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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：

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_gradientsw1 = 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)

参考：

1、

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