54 lines
1.6 KiB
Python
54 lines
1.6 KiB
Python
import torch
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from torch.nn.parameter import Parameter
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class AdaptiveExp(torch.nn.Module):
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"""
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Implementation of soft exponential activation.
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Shape:
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- Input: (N, *) where * means, any number of additional
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dimensions
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- Output: (N, *), same shape as the input
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Parameters:
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- alpha - trainable parameter
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References:
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- See related paper:
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https://arxiv.org/pdf/1602.01321.pdf
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Examples:
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>>> a1 = soft_exponential(256)
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>>> x = torch.randn(256)
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>>> x = a1(x)
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"""
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def __init__(self):
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"""
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Initialization.
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INPUT:
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- in_features: shape of the input
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- aplha: trainable parameter
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aplha is initialized with zero value by default
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"""
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super(AdaptiveExp, self).__init__()
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self.scale = Parameter(
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torch.normal(torch.tensor(1.0), torch.tensor(0.1))
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) # create a tensor out of alpha
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self.scale.requiresGrad = True # set requiresGrad to true!
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self.alpha = Parameter(
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torch.normal(torch.tensor(1.0), torch.tensor(0.1))
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) # create a tensor out of alpha
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self.alpha.requiresGrad = True # set requiresGrad to true!
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self.translate = Parameter(
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torch.normal(torch.tensor(0.0), torch.tensor(0.1))
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) # create a tensor out of alpha
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self.translate.requiresGrad = True # set requiresGrad to true!
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def forward(self, x):
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"""
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Forward pass of the function.
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Applies the function to the input elementwise.
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"""
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return self.scale * (x + self.translate)
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