train_on_batch: Single gradient update or model evaluation over one batch of...

In keras: R Interface to 'Keras'

Single gradient update or model evaluation over one batch of samples.

Description

Single gradient update or model evaluation over one batch of samples.

Usage

train_on_batch(object, x, y, class_weight = NULL, sample_weight = NULL)

test_on_batch(object, x, y, sample_weight = NULL)

Arguments

object	Keras model object
x	input data, as an array or list of arrays (if the model has multiple inputs).
y	labels, as an array.
class_weight	named list mapping classes to a weight value, used for scaling the loss function (during training only).
sample_weight	sample weights, as an array.

Value

Scalar training or test loss (if the model has no metrics) or list of scalars (if the model computes other metrics). The property model$metrics_names will give you the display labels for the scalar outputs.

See Also

Other model functions: compile.keras.engine.training.Model(), evaluate.keras.engine.training.Model(), evaluate_generator(), fit.keras.engine.training.Model(), fit_generator(), get_config(), get_layer(), keras_model(), keras_model_sequential(), multi_gpu_model(), pop_layer(), predict.keras.engine.training.Model(), predict_generator(), predict_on_batch(), predict_proba(), summary.keras.engine.training.Model()

keras documentation built on May 29, 2024, 3:20 a.m.