A
indices
A
indices
argpartition(A, 5)[:,:,:5]
A
indices
indices = np.array([[[5,4,3,2,1],[1,1,1,1,1],[1,1,1,1,1]],
[500,400,300,200,100],[100,100,100,100,100],[100,100,100,100,100]])
A = np.reshape(range(2 * 3 * 10000), (2,3,10000))
A[...,indices] # gives an array of size (2,3,2,3,5). I want a subset of these values
np.take(A, indices) # shape is right, but it flattens the array first
np.choose(indices, A) # fails because of shape mismatch.
A[i,j]
i<m
j<n
np.argpartition
You can use advancedindexing

m,n = A.shape[:2]
out = A[np.arange(m)[:,None,None],np.arange(n)[:,None],indices]
Sample run 
In [330]: A
Out[330]:
array([[[38, 21, 61, 74, 35, 29, 44, 46, 43, 38],
[22, 44, 89, 48, 97, 75, 50, 16, 28, 78],
[72, 90, 48, 88, 64, 30, 62, 89, 46, 20]],
[[81, 57, 18, 71, 43, 40, 57, 14, 89, 15],
[93, 47, 17, 24, 22, 87, 34, 29, 66, 20],
[95, 27, 76, 85, 52, 89, 69, 92, 14, 13]]])
In [331]: indices
Out[331]:
array([[[7, 8, 1],
[7, 4, 7],
[4, 8, 4]],
[[0, 7, 4],
[5, 3, 1],
[1, 4, 0]]])
In [332]: m,n = A.shape[:2]
In [333]: A[np.arange(m)[:,None,None],np.arange(n)[:,None],indices]
Out[333]:
array([[[46, 43, 21],
[16, 97, 16],
[64, 46, 64]],
[[81, 14, 43],
[87, 24, 47],
[27, 52, 95]]])
For getting those indices corresponding to the max 5 elements along the last axis, we would use argpartition
, like so 
indices = np.argpartition(A,5,axis=1)[...,:5]
To keep the order from highest to lowest, use range(5)
instead of 5
.