Pytorch : Loss function for binary classification
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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
asked 10 hours ago
raulraul
62
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$begingroup$
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
asked 10 hours ago
raulraul
62
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.
$endgroup$
add a comment |
$begingroup$
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
asked 10 hours ago
raulraul
62
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
asked 10 hours ago
raulraul
62
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
62
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
62
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
62
asked 10 hours ago
raulraul
62
62
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.
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.
add a comment |
add a comment |
1 Answer
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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.
answered 9 hours ago
Sajid AhmedSajid Ahmed
314
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1 Answer
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active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
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active
oldest
votes
$begingroup$
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.
answered 9 hours ago
Sajid AhmedSajid Ahmed
314
$endgroup$
add a comment |
$begingroup$
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.
answered 9 hours ago
Sajid AhmedSajid Ahmed
314
$endgroup$
add a comment |
$begingroup$
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.
answered 9 hours ago
Sajid AhmedSajid Ahmed
314
$endgroup$
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.
answered 9 hours ago
Sajid AhmedSajid Ahmed
314
answered 9 hours ago
Sajid AhmedSajid Ahmed
314
answered 9 hours ago
Sajid AhmedSajid Ahmed
314
answered 9 hours ago
Sajid AhmedSajid Ahmed
314
314
add a comment |
add a comment |
raul is a new contributor. Be nice, and check out our Code of Conduct.
raul is a new contributor. Be nice, and check out our Code of Conduct.
raul is a new contributor. Be nice, and check out our Code of Conduct.
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