Creates settings for a ResNet model
setResNet(
numLayers = c(1:8),
sizeHidden = c(2^(6:10)),
hiddenFactor = c(1:4),
residualDropout = c(seq(0, 0.5, 0.05)),
hiddenDropout = c(seq(0, 0.5, 0.05)),
sizeEmbedding = c(2^(6:9)),
estimatorSettings = setEstimator(learningRate = "auto", weightDecay = c(1e-06, 0.001),
device = "cpu", batchSize = 1024, epochs = 30, seed = NULL),
hyperParamSearch = "random",
randomSample = 100,
randomSampleSeed = NULL
)Arguments
- numLayers
Number of layers in network, default: 1:16
Amount of neurons in each default layer, default: 2^(6:10) (64 to 1024)
How much to grow the amount of neurons in each ResLayer, default: 1:4
- residualDropout
How much dropout to apply after last linear layer in ResLayer, default: seq(0, 0.3, 0.05)
How much dropout to apply after first linear layer in ResLayer, default: seq(0, 0.3, 0.05)
- sizeEmbedding
Size of embedding layer, default: 2^(6:9) '(64 to 512)
- estimatorSettings
created with “`setEstimator“`
- hyperParamSearch
Which kind of hyperparameter search to use random sampling or exhaustive grid search. default: 'random'
- randomSample
How many random samples from hyperparameter space to use
- randomSampleSeed
Random seed to sample hyperparameter combinations
Details
Model architecture from by https://arxiv.org/abs/2106.11959