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DESCRIPTION
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DESCRIPTION
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Package: mlr3torch
Title: Deep Learning with 'mlr3'
Version: 0.1.2-9000
Authors@R:
c(person(given = "Sebastian",
family = "Fischer",
role = c("cre", "aut"),
email = "[email protected]",
comment = c(ORCID = "0000-0002-9609-3197")),
person(given = "Bernd",
family = "Bischl",
role = "ctb",
email = "[email protected]",
comment = c(ORCID = "0000-0001-6002-6980")),
person(given = "Lukas",
family = "Burk",
role = "ctb",
email = "[email protected]",
comment = c(ORCID = "0000-0001-7528-3795")),
person(given = "Martin",
family = "Binder",
role = "aut",
email = "[email protected]"),
person(given = "Florian",
family = "Pfisterer",
role = "ctb",
email = "[email protected]",
comment = c(ORCID = "0000-0001-8867-762X")),
person(given = "Carson",
family = "Zhang",
role = "ctb",
email = "[email protected]")
)
Description: Deep Learning library that extends the mlr3 framework by building
upon the 'torch' package. It allows to conveniently build, train,
and evaluate deep learning models without having to worry about low level
details. Custom architectures can be created using the graph language
defined in 'mlr3pipelines'.
License: LGPL (>= 3)
BugReports: https://github.com/mlr-org/mlr3torch/issues
URL: https://mlr3torch.mlr-org.com/, https://github.com/mlr-org/mlr3torch/
Depends:
mlr3 (>= 0.20.0),
mlr3pipelines (>= 0.6.0),
torch (>= 0.13.0),
R (>= 3.5.0)
Imports:
backports,
checkmate (>= 2.2.0),
data.table,
lgr,
methods,
mlr3misc (>= 0.14.0),
paradox (>= 1.0.0),
R6,
withr
Suggests:
callr,
future,
ggplot2,
igraph,
jsonlite,
knitr,
mlr3tuning (>= 1.0.0),
progress,
rmarkdown,
rpart,
viridis,
visNetwork,
testthat (>= 3.0.0),
tfevents,
torchvision (>= 0.6.0),
waldo
Config/testthat/edition: 3
NeedsCompilation: no
ByteCompile: yes
Encoding: UTF-8
Roxygen: list(markdown = TRUE, r6 = TRUE)
RoxygenNote: 7.3.2
Collate:
'CallbackSet.R'
'zzz.R'
'TorchCallback.R'
'CallbackSetCheckpoint.R'
'CallbackSetEarlyStopping.R'
'CallbackSetHistory.R'
'CallbackSetProgress.R'
'CallbackSetTB.R'
'ContextTorch.R'
'DataBackendLazy.R'
'utils.R'
'DataDescriptor.R'
'LearnerTorch.R'
'LearnerTorchFeatureless.R'
'LearnerTorchImage.R'
'LearnerTorchMLP.R'
'task_dataset.R'
'shape.R'
'PipeOpTorchIngress.R'
'LearnerTorchModel.R'
'LearnerTorchTabResNet.R'
'LearnerTorchVision.R'
'ModelDescriptor.R'
'PipeOpModule.R'
'PipeOpTorch.R'
'PipeOpTaskPreprocTorch.R'
'PipeOpTorchActivation.R'
'PipeOpTorchAdaptiveAvgPool.R'
'PipeOpTorchAvgPool.R'
'PipeOpTorchBatchNorm.R'
'PipeOpTorchBlock.R'
'PipeOpTorchCallbacks.R'
'PipeOpTorchConv.R'
'PipeOpTorchConvTranspose.R'
'PipeOpTorchDropout.R'
'PipeOpTorchHead.R'
'PipeOpTorchLayerNorm.R'
'PipeOpTorchLinear.R'
'TorchLoss.R'
'PipeOpTorchLoss.R'
'PipeOpTorchMaxPool.R'
'PipeOpTorchMerge.R'
'PipeOpTorchModel.R'
'PipeOpTorchOptimizer.R'
'PipeOpTorchReshape.R'
'PipeOpTorchSoftmax.R'
'TaskClassif_lazy_iris.R'
'TaskClassif_mnist.R'
'TaskClassif_tiny_imagenet.R'
'TorchDescriptor.R'
'TorchOptimizer.R'
'bibentries.R'
'cache.R'
'lazy_tensor.R'
'learner_torch_methods.R'
'materialize.R'
'merge_graphs.R'
'nn.R'
'nn_graph.R'
'paramset_torchlearner.R'
'preprocess.R'
'rd_info.R'
'with_torch_settings.R'