Practical example of a CNN using data augmentation – Labeling Image Data Using Data Augmentation-2

Here, we are creating a simple CNN model with four convolutional layers and one fully connected layer. We are using ReLU activation for the convolutional layers and softmax activation for the output layer. We also compile the model with the categorical cross-entropy loss function, the Adam optimizer, and the accuracy metric.

In the preceding code snippet, a CNN model is being created using the Keras library. Let’s break down the components:

• Rectified Linear Unit (ReLU) Activation: activation=’relu’ is used for the convolutional and dense layers. ReLU is an activation function that introduces non-linearity to the model. It outputs the input directly if it is positive; otherwise, it outputs zero. ReLU is preferred for CNNs because it helps the model learn complex patterns and relationships in data. It is computationally efficient and mitigates the vanishing gradient problem.

The effect of ReLU: ReLU introduces non-linearity, enabling the model to learn complex features and relationships in the data. It helps address the vanishing gradient problem, promoting more efficient training by allowing the model to propagate gradients during backpropagation.

• Softmax activation: activation=’softmax’ is used for the output layer. Softmax is a function that converts raw scores (logits) into probabilities. It is often used in the output layer of a multi-class classification model. In this binary classification case (two classes), the softmax activation function normalizes the output scores for each class, assigning a probability to each class. The class with the highest probability is considered the model’s prediction. Softmax is useful for producing probability distributions over multiple classes, making it suitable for classification problems.

The effect of Softmax: Softmax converts raw model outputs into probability distributions over classes. It ensures that the predicted probabilities sum to 1, facilitating a meaningful interpretation of the model’s confidence in each class. In binary classification, it is often used in conjunction with categorical cross-entropy loss.

Why should we use them? ReLU is chosen for its simplicity, computational efficiency, and effectiveness in training deep neural networks. Softmax is selected for the output layer to obtain class probabilities, which are valuable for interpreting and evaluating the model’s predictions.

In summary, ReLU and softmax activations contribute to the effectiveness of the CNN model by introducing non-linearity, promoting efficient training, and producing meaningful probability distributions for classification. They are widely used in CNNs for image classification tasks.

In the provided code snippet, the model is compiled with three important components – categorical cross-entropy loss, the Adam optimizer, and the accuracy metric. Let’s delve into each of them:

• Categorical cross-entropy loss (loss=’categorical_crossentropy’):
  • Categorical cross-entropy is a loss function commonly used for multi-class classification problems.
  • In this context, the model is designed for binary classification (two classes), but it uses categorical cross-entropy to handle a case where there are more than two classes. The target labels are expected to be one-hot-encoded.
  • The loss function measures the dissimilarity between the predicted probabilities (obtained from the softmax activation in the output layer) and the true class labels.
  • The goal during training is to minimize this loss, effectively improving the model’s ability to make accurate class predictions.
• Adam optimizer (optimizer=’adam’):
  • Adaptive Moment Estimation (Adam) is an optimization algorithm widely used to train neural networks.
  • It combines ideas from two other optimization algorithms – Root Mean Square Propagation (RMSprop) and Momentum.
  • Adam adapts the learning rates of each parameter individually, making it well-suited for a variety of optimization problems.
  • It is known for its efficiency and effectiveness in training deep neural networks and is often a default choice for many applications.
• Accuracy metric (metrics=[‘accuracy’]):
  • Accuracy is a metric used to evaluate the performance of a classification model.
  • In the context of binary classification, accuracy measures the proportion of correctly classified instances (both true positives and true negatives) among all instances.
  • The accuracy metric is essential for assessing how well the model performs on the training and validation datasets.
  • While accuracy is a commonly used metric, it might not be sufficient for imbalanced datasets, where one class is much more prevalent than the other. In such cases, additional metrics such as precision, recall, or F1 score may be considered.

In summary, the choice of categorical cross-entropy loss, the Adam optimizer, and the accuracy metric during compilation reflects the best practices for training a binary classification model. These choices are based on their effectiveness in optimizing the model parameters, handling multi-class scenarios, and providing a straightforward evaluation of classification accuracy.

Step 5: Train the model using the augmented dataset:
model.fit(
    train_generator,
    steps_per_epoch=train_generator.samples // 32,
    validation_data=val_generator,
    validation_steps=val_generator.samples // 32,
    epochs=10
)