I have been looking to learn TensorFlow and I have noticed that different functions are used for the same goal. To square a variable for instance, I have seen tf.square(), tf.math.square() and tf.keras.backend.square(). This is the same for most math operations. Are all these the same or is there any difference?
What is the difference between tf.square, tf.math.square and tf.keras.backend.square?
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Mathematically, they should produce the same result. However Tensorflow functions in tensorflow.math.somefunction are used for operating Tensorflow tensors.
For example, when you write a custom loss or metric, the inputs and outputs should be Tensorflow tensors. So that Tensorflow knows how to take gradients of the functions. You can also use tf.keras.backend.* functions for custom loss etc.
Try to use tensorflow.math.somefunctions whenever you can, native operations are preferred. Because they are officially documented and guarateed to have backward compatibility between TF versions like TF 1.x and TF 2.x.
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