Hyperparameter Tuning

In CNNS, hyperparameter tuning involves optimizing different parameters to enhance model performance.

Learning rate

The learning rate determines the size of the steps taken to update the model parameters during training. It affects the speed at which the model learns and improves. A lower learning rate means slower learning but helps the model capture smaller details in the data. In contrast, a larger learning rate speeds up training but increases the risk of the model overshooting the best solution.

Batch size

The batch size is the number of training examples or samples processed during training when the data is divided into smaller subsets or batches of 64, 128, or 256. Each batch is processed independently through the network, and the network weights are updated based on the computed gradients for that batch. The batch size affects how the model learns and how quickly it trains. A larger batch size can make training faster because multiple examples are processed at once. However, it also requires more memory. On the other hand, using a smaller batch size can help the model generalize better and prevent overfitting. It allows the model to learn from different examples more frequently. That said, training can be slower because only a few images are processed at a time.

Number of epochs

The number of epochs determines how often the entire training dataset is passed through the network during training. Each epoch represents a complete iteration of the training data, where the network learns from the examples and updates its weights. The specific number of epochs is decided based on factors like the problem’s difficulty, the amount of available data, and how well the network is learning. It’s usually determined through trial and error—finding a balance between too few epochs (not learning enough) and too many epochs (overfitting or wasting time).

Optimizer

An optimizer is an algorithm or method used to update the parameters of the network during the training process. Optimization aims to minimize the difference between the predicted output of the network and the true output, typically measured by a loss function. The Adam algorithm is the most commonly used optimizer, which adjusts the learning rate for each part of the network based on incorrect predictions and what it has learned in the past. This helps the network learn better. Other optimizers like stochastic gradient descent (SGD), Root Mean Square Propagation (RMSProp), Adaptive Gradient, and adaptive delta work differently to adjust the network parameters.

Loss function

The loss function guides the network’s performance and calculates the difference between the network’s predictions and the correct answers. A widely used loss function in multiple-class classification is categorical cross-entropy, which helps the network learn by giving it feedback on its predictions to help it improve over time.

Practical implementation of hyperparameter tuning

The code given below implements hyperparameter tuning while training a CNN model. In the code, we change one parameter at a time, such as the optimizer, learning rate, or batch size. By changing one parameter at a time, we can easily grasp the impact of altering each parameter and better understand the tuning process.

Click the “Run” button to observe the output.