tf.gradients(
 ys,
 xs,
 grad_ys=None,
 name='gradients',
 colocate_gradients_with_ops=False,
 gate_gradients=False,
 aggregation_method=None,
 stop_gradients=None,
 unconnected_gradients=tf.UnconnectedGradients.NONE
)

import numpy as np
import tensorflow as tf
 
#构造数据集
x_pure = np.random.randint(-10, 100, 32)
x_train = x_pure + np.random.randn(32) / 32
y_train = 3 * x_pure + 2 + np.random.randn(32) / 32
 
x_input = tf.placeholder(tf.float32, name='x_input')
y_input = tf.placeholder(tf.float32, name='y_input')
w = tf.Variable(2.0, name='weight')
b = tf.Variable(1.0, name='biases')
y = tf.add(tf.multiply(x_input, w), b)
 
loss_op = tf.reduce_sum(tf.pow(y_input - y, 2)) / (2 * 32)
train_op = tf.train.GradientDescentOptimizer(0.01).minimize(loss_op)
gradients_node = tf.gradients(loss_op, w)
 
sess = tf.Session()
init = tf.global_variables_initializer()
sess.run(init)
 
for i in range(20):
 _, gradients, loss = sess.run([train_op, gradients_node, loss_op], feed_dict={x_input: x_train[i], y_input: y_train[i]})
 print("epoch: {} \t loss: {} \t gradients: {}".format(i, loss, gradients))
sess.close()

import numpy as np
import tensorflow as tf
 
#构造数据集
x_pure = np.random.randint(-10, 100, 32)
x_train = x_pure + np.random.randn(32) / 32
y_train = 3 * x_pure + 2 + np.random.randn(32) / 32
 
x_input = tf.placeholder(tf.float32, name='x_input')
y_input = tf.placeholder(tf.float32, name='y_input')
w = tf.Variable(2.0, name='weight')
b = tf.Variable(1.0, name='biases')
y = tf.add(tf.multiply(x_input, w), b)
 
loss_op = tf.reduce_sum(tf.pow(y_input - y, 2)) / (2 * 32)
# train_op = tf.train.GradientDescentOptimizer(0.01).minimize(loss_op)
 
#自定义权重更新
grad_w, grad_b = tf.gradients(loss_op, [w, b])
new_w = w.assign(w - 0.01 * grad_w)
new_b = b.assign(b - 0.01 * grad_b)
 
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)
 
for i in range(20):
 _, gradients, loss = sess.run([new_w, new_b, loss_op], feed_dict={x_input: x_train[i], y_input: y_train[i]})
 print("epoch: {} \t loss: {} \t gradients: {}".format(i, loss, gradients))
sess.close()

import numpy as np
import tensorflow as tf
 
a = tf.constant(0.)
b = 2 * a
c = a + b
g = tf.gradients(c, [a, b])
 
with tf.Session() as sess:
 tf.global_variables_initializer().run()
 print(sess.run(g))
 
#输出[3.0, 1.0]

import tensorflow as tf
 
#实验一
w1 = tf.Variable(2.0)
w2 = tf.Variable(2.0)
a = tf.multiply(w1, 3.0)
a_stoped = tf.stop_gradient(a)
 
# b=w1*3.0*w2
b = tf.multiply(a_stoped, w2)
gradients = tf.gradients(b, xs=[w1, w2])
print(gradients)
#输出[None, <tf.Tensor 'gradients/Mul_1_grad/Reshape_1:0' shape=() dtype=float32>]
 
#实验二
a = tf.Variable(1.0)
b = tf.Variable(1.0)
c = tf.add(a, b)
c_stoped = tf.stop_gradient(c)
d = tf.add(a, b)
e = tf.add(c_stoped, d)
gradients = tf.gradients(e, xs=[a, b])
with tf.Session() as sess:
 tf.global_variables_initializer().run()
 print(sess.run(gradients))
 
#因为梯度从另外地方传回，所以输出 [1.0, 1.0]