Function train_on_batch

tensorflow/python/keras/engine/training_eager.py:281–319

Calculates the loss and gradient updates for one input batch. Arguments: model: Model whose loss has to be calculated. inputs: Input batch data. targets: Target batch data. sample_weights: Sample weight batch data. output_loss_metrics: List of metrics that are used t

(model,
                   inputs,
                   targets,
                   sample_weights=None,
                   output_loss_metrics=None)

def train_on_batch(model,
                   inputs,
                   targets,
                   sample_weights=None,
                   output_loss_metrics=None):
  """Calculates the loss and gradient updates for one input batch.

  Arguments:
      model: Model whose loss has to be calculated.
      inputs: Input batch data.
      targets: Target batch data.
      sample_weights: Sample weight batch data.
      output_loss_metrics: List of metrics that are used to aggregated output
        loss values.

  Returns:
      Dict with three items:
        'total_loss': list with a single tensor for overall loss,
        'output_losses': list of tensors for loss corresponding to each of the
          model output. Could be a empty list when model has only one output.
        'metrics': list of tensors for metric specified.
  """
  inputs = training_utils.cast_to_model_input_dtypes(inputs, model)
  outs, total_loss, output_losses, masks = (
      _process_single_batch(
          model,
          inputs,
          targets,
          sample_weights=sample_weights,
          training=True,
          output_loss_metrics=output_loss_metrics))
  if not isinstance(outs, list):
    outs = [outs]
  metrics_results = _eager_metrics_fn(
      model, outs, targets, sample_weights=sample_weights, masks=masks)
  total_loss = nest.flatten(total_loss)
  return {'total_loss': total_loss,
          'output_losses': output_losses,
          'metrics': metrics_results}


def test_on_batch(model,