I try to write a cross entropy loss function by myself. My loss function gives the same loss value as the official one, but when i use my loss function in the code instead of official cross entropy loss function, the code does not converge. When i use the official cross entropy loss function, the code converges. Here is my code, please give me some suggestions. Thanks very much
The input 'out' is a tensor (B*C) and 'label' contains class indices (1 * B)
class MylossFunc(nn.Module):
def __init__(self):
super(MylossFunc, self).__init__()
def forward(self, out, label):
out = torch.nn.functional.softmax(out, dim=1)
n = len(label)
loss = torch.FloatTensor([0])
loss = Variable(loss, requires_grad=True)
tmp = torch.log(out)
#print(out)
torch.scalar_tensor(-100)
for i in range(n):
loss = loss - torch.max(tmp[i][label[i]], torch.scalar_tensor(-100) )/n
loss = torch.sum(loss)
return loss
Instead of using torch.softmax and torch.log, you should use torch.log_softmax, otherwise your training will become unstable with nan values everywhere.
This happens because when you take the softmax of your logits using the following line:
out = torch.nn.functional.softmax(out, dim=1)
you might get a zero in one of the components of out, and when you follow that by applying torch.log it will result in nan (since log(0) is undefined). That is why torch (and other common libraries) provide a single stable operation, log_softmax, to avoid the numerical instabilities that occur when you use torch.softmax and torch.log individually.
Related
I am currently working with torch.nn.CrossEntropyLoss. As far as I know, it is common to compute the loss batch-wise. However, is there a possibility to compute the loss over multiple batches?
More concretely, assume we are given the data
import torch
features = torch.randn(no_of_batches, batch_size, feature_dim)
targets = torch.randint(low=0, high=10, size=(no_of_batches, batch_size))
loss_function = torch.nn.CrossEntropyLoss()
Is there a way to compute in one line
loss = loss_function(features, targets) # raises RuntimeError: Expected target size [no_of_batches, feature_dim], got [no_of_batches, batch_size]
?
Thank you in advance!
You can compute multiple cross-entropy losses but you'll need to do your own reduction. Since cross-entropy loss assumes the feature dim is always the second dimension of the features tensor you will also need to permute it first.
loss_function = torch.nn.CrossEntropyLoss(reduction='none')
loss = loss_function(features.permute(0,2,1), targets).mean(dim=1)
which will result in a loss tensor with no_of_batches entries.
i'm trying to define the loss function of a two-class classification problem. However, the target label is not hard label 0,1, but a float number between 0~1.
torch.nn.CrossEntropy in Pytorch do not support soft label so i'm trying to write a cross entropy function by my self.
My function looks like this
def cross_entropy(self, pred, target):
loss = -torch.mean(torch.sum(target.flatten() * torch.log(pred.flatten())))
return loss
def step(self, batch: Any):
x, y = batch
logits = self.forward(x)
loss = self.criterion(logits, y)
preds = logits
# torch.argmax(logits, dim=1)
return loss, preds, y
however it does not work at all.
Can anyone give me a suggestion is there any mistake in my loss function?
It seems like BCELoss and the robust version BCEWithLogitsLoss are working with fuzzy targets "out of the box". They do not expect target to be binary" any number between zero and one is fine.
Please read the doc.
I am training a PyTorch model to perform binary classification. My minority class makes up about 10% of the data, so I want to use a weighted loss function. The docs for BCELoss and CrossEntropyLoss say that I can use a 'weight' for each sample.
However, when I declare CE_loss = nn.BCELoss() or nn.CrossEntropyLoss() and then do CE_Loss(output, target, weight=batch_weights), where output, target, and batch_weights are Tensors of batch_size, I get the following error message:
forward() got an unexpected keyword argument 'weight'
Another way you could accomplish your goal is to use reduction=none when initializing the loss and then multiply the resulting tensor by your weights before computing the mean.
e.g.
loss = torch.nn.BCELoss(reduction='none')
model = torch.sigmoid
weights = torch.rand(10,1)
inputs = torch.rand(10,1)
targets = torch.rand(10,1)
intermediate_losses = loss(model(inputs), targets)
final_loss = torch.mean(weights*intermediate_losses)
Of course for your scenario you still would need to calculate the weights tensor. But hopefully this helps!
Could it be that you want to apply separate fixed weights to all elements of class 0 and class 1 in your dataset? It is not clear what value you are passing for batch_weights here. If so, then that is not what the weight parameter in BCELoss does. The weight parameter expects you to pass a separate weight for every ELEMENT in the dataset, not for every CLASS. There are several ways around this. You could construct a weight table for every element. Alternatively, you could use a custom loss function that does what you want:
def BCELoss_class_weighted(weights):
def loss(input, target):
input = torch.clamp(input,min=1e-7,max=1-1e-7)
bce = - weights[1] * target * torch.log(input) - (1 - target) * weights[0] * torch.log(1 - input)
return torch.mean(bce)
return loss
Note that it is important to add a clamp to avoid numerical instability.
HTH Jeroen
the issue is wherein your providing the weight parameter. As it is mentioned in the docs, here, the weights parameter should be provided during module instantiation.
