When are Weights Updated in CNN?

Answer: Weights are updated in a Convolutional Neural Network (CNN) during the training phase through backpropagation and optimization algorithms, such as stochastic gradient descent, after computing the gradient of the loss with respect to the weights.

In a Convolutional Neural Network (CNN), the process of updating weights occurs during the training phase, and it involves several steps:

1. Forward Pass:
   • During the forward pass, input data is passed through the network layer by layer.
   • Convolutional layers apply filters to extract features from the input.
   • Activation functions introduce non-linearity to the network.
   • Pooling layers downsample the spatial dimensions.
2. Loss Computation:
   • The output of the network is compared to the actual target values to compute a loss.
   • The loss represents the difference between the predicted and actual values and quantifies the model’s performance.
3. Backward Pass (Backpropagation):
   • The backward pass is initiated to calculate the gradient of the loss with respect to the network’s parameters, including the weights.
   • The chain rule is applied to compute how much each parameter contributed to the overall loss.
4. Gradient Descent:
   • Optimization algorithms, commonly stochastic gradient descent (SGD) or its variants, are employed to minimize the loss.
   • The computed gradients are used to update the weights in the opposite direction of their respective gradients.
   • The learning rate determines the step size in this weight update process.
5. Weight Update Equation:
   • The general weight update equation for a parameter ω is given by:
     ω_new = ω_old − learning_rate × gradient.
6. Iteration:
   • The process of forward pass, loss computation, backward pass, and weight update is repeated iteratively for multiple batches of training data.
   • This iterative process continues until the model converges to a state where the loss is minimized.
7. Epochs:
   • One pass through the entire training dataset is called an epoch.
   • Training is typically conducted over multiple epochs to allow the model to learn complex patterns and relationships in the data.

Conclusion:

By updating the weights based on the computed gradients during each iteration, the CNN learns to adjust its parameters to better approximate the underlying patterns in the training data, ultimately improving its performance on the given task.