Pytorch : Loss function for binary classification












1












$begingroup$


Fairly newbie to Pytorch & neural nets world.Below is a code snippet from a binary classification being done using a simple 3 layer network :



n_input_dim = X_train.shape[1]

n_hidden = 100  # Number of hidden nodes

n_output = 1   # Number of output nodes = for binary classifier

# Build the network

model = nn.Sequential(

    nn.Linear(n_input_dim, n_hidden),

    nn.ELU(),

    nn.Linear(n_hidden, n_output),

    nn.Sigmoid())



x_tensor =  torch.from_numpy(X_train.values).float()

tensor([[ -1.0000,  -1.0000,  -1.0000,  ..., -99.0000, -99.0000, -99.0000],

       [ -1.0000,  -1.0000,  -1.0000,  ...,   0.1538,   5.0000,   0.1538],

       [ -1.0000,  -1.0000,  -1.0000,  ..., -99.0000,   6.0000,   0.2381],

       ...,

       [ -1.0000,  -1.0000,  -1.0000,  ..., -99.0000, -99.0000, -99.0000],

       [ -1.0000,  -1.0000,  -1.0000,  ..., -99.0000, -99.0000, -99.0000],

       [ -1.0000,  -1.0000,  -1.0000,  ..., -99.0000, -99.0000, -99.0000]])

y_tensor =  torch.from_numpy(Y_train).float()

tensor([0., 0., 1.,  ..., 0., 0., 0.])

#Loss Computation

loss_func = nn.BCELoss()

#Optimizer

learning_rate = 0.0001

optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)



train_loss = 

iters = 500

for i in range(iters):

    y_pred = model(x_tensor)

    loss = loss_func(y_pred, y_tensor)

    print " Loss in iteration :"

    print (i, loss.item())



    optimizer.zero_grad()

    loss.backward()

    optimizer.step()

    train_loss.append(loss.item())


In the above case , what i'm not sure about is loss is being computed on y_pred which is a set of probabilities ,computed from the model on the training data with y_tensor (which is binary 0/1).
Is this way of loss computation fine in Classification problem in pytorch? Shouldn't loss be computed between two probabilities set ideally ? If this is fine , then does loss function , BCELoss over here , scales the input in some manner ?



Any insights towards this will be highly appreciated






loss-function pytorch







share|improve this question







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    1












    $begingroup$


    Fairly newbie to Pytorch & neural nets world.Below is a code snippet from a binary classification being done using a simple 3 layer network :



    n_input_dim = X_train.shape[1]
    
n_hidden = 100  # Number of hidden nodes
    
n_output = 1   # Number of output nodes = for binary classifier
    
# Build the network
    
model = nn.Sequential(
    
    nn.Linear(n_input_dim, n_hidden),
    
    nn.ELU(),
    
    nn.Linear(n_hidden, n_output),
    
    nn.Sigmoid())
    

    
x_tensor =  torch.from_numpy(X_train.values).float()
    
tensor([[ -1.0000,  -1.0000,  -1.0000,  ..., -99.0000, -99.0000, -99.0000],
    
       [ -1.0000,  -1.0000,  -1.0000,  ...,   0.1538,   5.0000,   0.1538],
    
       [ -1.0000,  -1.0000,  -1.0000,  ..., -99.0000,   6.0000,   0.2381],
    
       ...,
    
       [ -1.0000,  -1.0000,  -1.0000,  ..., -99.0000, -99.0000, -99.0000],
    
       [ -1.0000,  -1.0000,  -1.0000,  ..., -99.0000, -99.0000, -99.0000],
    
       [ -1.0000,  -1.0000,  -1.0000,  ..., -99.0000, -99.0000, -99.0000]])
    
y_tensor =  torch.from_numpy(Y_train).float()
    
tensor([0., 0., 1.,  ..., 0., 0., 0.])
    
