Python tensorflow.python.ops.math_ops.logical_and() Examples

def _is_shape(expected_shape, actual_tensor, actual_shape=None):
  """Returns whether actual_tensor's shape is expected_shape.

  Args:
    expected_shape: Integer list defining the expected shape, or tensor of same.
    actual_tensor: Tensor to test.
    actual_shape: Shape of actual_tensor, if we already have it.
  Returns:
    New tensor.
  """
  with ops.name_scope('is_shape', values=[actual_tensor]) as scope:
    is_rank = _is_rank(array_ops.size(expected_shape), actual_tensor)
    if actual_shape is None:
      actual_shape = array_ops.shape(actual_tensor, name='actual')
    shape_equal = _all_equal(
        ops.convert_to_tensor(expected_shape, name='expected'),
        actual_shape)
    return math_ops.logical_and(is_rank, shape_equal, name=scope) 

def _is_shape(expected_shape, actual_tensor, actual_shape=None):
  """Returns whether actual_tensor's shape is expected_shape.

  Args:
    expected_shape: Integer list defining the expected shape, or tensor of same.
    actual_tensor: Tensor to test.
    actual_shape: Shape of actual_tensor, if we already have it.
  Returns:
    New tensor.
  """
  with ops.name_scope('is_shape', values=[actual_tensor]) as scope:
    is_rank = _is_rank(array_ops.size(expected_shape), actual_tensor)
    if actual_shape is None:
      actual_shape = array_ops.shape(actual_tensor, name='actual')
    shape_equal = _all_equal(
        ops.convert_to_tensor(expected_shape, name='expected'),
        actual_shape)
    return math_ops.logical_and(is_rank, shape_equal, name=scope) 

def _is_shape(expected_shape, actual_tensor, actual_shape=None):
  """Returns whether actual_tensor's shape is expected_shape.

  Args:
    expected_shape: Integer list defining the expected shape, or tensor of same.
    actual_tensor: Tensor to test.
    actual_shape: Shape of actual_tensor, if we already have it.
  Returns:
    New tensor.
  """
  with ops.name_scope('is_shape', values=[actual_tensor]) as scope:
    is_rank = _is_rank(array_ops.size(expected_shape), actual_tensor)
    if actual_shape is None:
      actual_shape = array_ops.shape(actual_tensor, name='actual')
    shape_equal = _all_equal(
        ops.convert_to_tensor(expected_shape, name='expected'),
        actual_shape)
    return math_ops.logical_and(is_rank, shape_equal, name=scope) 

def _process_matrix(self, matrix, min_rank, event_ndims):
    """Helper to __init__ which gets matrix in batch-ready form."""
    # Pad the matrix so that matmul works in the case of a matrix and vector
    # input.  Keep track if the matrix was padded, to distinguish between a
    # rank 3 tensor and a padded rank 2 tensor.
    # TODO(srvasude): Remove side-effects from functions. Its currently unbroken
    # but error-prone since the function call order may change in the future.
    self._rank_two_event_ndims_one = math_ops.logical_and(
        math_ops.equal(array_ops.rank(matrix), min_rank),
        math_ops.equal(event_ndims, 1))
    left = array_ops.where(self._rank_two_event_ndims_one, 1, 0)
    pad = array_ops.concat(
        [array_ops.ones(
            [left], dtype=dtypes.int32), array_ops.shape(matrix)],
        0)
    return array_ops.reshape(matrix, pad) 

def _mode(self):
    mode = (self.a - 1.)/ (self.a_b_sum - 2.)
    if self.allow_nan_stats:
      nan = np.array(np.nan, dtype=self.dtype.as_numpy_dtype())
      return array_ops.where(
          math_ops.logical_and(
              math_ops.greater(self.a, 1.),
              math_ops.greater(self.b, 1.)),
          mode,
          array_ops.fill(self.batch_shape(), nan, name="nan"))
    else:
      return control_flow_ops.with_dependencies([
          check_ops.assert_less(
              array_ops.ones((), dtype=self.dtype), self.a,
              message="Mode not defined for components of a <= 1."),
          check_ops.assert_less(
              array_ops.ones((), dtype=self.dtype), self.b,
              message="Mode not defined for components of b <= 1."),
      ], mode) 

