I am trying to understand the purpose of TF GRADIENT TAPE, in the following code:
import tensorflow as tf
var = tf.Variable(5.0)
with tf.GradientTape() as tape:
op = (2*var)+(var*var)
diff = tape.gradient(op,var)
print (diff)
Op:
diff = tf.Tensor(12.0, shape=(), dtype=float32)
I am confused because since var=5, the op=(2*5)+(5*5)=>35, and if I am calculating the derivative of a constant then diff should be 0
I understand the reason its 12, because its not taking the var as 5 instead (2*var)+(var*var)=> 2var+var**2 so calculating the derivative of this function becomes 2+2*var=>12.
But what I dont understand is, why the value given for var is not considered?
It is producing the right result.
tf.GradientTape context that will automatically record every operation
that involves a variable.
If you consider the partial derivative for the below function with regard to var
def v(var):
return 2 * var + var * var
It will be 2 + 2*var, when the value of var=5 it will be 2 + 2*5 which is 12 returned by the gradient below.
var = tf.Variable(5.0)
with tf.GradientTape() as tape:
z = v(var)
diff = tape.gradient(z,var)
print (diff)
tf.Tensor(12.0, shape=(), dtype=float32)
Try changing the variable to different values, for the function v, the equation 2 + 2*var holds good.