Is it possible to automatically infer the class_weight from flow_from_directory in Keras?

Just figured out a way of achieving this.

from collections import Counter
train_datagen = ImageDataGenerator()
train_generator = train_datagen.flow_from_directory(...)

counter = Counter(train_generator.classes)                          
max_val = float(max(counter.values()))       
class_weights = {class_id : max_val/num_images for class_id, num_images in counter.items()}                     

model.fit_generator(...,
                    class_weight=class_weights)

train_generator.classes is a list of classes for each image. Counter(train_generator.classes) creates a counter of the number of images in each class.

Note that these weights may not be good for convergence, but you can use it as a base for other type of weighting based on occurrence.

This answer was inspired by: https://github.com/fchollet/keras/issues/1875#issuecomment-273752868

Alternatively, you can simply do:

from sklearn.utils import class_weight
import numpy as np

class_weights = class_weight.compute_class_weight(
               'balanced',
                np.unique(train_generator.classes), 
                train_generator.classes)

You can then set (as per comment above):

model.fit_generator(..., class_weight=class_weights)