def _mode(self):
    mode = (self.concentration1 - 1.) / (self.total_concentration - 2.)
    if self.allow_nan_stats:
      nan = array_ops.fill(
          self.batch_shape_tensor(),
          np.array(np.nan, dtype=self.dtype.as_numpy_dtype()),
          name="nan")
      is_defined = math_ops.logical_and(self.concentration1 > 1.,
                                        self.concentration0 > 1.)
      return array_ops.where(is_defined, mode, nan)
    return control_flow_ops.with_dependencies([
        check_ops.assert_less(
            array_ops.ones([], dtype=self.dtype),
            self.concentration1,
            message="Mode undefined for concentration1 <= 1."),
        check_ops.assert_less(
            array_ops.ones([], dtype=self.dtype),
            self.concentration0,
            message="Mode undefined for concentration0 <= 1.")
    ], mode) 

def _is_shape(expected_shape, actual_tensor, actual_shape=None):
  """Returns whether actual_tensor's shape is expected_shape.

  Args:
    expected_shape: Integer list defining the expected shape, or tensor of same.
    actual_tensor: Tensor to test.
    actual_shape: Shape of actual_tensor, if we already have it.
  Returns:
    New tensor.
  """
  with ops.name_scope('is_shape', values=[actual_tensor]) as scope:
    is_rank = _is_rank(array_ops.size(expected_shape), actual_tensor)
    if actual_shape is None:
      actual_shape = array_ops.shape(actual_tensor, name='actual')
    shape_equal = _all_equal(
        ops.convert_to_tensor(expected_shape, name='expected'),
        actual_shape)
    return math_ops.logical_and(is_rank, shape_equal, name=scope) 

def _process_matrix(self, matrix, min_rank, event_ndims):
    """Helper to __init__ which gets matrix in batch-ready form."""
    # Pad the matrix so that matmul works in the case of a matrix and vector
    # input. Keep track if the matrix was padded, to distinguish between a
    # rank 3 tensor and a padded rank 2 tensor.
    # TODO(srvasude): Remove side-effects from functions. Its currently unbroken
    # but error-prone since the function call order may change in the future.
    self._rank_two_event_ndims_one = math_ops.logical_and(
        math_ops.equal(array_ops.rank(matrix), min_rank),
        math_ops.equal(event_ndims, 1))
    left = array_ops.where(self._rank_two_event_ndims_one, 1, 0)
    pad = array_ops.concat(
        [array_ops.ones(
            [left], dtype=dtypes.int32), array_ops.shape(matrix)],
        0)
    return array_ops.reshape(matrix, pad) 

def _mode(self):
    mode = (self.a - 1.)/ (self.a_b_sum - 2.)
    if self.allow_nan_stats:
      nan = np.array(np.nan, dtype=self.dtype.as_numpy_dtype())
      return array_ops.where(
          math_ops.logical_and(
              math_ops.greater(self.a, 1.),
              math_ops.greater(self.b, 1.)),
          mode,
          array_ops.fill(self.batch_shape(), nan, name="nan"))
    else:
      return control_flow_ops.with_dependencies([
          check_ops.assert_less(
              array_ops.ones((), dtype=self.dtype), self.a,
              message="Mode not defined for components of a <= 1."),
          check_ops.assert_less(
              array_ops.ones((), dtype=self.dtype), self.b,
              message="Mode not defined for components of b <= 1."),
      ], mode) 

def _mode(self):
    mode = (self.concentration1 - 1.) / (self.total_concentration - 2.)
    if self.allow_nan_stats:
      nan = array_ops.fill(
          self.batch_shape_tensor(),
          np.array(np.nan, dtype=self.dtype.as_numpy_dtype()),
          name="nan")
      is_defined = math_ops.logical_and(self.concentration1 > 1.,
                                        self.concentration0 > 1.)
      return array_ops.where(is_defined, mode, nan)
    return control_flow_ops.with_dependencies([
        check_ops.assert_less(
            array_ops.ones([], dtype=self.dtype),
            self.concentration1,
            message="Mode undefined for concentration1 <= 1."),
        check_ops.assert_less(
            array_ops.ones([], dtype=self.dtype),
            self.concentration0,
            message="Mode undefined for concentration0 <= 1.")
    ], mode) 

def _process_matrix(self, matrix, min_rank, event_ndims):
    """Helper to __init__ which gets matrix in batch-ready form."""
    # Pad the matrix so that matmul works in the case of a matrix and vector
    # input.  Keep track if the matrix was padded, to distinguish between a
    # rank 3 tensor and a padded rank 2 tensor.
    # TODO(srvasude): Remove side-effects from functions. Its currently unbroken
    # but error-prone since the function call order may change in the future.
    self._rank_two_event_ndims_one = math_ops.logical_and(
        math_ops.equal(array_ops.rank(matrix), min_rank),
        math_ops.equal(event_ndims, 1))
    left = array_ops.where(self._rank_two_event_ndims_one, 1, 0)
    pad = array_ops.concat(
        [array_ops.ones(
            [left], dtype=dtypes.int32), array_ops.shape(matrix)],
        0)
    return array_ops.reshape(matrix, pad) 

def _check_multiple_of(value, multiple_of):
  """Checks that value `value` is a non-zero multiple of `multiple_of`.

