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resnet katex fix

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Varuna Jayasiri
2022-04-10 13:41:33 +05:30
parent 1d2ce11ce4
commit 42257b7130
2 changed files with 40 additions and 40 deletions
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14
labml_nn/resnet/__init__.py
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@ -166,29 +166,29 @@ class BottleneckResidualBlock(Module):
![Bottlenext Block](bottleneck_block.svg)
The first convolution layer maps from `in_channels` to `bottleneck_channels` with a $1x1$
The first convolution layer maps from `in_channels` to `bottleneck_channels` with a $1 \times 1$
convolution,
where the `bottleneck_channels` is lower than `in_channels`.
The second $3x3$ convolution layer maps from `bottleneck_channels` to `bottleneck_channels`.
The second $3 \times 3$ convolution layer maps from `bottleneck_channels` to `bottleneck_channels`.
This can have a stride length greater than $1$ when we want to compress the
feature map size.
The third, final $1x1$ convolution layer maps to `out_channels`.
The third, final $1 \times 1$ convolution layer maps to `out_channels`.
`out_channels` is higher than `in_channels` if the stride length is greater than $1$;
otherwise, $out_channels$ is equal to `in_channels`.
`bottleneck_channels` is less than `in_channels` and the $3x3$ convolution is performed
on this shrunk space (hence the bottleneck). The two $1x1$ convolution decreases and increases
`bottleneck_channels` is less than `in_channels` and the $3 \times 3$ convolution is performed
on this shrunk space (hence the bottleneck). The two $1 \times 1$ convolution decreases and increases
the number of channels.
"""
def __init__(self, in_channels: int, bottleneck_channels: int, out_channels: int, stride: int):
"""
* `in_channels` is the number of channels in $x$
* `bottleneck_channels` is the number of channels for the $3x3$ convlution
* `bottleneck_channels` is the number of channels for the $3 \times 3$ convlution
* `out_channels` is the number of output channels
* `stride` is the stride length in the $3x3$ convolution operation.
* `stride` is the stride length in the $3 \times 3$ convolution operation.
"""
super().__init__()