tf.contrib.learn.DNNRegressor.predict()

Args:
  x: Matrix of shape [n_samples, n_features...]. Can be iterator that
     returns arrays of features. The training input samples for fitting the
     model. If set, `input_fn` must be `None`.
  input_fn: Input function. If set, `x` and 'batch_size' must be `None`.
  batch_size: Override default batch size. If set, 'input_fn' must be
    'None'.
  outputs: list of `str`, name of the output to predict.
    If `None`, returns all.
  as_iterable: If True, return an iterable which keeps yielding predictions
    for each example until inputs are exhausted. Note: The inputs must
    terminate if you want the iterable to terminate (e.g. be sure to pass
    num_epochs=1 if you are using something like read_batch_features).

Returns:
  A numpy array of predicted classes or regression values if the
  constructor's `model_fn` returns a `Tensor` for `predictions` or a `dict`
  of numpy arrays if `model_fn` returns a `dict`. Returns an iterable of
  predictions if as_iterable is True.

Raises:
  ValueError: If x and input_fn are both provided or both `None`.