  Args:
    value: an int32 scalar Tensor.
    multiple_of: an int or int32 scalar Tensor.

  Returns:
    new_value: an int32 scalar Tensor matching `value`, but which includes an
      assertion that `value` is a multiple of `multiple_of`.
  """
  assert isinstance(value, ops.Tensor)
  with ops.control_dependencies([
      control_flow_ops.Assert(
          math_ops.logical_and(
              math_ops.equal(math_ops.mod(value, multiple_of), 0),
              math_ops.not_equal(value, 0)),
          [string_ops.string_join(
              ["Tensor %s should be a multiple of: " % value.name,
               string_ops.as_string(multiple_of),
               ", but saw value: ",
               string_ops.as_string(value),
               ". Consider setting pad=True."])])]):
    new_value = array_ops.identity(
        value, name="multiple_of_checked")
    return new_value 

def false_negatives(labels, predictions, weights=None,
                    metrics_collections=None,
                    updates_collections=None,
                    name=None):
  """Computes the total number of false positives.

  If `weights` is `None`, weights default to 1. Use weights of 0 to mask values.

  Args:
    labels: The ground truth values, a `Tensor` whose dimensions must match
      `predictions`. Will be cast to `bool`.
    predictions: The predicted values, a `Tensor` of arbitrary dimensions. Will
      be cast to `bool`.
    weights: Optional `Tensor` whose rank is either 0, or the same rank as
      `labels`, and must be broadcastable to `labels` (i.e., all dimensions must
      be either `1`, or the same as the corresponding `labels` dimension).
    metrics_collections: An optional list of collections that the metric
      value variable should be added to.
    updates_collections: An optional list of collections that the metric update
      ops should be added to.
    name: An optional variable_scope name.

  Returns:
    value_tensor: A `Tensor` representing the current value of the metric.
    update_op: An operation that accumulates the error from a batch of data.

  Raises:
    ValueError: If `weights` is not `None` and its shape doesn't match `values`,
      or if either `metrics_collections` or `updates_collections` are not a list
      or tuple.
  """
  with variable_scope.variable_scope(
      name, 'false_negatives', (predictions, labels, weights)):

    labels = math_ops.cast(labels, dtype=dtypes.bool)
    predictions = math_ops.cast(predictions, dtype=dtypes.bool)
    predictions.get_shape().assert_is_compatible_with(labels.get_shape())
    is_false_negative = math_ops.logical_and(math_ops.equal(labels, True),
                                             math_ops.equal(predictions, False))
    return _count_condition(is_false_negative, weights, metrics_collections,
                            updates_collections) 

def _prune_invalid_ids(sparse_ids, sparse_weights):
  """Prune invalid IDs (< 0) from the input ids and weights."""
  is_id_valid = math_ops.greater_equal(sparse_ids.values, 0)
  if sparse_weights is not None:
    is_id_valid = math_ops.logical_and(
        is_id_valid, math_ops.greater(sparse_weights.values, 0))
  sparse_ids = sparse_ops.sparse_retain(sparse_ids, is_id_valid)
  if sparse_weights is not None:
    sparse_weights = sparse_ops.sparse_retain(sparse_weights, is_id_valid)
  return sparse_ids, sparse_weights 

def _streaming_false_negatives(predictions, labels, weights=None,
                               metrics_collections=None,
                               updates_collections=None,
                               name=None):
  """Computes the total number of false positives.

  If `weights` is `None`, weights default to 1. Use weights of 0 to mask values.

  Args:
    predictions: The predicted values, a `bool` `Tensor` of arbitrary
      dimensions.
    labels: The ground truth values, a `bool` `Tensor` whose dimensions must
      match `predictions`.
    weights: An optional `Tensor` whose shape is broadcastable to `predictions`.
    metrics_collections: An optional list of collections that the metric
      value variable should be added to.
    updates_collections: An optional list of collections that the metric update
      ops should be added to.
    name: An optional variable_scope name.

