I'm using flow_from_directory
to get the training set from a folder with the following structure:
train
class1
class2
class3
...
The generator is called as it follows:
train_generator = train_datagen.flow_from_directory(
train_data_dir,
target_size=(img_height, img_width),
batch_size=32,
class_mode='categorical')
I am not setting the argument classes
, but I was expecting to get the labels in alphabetical order.
classes: optional list of class subdirectories (e.g.
['dogs', 'cats']
). Default: None. If not provided, the list of classes will be automatically inferred (and the order of the classes, which will map to the label indices, will be alphanumeric).
However, when I classify the training images (for checking which labels are being returned), I'm don't get any specific ordering. The training goes well (accuracy of ~85%), and there is a consistency with the output labels when classifying images from the same class.
How can I infer the labels numbers generated by flow_from_directory
and map them to the classes?
You can see which class correspond to which integer looking at the variable ImageDataGenerator.class_indices
Here is an example on how to use it
def build(source=None):
datagen = ImageDataGenerator(rescale=1. / 255)
data_generator = datagen.flow_from_directory(
source, # this is the target directory
target_size=(150, 150), # all images will be resized to 150x150
batch_size=11,
class_mode='sparse')
class_dictionary = data_generator.class_indices
return data_generator, class_dictionary
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