Removing 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 array numpy
asked 28 mins ago
Brandon MacLeodBrandon MacLeod
11
New contributor
Brandon MacLeod is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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$endgroup$
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 array numpy
asked 28 mins ago
Brandon MacLeodBrandon MacLeod
11
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 array numpy
asked 28 mins ago
Brandon MacLeodBrandon MacLeod
11
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 array numpy
python performance array numpy
asked 28 mins ago
Brandon MacLeodBrandon MacLeod
11
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 28 mins ago
Brandon MacLeodBrandon MacLeod
11
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.
edited 6 mins ago
Brandon MacLeod
asked 28 mins ago
Brandon MacLeodBrandon MacLeod
11
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 28 mins ago
Brandon MacLeodBrandon MacLeod
11
asked 28 mins ago
Brandon MacLeodBrandon MacLeod
11
11
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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