How can I implement data augmentation in my machine learning model?

How can I implement data augmentation in my machine learning model?

Data augmentation involves creating new training samples by modifying existing ones. What type of data are you working with?

I'm working with image data for a classification task.

Great! You can use techniques like rotation, flipping, zooming, and color adjustment. Are you using any specific libraries?

Yes, I'm using TensorFlow. How can I apply these augmentations in TensorFlow?

You can use the ImageDataGenerator class in TensorFlow. It allows you to specify various augmentation parameters easily.

Can you provide a code example for that?

Sure! Here's a simple example:

from tensorflow.keras.preprocessing.image import ImageDataGenerator

datagen = ImageDataGenerator(
    rotation_range=40,
    width_shift_range=0.2,
    height_shift_range=0.2,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True,
    fill_mode='nearest'
)