#Loss Computation
    
loss_func = nn.BCELoss()
    
#Optimizer
    
learning_rate = 0.0001
    
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
    

    
train_loss = 
    
iters = 500
    
for i in range(iters):
    
    y_pred = model(x_tensor)
    
    loss = loss_func(y_pred, y_tensor)
    
    print " Loss in iteration :"
    
    print (i, loss.item())
    

    
    optimizer.zero_grad()
    
    loss.backward()
    
    optimizer.step()
    
    train_loss.append(loss.item())


    In the above case , what i'm not sure about is loss is being computed on y_pred which is a set of probabilities ,computed from the model on the training data with y_tensor (which is binary 0/1).
    Is this way of loss computation fine in Classification problem in pytorch? Shouldn't loss be computed between two probabilities set ideally ? If this is fine , then does loss function , BCELoss over here , scales the input in some manner ?



    Any insights towards this will be highly appreciated






    loss-function pytorch







    share|improve this question







    New contributor




    raul is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
    Check out our Code of Conduct.







    $endgroup$















      1












      1








      1





      $begingroup$


      Fairly newbie to Pytorch & neural nets world.Below is a code snippet from a binary classification being done using a simple 3 layer network :



      n_input_dim = X_train.shape[1]
      
n_hidden = 100  # Number of hidden nodes
      
n_output = 1   # Number of output nodes = for binary classifier
      
# Build the network
      
model = nn.Sequential(
      
    nn.Linear(n_input_dim, n_hidden),
      
    nn.ELU(),
      
    nn.Linear(n_hidden, n_output),
      
    nn.Sigmoid())
      

      
x_tensor =  torch.from_numpy(X_train.values).float()
      
tensor([[ -1.0000,  -1.0000,  -1.0000,  ..., -99.0000, -99.0000, -99.0000],
      
       [ -1.0000,  -1.0000,  -1.0000,  ...,   0.1538,   5.0000,   0.1538],
      
       [ -1.0000,  -1.0000,  -1.0000,  ..., -99.0000,   6.0000,   0.2381],
      
       ...,
      
       [ -1.0000,  -1.0000,  -1.0000,  ..., -99.0000, -99.0000, -99.0000],
      
       [ -1.0000,  -1.0000,  -1.0000,  ..., -99.0000, -99.0000, -99.0000],
      
       [ -1.0000,  -1.0000,  -1.0000,  ..., -99.0000, -99.0000, -99.0000]])
      
y_tensor =  torch.from_numpy(Y_train).float()
      
tensor([0., 0., 1.,  ..., 0., 0., 0.])
      
#Loss Computation
      
loss_func = nn.BCELoss()
      
#Optimizer
      
learning_rate = 0.0001
      
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
      

      
train_loss = 
      
iters = 500
      
for i in range(iters):
      
    y_pred = model(x_tensor)
      
    loss = loss_func(y_pred, y_tensor)
      
    print " Loss in iteration :"
      
    print (i, loss.item())
      

      
    optimizer.zero_grad()
      
    loss.backward()
      
    optimizer.step()
      
    train_loss.append(loss.item())


      In the above case , what i'm not sure about is loss is being computed on y_pred which is a set of probabilities ,computed from the model on the training data with y_tensor (which is binary 0/1).
      Is this way of loss computation fine in Classification problem in pytorch? Shouldn't loss be computed between two probabilities set ideally ? If this is fine , then does loss function , BCELoss over here , scales the input in some manner ?



      Any insights towards this will be highly appreciated






      loss-function pytorch







      share|improve this question







      New contributor




      raul is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.







