Delete log_loss.py

neural_filters/log_loss.py

-import torch
-from torch.nn import MSELoss
-from torch.nn import L1Loss
-
-
-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().__init__(size_average, reduce)
-        self.epsilon = epsilon
-
-    def forward(self, input, target):
-        loss = super().forward(input, target)
-        return torch.log(loss + self.epsilon)
-
-class LogL1Loss(L1Loss):
-    r"""Creates a criterion that measures the logarithm of the mean absolute value of the
-    element-wise difference between input `x` and target `y`:
-
-    :math:`{loss}(x, y)  = \log( 1/n \sum |x_i - y_i| + 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 constructor argument
-    `size_average=False`.
-
-    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. Ignored when reduce is ``False``. Default: ``True``
-        reduce (bool, optional): By default, the losses are averaged or summed
-           for each minibatch. When reduce is ``False``, the loss function 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
-        - Output: scalar. If reduce is ``False``, then
-          :math:`(N, *)`, same shape as the input
-
-    Examples::
-
-        >>> loss = neural_filters.LogL1Loss()
-        >>> 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().__init__(size_average, reduce)
-        self.epsilon = epsilon
-
-    def forward(self, input, target):
-        loss = super().forward(input, target)
-        return torch.log(loss + self.epsilon)