  Returns:
    value_tensor: A tensor representing the current value of the metric.
    update_op: An operation that accumulates the error from a batch of data.

  Raises:
    ValueError: If `weights` is not `None` and its shape doesn't match `values`,
      or if either `metrics_collections` or `updates_collections` are not a list
      or tuple.
  """
  with variable_scope.variable_scope(
      name, 'false_negatives', [predictions, labels]):

    predictions.get_shape().assert_is_compatible_with(labels.get_shape())
    is_false_negative = math_ops.logical_and(math_ops.equal(labels, 1),
                                             math_ops.equal(predictions, 0))
    return _count_condition(is_false_negative, weights, metrics_collections,
                            updates_collections) 

def __and__(self, other):
    return logical_and(self, other) 

def ordered_pair_accuracy(labels, predictions, weights=None, name=None):
    """Computes the percentage of correctedly ordered pair.

    For any pair of examples, we compare their orders determined by `labels` and
    `predictions`. They are correctly ordered if the two orders are compatible.
    That is, labels l_i > l_j and predictions s_i > s_j and the weight for this
    pair is the weight from the l_i.

    Args:
      labels: A `Tensor` of the same shape as `predictions`.
      predictions: A `Tensor` with shape [batch_size, list_size]. Each value is
        the ranking score of the corresponding example.
      weights: A `Tensor` of the same shape of predictions or [batch_size, 1]. The
        former case is per-example and the latter case is per-list.
      name: A string used as the name for this metric.

    Returns:
      A metric for the accuracy or ordered pairs.
    """
    with ops.name_scope(name, 'ordered_pair_accuracy',
                        (labels, predictions, weights)):
        clean_labels, predictions, weights, _ = _prepare_and_validate_params(
            labels, predictions, weights)
        label_valid = math_ops.equal(clean_labels, labels)
        valid_pair = math_ops.logical_and(
            array_ops.expand_dims(label_valid, 2),
            array_ops.expand_dims(label_valid, 1))
        pair_label_diff = array_ops.expand_dims(
            clean_labels, 2) - array_ops.expand_dims(clean_labels, 1)
        pair_pred_diff = array_ops.expand_dims(
            predictions, 2) - array_ops.expand_dims(predictions, 1)
        # Correct pairs are represented twice in the above pair difference tensors.
        # We only take one copy for each pair.
        correct_pairs = math_ops.to_float(pair_label_diff > 0) * math_ops.to_float(
            pair_pred_diff > 0)
        pair_weights = math_ops.to_float(
            pair_label_diff > 0) * array_ops.expand_dims(
                weights, 2) * math_ops.to_float(valid_pair)
        return math_ops.reduce_mean(correct_pairs * pair_weights) 

def should_stop(scalar_stopping_signal):
    """Detects whether scalar_stopping_signal indicates stopping."""
    if isinstance(scalar_stopping_signal, ops.Tensor):
      # STOPPING_SIGNAL is a constant True. Here, the logical_and is just the TF
      # way to express the bool check whether scalar_stopping_signal is True.
      return math_ops.logical_and(scalar_stopping_signal,
                                  _StopSignals.STOPPING_SIGNAL)
    else:
      # For non Tensor case, it is used in SessionRunHook. So, we cannot modify
      # the graph anymore. Here, we use pure Python.
      return bool(scalar_stopping_signal) 

def should_stop(scalar_stopping_signal):
    """Detects whether scalar_stopping_signal indicates stopping."""
    if isinstance(scalar_stopping_signal, ops.Tensor):
      # STOPPING_SIGNAL is a constant True. Here, the logical_and is just the TF
      # way to express the bool check whether scalar_stopping_signal is True.
      return math_ops.logical_and(
          scalar_stopping_signal, _StopSignals.STOPPING_SIGNAL)
    else:
      # For non Tensor case, it is used in SessionRunHook. So, we cannot modify
      # the graph anymore. Here, we use pure Python.
      return bool(scalar_stopping_signal) 

def is_mask_update_iter(self, global_step, last_update_step):
    """Function for checking if the current step is a mask update step.

    It also creates the drop_fraction op and assigns it to the self object.

    Args:
      global_step: tf.Variable(int), current training step.
      last_update_step: tf.Variable(int), holding the last iteration the mask
        is updated. Used to determine whether current iteration is a mask
        update step.


