Python math_ops.reduce_any方法代碼示例

# 需要導入模塊: from tensorflow.python.ops import math_ops [as 別名]
# 或者: from tensorflow.python.ops.math_ops import reduce_any [as 別名]
def insert(self, ids, scores):
    """Insert the ids and scores into the TopN."""
    with ops.control_dependencies(self.last_ops):
      scatter_op = state_ops.scatter_update(self.id_to_score, ids, scores)
      larger_scores = math_ops.greater(scores, self.sl_scores[0])

      def shortlist_insert():
        larger_ids = array_ops.boolean_mask(
            math_ops.to_int64(ids), larger_scores)
        larger_score_values = array_ops.boolean_mask(scores, larger_scores)
        shortlist_ids, new_ids, new_scores = tensor_forest_ops.top_n_insert(
            self.sl_ids, self.sl_scores, larger_ids, larger_score_values)
        u1 = state_ops.scatter_update(self.sl_ids, shortlist_ids, new_ids)
        u2 = state_ops.scatter_update(self.sl_scores, shortlist_ids, new_scores)
        return control_flow_ops.group(u1, u2)

      # We only need to insert into the shortlist if there are any
      # scores larger than the threshold.
      cond_op = control_flow_ops.cond(
          math_ops.reduce_any(larger_scores), shortlist_insert,
          control_flow_ops.no_op)
      with ops.control_dependencies([cond_op]):
        self.last_ops = [scatter_op, cond_op] 

# 需要導入模塊: from tensorflow.python.ops import math_ops [as 別名]
# 或者: from tensorflow.python.ops.math_ops import reduce_any [as 別名]
def any(x, axis=None, keepdims=False):
  """Bitwise reduction (logical OR).

  Arguments:
      x: Tensor or variable.
      axis: axis along which to perform the reduction.
      keepdims: whether the drop or broadcast the reduction axes.

  Returns:
      A uint8 tensor (0s and 1s).
  """
  axis = _normalize_axis(axis, ndim(x))
  x = math_ops.cast(x, dtypes_module.bool)
  return math_ops.reduce_any(x, reduction_indices=axis, keep_dims=keepdims) 

# 需要導入模塊: from tensorflow.python.ops import math_ops [as 別名]
# 或者: from tensorflow.python.ops.math_ops import reduce_any [as 別名]
def test_name(self):
    result_lt = ops.reduce_any(self.bool_lt, {'channel'})
    self.assertIn('lt_reduce_any', result_lt.name) 

# 需要導入模塊: from tensorflow.python.ops import math_ops [as 別名]
# 或者: from tensorflow.python.ops.math_ops import reduce_any [as 別名]
def test(self):
    result_lt = ops.reduce_any(self.bool_lt, {'channel'})
    golden_lt = core.LabeledTensor(
        math_ops.reduce_any(self.bool_tensor, 1), [self.a0, self.a2, self.a3])
    self.assertLabeledTensorsEqual(result_lt, golden_lt) 

# 需要導入模塊: from tensorflow.python.ops import math_ops [as 別名]
# 或者: from tensorflow.python.ops.math_ops import reduce_any [as 別名]
def get_cluster_assignment(pairwise_distances, centroid_ids):
  """Assign data points to the neareset centroids.

  Tensorflow has numerical instability and doesn't always choose
    the data point with theoretically zero distance as it's nearest neighbor.
    Thus, for each centroid in centroid_ids, explicitly assign
    the centroid itself as the nearest centroid.
    This is done through the mask tensor and the constraint_vect tensor.

  Args:
    pairwise_distances: 2-D Tensor of pairwise distances.
    centroid_ids: 1-D Tensor of centroid indices.

