mlr3torch - mlr3torch 0.3.1

Bug Fixes

• FT Transformer can now be (un-)marshaled after being trained on categorical data (#412).
• Parameters (batch)-sampler now work (#420, thanks @tdhock)

Features

• Better error messages.

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Published by sebffischer 6 months ago

mlr3torch - 0.3.0

Breaking Changes:

• The output dimension of neural networks for binary classification tasks is now expected to be 1 and not 2 as before. The behavior of nn("head") was also changed to match this. This means that for binary classification tasks, t_loss("cross_entropy") now generates nn_bce_with_logits_loss instead of nn_cross_entropy_loss. This also came with a reparametrization of the t_loss("cross_entropy") loss (thanks to @tdhock, #374).

New Features:

PipeOps & Learners:

• Added po("nn_identity")
• Added po("nn_fn") for calling custom functions in a network.
• Added the FT Transformer model for tabular data.
• Added encoders for numericals and categoricals
• nn("block") (which allows to repeat the same network segment multiple times) now has an extra argument trafo, which allows to modify the parameter values per layer.

Callbacks:

• The context for callbacks now includes the network prediction (y_hat).
• The lr_one_cycle callback now infers the total number of steps.
• Progress callback got argument digits for controlling the precision with which validation/training scores are logged.

Other:

• TorchIngressToken now also can take a Selector as argument features.
• Added function lazy_shape() to get the shape of a lazy tensor.
• Better error messages for MLP and TabResNet learners.
• TabResNet learner now supports lazy tensors.
• The LearnerTorch base class now supports the private method $.ingress_tokens(task, param_vals) for generating the torch::dataset.
• Shapes can now have multiple NAs and not only the batch dimension can be missing. However, most nn() operators still expect only one missing values and will throw an error if multiple dimensions are unknown.
• Training now does not fail anymore when encountering a missing value during validation but uses NA instead.
• It is now possible to specify parameter groups for optimizers via the param_groups parameter.

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Published by sebffischer 8 months ago

mlr3torch - 0.2.1

See NEWS.md

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Published by sebffischer about 1 year ago

mlr3torch - 0.2.0

See NEWS.md

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Published by sebffischer about 1 year ago

mlr3torch - 0.1.2

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Published by sebffischer over 1 year ago

mlr3torch - 0.1.1

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Published by sebffischer over 1 year ago

mlr3torch - 0.1.0

Initial CRAN release

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Published by sebffischer over 1 year ago