链式法则

目录

• Derivative Rules
• Chain rule

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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>