  Returns:
    y_fixed: 1-D tensor of cluster assignment.
  """
  predictions = math_ops.argmin(
      array_ops.gather(pairwise_distances, centroid_ids), dimension=0)
  batch_size = array_ops.shape(pairwise_distances)[0]

  # Deal with numerical instability
  mask = math_ops.reduce_any(array_ops.one_hot(
      centroid_ids, batch_size, True, False, axis=-1, dtype=dtypes.bool),
                             axis=0)
  constraint_one_hot = math_ops.multiply(
      array_ops.one_hot(centroid_ids,
                        batch_size,
                        array_ops.constant(1, dtype=dtypes.int64),
                        array_ops.constant(0, dtype=dtypes.int64),
                        axis=0,
                        dtype=dtypes.int64),
      math_ops.cast(math_ops.range(array_ops.shape(centroid_ids)[0]),
                    dtypes.int64))
  constraint_vect = math_ops.reduce_sum(
      array_ops.transpose(constraint_one_hot), axis=0)

  y_fixed = array_ops.where(mask, constraint_vect, predictions)
  return y_fixed 

# 需要導入模塊: from tensorflow.python.ops import math_ops [as 別名]
# 或者: from tensorflow.python.ops.math_ops import reduce_any [as 別名]
def get_cluster_assignment(pairwise_distances, centroid_ids):
  """Assign data points to the neareset centroids.

  Tensorflow has numerical instability and doesn't always choose
    the data point with theoretically zero distance as it's nearest neighbor.
    Thus, for each centroid in centroid_ids, explicitly assign
    the centroid itself as the nearest centroid.
    This is done through the mask tensor and the constraint_vect tensor.

  Args:
    pairwise_distances: 2-D Tensor of pairwise distances.
    centroid_ids: 1-D Tensor of centroid indices.

  Returns:
    y_fixed: 1-D tensor of cluster assignment.
  """
  predictions = math_ops.argmin(
      array_ops.gather(pairwise_distances, centroid_ids), dimension=0)
  batch_size = array_ops.shape(pairwise_distances)[0]

  # Deal with numerical instability
  mask = math_ops.reduce_any(array_ops.one_hot(
      centroid_ids, batch_size, True, False, axis=-1, dtype=dtypes.bool),
                             axis=0)
  constraint_one_hot = math_ops.multiply(
      array_ops.one_hot(centroid_ids,
                        batch_size,
                        array_ops.constant(1, dtype=dtypes.int64),
                        array_ops.constant(0, dtype=dtypes.int64),
                        axis=0,
                        dtype=dtypes.int64),
      math_ops.to_int64(math_ops.range(array_ops.shape(centroid_ids)[0])))
  constraint_vect = math_ops.reduce_sum(
      array_ops.transpose(constraint_one_hot), axis=0)

  y_fixed = array_ops.where(mask, constraint_vect, predictions)
  return y_fixed 

# 需要導入模塊: from tensorflow.python.ops import math_ops [as 別名]
# 或者: from tensorflow.python.ops.math_ops import reduce_any [as 別名]
def __while_loop(self, b, a, d, n, seed):
        def __cond(w, e, bool_mask, b, a, d):
            return math_ops.reduce_any(bool_mask)

        def __body(w_, e_, bool_mask, b, a, d):
            e = math_ops.cast(Beta((self.__mf - 1) / 2, (self.__mf - 1) / 2).sample(
                shape, seed=seed), dtype=self.dtype)

            u = random_ops.random_uniform(shape, dtype=self.dtype, seed=seed)

            w = (1 - (1 + b) * e) / (1 - (1 - b) * e)
            t = (2 * a * b) / (1 - (1 - b) * e)

            accept = gen_math_ops.greater(((self.__mf - 1) * math_ops.log(t) - t + d), math_ops.log(u))
            reject = gen_math_ops.logical_not(accept)

            w_ = array_ops.where(gen_math_ops.logical_and(bool_mask, accept), w, w_)
            e_ = array_ops.where(gen_math_ops.logical_and(bool_mask, accept), e, e_)
            bool_mask = array_ops.where(gen_math_ops.logical_and(bool_mask, accept), reject, bool_mask)

            return w_, e_, bool_mask, b, a, d

        shape = array_ops.concat([[n], self.batch_shape_tensor()[:-1], [1]], 0)
        b, a, d = [gen_array_ops.tile(array_ops.expand_dims(e, axis=0), [n] + [1] * len(e.shape)) for e in (b, a, d)]

        w, e, bool_mask, b, a, d = control_flow_ops.while_loop(__cond, __body,
                                                               [array_ops.zeros_like(b, dtype=self.dtype),
                                                                array_ops.zeros_like(b, dtype=self.dtype),
                                                                array_ops.ones_like(b, dtypes.bool),
                                                                b, a, d])

        return e, w 

# 需要導入模塊: from tensorflow.python.ops import math_ops [as 別名]
# 或者: from tensorflow.python.ops.math_ops import reduce_any [as 別名]
def _test_reduce_any(data, keep_dims=None):
    """ One iteration of reduce_any """
    return _test_reduce(math_ops.reduce_any, data, keep_dims) 

