4. tf.clip_by_global_norm tf.clip_by_global_norm( t_list, clip_norm, use_norm=None, name
方法二:tf.clip_by_global_norm def clip_by_global_norm(t_list, clip_norm, use_norm=None, name=None): 其中,...tf.train.AdamOptimizer(learning_rate) gradients, v = zip(*optimizer.compute_gradients(loss)) gradients, _ = tf.clip_by_global_norm
gradients for params gradients = tf.gradients(loss, params) #process gradients clipped_gradients, norm = tf.clip_by_global_norm
AliHPC-G41-211 test]# python tt.py --str_name test_str --int_name 99 --bool_name True test_str 99 True 2.2 tf.clip_by_global_norm...tf.clip_by_global_norm函数的作用就是通过权重梯度的总和的比率来截取多个张量的值。...使用方式如下: tf.clip_by_global_norm(t_list, clip_norm, use_norm=None, name=None) t_list 是梯度张量, clip_norm...gradients = tf.gradients(self.loss, trainable_params) clip_gradients, _ = tf.clip_by_global_norm...参考文献 1、tensorflow 学习(三)使用flags定义命令行参数:http://blog.csdn.net/leiting_imecas/article/details/72367937 2、tf.clip_by_global_norm
predictor, envWrap, noReward) # clip gradients grads, _ = tf.clip_by_global_norm...grads_and_vars = list(zip(grads, self.network.var_list)) if self.unsup: predgrads, _ = tf.clip_by_global_norm
learning_rate) gradients, v = zip(*optimizer.compute_gradients(loss)) gradients, _ = tf.clip_by_global_norm
tf.trainable_variables() gradients = tf.gradients(self.loss, trainable_params) clip_gradients, _ = tf.clip_by_global_norm
self.losses): # 用梯度下降法优化 gradients = tf.gradients(loss, params) clipped_gradients, norm = tf.clip_by_global_norm...self.losses): # 用梯度下降法优化 gradients = tf.gradients(loss, params) clipped_gradients, norm = tf.clip_by_global_norm
[x,w,y])) >>> [(3.0, 4.0)] [3.0, 4.0, 12.0] 其他的tf.clip_by_*方法可参看TensorFlow学习笔记之--[tf.clip_by_global_norm
tf.trainable_variables() 4gradients = tf.gradients(self.loss, trainable_params) 5clip_gradients, _ = tf.clip_by_global_norm
learning_rate = tf.Variable(0.0, trainable = False) tvars = tf.trainable_variables() grads, _ = tf.clip_by_global_norm
self.learning_rate = tf.Variable(0.0, trainable=False) tvars = tf.trainable_variables() grads, _ = tf.clip_by_global_norm
tf.Tensor: id=127, shape=(3,), dtype=float32, numpy=array([8.945623, 7.826795, 8.258204], dtype=float32)>] tf.clip_by_global_norm
dtype=tf.float32)], wordNum) cost = tf.reduce_mean(loss) tvars = tf.trainable_variables() grads, a = tf.clip_by_global_norm
_lr = tf.Variable(0.0, trainable=False) tvars = tf.trainable_variables() grads, _ = tf.clip_by_global_norm
build_optimizer(loss, learning_rate, grad_clip): tvars = tf.trainable_variables() grads, _ = tf.clip_by_global_norm
领取专属 10元无门槛券
手把手带您无忧上云