70: divide_no_nan | TensorFlow | Tutorial

In TensorFlow, the divide_no_nan function allows you to perform element-wise division on two tensors,
without dividing by zero and replacing NaN values with a specified value. This function can be useful when dealing with
numerical data where division by zero can lead to undefined results or errors.

Example:

Suppose we have two tensors A = [1.0, 2.0, 3.0] and B = [0.0, 2.0, 0.0]. If we were to
perform a simple division operation C = A / B, we would encounter a divide by zero error. However, using
divide_no_nan function, we can avoid this issue:

        import tensorflow as tf

        A = tf.constant([1.0, 2.0, 3.0])
        B = tf.constant([0.0, 2.0, 0.0])

        C = tf.math.divide_no_nan(A, B)

        print(C)
    

The output of the above code snippet would be [1.0, 1.0, 1.0], as the function replaces the division by zero
with 1.0.

Parameters:

The divide_no_nan function takes two parameters – x and y, which are the input
tensors to be divided. The function returns an output tensor of the same shape as the input tensors, with division
performed element-wise and NaN values replaced with a specified value.

Conclusion:

Using the divide_no_nan function in TensorFlow can help you avoid divide by zero errors and handle NaN values
gracefully in your numerical computations. This function is particularly useful when working with large datasets or
complex mathematical operations where division errors can occur frequently. Experimenting with this function in
different scenarios can help you better understand its utility and improve the robustness of your TensorFlow models.