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import tensorflow as tf
import numpy as np

a = tf.constant([[1.0,2],[-3,4.0]])
b = tf.constant([[5.0,6],[7.0,8.0]])
a+b  #运算符重载

<tf.Tensor: shape=(2, 2), dtype=float32, numpy=
array([[ 6.,  8.],
       [ 4., 12.]], dtype=float32)>
a-b
<tf.Tensor: shape=(2, 2), dtype=float32, numpy=
array([[ -4.,  -4.],
       [-10.,  -4.]], dtype=float32)>
a*b
<tf.Tensor: shape=(2, 2), dtype=float32, numpy=
array([[  5.,  12.],
       [-21.,  32.]], dtype=float32)>

a/b
<tf.Tensor: shape=(2, 2), dtype=float32, numpy=
array([[ 0.2       ,  0.33333334],
       [-0.42857143,  0.5       ]], dtype=float32)>

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a**2
<tf.Tensor: shape=(2, 2), dtype=float32, numpy=
array([[ 1.,  4.],
       [ 9., 16.]], dtype=float32)>

a**0.5
<tf.Tensor: shape=(2, 2), dtype=float32, numpy=
array([[1.       , 1.4142135],
       [      nan, 2.       ]], dtype=float32)>

a%3  #mod的运算符重载，等价于m = tf.math.mod(a,3)
<tf.Tensor: shape=(3,), dtype=int32, numpy=array([1, 2, 0], dtype=int32)>

a//3  #地板除法
<tf.Tensor: shape=(2, 2), dtype=float32, numpy=
array([[ 0.,  0.],
       [-1.,  1.]], dtype=float32)>

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(a>=2)
<tf.Tensor: shape=(2, 2), dtype=bool, numpy=
array([[False,  True],
       [False,  True]])>

(a>=2)&(a<=3)
<tf.Tensor: shape=(2, 2), dtype=bool, numpy=
array([[False,  True],
       [False, False]])>

(a>=2)|(a<=3)
<tf.Tensor: shape=(2, 2), dtype=bool, numpy=
array([[ True,  True],
       [ True,  True]])>

a==5 #tf.equal(a,5)
<tf.Tensor: shape=(3,), dtype=bool, numpy=array([False, False, False])>

tf.sqrt(a)
<tf.Tensor: shape=(2, 2), dtype=float32, numpy=
array([[1.       , 1.4142135],
       [      nan, 2.       ]], dtype=float32)>

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a = tf.constant([1.0,8.0])
b = tf.constant([5.0,6.0])
c = tf.constant([6.0,7.0])
tf.add_n([a,b,c])

<tf.Tensor: shape=(2,), dtype=float32, numpy=array([12., 21.], dtype=float32)>

tf.print(tf.maximum(a,b))
[5 8]

tf.print(tf.minimum(a,b))
[1 6]

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#向量reduce
a = tf.range(1,10)
tf.print(tf.reduce_sum(a))
tf.print(tf.reduce_mean(a))
tf.print(tf.reduce_max(a))
tf.print(tf.reduce_min(a))
tf.print(tf.reduce_prod(a))


#45
#5
#9
#1
#362880

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#张量指定维度进行reduce
b = tf.reshape(a,(3,3))
tf.print(tf.reduce_sum(b, axis=1, keepdims=True))
tf.print(tf.reduce_sum(b, axis=0, keepdims=True))

#[[6]
# [15]
# [24]]
#[[12 15 18]]

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#bool类型的reduce
p = tf.constant([True,False,False])
q = tf.constant([False,False,True])
tf.print(tf.reduce_all(p))
tf.print(tf.reduce_any(q))

0
1

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#利用tf.foldr实现tf.reduce_sum
s = tf.foldr(lambda a,b:a+b,tf.range(10)) 
tf.print(s)

45

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#cum扫描累积
a = tf.range(1,10)
tf.print(tf.math.cumsum(a))
tf.print(tf.math.cumprod(a))

[1 3 6 ... 28 36 45]
[1 2 6 ... 5040 40320 362880]

