How to eliminate for loops in Python?
$begingroup$
I'm writing a loss function for a linear classifier. The function takes in several arrays and outputs the loss. 's' is a score array which contains 10 entries that are the scores for each class. y is the array that contains the correct class assignments. My loss function takes the max of 0 and the calculation between the correct score and all the other scores to see if there is a higher value in 's' than y[i]. Here's my code.
n = x.shape[0]
lossSize = 1/n
Li = 0.0
loss = 0.0
for i in range(n):
s = (np.dot(W.transpose(), x[i])) + b
for j in range (W.shape[1]):
if (j != y[i]):
Li += max(0.0, (s[j] - s[y[i]] + 1.0))
loss += Li
Li = 0.0
loss *= LossSize
return loss`
This produces what I want but now I need to optimize it. My question is, is there a way to get rid of the two for loops since for loops can be rather slow and I feel like there is a more efficient way of doing this.
python performance numpy
asked 2 mins ago
Brandon MacLeodBrandon MacLeod
1
New contributor
Brandon MacLeod is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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add a comment |
$begingroup$
I'm writing a loss function for a linear classifier. The function takes in several arrays and outputs the loss. 's' is a score array which contains 10 entries that are the scores for each class. y is the array that contains the correct class assignments. My loss function takes the max of 0 and the calculation between the correct score and all the other scores to see if there is a higher value in 's' than y[i]. Here's my code.
n = x.shape[0]
lossSize = 1/n
Li = 0.0
loss = 0.0
for i in range(n):
s = (np.dot(W.transpose(), x[i])) + b
for j in range (W.shape[1]):
if (j != y[i]):
Li += max(0.0, (s[j] - s[y[i]] + 1.0))
loss += Li
Li = 0.0
loss *= LossSize
return loss`
This produces what I want but now I need to optimize it. My question is, is there a way to get rid of the two for loops since for loops can be rather slow and I feel like there is a more efficient way of doing this.
python performance numpy
asked 2 mins ago
Brandon MacLeodBrandon MacLeod
1
New contributor
Brandon MacLeod is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
I'm writing a loss function for a linear classifier. The function takes in several arrays and outputs the loss. 's' is a score array which contains 10 entries that are the scores for each class. y is the array that contains the correct class assignments. My loss function takes the max of 0 and the calculation between the correct score and all the other scores to see if there is a higher value in 's' than y[i]. Here's my code.
n = x.shape[0]
lossSize = 1/n
Li = 0.0
loss = 0.0
for i in range(n):
s = (np.dot(W.transpose(), x[i])) + b
for j in range (W.shape[1]):
if (j != y[i]):
Li += max(0.0, (s[j] - s[y[i]] + 1.0))
loss += Li
Li = 0.0
loss *= LossSize
return loss`
This produces what I want but now I need to optimize it. My question is, is there a way to get rid of the two for loops since for loops can be rather slow and I feel like there is a more efficient way of doing this.
python performance numpy
asked 2 mins ago
Brandon MacLeodBrandon MacLeod
1
New contributor
Brandon MacLeod is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I'm writing a loss function for a linear classifier. The function takes in several arrays and outputs the loss. 's' is a score array which contains 10 entries that are the scores for each class. y is the array that contains the correct class assignments. My loss function takes the max of 0 and the calculation between the correct score and all the other scores to see if there is a higher value in 's' than y[i]. Here's my code.
n = x.shape[0]
lossSize = 1/n
Li = 0.0
loss = 0.0
for i in range(n):
s = (np.dot(W.transpose(), x[i])) + b
for j in range (W.shape[1]):
if (j != y[i]):
Li += max(0.0, (s[j] - s[y[i]] + 1.0))
loss += Li
Li = 0.0
loss *= LossSize
return loss`
This produces what I want but now I need to optimize it. My question is, is there a way to get rid of the two for loops since for loops can be rather slow and I feel like there is a more efficient way of doing this.
python performance numpy
python performance numpy
asked 2 mins ago
Brandon MacLeodBrandon MacLeod
1
New contributor
Brandon MacLeod is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 2 mins ago
Brandon MacLeodBrandon MacLeod
1
New contributor
Brandon MacLeod is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 2 mins ago
Brandon MacLeodBrandon MacLeod
1
New contributor
Brandon MacLeod is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 2 mins ago
Brandon MacLeodBrandon MacLeod
1
asked 2 mins ago
Brandon MacLeodBrandon MacLeod
1
1
New contributor
Brandon MacLeod is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Brandon MacLeod is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Brandon MacLeod is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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