Difference between tf.constant and tf.Variable (trainable= False)
If you declare something with tf.constant() you won't be able to change the value in future. But, tf.Variable() let's you change the variable in future. You can assign some other value to it. If it is not trainable, then the gradient won't flow through it.
I came across some code where tf.Variable(... trainable=False) was used and I wondered whether there was any difference between using tf.constant(...) and tf.Variable(with the trainable argument set to False)
It seems a bit redundant to have the trainable argument option available with tf.constant is available.