Runs a single gradient update on a single batch of data.
Source:R/model-training.R
train_on_batch.RdRuns a single gradient update on a single batch of data.
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_weightis specified and targets have a rank of 2 or greater, eitherymust be one-hot encoded, or an explicit final dimension of 1 must be included for sparse class labels.