train_on_batch {keras3}

Runs a single gradient update on a single batch of data.

Description

Runs a single gradient update on a single batch of data.

Usage

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

Arguments

object

Keras model object

x

Input data. Must be array-like.

y

Target data. Must be array-like.

sample_weight

Optional array of the same length as x, containing weights to apply to the model's loss for each sample. In the case of temporal data, you can pass a 2D array with shape ⁠(samples, sequence_length)⁠, to apply a different weight to every timestep of every sample.

class_weight

Optional named list mapping class indices (integers, 0-based) to a weight (float) to apply to the model's loss for the samples from this class during training. This can be useful to tell the model to "pay more attention" to samples from an under-represented class. When class_weight is specified and targets have a rank of 2 or greater, either y must be one-hot encoded, or an explicit final dimension of 1 must be included for sparse class labels.

Value

A scalar loss value (when no metrics), or a named list of loss and metric values (if there are metrics). The property model$metrics_names will give you the display labels for the scalar outputs.

See Also

• https://keras.io/api/models/model_training_apis#trainonbatch-method

Other model training:
compile.keras.src.models.model.Model()
evaluate.keras.src.models.model.Model()
predict.keras.src.models.model.Model()
predict_on_batch()
test_on_batch()