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Merge pull request #313 from glynpu/fix_comments

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fix comments
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Daniel Povey 2022-04-13 14:03:02 +08:00 committed by GitHub
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2 changed files with 3 additions and 3 deletions
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4
egs/librispeech/ASR/pruned_transducer_stateless2/scaling.py
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@ -367,7 +367,7 @@ class ActivationBalancer(torch.nn.Module):
min_positive: the minimum, per channel, of the proportion of the time min_positive: the minimum, per channel, of the proportion of the time
that (x > 0), below which we start to modify the derivatives. that (x > 0), below which we start to modify the derivatives.
max_positive: the maximum, per channel, of the proportion of the time max_positive: the maximum, per channel, of the proportion of the time
that (x > 0), below which we start to modify the derivatives. that (x > 0), above which we start to modify the derivatives.
max_factor: the maximum factor by which we modify the derivatives for max_factor: the maximum factor by which we modify the derivatives for
either the sign constraint or the magnitude constraint; either the sign constraint or the magnitude constraint;
e.g. with max_factor=0.02, the the derivatives would be multiplied by e.g. with max_factor=0.02, the the derivatives would be multiplied by
@ -413,7 +413,7 @@ class DoubleSwishFunction(torch.autograd.Function):
""" """
double_swish(x) = x * torch.sigmoid(x-1) double_swish(x) = x * torch.sigmoid(x-1)
This is a definition, originally motivated by its close numerical This is a definition, originally motivated by its close numerical
similarity to swish(swish(x), where swish(x) = x * sigmoid(x). similarity to swish(swish(x)), where swish(x) = x * sigmoid(x).
Memory-efficient derivative computation: Memory-efficient derivative computation:
double_swish(x) = x * s, where s(x) = torch.sigmoid(x-1) double_swish(x) = x * s, where s(x) = torch.sigmoid(x-1)

2
icefall/diagnostics.py
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@ -111,7 +111,7 @@ def get_diagnostics_for_dim(
options object options object
sizes_same: sizes_same:
True if all the tensor sizes are the same on this dimension True if all the tensor sizes are the same on this dimension
stats_type: either "abs" or "positive" or "eigs" or "value", stats_type: either "abs" or "positive" or "eigs" or "value",
imdictates the type of stats we accumulate, abs is mean absolute imdictates the type of stats we accumulate, abs is mean absolute
value, "positive" is proportion of positive to nonnegative values, value, "positive" is proportion of positive to nonnegative values,
"eigs" is eigenvalues after doing outer product on this dim, sum "eigs" is eigenvalues after doing outer product on this dim, sum