In the previous blog, we learned how to generate batches of augmented data using the flow method. In that, the data was loaded in the memory. But this is not always the case. Sometimes, the datasets we download contains folders of data corresponding to the respective classes. To use the flow method, one may first need to append the data and corresponding labels into an array and then use the flow method on those arrays. Thus overall it is a tedious task.

This led to the need for a method that takes the path to a directory and generates batches of augmented data. In Keras, this is done using the flow_from_directory method. So, let’s discuss this method in detail.

Keras API

Here, the directory is the path of the directory that contains the sub-directories of the respective classes. Each subdirectory is treated as a different class. The name of the class can either be inferred from the subdirectory name or can be passed using the “classes” argument. The labels to these classes are assigned alphanumerically.

For instance, suppose you have a directory structure as shown below

So, in this case, the directory will be the path to the train folder. If we set the “classes=None“, the class names will be inferred from the sub-directory names as “dogs” and “cats”. Because the labels are assigned alphanumerically, the labels for this will be {‘cats’: 0, ‘dogs’: 1}. If we have passed the argument classes=[‘Dog’,’Cat’], then the labels will be {‘Cat’: 0, ‘Dog’: 1}.

To check the class labels, we can use the “class_indices” argument as

This returns a dictionary containing the mapping from class names to class indices. 

The labels generated depends on the “class_mode” argument. This can take one of “categorical“, “binary“, “sparse“, “input“, or None. Default is “categorical”. 

• If “binary“, the labels are “0” and “1”.
• For “categorical“, we will have 2D one-hot encoded labels
• If “sparse”, 1D integer labels
• For autoencoders, pass this as “input“
• Since during test time we have no labels, so pass as None.

Sometimes the datasets contain images that are not of the same size. So, using the “target_size” argument, we can resize the images to a fixed size using an interpolation method specified by the “interpolation” argument. Default is the nearest neighbor interpolation method.

You can also convert the color of the images using the “color_mode” argument. Available options are “grayscale“, “rgb“, “rgba“. Default is “rgb“.

You can also save the augmented images to the disk by specifying the “save_to_dir” argument. You can also select which format to save the image files and what prefix to use, using the “save_format” and “save_prefix” arguments respectively.

To see an example of flow_from_directory() method, you can refer to this blog.

Hope you enjoy reading.

If you have any doubt/suggestion please feel free to ask and I will do my best to help or improve myself. Good-bye until next time.