What is data augmentation in TensorFlow?
An example of an AI chatbot conversation about data augmentation techniques in TensorFlow.
What is data augmentation in TensorFlow?
Data augmentation in TensorFlow refers to techniques used to artificially expand the size of a training dataset by creating modified versions of images or data points.
Can you give me some examples of data augmentation techniques?
Sure! Common techniques include rotation, flipping, scaling, cropping, and color adjustments.
How do I implement data augmentation in my TensorFlow model?
You can use the ImageDataGenerator class from tf.keras.preprocessing.image to easily apply data augmentation during training.
Are there any specific parameters I should consider when using ImageDataGenerator?
Yes, you can adjust parameters like rotation_range, width_shift_range, height_shift_range, and zoom_range to customize the augmentation.
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