get_keras_optimizer

<code>(character(1))</code> Optimizer to construct, see details for those available.
Default is <code>"adam"</code>.

optimizer

<code>(numeric(1))</code> Passed to all optimizers except <code>adadelta</code> and <code>adagrad</code>.

<code>(numeric(1))</code> Passed to <code>adamax</code>, <code>adam</code>, and <code>nadam</code>.

beta_1, beta_2, epsilon

<code>(numeric(1))</code> Passed to <code>adamax</code>, <code>adam</code>, and <code>sgd</code>.

decay

<code>(numeric(1))</code> Passed to <code>adamax</code>, <code>adam</code>, <code>nadam</code>, and <code>sgd</code>.

clipnorm, clipvalue

<code>(numeric(1))</code> Passed to <code>nadam</code>.

schedule_decay

<code>(numeric(1))</code> Passed to <code>sgd</code>.

momentum

<code>(logical(1))</code> Passed to <code>sgd</code>.

nesterov

Utility function to construct optimiser from keras, primarily for
internal use.

Implementations of classical and machine learning models for survival analysis, including deep neural networks via 'keras' and 'tensorflow'. Each model includes a separated fit and predict interface with consistent prediction types for predicting risk, survival probabilities, or survival distributions with 'distr6' <https://CRAN.R-project.org/package=distr6>. Models are either implemented from 'Python' via 'reticulate' <https://CRAN.R-project.org/package=reticulate>, from code in GitHub packages, or novel implementations using 'Rcpp' <https://CRAN.R-project.org/package=Rcpp>. Novel machine learning survival models wil be included in the package in near-future updates. Neural networks are implemented from the 'Python' package 'pycox' <https://github.com/havakv/pycox> and are detailed by Kvamme et al. (2019) <https://jmlr.org/papers/v20/18-424.html>. The 'Akritas' estimator is defined in Akritas (1994) <doi:10.1214/aos/1176325630>. 'DNNSurv' is defined in Zhao and Feng (2020) <arXiv:1908.02337>.

get_keras_optimizer: Get Keras Optimizer

Description

Usage

Arguments

Details

Examples