Derivative Rules
Chain rule
import tensorflow as tf
x = tf.constant(1.)
w1 = tf.constant(2.)
b1 = tf.constant(1.)
w2 = tf.constant(2.)
b2 = tf.constant(1.)
with tf.GradientTape(persistent=True) as tape:
tape.watch([w1, b1, w2, b2])
y1 = x * w1 + b1
y2 = y1 * w2 + b2
dy2_dy1 = tape.gradient(y2, [y1])[0]
dy1_dw1 = tape.gradient(y1, [w1])[0]
dy2_dw1 = tape.gradient(y2, [w1])[0]
dy2_dy1 * dy1_dw1
<tf.Tensor: id=132, shape=(), dtype=float32, numpy=2.0>
dy2_dw1
<tf.Tensor: id=138, shape=(), dtype=float32, numpy=2.0>
