EfficientAttentionLayer2D: Efficient attention layer (2-D)
In ANTsX/ANTsRNet: Neural Networks for Medical Image Processing
EfficientAttentionLayer2D R Documentation
Efficient attention layer (2-D)
Description
Efficient attention layer (2-D)
Value
output of tensor shape.
Arguments
- numberOfFiltersFG
number of filters for F and G layers.
- numberOfFiltersH
number of filters for H. If = NA, only
use filter F for efficiency.
- poolSize
pool_size in max pool layer.
- doConcatenateFinalLayers
concatenate final layer with input.
Alternatively, add.
Details
$initialize instantiates a new class.
$call main body.
$compute_output_shape computes the output shape.
Author(s)
BB Avants, NJ Tustison
ANTsX/ANTsRNet documentation built on Nov. 21, 2024, 4:07 a.m.
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| EfficientAttentionLayer2D | R Documentation |
Efficient attention layer (2-D)
Description
Efficient attention layer (2-D)
Value
output of tensor shape.
Arguments
- numberOfFiltersFG
number of filters for F and G layers.
- numberOfFiltersH
number of filters for H. If = NA, only use filter F for efficiency.
- poolSize
pool_size in max pool layer.
- doConcatenateFinalLayers
concatenate final layer with input. Alternatively, add.
Details
$initialize instantiates a new class.
$call main body.
$compute_output_shape computes the output shape.
Author(s)
BB Avants, NJ Tustison
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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