ConvLayer

Create a sequence of convolutional (`ni` to `nf`), ReLU (if `use_activ`) and `norm_type` layers.

ConvLayer(
  ni,
  nf,
  ks = 3,
  stride = 1,
  padding = NULL,
  bias = NULL,
  ndim = 2,
  norm_type = 1,
  bn_1st = TRUE,
  act_cls = nn()$ReLU,
  transpose = FALSE,
  init = "auto",
  xtra = NULL,
  bias_std = 0.01,
  dilation = 1,
  groups = 1,
  padding_mode = "zeros"
)

Arguments

ni

number of inputs

nf

outputs/ number of features

ks

kernel size

stride

stride

padding

padding

bias

bias

ndim

dimension number

norm_type

normalization type

bn_1st

batch normalization 1st

act_cls

activation

transpose

transpose

init

initializer

xtra

xtra

bias_std

bias standard deviation

dilation

specify the dilation rate to use for dilated convolution

groups

groups size

padding_mode

padding mode, e.g 'zeros'

Value

None