip(gradients, model.trainable_variables))

    # Print progress
    print(f'Epoch {epoch+1}, Batch {batch+1}, Loss: {loss.numpy()}')

Conclusion

Epochs and iterations are fundamental concepts in neural network training, representing the overall training process and individual parameter updates, respectively. Understanding their relationship and practical considerations helps you optimize the training process and achieve desired model performance.