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@ -367,7 +367,7 @@ class ActivationBalancer(torch.nn.Module):
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min_positive: the minimum, per channel, of the proportion of the time
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that (x > 0), below which we start to modify the derivatives.
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max_positive: the maximum, per channel, of the proportion of the time
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that (x > 0), below which we start to modify the derivatives.
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that (x > 0), above which we start to modify the derivatives.
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max_factor: the maximum factor by which we modify the derivatives for
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either the sign constraint or the magnitude constraint;
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e.g. with max_factor=0.02, the the derivatives would be multiplied by
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@ -413,7 +413,7 @@ class DoubleSwishFunction(torch.autograd.Function):
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"""
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double_swish(x) = x * torch.sigmoid(x-1)
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This is a definition, originally motivated by its close numerical
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similarity to swish(swish(x), where swish(x) = x * sigmoid(x).
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similarity to swish(swish(x)), where swish(x) = x * sigmoid(x).
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Memory-efficient derivative computation:
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double_swish(x) = x * s, where s(x) = torch.sigmoid(x-1)
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