Function test_on_batch

tensorflow/python/keras/engine/training_v2_utils.py:273–333

Test the model on a single batch of samples. Arguments: model: The model to test. x: Input data. It could be: - A Numpy array (or array-like), or a list of arrays (in case the model has multiple inputs). - A TensorFlow tensor, or a list of tensors (

(model, x, y=None, sample_weight=None, reset_metrics=True)

def test_on_batch(model, x, y=None, sample_weight=None, reset_metrics=True):
  """Test the model on a single batch of samples.

  Arguments:
      model: The model to test.
      x: Input data. It could be:
        - A Numpy array (or array-like), or a list of arrays
          (in case the model has multiple inputs).
        - A TensorFlow tensor, or a list of tensors
          (in case the model has multiple inputs).
        - A dict mapping input names to the corresponding array/tensors,
          if the model has named inputs.
        - A `tf.data` dataset.
      y: Target data. Like the input data `x`,
        it could be either Numpy array(s) or TensorFlow tensor(s).
        It should be consistent with `x` (you cannot have Numpy inputs and
        tensor targets, or inversely). If `x` is a dataset,
        `y` should not be specified
        (since targets will be obtained from the iterator).
      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.
          In this case you should make sure to specify
          sample_weight_mode="temporal" in compile(). This argument is not
          supported when `x` is a dataset.
      reset_metrics: If `True`, the metrics returned will be only for this
        batch. If `False`, the metrics will be statefully accumulated across
        batches.

  Returns:
      Scalar test loss (if the model has a single output and no metrics)
      or list of scalars (if the model has multiple outputs
      and/or metrics). The attribute `model.metrics_names` will give you
      the display labels for the scalar outputs.

  Raises:
      ValueError: In case of invalid user-provided arguments.
  """
  model._assert_compile_was_called()

  # TODO(scottzhu): Standardization should happen in the data handlers,
  ## not on a per batch basis in the *_on_batch methods
  # Validate and standardize user data.
  x, y, sample_weights = model._standardize_user_data(
      x, y, sample_weight=sample_weight, extract_tensors_from_dataset=True)

  batch_size = array_ops.shape(nest.flatten(x, expand_composites=True)[0])[0]
  outputs = training_eager.test_on_batch(
      model,
      x,
      y,
      sample_weights=sample_weights,
      output_loss_metrics=model._output_loss_metrics)

  if reset_metrics:
    model.reset_metrics()