Method evaluate

tensorflow/python/keras/engine/training.py:729–832

Returns the loss value & metrics values for the model in test mode. Computation is done in batches. Arguments: 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 te

(self,
               x=None,
               y=None,
               batch_size=None,
               verbose=1,
               sample_weight=None,
               steps=None,
               callbacks=None,
               max_queue_size=10,
               workers=1,
               use_multiprocessing=False)

        use_multiprocessing=use_multiprocessing)

  def evaluate(self,
               x=None,
               y=None,
               batch_size=None,
               verbose=1,
               sample_weight=None,
               steps=None,
               callbacks=None,
               max_queue_size=10,
               workers=1,
               use_multiprocessing=False):
    """Returns the loss value & metrics values for the model in test mode.

    Computation is done in batches.

    Arguments:
        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.
          - A generator or `keras.utils.Sequence` instance.
        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, generator or
          `keras.utils.Sequence` instance, `y` should not be specified (since
          targets will be obtained from the iterator/dataset).
        batch_size: Integer or `None`.
            Number of samples per gradient update.
            If unspecified, `batch_size` will default to 32.
            Do not specify the `batch_size` is your data is in the
            form of symbolic tensors, dataset,
            generators, or `keras.utils.Sequence` instances (since they generate
            batches).
        verbose: 0 or 1. Verbosity mode.
            0 = silent, 1 = progress bar.
        sample_weight: Optional Numpy array of weights for
            the test samples, used for weighting the loss function.
            You can either pass a flat (1D)
            Numpy array with the same length as the input samples
            (1:1 mapping between weights and samples),
            or 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, instead pass
            sample weights as the third element of `x`.
        steps: Integer or `None`.
            Total number of steps (batches of samples)
            before declaring the evaluation round finished.
            Ignored with the default value of `None`.