    Returns:
      bool, whether the current iteration is a mask_update step.
    """
    gs_dtype = global_step.dtype
    self._begin_step = math_ops.cast(self._begin_step, gs_dtype)
    self._end_step = math_ops.cast(self._end_step, gs_dtype)
    self._frequency = math_ops.cast(self._frequency, gs_dtype)
    is_step_within_update_range = math_ops.logical_and(
        math_ops.greater_equal(global_step, self._begin_step),
        math_ops.logical_or(
            math_ops.less_equal(global_step, self._end_step),
            # If _end_step is negative, we never stop updating the mask.
            # In other words we update the mask with given frequency until the
            # training ends.
            math_ops.less(self._end_step, 0)))
    is_update_step = math_ops.less_equal(
        math_ops.add(last_update_step, self._frequency), global_step)
    is_mask_update_iter_op = math_ops.logical_and(
        is_step_within_update_range, is_update_step)
    self.drop_fraction = self.get_drop_fraction(global_step,
                                                is_mask_update_iter_op)
    return is_mask_update_iter_op 

def should_stop(scalar_stopping_signal):
    """Detects whether scalar_stopping_signal indicates stopping."""
    if isinstance(scalar_stopping_signal, ops.Tensor):
      # STOPPING_SIGNAL is a constant True. Here, the logical_and is just the TF
      # way to express the bool check whether scalar_stopping_signal is True.
      return math_ops.logical_and(scalar_stopping_signal,
                                  _StopSignals.STOPPING_SIGNAL)
    else:
      # For non Tensor case, it is used in SessionRunHook. So, we cannot modify
      # the graph anymore. Here, we use pure Python.
      return bool(scalar_stopping_signal) 

def _test_forward_logical_and(data):
    """ One iteration of logical and """
    return _test_logical_binary(math_ops.logical_and, data) 

def _logical_and(*args):
  """Convenience function which attempts to statically `reduce_all`."""
  args_ = [_static_value(x) for x in args]
  if any(x is not None and not bool(x) for x in args_):
    return constant_op.constant(False)
  if all(x is not None and bool(x) for x in args_):
    return constant_op.constant(True)
  if len(args) == 2:
    return math_ops.logical_and(*args)
  return math_ops.reduce_all(args) 

def _logical_and(*args):
  """Convenience function which attempts to statically `reduce_all`."""
  args_ = [_static_value(x) for x in args]
  if any(x is not None and not bool(x) for x in args_):
    return constant_op.constant(False)
  if all(x is not None and bool(x) for x in args_):
    return constant_op.constant(True)
  if len(args) == 2:
    return math_ops.logical_and(*args)
  return math_ops.reduce_all(args) 

def _check_multiple_of(value, multiple_of):
  """Checks that value `value` is a non-zero multiple of `multiple_of`.

  Args:
    value: an int32 scalar Tensor.
    multiple_of: an int or int32 scalar Tensor.

  Returns:
    new_value: an int32 scalar Tensor matching `value`, but which includes an
      assertion that `value` is a multiple of `multiple_of`.
  """
  assert isinstance(value, ops.Tensor)
  with ops.control_dependencies([
      control_flow_ops.Assert(
          math_ops.logical_and(
              math_ops.equal(math_ops.mod(value, multiple_of), 0),
              math_ops.not_equal(value, 0)), [
                  string_ops.string_join([
                      "Tensor %s should be a multiple of: " % value.name,
                      string_ops.as_string(multiple_of), ", but saw value: ",
                      string_ops.as_string(value),
                      ". Consider setting pad=True."
                  ])
              ])
  ]):
    new_value = array_ops.identity(value, name="multiple_of_checked")
    return new_value 

def _streaming_false_positives(predictions, labels, weights=None,
                               metrics_collections=None,
                               updates_collections=None,
                               name=None):
  """Sum the weights of false positives.

  If `weights` is `None`, weights default to 1. Use weights of 0 to mask values.

  Args:
    predictions: The predicted values, a `bool` `Tensor` of arbitrary
      dimensions.
    labels: The ground truth values, a `bool` `Tensor` whose dimensions must
      match `predictions`.
    weights: An optional `Tensor` whose shape is broadcastable to `predictions`.
    metrics_collections: An optional list of collections that the metric
      value variable should be added to.
    updates_collections: An optional list of collections that the metric update
      ops should be added to.
    name: An optional variable_scope name.

  Returns:
    value_tensor: A tensor representing the current value of the metric.
    update_op: An operation that accumulates the error from a batch of data.

