矩陣M1由tf.inverse（M1）乘以不會產生單位矩陣

Question

使用Python中TensorFlow，我有以下代碼：

sess = tf.InteractiveSession() # so I can eval() 
t1 = tf.convert_to_tensor([[1,4,5],[34,5,1],[53,1,4]],dtype=tensorflow.float32) 
t1.eval() 
OUTPUT>> array([[ 1., 4., 5.], 
     [ 34., 5., 1.], 
     [ 53., 1., 4.]], dtype=float32) 
# so far, so good! 

t1_inverse = tf.matrix_inverse(t1) 
t1_inverse.eval() 
OUTPUT>> array([[-0.01294278, 0.00749319, 0.01430518], 
    [ 0.05653951, 0.17779292, -0.11512262], 
    [ 0.15735695, -0.14373296, 0.08923706]], dtype=float32) 
# I'm not a math whiz but this looks like an inverted matrix to me! 

(t1*t1_inverse).eval() # should yield identity matrix, but.. 
OUTPUT>> array([[-0.01294278, 0.02997275, 0.07152588], 
     [ 1.92234337, 0.88896459, -0.11512262], 
     [ 8.33991814, -0.14373296, 0.35694823]], dtype=float32) 

所以我的問題是，爲什麼矩陣T1乘以它的逆不產生單位矩陣，或[[1,0,0]，[0,1,0]，[0,0,1]]？

Answer 1

您使用的是正常的乘號在你的代碼：

(t1*t1_inverse).eval() 

我以爲會做一個broadcast multiplication

要使用的是matmul

tf.matmul(t1,t1_inverse) 

Answer 2

這裏t1*t1_inverse是逐元素相乘，你需要使用tf.matmul

idenity_mat = tf.matmul(t1, t1_inverse) 
sess.run(identity_mat) 

# Results: array([[ 1.00000000e+00, 5.96046448e-08, 0.00000000e+00], 
        [ 0.00000000e+00, 1.00000000e+00, -6.70552254e-08], 
        [ 0.00000000e+00, 5.96046448e-08, 9.99999881e-01]], dtype=float32)