TensorFlow2.0:数据统计

最新推荐文章于 2023-07-07 20:31:50 发布

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最新推荐文章于 2023-07-07 20:31:50 发布

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分类专栏： tensorflow2.0

本文链接：https://blog.youkuaiyun.com/meijie2018_1/article/details/99447767

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一 tf.norm( )函数–求范数

In [30]: a = tf.ones([2,2])                                                                                                                                                                                 

In [31]: tf.norm(a)#求a的二范数                                                                                                                                                                             
Out[31]: <tf.Tensor: id=57, shape=(), dtype=float32, numpy=2.0>

In [32]: tf.sqrt(tf.reduce_sum(tf.square(a)))#求a的二范数                                                                                                                                                   
Out[32]: <tf.Tensor: id=62, shape=(), dtype=float32, numpy=2.0>

In [33]: a = tf.ones([4,28,28,3])                                                                                                                                                                           

In [34]: tf.norm(a)                                                                                                                                                                                         
Out[34]: <tf.Tensor: id=71, shape=(), dtype=float32, numpy=96.99484>

In [35]: tf.sqrt(tf.reduce_sum(tf.square(a)))#求a的二范数                                                                                                                                                   
Out[35]: <tf.Tensor: id=76, shape=(), dtype=float32, numpy=96.99484>

In [36]: b = tf.ones([2,2])                                                                                                                                                                                 

In [37]: tf.norm(b)                                                                                                                                                                                         
Out[37]: <tf.Tensor: id=85, shape=(), dtype=float32, numpy=2.0>

In [38]: tf.norm(b,ord=2,axis=1)#求b在轴1方向的二范数                                                                                                                                                       
Out[38]: <tf.Tensor: id=91, shape=(2,), dtype=float32, numpy=array([1.4142135, 1.4142135], dtype=float32)>

In [39]: tf.norm(b,ord=1)#求b的一范数                                                                                                                                                                       
Out[39]: <tf.Tensor: id=96, shape=(), dtype=float32, numpy=4.0>

In [40]: tf.norm(b,ord=1,axis=0)#求b在轴0方向上的一范数                                                                                                                                                     
Out[40]: <tf.Tensor: id=101, shape=(2,), dtype=float32, numpy=array([2., 2.], dtype=float32)>

In [41]: tf.norm(b,ord=1,axis=1)#求b在轴1方向上的一范数                                                                                                                                                     
Out[41]: <tf.Tensor: id=106, shape=(2,), dtype=float32, numpy=array([2., 2.], dtype=float32)>

二 tf.reduce_min/max/mean( )函数–求解最小值最大值和均值函数

In [2]: a = tf.random.normal([4,10])                                                                                                                                                                        

In [3]: tf.reduce_min(a)                                                                                                                                                                                    
Out[3]: <tf.Tensor: id=7, shape=(), dtype=float32, numpy=-1.8195488>

In [4]: tf.reduce_max(a)                                                                                                                                                                                    
Out[4]: <tf.Tensor: id=10, shape=(), dtype=float32, numpy=2.3925323>

In [5]: tf.reduce_mean(a)                                                                                                                                                                                   
Out[5]: <tf.Tensor: id=13, shape=(), dtype=float32, numpy=0.17027304>

In [6]: tf.reduce_min(a,axis=1)#对轴1方向取最小值                                                                                                                                                           
Out[6]: <tf.Tensor: id=16, shape=(4,), dtype=float32, numpy=array([-0.3108449, -1.4913915, -1.3940301, -1.8195488], dtype=float32)>

In [7]: tf.reduce_max(a,axis=1)#对轴1方向取最大值                                                                                                                                                           
Out[7]: <tf.Tensor: id=19, shape=(4,), dtype=float32, numpy=array([1.9342865, 0.8926946, 2.3925323, 1.7844682], dtype=float32)>

In [8]: tf.reduce_mean(a,axis=1)#对轴1方向取均值                                                                                                                                                            
Out[8]: <tf.Tensor: id=22, shape=(4,), dtype=float32, numpy=array([ 0.7435825 , -0.08960855,  0.25611132, -0.2289931 ], dtype=float32)>

三 tf.argmax( )和tf.argmin( )函数-求最大最小值位置

In [9]: a = tf.random.normal([4,10])                                                                                                                                                                        

In [10]: tf.argmax(a).shape                                                                                                                                                                                 
Out[10]: TensorShape([10])

In [11]: tf.argmax(a)                                                                                                                                                                                       
Out[11]: <tf.Tensor: id=33, shape=(10,), dtype=int64, numpy=array([1, 0, 0, 1, 3, 3, 1, 1, 2, 0])>

