我就廢話不多說(shuō)了，直接上代碼吧！

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) 

輸出結(jié)果為：

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

可以這樣看res與w1有關(guān)，w1的參數(shù)設(shè)為[a1,a2],則：

2*a1 + a2 = res

所以res對(duì)a1,a2求導(dǎo)可得 [[2,1]]為w1對(duì)應(yīng)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_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)

以上這篇TensorFlow梯度求解tf.gradients實(shí)例就是小編分享給大家的全部?jī)?nèi)容了，希望能給大家一個(gè)參考，也希望大家多多支持武林站長(zhǎng)站。