Keras Neural Network with custom architecture (Classification)

Source: R/LearnerClassifKeras.R

Neural Network using Keras and Tensorflow. This learner allows for supplying a custom architecture. Calls keras::fit() from package keras.

Parameters: Most of the parameters can be obtained from the keras documentation. Some exceptions are documented here.

  • model: A compiled keras model suited for the task.

  • class_weight: A named list of class-weights for the different classes numbered from 0 to c-1 (for c classes).

    Example:
wts = c(0.5, 1)
setNames(as.list(wts), seq_len(length(wts)) - 1)

  • callbacks: A list of keras callbacks. See ?callbacks.

Format

R6::R6Class() inheriting from mlr3::LearnerClassif.

Construction

LearnerClassifKeras$new()
mlr3::mlr_learners$get("classif.keras")
mlr3::lrn("classif.keras")

Learner Methods

Keras Learners offer several methods for easy access to the stored models.

  • .$plot()
    Plots the history, i.e. the train-validation loss during training.

  • .$save(file_path)
    Dumps the model to a provided file_path in 'h5' format.

  • .$load_model_from_file(file_path)
    Loads a model saved using saved back into the learner. The model needs to be saved separately when the learner is serialized. In this case, the learner can be restored from this function. Currently not implemented for 'TabNet'.

  • .$lr_find(task, epochs, lr_min, lr_max, batch_size)
    Employ an implementation of the learning rate finder as popularized by Jeremy Howard in fast.ai (http://course.fast.ai/) for the learner. For more info on parameters, see find_lr.

See also

Dictionary of Learners: mlr3::mlr_learners

Examples

 # Define a model
 library(keras)
 model = keras_model_sequential() %>%
 layer_dense(units = 12L, input_shape = 4L, activation = "relu") %>%
 layer_dense(units = 12L, activation = "relu") %>%
 layer_dense(units = 3L, activation = "softmax") %>%
   compile(optimizer = optimizer_sgd(),
     loss = "categorical_crossentropy",
     metrics = "accuracy")
 # Create the learner
 learner = LearnerClassifKeras$new()
 learner$param_set$values$model = model
 learner$train(mlr3::mlr_tasks$get("iris"))