In [12]: tf.argmin(a).shape                                                                                                                                                                                 
Out[12]: TensorShape([10])

In [13]: tf.argmin(a)                                                                                                                                                                                       
Out[13]: <tf.Tensor: id=38, shape=(10,), dtype=int64, numpy=array([3, 3, 2, 0, 2, 2, 0, 2, 0, 1])>

In [14]: tf.argmax(a,axis=1)                                                                                                                                                                                
Out[14]: <tf.Tensor: id=41, shape=(4,), dtype=int64, numpy=array([0, 0, 8, 4])>

In [15]: tf.argmin(a,axis=1)  
Out[15]: <tf.Tensor: id=44, shape=(4,), dtype=int64, numpy=array([6, 9, 4, 1])>

四 tf.equal( )函数

In [22]: a = tf.constant([0,2,3,2,4])                                                                                                                                                                       

In [23]: b = tf.range(5)                                                                                                                                                                                    

In [24]: res = tf.equal(a,b)                                                                                                                                                                                

In [25]: a,b,res                                                                                                                                                                                            
Out[25]: 
(<tf.Tensor: id=55, shape=(5,), dtype=int32, numpy=array([0, 2, 3, 2, 4], dtype=int32)>,
 <tf.Tensor: id=59, shape=(5,), dtype=int32, numpy=array([0, 1, 2, 3, 4], dtype=int32)>,
 <tf.Tensor: id=60, shape=(5,), dtype=bool, numpy=array([ True, False, False, False,  True])>)

In [26]: tf.reduce_sum(tf.cast(res,dtype=tf.int32))                                                                                                                                                         
Out[26]: <tf.Tensor: id=66, shape=(), dtype=int32, numpy=2>

求解准确度

In [34]: a = tf.constant([[0.1,0.2,0.7],[0.9,0.05,0.05],[0.1,0.8,0.1],[0.01,0.99,0]])                                                                                                                       

In [35]: pred = tf.cast(tf.argmax(a,axis=1),dtype=tf.int32)                                                                                                                                                 

In [36]: a,pred                                                                                                                                                                                             
Out[36]: 
(<tf.Tensor: id=78, shape=(4, 3), dtype=float32, numpy=
 array([[0.1 , 0.2 , 0.7 ],
        [0.9 , 0.05, 0.05],
        [0.1 , 0.8 , 0.1 ],
        [0.01, 0.99, 0.  ]], dtype=float32)>,
 <tf.Tensor: id=81, shape=(4,), dtype=int32, numpy=array([2, 0, 1, 1], dtype=int32)>)

In [37]: y = tf.constant([2,0,1,2])                                                                                                                                                                                                                                                                                                  
In [39]: correct_num = tf.reduce_sum(tf.cast(tf.equal(pred,y),dtype=tf.int32))                                                                                                                              

In [40]: accuracy = correct_num /a.shape[0]                                                                                                                                                                 

In [41]: correct_num,accuracy                                                                                                                                                                               
Out[41]: 
(<tf.Tensor: id=92, shape=(), dtype=int32, numpy=3>,
 <tf.Tensor: id=96, shape=(), dtype=float64, numpy=0.75>)

五 tf.unique( )函数-找不重复部分

In [42]: a = tf.range(5)                                                                                                                                                                                    

In [43]: Unique,idx = tf.unique(a)                                                                                                                                                                          

In [44]: Unique,idx                                                                                                                                                                                         
Out[44]: 
(<tf.Tensor: id=103, shape=(5,), dtype=int32, numpy=array([0, 1, 2, 3, 4], dtype=int32)>,
 <tf.Tensor: id=104, shape=(5,), dtype=int32, numpy=array([0, 1, 2, 3, 4], dtype=int32)>)

In [45]: a = tf.constant([4,2,2,4,3])                                                                                                                                                                       

In [46]: Unique,idx = tf.unique(a)                                                                                                                                                                          

In [47]: Unique,idx                                                                                                                                                                                         
Out[47]: 
(<tf.Tensor: id=108, shape=(3,), dtype=int32, numpy=array([4, 2, 3], dtype=int32)>,
 <tf.Tensor: id=109, shape=(5,), dtype=int32, numpy=array([0, 1, 1, 0, 2], dtype=int32)>)

In [48]: aa = tf.gather(Unique,idx)                                                                                                                                                                         

In [49]: aa                                                                                                                                                                                                 
Out[49]: <tf.Tensor: id=113, shape=(5,), dtype=int32, numpy=array([4, 2, 2, 4, 3], dtype=int32)>