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#arg最大最小值索引
a = tf.range(1,10)
tf.print(tf.argmax(a))
tf.print(tf.argmin(a))

8
0

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#tf.math.top_k可以用于对张量排序
a = tf.constant([1,3,7,5,4,8])

values,indices = tf.math.top_k(a,3,sorted=True)
tf.print(values)
tf.print(indices)

#利用tf.math.top_k可以在TensorFlow中实现KNN算法

[8 7 5]
[5 2 3]

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#矩阵乘法
a = tf.constant([[1,2],[3,4]])
b = tf.constant([[2,0],[0,2]])
a@b  #等价于tf.matmul(a,b)

<tf.Tensor: shape=(2, 2), dtype=int32, numpy=
array([[2, 4],
       [6, 8]], dtype=int32)>

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#矩阵转置
a = tf.constant([[1.0,2],[3,4]])
tf.transpose(a)

<tf.Tensor: shape=(2, 2), dtype=float32, numpy=
array([[1., 3.],
       [2., 4.]], dtype=float32)>

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#矩阵逆，必须为tf.float32或tf.double类型
a = tf.constant([[1.0,2],[3.0,4]],dtype = tf.float32)
tf.linalg.inv(a)

<tf.Tensor: shape=(2, 2), dtype=float32, numpy=
array([[-2.0000002 ,  1.0000001 ],
       [ 1.5000001 , -0.50000006]], dtype=float32)>

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#矩阵求trace
a = tf.constant([[1.0,2],[3,4]])
tf.linalg.trace(a)

<tf.Tensor: shape=(), dtype=float32, numpy=5.0>

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#矩阵求范数
a = tf.constant([[1.0,2],[3,4]])
tf.linalg.norm(a)

<tf.Tensor: shape=(), dtype=float32, numpy=5.477226>

#矩阵行列式
a = tf.constant([[1.0,2],[3,4]])
tf.linalg.det(a)

<tf.Tensor: shape=(), dtype=float32, numpy=-2.0>

#矩阵特征值
tf.linalg.eigvalsh(a)

<tf.Tensor: shape=(2,), dtype=float32, numpy=array([-0.8541021,  5.854102 ], dtype=float32)>

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#矩阵qr分解
a  = tf.constant([[1.0,2.0],[3.0,4.0]],dtype = tf.float32)
q,r = tf.linalg.qr(a)
tf.print(q)
tf.print(r)
tf.print(q@r)

[[-0.316227794 -0.948683321]
 [-0.948683321 0.316227734]]
[[-3.1622777 -4.4271884]
 [0 -0.632455349]]
[[1.00000012 1.99999976]
 [3 4]]


#矩阵svd分解
a  = tf.constant([[1.0,2.0],[3.0,4.0]],dtype = tf.float32)
v,s,d = tf.linalg.svd(a)
tf.matmul(tf.matmul(s,tf.linalg.diag(v)),d)

#利用svd分解可以在TensorFlow中实现主成分分析降维

<tf.Tensor: shape=(2, 2), dtype=float32, numpy=
array([[0.9999996, 1.9999996],
       [2.9999998, 4.       ]], dtype=float32)>

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a = tf.constant([1,2,3])
b = tf.constant([[0,0,0],[1,1,1],[2,2,2]])
b + a  #等价于 b + tf.broadcast_to(a,b.shape)

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

tf.broadcast_to(a,b.shape)

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

#计算广播后计算结果的形状，静态形状，TensorShape类型参数
tf.broadcast_static_shape(a.shape,b.shape)

TensorShape([3, 3])

#计算广播后计算结果的形状，动态形状，Tensor类型参数
c = tf.constant([1,2,3])
d = tf.constant([[1],[2],[3]])
tf.broadcast_dynamic_shape(tf.shape(c),tf.shape(d))

<tf.Tensor: shape=(2,), dtype=int32, numpy=array([3, 3], dtype=int32)>

#广播效果
c+d #等价于 tf.broadcast_to(c,[3,3]) + tf.broadcast_to(d,[3,3])

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