# 需要導入模塊: from tensorflow.python.ops import math_ops [as 別名]
# 或者: from tensorflow.python.ops.math_ops import reduce_any [as 別名]
def any(x, axis=None, keepdims=False):
  """Bitwise reduction (logical OR).

  Arguments:
      x: Tensor or variable.
      axis: axis along which to perform the reduction.
      keepdims: whether the drop or broadcast the reduction axes.

  Returns:
      A uint8 tensor (0s and 1s).
  """
  x = math_ops.cast(x, dtypes_module.bool)
  return math_ops.reduce_any(x, axis=axis, keep_dims=keepdims) 

# 需要導入模塊: from tensorflow.python.ops import math_ops [as 別名]
# 或者: from tensorflow.python.ops.math_ops import reduce_any [as 別名]
def kl_divergence(distribution_a, distribution_b,
                  allow_nan_stats=True, name=None):
  """Get the KL-divergence KL(distribution_a || distribution_b).

  If there is no KL method registered specifically for `type(distribution_a)`
  and `type(distribution_b)`, then the class hierarchies of these types are
  searched.

  If one KL method is registered between any pairs of classes in these two
  parent hierarchies, it is used.

  If more than one such registered method exists, the method whose registered
  classes have the shortest sum MRO paths to the input types is used.

  If more than one such shortest path exists, the first method
  identified in the search is used (favoring a shorter MRO distance to
  `type(distribution_a)`).

  Args:
    distribution_a: The first distribution.
    distribution_b: The second distribution.
    allow_nan_stats: Python `bool`, default `True`. When `True`,
      statistics (e.g., mean, mode, variance) use the value "`NaN`" to
      indicate the result is undefined. When `False`, an exception is raised
      if one or more of the statistic's batch members are undefined.
    name: Python `str` name prefixed to Ops created by this class.

  Returns:
    A Tensor with the batchwise KL-divergence between `distribution_a`
    and `distribution_b`.

  Raises:
    NotImplementedError: If no KL method is defined for distribution types
      of `distribution_a` and `distribution_b`.
  """
  kl_fn = _registered_kl(type(distribution_a), type(distribution_b))
  if kl_fn is None:
    raise NotImplementedError(
        "No KL(distribution_a || distribution_b) registered for distribution_a "
        "type %s and distribution_b type %s"
        % (type(distribution_a).__name__, type(distribution_b).__name__))

  with ops.name_scope("KullbackLeibler"):
    kl_t = kl_fn(distribution_a, distribution_b, name=name)
    if allow_nan_stats:
      return kl_t

    # Check KL for NaNs
    kl_t = array_ops.identity(kl_t, name="kl")

    with ops.control_dependencies([
        control_flow_ops.Assert(
            math_ops.logical_not(
                math_ops.reduce_any(math_ops.is_nan(kl_t))),
            ["KL calculation between %s and %s returned NaN values "
             "(and was called with allow_nan_stats=False). Values:"
             % (distribution_a.name, distribution_b.name), kl_t])]):
      return array_ops.identity(kl_t, name="checked_kl") 

# 需要導入模塊: from tensorflow.python.ops import math_ops [as 別名]
# 或者: from tensorflow.python.ops.math_ops import reduce_any [as 別名]
def kl(dist_a, dist_b, allow_nan=False, name=None):
  """Get the KL-divergence KL(dist_a || dist_b).

  If there is no KL method registered specifically for `type(dist_a)` and
  `type(dist_b)`, then the class hierarchies of these types are searched.

  If one KL method is registered between any pairs of classes in these two
  parent hierarchies, it is used.

  If more than one such registered method exists, the method whose registered
  classes have the shortest sum MRO paths to the input types is used.

  If more than one such shortest path exists, the first method
  identified in the search is used (favoring a shorter MRO distance to
  `type(dist_a)`).

