Removing for loops in Python












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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.










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    0












    $begingroup$


    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.










    share|improve this question









    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.







    $endgroup$















      0












      0








      0





      $begingroup$


      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.










      share|improve this question









      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






      share|improve this question









      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.











      share|improve this question









      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.









      share|improve this question




      share|improve this question








      edited 6 mins ago







      Brandon MacLeod













      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

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      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.






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      Check out our Code of Conduct.






















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