Method predict

tensorflow/python/keras/engine/training.py:834–908

Generates output predictions for the input samples. Computation is done in batches. Arguments: x: Input samples. 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 li

(self,
              x,
              batch_size=None,
              verbose=0,
              steps=None,
              callbacks=None,
              max_queue_size=10,
              workers=1,
              use_multiprocessing=False)

        use_multiprocessing=use_multiprocessing)

  def predict(self,
              x,
              batch_size=None,
              verbose=0,
              steps=None,
              callbacks=None,
              max_queue_size=10,
              workers=1,
              use_multiprocessing=False):
    """Generates output predictions for the input samples.

    Computation is done in batches.

    Arguments:
        x: Input samples. 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 `tf.data` dataset.
          - A generator or `keras.utils.Sequence` instance.
        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: Verbosity mode, 0 or 1.
        steps: Total number of steps (batches of samples)
            before declaring the prediction round finished.
            Ignored with the default value of `None`. If x is a `tf.data`
            dataset and `steps` is None, `predict` will
            run until the input dataset is exhausted.
        callbacks: List of `keras.callbacks.Callback` instances.
            List of callbacks to apply during prediction.
            See [callbacks](/api_docs/python/tf/keras/callbacks).
        max_queue_size: Integer. Used for generator or `keras.utils.Sequence`
            input only. Maximum size for the generator queue.
            If unspecified, `max_queue_size` will default to 10.
        workers: Integer. Used for generator or `keras.utils.Sequence` input
            only. Maximum number of processes to spin up when using
            process-based threading. If unspecified, `workers` will default
            to 1. If 0, will execute the generator on the main thread.
        use_multiprocessing: Boolean. Used for generator or
            `keras.utils.Sequence` input only. If `True`, use process-based
            threading. If unspecified, `use_multiprocessing` will default to
            `False`. Note that because this implementation relies on
            multiprocessing, you should not pass non-picklable arguments to
            the generator as they can't be passed easily to children processes.


    Returns:
        Numpy array(s) of predictions.

    Raises:
        ValueError: In case of mismatch between the provided
            input data and the model's expectations,