`tf.reciprocal` vs `tf.inv`: is there any difference?

Question

tf.reciprocal and tf.inv seem to be equivalent. Is there any difference? They are implemented as separate TF ops and also have separate gradient implementations, which also seem equivalent.

Answer 1

They mean the same. In fact, tf.inv was renamed to tf.reciprocal and tf.inv is no longer exposed to the top level module in the latest versions (though both still exist in gen_math_ops.py).

From the migration documentation:

Many functions have been renamed to match NumPy. This was done to make the transition between NumPy and TensorFlow as easy as possible. There are still numerous cases where functions do not match, so this is far from a hard and fast rule, but we have removed several commonly noticed inconsistencies.

• tf.inv
  • should be renamed to tf.reciprocal
  • This was done to avoid confusion with NumPy's matrix inverse np.inv

You can see there several more functions that were renamed, like tf.mul and tf.neg.