TensorFlow梯度求解tf.gradients实例

我就废话不多说了，直接上代码吧！

import tensorflow as tf

w1 = tf.Variable([[1,2]])
w2 = tf.Variable([[3,4]])

res = tf.matmul(w1, [[2],[1]])

grads = tf.gradients(res,[w1])

with tf.Session() as sess:
tf.global_variables_initializer().run()
print sess.run(res)
print sess.run(grads)

输出结果为：

[[4]]
[array([[2, 1]], dtype=int32)]

可以这样看res与w1有关，w1的参数设为[a1,a2],则：

2*a1 + a2 = res

所以res对a1,a2求导可得 [[2,1]]为w1对应的梯度信息。

import tensorflow as tf
def gradient_clip(gradients, max_gradient_norm):
"""Clipping gradients of a model."""
clipped_gradients, gradient_norm = tf.clip_by_global_norm(
  gradients, max_gradient_norm)
gradient_norm_summary = [tf.summary.scalar("grad_norm", gradient_norm)]
gradient_norm_summary.append(
 tf.summary.scalar("clipped_gradient", tf.global_norm(clipped_gradients)))

return clipped_gradients
w1 = tf.Variable([[3.0,2.0]])
# w2 = tf.Variable([[3,4]])
params = tf.trainable_variables()
res = tf.matmul(w1, [[3.0],[1.]])
opt = tf.train.GradientDescentOptimizer(1.0)
grads = tf.gradients(res,[w1])
clipped_gradients = gradient_clip(grads,2.0)
global_step = tf.Variable(0, name='global_step', trainable=False)
#update = opt.apply_gradients(zip(clipped_gradients,params), global_step=global_step)
with tf.Session() as sess:
tf.global_variables_initializer().run()
print sess.run(res)
print sess.run(grads)
print sess.run(clipped_gradients)

来源：https://blog.csdn.net/taoyanqi8932/article/details/77602721