Practical example of a CNN using data augmentation – Labeling Image Data Using Data Augmentation-3

We use the fit() function to train the model on the augmented dataset. We specify the training and validation generators, the number of steps per epoch, the validation steps, and the number of epochs.

In this code snippet, the fit() function is used to train the model on an augmented dataset. Let’s break down the key components:

• Training generator (train_generator):The training generator is an instance of a data generator that generates batches of training data with augmentation on the fly in Step 3. A data generator is a way to efficiently load and preprocess data in chunks during training rather than loading the entire dataset into memory. train_generator is responsible for providing the model with batches of augmented training data.
• Validation generator (val_generator): Similar to the training generator, the validation generator is an instance of a data generator that generates batches of validation data. The validation generator provides a separate set of data that the model has not seen during training. It helps assess the model’s generalization to unseen examples and prevents overfitting.
• Number of steps per epoch (steps_per_epoch=train_generator.samples // 32): steps_per_epoch specifies the number of batches of data to process in each epoch of training. It is calculated as the total number of samples in the training dataset divided by the batch size (32 in this case). Each step involves a forward pass (prediction) and a backward pass (gradient computation and parameter updates) on a batch of data. A smaller steps_per_epoch value means that the model will see fewer batches in each epoch, potentially leading to faster training but with less exposure to the entire dataset.
• Validation steps (validation_steps=val_generator.samples // 32): validation_steps is similar to steps_per_epoch but for the validation dataset. It determines the number of batches processed during each validation epoch. Like steps_per_epoch, it is calculated based on the total number of samples in the validation dataset divided by the batch size.
• Number of epochs (epochs=10): Epoch specifies the number of times the entire dataset is processed during training. Training for more epochs allows the model to learn from the data over multiple passes, potentially improving performance. However, training for too many epochs may lead to overfitting, where the model memorizes the training data but fails to generalize to new data.

Adjusting the batch size, steps per epoch, and validation steps can impact the training speed and memory requirements. A larger batch size and more steps per epoch may lead to slower training but can be more memory-efficient. The number of epochs should be chosen carefully to balance model training and prevent overfitting.

In summary, the settings provided to fit() control how the model is trained, the data it sees in each epoch, and the evaluation of the validation set. Properly tuning these settings is crucial to achieving good model performance and preventing issues such as overfitting.

By following these steps, you can implement supervised CNNs using image data augmentation in Keras. This can help improve the performance of your model and make it more robust to variations in the input data.