tensorflow2.0——自动求导GradientTape

该参数表示是否监视可训练变量，若为False，则无法监视该变量，则输出也为None

 手动添加监视

 

 

import tensorflow as tf

############################### tf.GradientTape(persistent,watch_accessed_variables)
print('###############一元函数求导##############')
x = tf.Variable(3.)
# x = tf.constant(3.)
with tf.GradientTape(persistent = True,watch_accessed_variables = True)as tape:                     #   persistent = True表示可以再次使用这个tape而不会立即销毁
    # tape.watch(x)                           #   手动添加监视
    y = 3 * pow(x, 3) + 2 * x
    z = pow(x,4)
dy_dx = tape.gradient(y,x)
dz_dx = tape.gradient(z,x)
print('y:',y)
print('y对x的导数为:',dy_dx)
print('z:',z)
print('z对x的导数为:',dz_dx)
print()
del tape
print('###############一元函数求二阶导##############')
x = tf.Variable(10.)
with tf.GradientTape() as tape1:
    with tf.GradientTape() as tape2:
        y = pow(x,2)
    y2 = tape2.gradient(y,x)
y3 = tape1.gradient(y2,x)
print('x**2在x=10的二阶导数为:',y3)
print()

print('###############多元函数求偏导##############')
x = tf.Variable(4.)
y = tf.Variable(2.)
with tf.GradientTape(persistent = True) as tape:
    z = pow(x,2) + x * y
# dz_dx = tape.gradient(z,x)
# dz_dy = tape.gradient(z,y)
dz_dx,dz_dy = tape.gradient(z,[x,y])
result = tape.gradient(z,[x,y])
print('z:',z)
print('z对x的导数为:',dz_dx)
print('z对y的导数为:',dz_dy)
print('result:
',result)
print()
print('###############对向量求偏导##############')
x = tf.Variable([[1.,2.,3.]])
with tf.GradientTape() as tape:
    y = 3 * pow(x,2)
dy_dx = tape.gradient(y,x)
print('向量求导dy_dx:',dy_dx)