decoder_small: Network small

In quon-titative-biology/scAlign: An alignment and integration method for single cell genomics

Description

Defines decoder network architecture for scAlign.

Usage

decoder_small(
  inputs,
  complexity,
  final_dim,
  emb_size = 32,
  l2_weight = 1e-08,
  dropout = TRUE,
  dropout_rate = 0.3,
  is_training = TRUE,
  batch_norm = TRUE,
  shared_ae = FALSE
)

Arguments

inputs	Mini-batch placeholder
complexity	Determines the depth and width of an automatically created network
final_dim	Number of features in high dimensional data
emb_size	Number of hidden nodes in final (embedding) hidden layer
l2_weight	Weight on l2_regularizer
dropout_rate	Probability for dropout.
is_training	Determines if dropout and batch norm should be include in pass through network
batch_norm	Determines if batch normalization layers should be included

Value

Neural network graph op

quon-titative-biology/scAlign documentation built on Nov. 17, 2021, 9:57 a.m.