| train_on_batch {keras} | R Documentation |
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()