LogMSELoss

neural_filters/LogMSELoss.py

+import torch
+from torch.nn import MSELoss
+
+class LogMSELoss(MSELoss):
+    r"""Creates a criterion that measures the logarithmic mean squared error between
+    `n` elements in the input `x` and target `y`:
+
+    :math:`{loss}(x, y)  = \log( 1/n \sum |x_i - y_i|^2 + epsilon)`
+
+    `x` and `y` arbitrary shapes with a total of `n` elements each.
+
+    The sum operation still operates over all the elements, and divides by `n`.
+
+    The division by `n` can be avoided if one sets the internal variable
+    `size_average` to ``False``.
+
+    To get a batch of losses, a loss per batch element, set `reduce` to
+    ``False``. These losses are not averaged and are not affected by
+    `size_average`.
+
+    The epsilon is a positive float used to avoid log(0) leading to NaN.
+
+    Args:
+        size_average (bool, optional): By default, the losses are averaged
+           over observations for each minibatch. However, if the field
+           size_average is set to ``False``, the losses are instead summed for
+           each minibatch. Only applies when reduce is ``True``. Default: ``True``
+        reduce (bool, optional): By default, the losses are averaged
+           over observations for each minibatch, or summed, depending on
+           size_average. When reduce is ``False``, returns a loss per batch
+           element instead and ignores size_average. Default: ``True``
+        epsilon (float, optional): add a small positive term to the MSE before
+            taking the log to avoid NaN with log(0). Default: ``0.05``
+
+    Shape:
+        - Input: :math:`(N, *)` where `*` means, any number of additional
+          dimensions
+        - Target: :math:`(N, *)`, same shape as the input
+
+    Examples::
+
+        >>> loss = neural_filters.LogMSELoss()
+        >>> input = autograd.Variable(torch.randn(3, 5), requires_grad=True)
+        >>> target = autograd.Variable(torch.randn(3, 5))
+        >>> output = loss(input, target)
+        >>> output.backward()
+    """
+    def __init__(self, size_average=True, reduce=True, epsilon=0.05):
+        super(LogMSELoss, self).__init__(size_average, reduce)
+        self.epsilon = epsilon
+
+    def forward(self, input, target):
+        loss = super(LogMSELoss, self).forward(input, target)
+        return torch.log(loss + self.epsilon)
\ No newline at end of file

neural_filters/__init__.py

@@ -2,4 +2,5 @@ from .NeuralFilterCell import *
 from .NeuralFilter1P import *
 from .NeuralFilter2R import *
 from .NeuralFilter2CC import *
-from .NeuralFilter2CD import *
\ No newline at end of file
+from .NeuralFilter2CD import *
+from .LogMSELoss import *