{"id":47,"date":"2026-04-07T03:55:04","date_gmt":"2026-04-07T03:55:04","guid":{"rendered":"https:\/\/gigz.pk\/dl\/?post_type=lesson&p=47"},"modified":"2026-04-07T03:55:21","modified_gmt":"2026-04-07T03:55:21","slug":"training-loop","status":"publish","type":"lesson","link":"https:\/\/gigz.pk\/dl\/index.php\/lesson\/training-loop\/","title":{"rendered":"Training Loop"},"content":{"rendered":"\n

Introduction<\/strong>
The training loop is the core process used to train a deep learning model. It repeatedly feeds data into the model, calculates errors, updates weights, and improves performance over time. Understanding the training loop is essential for building and optimizing neural networks.<\/p>\n\n\n\n

What is a Training Loop?<\/strong>
A training loop is a sequence of steps that runs multiple times (epochs) to train a model. In each iteration, the model learns from data by adjusting its parameters based on the error it produces.<\/p>\n\n\n\n

Key Components of a Training Loop<\/strong><\/p>\n\n\n\n

1. Epochs<\/strong>
An epoch is one complete pass through the entire training dataset. Models are typically trained over multiple epochs to improve accuracy.<\/p>\n\n\n\n

2. Batches<\/strong>
Instead of processing the entire dataset at once, data is divided into smaller groups called batches. This makes training faster and more efficient.<\/p>\n\n\n\n

3. Forward Pass<\/strong>
The input data is passed through the model to generate predictions.<\/p>\n\n\n\n

4. Loss Calculation<\/strong>
The predicted output is compared with the actual output using a loss function to measure error.<\/p>\n\n\n\n

5. Backward Pass<\/strong>
Gradients are calculated using backpropagation to determine how to adjust weights.<\/p>\n\n\n\n

6. Weight Update<\/strong>
Weights and biases are updated using an optimization algorithm such as gradient descent or Adam.<\/p>\n\n\n\n

7. Repeat Process<\/strong>
The loop continues for all batches and epochs until the model learns effectively.<\/p>\n\n\n\n

Step-by-Step Training Loop<\/strong><\/p>\n\n\n\n

    \n
  1. Initialize model parameters (weights and biases)<\/li>\n\n\n\n
  2. Loop through epochs<\/li>\n\n\n\n
  3. Divide dataset into batches<\/li>\n\n\n\n
  4. Perform forward propagation<\/li>\n\n\n\n
  5. Compute loss<\/li>\n\n\n\n
  6. Perform backpropagation<\/li>\n\n\n\n
  7. Update weights<\/li>\n\n\n\n
  8. Repeat until training is complete<\/li>\n<\/ol>\n\n\n\n

    Example: Simple Training Loop in Python<\/strong><\/p>\n\n\n\n

    import numpy as np# Dummy dataset
    X = np.array([[1], [2], [3], [4]])
    y = np.array([2, 4, 6, 8])# Initialize weight
    w = 0.0
    learning_rate = 0.01# Training loop
    for epoch in range(100):
        total_loss = 0
        
        for i in range(len(X)):
            # Forward pass
            y_pred = w * X[i]
            
            # Loss (Mean Squared Error)
            loss = (y_pred - y[i]) ** 2
            
            # Gradient
            grad = 2 * (y_pred - y[i]) * X[i]
            
            # Update weight
            w = w - learning_rate * grad
            
            total_loss += loss
        
        if epoch % 10 == 0:
            print(f\"Epoch {epoch}, Loss: {total_loss}\")print(\"Trained weight:\", w)<\/pre>\n\n\n\n
    Best Practices for Training Loops<\/strong><\/p>\n\n\n\n