目录
- Outline
-
Vector nORM
- Eukl. Norm
- L1 Norm
- reduce_min/max/mean
- argmax/argmin
- tf.equal
- Accuracy
- tf.unique
tf.norm
tf.reduce_min/max/mean
- tf.argmax/argmin
- tf.equal
tf.unique
- Eukl. Norm
\[ ||x||_2=|\sum_{k}x_k^2|^{\frac{1}{2}} \] - Max.norm
\[ ||x||_{\infty}=max_k|x_k| \] L1-Norm
\[ ||x||_1=\sum_{k}|x_k| \]Here talks about Vector Norm
Eukl. Norm
import Tensorflow as tfa = tf.ones([2, 2])
a<tf.Tensor: id=11, shape=(2, 2), dtype=float32, numpy=
array([[1., 1.],
[1., 1.]], dtype=float32)>tf.norm(a)<tf.Tensor: id=7, shape=(), dtype=float32, numpy=2.0>tf.sqrt(tf.reduce_sum(tf.square(a)))<tf.Tensor: id=16, shape=(), dtype=float32, numpy=2.0>a = tf.ones([4, 28, 28, 3])
a.shapeTensorShape([4, 28, 28, 3])tf.norm(a)<tf.Tensor: id=25, shape=(), dtype=float32, numpy=96.99484>tf.sqrt(tf.reduce_sum(tf.square(a)))<tf.Tensor: id=30, shape=(), dtype=float32, numpy=96.99484>L1 Norm
b = tf.ones([2, 2])tf.norm(b)<tf.Tensor: id=45, shape=(), dtype=float32, numpy=2.0>tf.norm(b, ord=2, axis=1)<tf.Tensor: id=51, shape=(2,), dtype=float32, numpy=array([1.4142135, 1.4142135], dtype=float32)>tf.norm(b, ord=1)<tf.Tensor: id=56, shape=(), dtype=float32, numpy=4.0># 列为整体
tf.norm(b, ord=1, axis=0)<tf.Tensor: id=66, shape=(2,), dtype=float32, numpy=array([2., 2.], dtype=float32)># 行为整体
tf.norm(b, ord=1, axis=1)<tf.Tensor: id=71, shape=(2,), dtype=float32, numpy=array([2., 2.], dtype=float32)>- reduce，操作可能会有减维的功能，如[2,2]，对行求max，会变成[2]
a = tf.random.normal([4, 10])tf.reduce_min(a), tf.reduce_max(a), tf.reduce_mean(a)(<tf.Tensor: id=80, shape=(), dtype=float32, numpy=-2.215113>,
<tf.Tensor: id=82, shape=(), dtype=float32, numpy=1.9458845>,
<tf.Tensor: id=84, shape=(), dtype=float32, numpy=-0.045550883>)# 对某一行求max
tf.reduce_min(a, axis=1), tf.reduce_max(a, axis=1), tf.reduce_mean(a, axis=1)(<tf.Tensor: id=98, shape=(4,), dtype=float32, numpy=array([-2.215113 , -1.5824796, -1.4861531, -1.3477703], dtype=float32)>,
<tf.Tensor: id=100, shape=(4,), dtype=float32, numpy=array([0.9380455, 1.1625607, 1.9458845, 1.492183 ], dtype=float32)>,
<tf.Tensor: id=102, shape=(4,), dtype=float32, numpy=array([-0.48791748, 0.25639585, 0.07420422, -0.02488617], dtype=float32)>)a.shapeTensorShape([4, 10])tf.argmax(a).shapeTensorShape([10])# 返回index
tf.argmax(a)<tf.Tensor: id=112, shape=(10,), dtype=int64, numpy=array([1, 1, 2, 3, 2, 1, 3, 1, 2, 1])># 对第1维作用
tf.argmin(a).shapeTensorShape([10])# 对第2维作用
tf.argmin(a, axis=1).shapeTensorShape([4])a = tf.constant([1, 2, 3, 2, 5])b = tf.range(5)tf.equal(a, b)<tf.Tensor: id=186, shape=(5,), dtype=bool, numpy=array([False, False, False, False, False])>res = tf.equal(a, b)# 对True和False转换为1和0
tf.reduce_sum(tf.cast(res, dtype=tf.int32))<tf.Tensor: id=191, shape=(), dtype=int32, numpy=0>a = tf.random.normal([2, 3])
a<tf.Tensor: id=198, shape=(2, 3), dtype=float32, numpy=
array([[ 0.25201225, -1.3897187 , 0.29240564],
[-1.0671712 , 2.1487093 , 0.690736 ]], dtype=float32)>pred = tf.cast(tf.argmax(a, axis=1), dtype=tf.int32)
pred.shapeTensorShape([2])y = tf.constant([2, 1])
y<tf.Tensor: id=163, shape=(2,), dtype=int32, numpy=array([2, 1], dtype=int32)>tf.equal(y, pred)<tf.Tensor: id=165, shape=(2,), dtype=bool, numpy=array([ True, True])>correct = tf.reduce_sum(tf.cast(tf.equal(y, pred), dtype=tf.int32))correct<tf.Tensor: id=170, shape=(), dtype=int32, numpy=2>correct / 2<tf.Tensor: id=175, shape=(), dtype=float64, numpy=1.0>- 用于去重
a = tf.range(5)
a<tf.Tensor: id=235, shape=(5,), dtype=int32, numpy=array([0, 1, 2, 3, 4], dtype=int32)># 返回索引
tf.unique(a)Unique(y=<tf.Tensor: id=237, shape=(5,), dtype=int32, numpy=array([0, 1, 2, 3, 4], dtype=int32)>, idx=<tf.Tensor: id=238, shape=(5,), dtype=int32, numpy=array([0, 1, 2, 3, 4], dtype=int32)>)a = tf.constant([4, 2, 2, 4, 3])
a<tf.Tensor: id=226, shape=(5,), dtype=int32, numpy=array([4, 2, 2, 4, 3], dtype=int32)>res = tf.unique(a)Unique(y=<tf.Tensor: id=228, shape=(3,), dtype=int32, numpy=array([4, 2, 3], dtype=int32)>, idx=<tf.Tensor: id=229, shape=(5,), dtype=int32, numpy=array([0, 1, 1, 0, 2], dtype=int32)>)
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