数据统计

目录

* Outline <https://www.cnblogs.com/nickchen121/p/10851359.html#outline>
* Vector norm
<https://www.cnblogs.com/nickchen121/p/10851359.html#vector-norm>
* Eukl. Norm <https://www.cnblogs.com/nickchen121/p/10851359.html#eukl.-norm>
* L1 Norm <https://www.cnblogs.com/nickchen121/p/10851359.html#l1-norm>
* reduce_min/max/mean
<https://www.cnblogs.com/nickchen121/p/10851359.html#reduce_minmaxmean>
* argmax/argmin
<https://www.cnblogs.com/nickchen121/p/10851359.html#argmaxargmin>
* tf.equal <https://www.cnblogs.com/nickchen121/p/10851359.html#tf.equal>
* Accuracy <https://www.cnblogs.com/nickchen121/p/10851359.html#accuracy>
* tf.unique <https://www.cnblogs.com/nickchen121/p/10851359.html#tf.unique>
Outline

*
tf.norm

*
tf.reduce_min/max/mean

* tf.argmax/argmin
* tf.equal
*
tf.unique

Vector norm

* 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 tf a = 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.shape TensorShape([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_min/max/mean

* 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)>)
argmax/argmin
a.shape TensorShape([4, 10]) tf.argmax(a).shape TensorShape([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).shape TensorShape([10]) #
对第2维作用 tf.argmin(a, axis=1).shape TensorShape([4])
tf.equal
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>
Accuracy
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.shape TensorShape([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>
tf.unique

* 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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