This learner supports long short-term memory (LSTM) recurrent neural network
algorithm. This learner uses the kerasR
package, and in order to use
it, you will need keras
Python module 2.0.0 or higher. Note that all
preprocessing, such as differencing and seasonal effects for time series,
should be addressed before using this learner.
R6Class
object.
Lrnr_base
object with methods for training and prediction
units
Positive integer, dimensionality of the output space.
loss
Name of a loss function used.
optimizer
name of optimizer, or optimizer object.
batch_size
Number of samples per gradient update.
epochs
Number of epochs to train the model.
window
Size of the sliding window input.
activation
The activation function to use.
dense
regular, densely-connected NN layer. Default is 1.
dropout
float between 0 and 1. Fraction of the input units to drop.
early_stopping
logical indicating whether ot not to interrupt training when the validation loss is not decreasing anymore.
patience
number of epochs with no improvement after which training will be stopped, only used when early_stopping = TRUE.
validation_split
float between 0 and 1. Fraction of the data to use as held-out validation data, only used when early_stopping = TRUE.
Other Learners:
Custom_chain
,
Lrnr_HarmonicReg
,
Lrnr_arima
,
Lrnr_bartMachine
,
Lrnr_base
,
Lrnr_bayesglm
,
Lrnr_bilstm
,
Lrnr_caret
,
Lrnr_cv_selector
,
Lrnr_cv
,
Lrnr_dbarts
,
Lrnr_define_interactions
,
Lrnr_density_discretize
,
Lrnr_density_hse
,
Lrnr_density_semiparametric
,
Lrnr_earth
,
Lrnr_expSmooth
,
Lrnr_gam
,
Lrnr_gbm
,
Lrnr_glm_fast
,
Lrnr_glmnet
,
Lrnr_glm
,
Lrnr_grf
,
Lrnr_gru_keras
,
Lrnr_gts
,
Lrnr_h2o_grid
,
Lrnr_hal9001
,
Lrnr_haldensify
,
Lrnr_hts
,
Lrnr_independent_binomial
,
Lrnr_lightgbm
,
Lrnr_lstm_keras
,
Lrnr_mean
,
Lrnr_multiple_ts
,
Lrnr_multivariate
,
Lrnr_nnet
,
Lrnr_nnls
,
Lrnr_optim
,
Lrnr_pca
,
Lrnr_pkg_SuperLearner
,
Lrnr_polspline
,
Lrnr_pooled_hazards
,
Lrnr_randomForest
,
Lrnr_ranger
,
Lrnr_revere_task
,
Lrnr_rpart
,
Lrnr_rugarch
,
Lrnr_screener_augment
,
Lrnr_screener_coefs
,
Lrnr_screener_correlation
,
Lrnr_screener_importance
,
Lrnr_sl
,
Lrnr_solnp_density
,
Lrnr_solnp
,
Lrnr_stratified
,
Lrnr_subset_covariates
,
Lrnr_svm
,
Lrnr_tsDyn
,
Lrnr_ts_weights
,
Lrnr_xgboost
,
Pipeline
,
Stack
,
define_h2o_X()
,
undocumented_learner