CODEWITHSUNDEEP
pythontensorflow
Jonathan
Jonathan

Reputation: 1936

Python: Extend for loop operation to each row in matrix without using iteration

I have a piece of code like this:

a = Y[0]; b = Z[0]
print(a, b)
loss = 0
for i in range(len(a)):
    k = len(a)-i
    loss += (2**(k-1))*np.abs(a[i]-b[i])
print(loss)

Where Y and Z are of dimensions 250 x 10 and each row is 10 bit binary value. For example, print(a,b) prints this: [1 0 0 0 0 0 0 0 1 0] [0 0 0 1 1 1 1 1 0 0]

Now I want to apply the two line function inside the for loop for corresponding rows between Y and Z. But I don't want to do something like this:

for j in range(Y.shape[0]):
    a = Y[j]; b = Z[j]
    loss = 0
    for i in range(len(a)):
        k = len(a)-i
        loss += (2**(k-1))*np.abs(a[i]-b[i])
    print(loss)

I am essentially trying to make a custom loss function in keras/tensorflow. And that for loop example doesn't scale for large tensor operations. How do I do it with some sort of batch matrix operation instead of for loops?

Upvotes: 3

Views: 159

Answers (2)

fountainhead
fountainhead

Reputation: 3722

You could do this:

factor = 2**np.arange(Y.shape[1])[::-1]
loss = np.sum(factor * np.abs(Y-Z), axis=-1)

Upvotes: 1

anurag
anurag

Reputation: 1932

If only the inner loop needs to be made numpy parallelized:

import numpy as np

for j in range(Y.shape[0]):
    a = Y[j]; b = Z[j]
    loss = 0
    """
    for i in range(len(a)):
        k = len(a)-i
        loss += (2**(k-1))*np.abs(a[i]-b[i])
    """
    k = np.arange(len(a), 0, -1)
    loss = np.sum(np.multiply(2**(k-1), np.abs(a-b)))
    print(loss)

EDIT

To make it even more numpy parallelized, use the following approach:

import numpy as np

# This function computes loss for row pairs
def get_loss(row, sz):
    loss = 0
    k = np.arange(sz, 0, -1)
    loss = np.sum(np.multiply(2**(k-1), np.abs(row[:sz]-row[sz:])))
    return loss

# Sample input matrices
A = np.random.random((5, 10))
B = np.random.random((5, 10))

# Concatenate the input matrices
AB = np.concatenate((A, B), axis=1)

# apply the function on each row pair
result = np.apply_along_axis(get_loss, 1, AB, A.shape[1])

# result is a 1D array of the losses
print(result.shape)

Upvotes: 1

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