Introduction

In the world of deep learning, particularly when dealing with image data, Keras offers a powerful utility function called `flow_from_directory` which simplifies the task of importing and preprocessing images. This function is part of the `ImageDataGenerator` class in Keras and is extensively used for data augmentation and feeding data into a model. A common question among practitioners is whether it's possible to use `flow_from_directory` to fetch both training and validation data from the same directory.

Understanding `flow_from_directory`

The `flow_from_directory` function expects a directory structure where each subdirectory represents a class, and images belonging to that class reside within it. Here's a basic structure:

• `directory`: Path to the target directory.
• `target_size`: Tuple specifying the dimensions to which all images found will be resized.
• `batch_size`: Size of the batches of data.
• `class_mode`: Determines the type of label arrays that are returned: `"categorical"`, `"binary"`, `"sparse"`, `"input"`, or `None`.
• `shuffle`: Whether to shuffle the data (default: `True`).
• `subset`: Subset of data to use (either `"training"` or `"validation"`), applicable if a validation split is provided.
• `seed`: Random seed for shuffling and transformations.
• Validation Split: When `validation_split` is specified, the `ImageDataGenerator` splits the data into two non-overlapping subsets according to this fraction.
• Subsets: You specify which subset you want (`"training"` or `"validation"`) through the `subset` parameter in the `flow_from_directory` method.
• Same Directory: Both `train_generator` and `validation_generator` fetch data from the same directory but ensure they access exclusive data points by virtue of the `subset` parameter.
• Simplicity: Eliminates the need to create separate directories for training and validation data manually.
• Consistency: Using the same `ImageDataGenerator` for both subsets ensures consistent preprocessing and augmentation.
• Random Splits: Unless a `seed` is set, the split is random, which can lead to variability across different runs.
• Single Split: You cannot have more complex data partitions, such as test splits, using this functionality alone.