1. 函数求一阶导

import tensorflow as tf

tf.enable_eager_execution()

tfe=tf.contrib.eager

from math import pi

def f(x):

return tf.square(tf.sin(x))

assert f(pi/2).numpy()==1.0

sess=tf.Session()

grad_f=tfe.gradients_function(f)

print(grad_f(np.zeros(1))[0].numpy())

2. 高阶函数求导

import numpy as np

def f(x):

return tf.square(tf.sin(x))

def grad(f):

return lambda x:tfe.gradients_function(f)(x)[0]

x=tf.lin_space(-2*pi,2*pi,100)

# print(grad(f)(x).numpy())

x=x.numpy()

import matplotlib.pyplot as plt

plt.plot(x,f(x).numpy(),label="f")

plt.plot(x,grad(f)(x).numpy(),label="first derivative")#一阶导

plt.plot(x,grad(grad(f))(x).numpy(),label="second derivative")#二阶导

plt.plot(x,grad(grad(grad(f)))(x).numpy(),label="third derivative")#三阶导

plt.legend()

plt.show()

def f(x,y):

output=1

for i in range(int(y)):

output=tf.multiply(output,x)

return output

def g(x,y):

return tfe.gradients_function(f)(x,y)[0]

print(f(3.0,2).numpy()) #f(x)=x^2

print(g(3.0,2).numpy()) #f'(x)=2*x

print(f(4.0,3).numpy())#f(x)=x^3

print(g(4.0,3).numpy())#f(x)=3x^2

3. 函数求一阶偏导

x=tf.ones((2,2))

with tf.GradientTape(persistent=True) as t:

t.watch(x)

y=tf.reduce_sum(x)

z=tf.multiply(y,y)

dz_dy=t.gradient(z,y)

print(dz_dy.numpy())

dz_dx=t.gradient(z,x)

print(dz_dx.numpy())

for i in [0, 1]:

for j in [0, 1]:

print(dz_dx[i][j].numpy() )

4. 函数求二阶偏导

x=tf.constant(2.0)

with tf.GradientTape() as t:

with tf.GradientTape() as t2:

t2.watch(x)

y=x*x*x

dy_dx=t2.gradient(y,x)

d2y_dx2=t.gradient(dy_dx,x)

print(dy_dx.numpy())

print(d2y_dx2.numpy())

以上这篇tensorflow求导和梯度计算实例就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持python博客。