For example, something like,
from torch import nn
weights = torch.FloatTensor([2.0, 1.2])
loss = nn.BCELoss(weights=weights)
You can find a more concrete example here or another helpful PT forum discussion here.
you need to pass weights like below:
CE_loss = CrossEntropyLoss(weight=[…])
This is similar to the idea of #Jeroen Vuurens, but the class weights are determined by the target mean:
y_train_mean = y_train.mean()
bi_cls_w2 = 1/(1 - y_train_mean)
bi_cls_w1 = 1/y_train_mean - bi_cls_w2
bce_loss = nn.BCELoss(reduction='none')
loss_fun = lambda pred, target: ((bi_cls_w1*target + bi_cls_w2) * bce_loss(pred, target)).mean()
Suppose a model as in:
model = Model(inputs=[A, B], outputs=C)
With custom loss:
def actor_loss(y_true, y_pred):
log_lik = y_true * K.log(y_pred)
loss = -K.sum(log_lik * K.stop_gradient(B))
return loss
Now I'm trying to define a function that returns the gradients of the loss wrt to the weights for a given pair of input and target output and expose it as such.
Here is an idea of what I mean in pseudocode
def _get_grads(inputs, targets):
loss = model.loss(targets, model.output)
weights = model.trainable_weights
grads = K.gradients(loss, weights)
model.input[0] (aka 'A') <----inputs[0]
model.input[1] (aka 'B') <----inputs[1]
return K.function(model.input, grads)
self.get_grads = _get_grads
My question is how do I feed inputs argument to the graph inside said function.
(So far I've only worked with .fit and not with .gradients and I can't find any decent documentation with custom loss or multiple inputs)
If you call K.function, you get an actual callable function, so you should just call it with some parameter values. The format is exactly the same as model.fit, in your case it should be two arrays of values, including the batch dimension:
self.get_grads = _get_grads(inputs, targets)
grad_value = self.get_grads([input1, input2])
Where input1 and input2 are numpy arrays that include the batch dimension.
My understanding of K.function ,K.gradients and custom loss was fundamentally wrong. You use the function to construct a mini-graph that computes gradients of loss wrt to weights. No need for the function itself to have arguments.
def _get_grads():
targets = Input(shape=...)
loss = model.loss(targets, model.output)
weights = model.trainable_weights
grads = K.gradients(loss, weights)
return K.function(model.input + [targets], grads)
I was under the impression that _get_grads was itself K.function but that was wrong. _get_grads() returns K.function. And then you use that as
f = _get_grads() # constructs the mini-graph that gives gradients
grads = f([inputs, labels])
inputs is fed to model.inputs, labels to targets and it returns grads.
I have a 1000 classes in the network and they have multi-label outputs. For each training example, the number of positive output is same(i.e 10) but they can be assigned to any of the 1000 classes. So 10 classes have output 1 and rest 990 have output 0.
For the multi-label classification, I am using 'binary-cross entropy' as cost function and 'sigmoid' as the activation function. When I tried this rule of 0.5 as the cut-off for 1 or 0. All of them were 0. I understand this is a class imbalance problem. From this link, I understand that, I might have to create extra output labels.Unfortunately, I haven't been able to figure out how to incorporate that into a simple neural network in keras.
nclasses = 1000
# if we wanted to maximize an imbalance problem!
#class_weight = {k: len(Y_train)/(nclasses*(Y_train==k).sum()) for k in range(nclasses)}
inp = Input(shape=[X_train.shape[1]])
x = Dense(5000, activation='relu')(inp)
x = Dense(4000, activation='relu')(x)
x = Dense(3000, activation='relu')(x)
x = Dense(2000, activation='relu')(x)
x = Dense(nclasses, activation='sigmoid')(x)
model = Model(inputs=[inp], outputs=[x])
adam=keras.optimizers.adam(lr=0.00001)
model.compile('adam', 'binary_crossentropy')
history = model.fit(
X_train, Y_train, batch_size=32, epochs=50,verbose=0,shuffle=False)
Could anyone help me with the code here and I would also highly appreciate if you could suggest a good 'accuracy' metric for this problem?
Thanks a lot :) :)
I have a similar problem and unfortunately have no answer for most of the questions. Especially the class imbalance problem.
In terms of metric there are several possibilities: In my case I use the top 1/2/3/4/5 results and check if one of them is right. Because in your case you always have the same amount of labels=1 you could take your top 10 results and see how many percent of them are right and average this result over your batch size. I didn't find a possibility to include this algorithm as a keras metric. Instead, I wrote a callback, which calculates the metric on epoch end on my validation data set.
Also, if you predict the top n results on a test dataset, see how many times each class is predicted. The Counter Class is really convenient for this purpose.
Edit: If found a method to include class weights without splitting the output.
You need a numpy 2d array containing weights with shape [number classes to predict, 2 (background and signal)].
Such an array could be calculated with this function:
def calculating_class_weights(y_true):
from sklearn.utils.class_weight import compute_class_weight
number_dim = np.shape(y_true)[1]
weights = np.empty([number_dim, 2])
for i in range(number_dim):
weights[i] = compute_class_weight('balanced', [0.,1.], y_true[:, i])
return weights
The solution is now to build your own binary crossentropy loss function in which you multiply your weights yourself:
def get_weighted_loss(weights):
def weighted_loss(y_true, y_pred):
return K.mean((weights[:,0]**(1-y_true))*(weights[:,1]**(y_true))*K.binary_crossentropy(y_true, y_pred), axis=-1)
return weighted_loss
weights[:,0] is an array with all the background weights and weights[:,1] contains all the signal weights.
All that is left is to include this loss into the compile function:
model.compile(optimizer=Adam(), loss=get_weighted_loss(class_weights))