The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

brulee 0.6.0

• Transition from the magrittr pipe to the base R pipe.

• To try to help avoiding numeric overflow in the loss functions:

  • Tensors are stored as a 64-bit float instead of 32-bit.

  • Starting values were transitioned to using Gaussian distribution (instead of uniform) with a smaller standard deviation.

  • The results always contain the initial results to use as a fallback if there is overflow during the first epoch.

  • brulee_mlp() has two additional parameters, grad_value_clip and grad_value_clip, that prevent issues.

  • The warning was changed to “Early stopping occurred at epoch {X} due to numerical overflow of the loss function.”

• Several new SGD optimizers were added: "ADAMw", "Adadelta", "Adagrad", and "RMSprop".

• Mixture parameter values different than zero cannot be used for several optimizers since they require L2 penalties.

brulee 0.5.0

• Removed a unit test for numerical overflow since it occurs less frequently and has become increasingly more challenging to reproduce.

brulee 0.4.0

• Added a convenience function, brulee_mlp_two_layer(), to more easily fit two-layer networks with parsnip.

• Various changes and improvements to error and warning messages.

• Fixed a bug that occurred when linear activation was used for neural networks (#68).

brulee 0.3.0

• Fixed bug where coef() didn’t would error if used on a brulee_logistic_reg() that was trained with a recipe. (#66)

• Fixed a bug where SGD always being used as the optimizer (#61).

• Additional activation functions were added (#74).

brulee 0.2.0

• Several learning rate schedulers were added to the modeling functions (#12).

• An optimizer was added to [brulee_mlp()], with a new default being LBFGS instead of stochastic gradient descent.

brulee 0.1.0

• Modeling functions gained a mixture argument for the proportion of L1 penalty that is used. (#50)

• Penalization was not occurring when quasi-Newton optimization was chosen. (#50)

brulee 0.0.1

First CRAN release.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.