train_on_batch

FullyConnected.train_on_batch(x, y=None, sample_weight=None, class_weight=None, return_dict=False)

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

Parameters:

• 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 dictionary mapping class indices (integers) 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.

• return_dict – If True, loss and metric results are returned as a dict, with each key being the name of the metric. If False, they are returned as a list.

Returns:

A scalar loss value (when no metrics and return_dict=False), a list of loss and metric values (if there are metrics and return_dict=False), or a dict of metric and loss values (if return_dict=True).