  Args:
    dist_a: The first distribution.
    dist_b: The second distribution.
    allow_nan: If `False` (default), a runtime error is raised
      if the KL returns NaN values for any batch entry of the given
      distributions.  If `True`, the KL may return a NaN for the given entry.
    name: (optional) Name scope to use for created operations.

  Returns:
    A Tensor with the batchwise KL-divergence between dist_a and dist_b.

  Raises:
    NotImplementedError: If no KL method is defined for distribution types
      of dist_a and dist_b.
  """
  kl_fn = _registered_kl(type(dist_a), type(dist_b))
  if kl_fn is None:
    raise NotImplementedError(
        "No KL(dist_a || dist_b) registered for dist_a type %s and dist_b "
        "type %s" % ((type(dist_a).__name__, type(dist_b).__name__)))

  with ops.name_scope("KullbackLeibler"):
    kl_t = kl_fn(dist_a, dist_b, name=name)
    if allow_nan:
      return kl_t

    # Check KL for NaNs
    kl_t = array_ops.identity(kl_t, name="kl")

    with ops.control_dependencies([
        control_flow_ops.Assert(
            math_ops.logical_not(
                math_ops.reduce_any(math_ops.is_nan(kl_t))),
            ["KL calculation between %s and %s returned NaN values "
             "(and was called with allow_nan=False).  Values:"
             % (dist_a.name, dist_b.name), kl_t])]):
      return array_ops.identity(kl_t, name="checked_kl") 

# 需要導入模塊: from tensorflow.python.ops import math_ops [as 別名]
# 或者: from tensorflow.python.ops.math_ops import reduce_any [as 別名]
def _process_scale(self, scale, event_ndims):
    """Helper to __init__ which gets scale in batch-ready form.

    This function expands dimensions of `scale` according to the following
    table:
                     event_ndims
    scale.ndims   0            1
              0  [1]+S+[1,1]   "silent error"
              1  [ ]+S+[1,1]   "silent error"
              2  [ ]+S+[1,1]   [1]+S+[ ]
              3  [ ]+S+[1,1]   [ ]+S+[ ]
            ...  (same)        (same)

    The idea is that we want to convert `scale` into something which can always
    work for, say, the left-hand argument of `batch_matmul`.

    Args:
      scale: `Tensor`.
      event_ndims: `Tensor` (0D, `int32`).

    Returns:
      scale: `Tensor` with dims expanded according to [above] table.
      batch_ndims: `Tensor` (0D, `int32`).  The ndims of the `batch` portion.
    """
    ndims = array_ops.rank(scale)
    left = math_ops.select(
        math_ops.reduce_any([
            math_ops.reduce_all([
                math_ops.equal(ndims, 0),
                math_ops.equal(event_ndims, 0)
            ]),
            math_ops.reduce_all([
                math_ops.equal(ndims, 2),
                math_ops.equal(event_ndims, 1)
            ])]), 1, 0)
    right = math_ops.select(math_ops.equal(event_ndims, 0), 2, 0)
    pad = array_ops.concat(0, (
        array_ops.ones([left], dtype=dtypes.int32),
        array_ops.shape(scale),
        array_ops.ones([right], dtype=dtypes.int32)))
    scale = array_ops.reshape(scale, pad)
    batch_ndims = ndims - 2 + right
    # For safety, explicitly zero-out the upper triangular part.
    scale = array_ops.matrix_band_part(scale, -1, 0)
    if self.validate_args:
      # matrix_band_part will fail if scale is not at least rank 2.
      shape = array_ops.shape(scale)
      assert_square = check_ops.assert_equal(
          shape[-2], shape[-1],
          message="Input must be a (batch of) square matrix.")
      # Assuming lower-triangular means we only need check diag != 0.
      diag = array_ops.matrix_diag_part(scale)
      is_non_singular = math_ops.logical_not(
          math_ops.reduce_any(
              math_ops.equal(diag, ops.convert_to_tensor(0, dtype=diag.dtype))))
      assert_non_singular = control_flow_ops.Assert(
          is_non_singular, ["Singular matrix encountered", diag])
      scale = control_flow_ops.with_dependencies(
          [assert_square, assert_non_singular], scale)
    return scale, batch_ndims