张量限幅

目录

• Outline
• clip_by_value
  • relu
  • clip_by_norm
  • gradient clipping

Outline

• clip_by_value

• relu

• clip_by_norm

• gradient clipping

clip_by_value

import tensorflow as tf

a = tf.range(10)
a

<tf.Tensor: id=3, shape=(10,), dtype=int32, numpy=array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=int32)>

# a中小于2的元素值为2
tf.maximum(a, 2)

<tf.Tensor: id=6, shape=(10,), dtype=int32, numpy=array([2, 2, 2, 3, 4, 5, 6, 7, 8, 9], dtype=int32)>

# a中大于8的元素值为8
tf.minimum(a, 8)

<tf.Tensor: id=9, shape=(10,), dtype=int32, numpy=array([0, 1, 2, 3, 4, 5, 6, 7, 8, 8], dtype=int32)>

# a中的元素值限制在[2,8]区间内
tf.clip_by_value(a, 2, 8)

<tf.Tensor: id=14, shape=(10,), dtype=int32, numpy=array([2, 2, 2, 3, 4, 5, 6, 7, 8, 8], dtype=int32)>

relu

a = a - 5
a

<tf.Tensor: id=17, shape=(10,), dtype=int32, numpy=array([-5, -4, -3, -2, -1,  0,  1,  2,  3,  4], dtype=int32)>

tf.nn.relu(a)

<tf.Tensor: id=19, shape=(10,), dtype=int32, numpy=array([0, 0, 0, 0, 0, 0, 1, 2, 3, 4], dtype=int32)>

tf.maximum(a, 0)

<tf.Tensor: id=22, shape=(10,), dtype=int32, numpy=array([0, 0, 0, 0, 0, 0, 1, 2, 3, 4], dtype=int32)>

clip_by_norm

• 缩放时不改变梯度方向

a = tf.random.normal([2, 2], mean=10)
a

<tf.Tensor: id=35, shape=(2, 2), dtype=float32, numpy=
array([[ 8.630464, 10.737844],
       [ 9.764073, 10.382202]], dtype=float32)>

tf.norm(a)

<tf.Tensor: id=41, shape=(), dtype=float32, numpy=19.822044>

# 等比例的放缩a, norm为15
aa = tf.clip_by_norm(a, 15)
aa

<tf.Tensor: id=58, shape=(2, 2), dtype=float32, numpy=
array([[6.5309587, 8.125684 ],
       [7.388799 , 7.8565574]], dtype=float32)>

tf.norm(aa)

<tf.Tensor: id=64, shape=(), dtype=float32, numpy=15.0>

gradient clipping

• Gradient Exploding or vanishing

• set lr=1

• new_grads,total_norm = tf.clip_by_global_norm(grads,25)

• 裁剪所有向量，但是所有向量的梯度方向都不变化