  Raises:
    ValueError: If `predictions` and `labels` have mismatched shapes, or if
      `weights` is not `None` and its shape doesn't match `predictions`, or if
      either `metrics_collections` or `updates_collections` are not a list or
      tuple.
  """
  with variable_scope.variable_scope(
      name, 'false_positives', [predictions, labels]):

    predictions.get_shape().assert_is_compatible_with(labels.get_shape())
    is_false_positive = math_ops.logical_and(math_ops.equal(labels, 0),
                                             math_ops.equal(predictions, 1))
    return _count_condition(is_false_positive, weights, metrics_collections,
                            updates_collections) 

def _prune_invalid_ids(sparse_ids, sparse_weights):
  """Prune invalid IDs (< 0) from the input ids and weights."""
  is_id_valid = math_ops.greater_equal(sparse_ids.values, 0)
  if sparse_weights is not None:
    is_id_valid = math_ops.logical_and(
        is_id_valid, math_ops.greater(sparse_weights.values, 0))
  sparse_ids = sparse_ops.sparse_retain(sparse_ids, is_id_valid)
  if sparse_weights is not None:
    sparse_weights = sparse_ops.sparse_retain(sparse_weights, is_id_valid)
  return sparse_ids, sparse_weights 

def _check_multiple_of(value, multiple_of):
  """Checks that value `value` is a non-zero multiple of `multiple_of`.

  Args:
    value: an int32 scalar Tensor.
    multiple_of: an int or int32 scalar Tensor.

  Returns:
    new_value: an int32 scalar Tensor matching `value`, but which includes an
      assertion that `value` is a multiple of `multiple_of`.
  """
  assert isinstance(value, ops.Tensor)
  with ops.control_dependencies([
      control_flow_ops.Assert(
          math_ops.logical_and(
              math_ops.equal(math_ops.mod(value, multiple_of), 0),
              math_ops.not_equal(value, 0)), [
                  string_ops.string_join([
                      "Tensor %s should be a multiple of: " % value.name,
                      string_ops.as_string(multiple_of), ", but saw value: ",
                      string_ops.as_string(value),
                      ". Consider setting pad=True."
                  ])
              ])
  ]):
    new_value = array_ops.identity(value, name="multiple_of_checked")
    return new_value 

def _logical_and(*args):
  """Convenience function which attempts to statically `reduce_all`."""
  args_ = [_static_value(x) for x in args]
  if any(x is not None and not bool(x) for x in args_):
    return constant_op.constant(False)
  if all(x is not None and bool(x) for x in args_):
    return constant_op.constant(True)
  if len(args) == 2:
    return math_ops.logical_and(*args)
  return math_ops.reduce_all(args) 

def false_negatives(labels, predictions, weights=None,
                    metrics_collections=None,
                    updates_collections=None,
                    name=None):
  """Computes the total number of false positives.

  If `weights` is `None`, weights default to 1. Use weights of 0 to mask values.

  Args:
    labels: The ground truth values, a `Tensor` whose dimensions must match
      `predictions`. Will be cast to `bool`.
    predictions: The predicted values, a `Tensor` of arbitrary dimensions. Will
      be cast to `bool`.
    weights: Optional `Tensor` whose rank is either 0, or the same rank as
      `labels`, and must be broadcastable to `labels` (i.e., all dimensions must
      be either `1`, or the same as the corresponding `labels` dimension).
    metrics_collections: An optional list of collections that the metric
      value variable should be added to.
    updates_collections: An optional list of collections that the metric update
      ops should be added to.
    name: An optional variable_scope name.

  Returns:
    value_tensor: A `Tensor` representing the current value of the metric.
    update_op: An operation that accumulates the error from a batch of data.

  Raises:
    ValueError: If `weights` is not `None` and its shape doesn't match `values`,
      or if either `metrics_collections` or `updates_collections` are not a list
      or tuple.
  """
  with variable_scope.variable_scope(
      name, 'false_negatives', (predictions, labels, weights)):

    labels = math_ops.cast(labels, dtype=dtypes.bool)
    predictions = math_ops.cast(predictions, dtype=dtypes.bool)
    predictions.get_shape().assert_is_compatible_with(labels.get_shape())
    is_false_negative = math_ops.logical_and(math_ops.equal(labels, True),
                                             math_ops.equal(predictions, False))
    return _count_condition(is_false_negative, weights, metrics_collections,
                            updates_collections)