Get True Labels from Keras Generator

When working with deep learning models in Keras, it is common to use data generators to efficiently feed data into the model during training. However, one challenge that may arise is how to get the true labels of the data generated by the generator.

One way to get the true labels from a Keras generator is by using the `classes` attribute of the generator. The `classes` attribute gives the class index that each sample belongs to. This can be useful for tasks such as evaluating the model’s performance or analyzing the predicted outputs.

Here is an example code snippet that demonstrates how to get the true labels from a Keras generator:

    
from keras.preprocessing.image import ImageDataGenerator

# Create a data generator
datagen = ImageDataGenerator()

# Specify the directory containing the images
datagen_flow = datagen.flow_from_directory('path_to_directory', batch_size=32)

# Get the true labels
true_labels = datagen_flow.classes

print(true_labels)
    
  

In the code snippet above, we first create an `ImageDataGenerator` object and then use the `flow_from_directory` method to create a generator that reads images from a specified directory. We then use the `classes` attribute to get the true labels of the generated data.

By utilizing the `classes` attribute of a Keras generator, you can easily access the true labels of the data being fed into your deep learning model. This can be helpful for various tasks such as model evaluation, performance analysis, and debugging.