      $endgroup$




      Fairly newbie to Pytorch & neural nets world.Below is a code snippet from a binary classification being done using a simple 3 layer network :



      n_input_dim = X_train.shape[1]
      
n_hidden = 100  # Number of hidden nodes
      
n_output = 1   # Number of output nodes = for binary classifier
      
# Build the network
      
model = nn.Sequential(
      
    nn.Linear(n_input_dim, n_hidden),
      
    nn.ELU(),
      
    nn.Linear(n_hidden, n_output),
      
    nn.Sigmoid())
      

      
x_tensor =  torch.from_numpy(X_train.values).float()
      
tensor([[ -1.0000,  -1.0000,  -1.0000,  ..., -99.0000, -99.0000, -99.0000],
      
       [ -1.0000,  -1.0000,  -1.0000,  ...,   0.1538,   5.0000,   0.1538],
      
       [ -1.0000,  -1.0000,  -1.0000,  ..., -99.0000,   6.0000,   0.2381],
      
       ...,
      
       [ -1.0000,  -1.0000,  -1.0000,  ..., -99.0000, -99.0000, -99.0000],
      
       [ -1.0000,  -1.0000,  -1.0000,  ..., -99.0000, -99.0000, -99.0000],
      
       [ -1.0000,  -1.0000,  -1.0000,  ..., -99.0000, -99.0000, -99.0000]])
      
y_tensor =  torch.from_numpy(Y_train).float()
      
tensor([0., 0., 1.,  ..., 0., 0., 0.])
      
#Loss Computation
      
loss_func = nn.BCELoss()
      
#Optimizer
      
learning_rate = 0.0001
      
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
      

      
train_loss = 
      
iters = 500
      
for i in range(iters):
      
    y_pred = model(x_tensor)
      
    loss = loss_func(y_pred, y_tensor)
      
    print " Loss in iteration :"
      
    print (i, loss.item())
      

      
    optimizer.zero_grad()
      
    loss.backward()
      
    optimizer.step()
      
    train_loss.append(loss.item())


      In the above case , what i'm not sure about is loss is being computed on y_pred which is a set of probabilities ,computed from the model on the training data with y_tensor (which is binary 0/1).
      Is this way of loss computation fine in Classification problem in pytorch? Shouldn't loss be computed between two probabilities set ideally ? If this is fine , then does loss function , BCELoss over here , scales the input in some manner ?



      Any insights towards this will be highly appreciated






      loss-function pytorch




      loss-function pytorch






      share|improve this question







      New contributor




      raul 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




      raul 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






      New contributor




      raul is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.









      asked 10 hours ago









      raulraul

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      New contributor





      raul is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.






      raul 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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          $begingroup$

          You are right about the fact that cross entropy is computed between 2 distributions, however, in the case of the y_tensor values, we know for sure which class the example should actually belong to which is the ground truth. So, you can think of the binary values as probability distributions over possible classes in which case the loss function is absolutely correct and the way to go for the problem. Hope that helps.






          share|improve this answer









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            $begingroup$

            You are right about the fact that cross entropy is computed between 2 distributions, however, in the case of the y_tensor values, we know for sure which class the example should actually belong to which is the ground truth. So, you can think of the binary values as probability distributions over possible classes in which case the loss function is absolutely correct and the way to go for the problem. Hope that helps.






            share|improve this answer









            $endgroup$


















              0












              $begingroup$

              You are right about the fact that cross entropy is computed between 2 distributions, however, in the case of the y_tensor values, we know for sure which class the example should actually belong to which is the ground truth. So, you can think of the binary values as probability distributions over possible classes in which case the loss function is absolutely correct and the way to go for the problem. Hope that helps.






              share|improve this answer









              $endgroup$
















                0












                0








                0





                $begingroup$

                You are right about the fact that cross entropy is computed between 2 distributions, however, in the case of the y_tensor values, we know for sure which class the example should actually belong to which is the ground truth. So, you can think of the binary values as probability distributions over possible classes in which case the loss function is absolutely correct and the way to go for the problem. Hope that helps.






                share|improve this answer









                $endgroup$



                You are right about the fact that cross entropy is computed between 2 distributions, however, in the case of the y_tensor values, we know for sure which class the example should actually belong to which is the ground truth. So, you can think of the binary values as probability distributions over possible classes in which case the loss function is absolutely correct and the way to go for the problem. Hope that helps.







                share|improve this answer












                share|improve this answer



                share|improve this answer










                answered 9 hours ago









                Sajid AhmedSajid Ahmed

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