reservr (version 0.0.2)

tf_compile_model: Compile a Keras model for truncated data under dist

Description

Compile a Keras model for truncated data under dist

Usage

tf_compile_model(
  inputs,
  intermediate_output,
  dist,
  optimizer,
  censoring = TRUE,
  truncation = TRUE,
  metrics = NULL,
  sample_weight_mode = NULL,
  weighted_metrics = NULL,
  target_tensors = NULL
)

Value

A reservr_keras_model that can be used to train truncated and censored observations from dist based on input data from inputs.

Arguments

inputs

List of keras input layers

intermediate_output

Intermediate model layer to be used as input to distribution parameters

dist

A Distribution to use for compiling the loss and parameter outputs

optimizer

String (name of optimizer) or optimizer instance. For most models, this defaults to "rmsprop"

censoring

A flag, whether the compiled model should support censored observations. Set to FALSE for higher efficiency. fit(...) will error if the resulting model is used to fit censored observations.

truncation

A flag, whether the compiled model should support truncated observations. Set to FALSE for higher efficiency. fit(...) will warn if the resuting model is used to fit truncated observations.

metrics

List of metrics to be evaluated by the model during training and testing. Each of this can be a string (name of a built-in function), function or a keras$metrics$Metric class instance. See ?tf$keras$metrics. Typically you will use metrics=list('accuracy'). A function is any callable with the signature result = fn(y_true, y_pred). To specify different metrics for different outputs of a multi-output model, you could also pass a dictionary, such as metrics=list(output_a = 'accuracy', output_b = c('accuracy', 'mse')). You can also pass a list to specify a metric or a list of metrics for each output, such as metrics=list(list('accuracy'), list('accuracy', 'mse')) or metrics=list('accuracy', c('accuracy', 'mse')). When you pass the strings 'accuracy' or 'acc', this is converted to one of tf.keras.metrics.BinaryAccuracy, tf.keras.metrics.CategoricalAccuracy, tf.keras.metrics.SparseCategoricalAccuracy based on the loss function used and the model output shape. A similar conversion is done for the strings 'crossentropy' and 'ce'.

sample_weight_mode

If you need to do timestep-wise sample weighting (2D weights), set this to "temporal". NULL defaults to sample-wise weights (1D). If the model has multiple outputs, you can use a different sample_weight_mode on each output by passing a list of modes.

weighted_metrics

List of metrics to be evaluated and weighted by sample_weight or class_weight during training and testing.

target_tensors

By default, Keras will create a placeholder for the model's target, which will be fed with the target data during training. If instead you would like to use your own target tensor (in turn, Keras will not expect external data for these targets at training time), you can specify them via the target_tensors argument. It should be a single tensor (for a single-output sequential model).

Examples

dist <- dist_exponential()
params <- list(rate = 1.0)
N <- 100L
rand_input <- runif(N)
x <- dist$sample(N, with_params = params)

if (interactive() && keras::is_keras_available()) {
  tf_in <- keras::layer_input(1L)
  mod <- tf_compile_model(
    inputs = list(tf_in),
    intermediate_output = tf_in,
    dist = dist,
    optimizer = keras::optimizer_adam(),
    censoring = FALSE,
    truncation = FALSE
  )
}