GraphCovolution: Graph Convolutional Neural Network Layer
In ff98li/scRGNet: Perform scRNA-seq Analysis using Single-cell Graphical Neural Network (scGNN) Framework
Description
Usage
Arguments
Details
Value
Fields
Description
Create a new GCN layer object.
Forward feeding
Reset weight
Usage
1
GraphCovolution(in_features, out_features, dropout = 0, act = torch::nnf_relu)
Arguments
in_features
in_features.
out_features
out_features
dropout
default 0
act
The activation function used by the neuron. Default ReLU
Details
A GCN layer class
#' @references
\insertRefGCNscRGNet
Value
A new GraphCovolution object.
Loss
Fields
in_featuresin_features
out_featuresout_features
dropoutdropout
actactivation function
weightweight of the model
ff98li/scRGNet documentation built on Jan. 14, 2022, 4:58 a.m.
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Description Usage Arguments Details Value Fields
Description
Create a new GCN layer object.
Forward feeding
Reset weight
Usage
1 | GraphCovolution(in_features, out_features, dropout = 0, act = torch::nnf_relu)
|
Arguments
in_features |
in_features. |
out_features |
out_features |
dropout |
default 0 |
act |
The activation function used by the neuron. Default ReLU |
Details
A GCN layer class
#' @references \insertRefGCNscRGNet
Value
A new GraphCovolution object.
Loss
Fields
in_featuresin_features
out_featuresout_features
dropoutdropout
actactivation function
weightweight of the model
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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