Python v2.bool方法代码示例

# 需要导入模块: from tensorflow.compat import v2 [as 别名]
# 或者: from tensorflow.compat.v2 import bool [as 别名]
def all(a, axis=None, keepdims=None):  # pylint: disable=redefined-builtin
  """Whether all array elements or those along an axis evaluate to true.

  Casts the array to bool type if it is not already and uses `tf.reduce_all` to
  compute the result.

  Args:
    a: array_like. Could be an ndarray, a Tensor or any object that can
      be converted to a Tensor using `tf.convert_to_tensor`.
    axis: Optional. Could be an int or a tuple of integers. If not specified,
      the reduction is performed over all array indices.
    keepdims: If true, retains reduced dimensions with length 1.

  Returns:
    An ndarray. Note that unlike NumPy this does not return a scalar bool if
    `axis` is None.
  """
  a = asarray(a, dtype=bool)
  return utils.tensor_to_ndarray(
      tf.reduce_all(input_tensor=a.data, axis=axis, keepdims=keepdims)) 

# 需要导入模块: from tensorflow.compat import v2 [as 别名]
# 或者: from tensorflow.compat.v2 import bool [as 别名]
def any(a, axis=None, keepdims=None):  # pylint: disable=redefined-builtin
  """Whether any element in the entire array or in an axis evaluates to true.

  Casts the array to bool type if it is not already and uses `tf.reduce_any` to
  compute the result.

  Args:
    a: array_like. Could be an ndarray, a Tensor or any object that can
      be converted to a Tensor using `tf.convert_to_tensor`.
    axis: Optional. Could be an int or a tuple of integers. If not specified,
      the reduction is performed over all array indices.
    keepdims: If true, retains reduced dimensions with length 1.

  Returns:
    An ndarray. Note that unlike NumPy this does not return a scalar bool if
    `axis` is None.
  """
  a = asarray(a, dtype=bool)
  return utils.tensor_to_ndarray(
      tf.reduce_any(input_tensor=a.data, axis=axis, keepdims=keepdims)) 

# 需要导入模块: from tensorflow.compat import v2 [as 别名]
# 或者: from tensorflow.compat.v2 import bool [as 别名]
def tril(m, k=0):  # pylint: disable=missing-docstring
  m = asarray(m).data
  m_shape = m.shape.as_list()

  if len(m_shape) < 2:
    raise ValueError('Argument to tril must have rank at least 2')

  if m_shape[-1] is None or m_shape[-2] is None:
    raise ValueError('Currently, the last two dimensions of the input array '
                     'need to be known.')

  z = tf.constant(0, m.dtype)

  mask = tri(*m_shape[-2:], k=k, dtype=bool)
  return utils.tensor_to_ndarray(
      tf.where(tf.broadcast_to(mask, tf.shape(m)), m, z)) 

# 需要导入模块: from tensorflow.compat import v2 [as 别名]
# 或者: from tensorflow.compat.v2 import bool [as 别名]
def triu(m, k=0):  # pylint: disable=missing-docstring
  m = asarray(m).data
  m_shape = m.shape.as_list()

  if len(m_shape) < 2:
    raise ValueError('Argument to triu must have rank at least 2')

  if m_shape[-1] is None or m_shape[-2] is None:
    raise ValueError('Currently, the last two dimensions of the input array '
                     'need to be known.')

  z = tf.constant(0, m.dtype)

  mask = tri(*m_shape[-2:], k=k - 1, dtype=bool)
  return utils.tensor_to_ndarray(
      tf.where(tf.broadcast_to(mask, tf.shape(m)), z, m)) 

# 需要导入模块: from tensorflow.compat import v2 [as 别名]
# 或者: from tensorflow.compat.v2 import bool [as 别名]
def diff(a, n=1, axis=-1):
  def f(a):
    nd = a.shape.rank
    if (axis + nd if axis < 0 else axis) >= nd:
      raise ValueError("axis %s is out of bounds for array of dimension %s" %
                       (axis, nd))
    if n < 0:
      raise ValueError("order must be non-negative but got %s" % n)
    slice1 = [slice(None)] * nd
    slice2 = [slice(None)] * nd
    slice1[axis] = slice(1, None)
    slice2[axis] = slice(None, -1)
    slice1 = tuple(slice1)
    slice2 = tuple(slice2)
    op = tf.not_equal if a.dtype == tf.bool else tf.subtract
    for _ in range(n):
      a = op(a[slice1], a[slice2])
    return a
  return _scalar(f, a) 

# 需要导入模块: from tensorflow.compat import v2 [as 别名]
# 或者: from tensorflow.compat.v2 import bool [as 别名]
def __init__(self,
                 shift,
                 validate_args=False,
                 name='shift'):
        """Instantiates the `Shift` bijector which computes `Y = g(X; shift) = X + shift`
        where `shift` is a numeric `Tensor`.
        Args:
          shift: Floating-point `Tensor`.
          validate_args: Python `bool` indicating whether arguments should be
            checked for correctness.
          name: Python `str` name given to ops managed by this object.
        """
        with tf.name_scope(name) as name:
            dtype = dtype_util.common_dtype([shift], dtype_hint=tf.float32)
            self._shift = tensor_util.convert_nonref_to_tensor(shift, dtype=dtype, name='shift')
            super(Shift, self).__init__(
              forward_min_event_ndims=0,
              is_constant_jacobian=True,
              dtype=dtype,
              validate_args=validate_args,
              name=name
            ) 

# 需要导入模块: from tensorflow.compat import v2 [as 别名]
# 或者: from tensorflow.compat.v2 import bool [as 别名]
def transform(self, features=None, training=None, mask=None):
    """Transforms the features into dense context features and example features.

    The user can overwrite this function for custom transformations.
    Mask is provided as an argument so that inherited models can have access
    to it for custom feature transformations, without modifying
    `call` explicitly.

    Args:
      features: (dict) with a mix of context (2D) and example features (3D).
      training: (bool) whether in train or inference mode.
      mask: (tf.Tensor) Mask is a tensor of shape [batch_size, list_size], which
        is True for a valid example and False for invalid one.

    Returns:
      context_features: (dict) context feature names to dense 2D tensors of
        shape [batch_size, feature_dims].
      example_features: (dict) example feature names to dense 3D tensors of
        shape [batch_size, list_size, feature_dims].
    """
    del mask
    context_features, example_features = self._listwise_dense_layer(
        inputs=features, training=training)
    return context_features, example_features 

# 需要导入模块: from tensorflow.compat import v2 [as 别名]
# 或者: from tensorflow.compat.v2 import bool [as 别名]
def compute_logits(self,
                     context_features=None,
                     example_features=None,
                     training=None,
                     mask=None):
    """Scores context and examples to return a score per document.

    Args:
      context_features: (dict) context feature names to 2D tensors of shape
        [batch_size, feature_dims].
      example_features: (dict) example feature names to 3D tensors of shape
        [batch_size, list_size, feature_dims].
      training: (bool) whether in train or inference mode.
      mask: (tf.Tensor) Mask is a tensor of shape [batch_size, list_size], which
        is True for a valid example and False for invalid one. If mask is None,
        all entries are valid.

    Returns:
      (tf.Tensor) A score tensor of shape [batch_size, list_size].
    """
    raise NotImplementedError('Calling an abstract method, '
                              'tfr.keras.RankingModel.compute_logits().') 

# 需要导入模块: from tensorflow.compat import v2 [as 别名]
# 或者: from tensorflow.compat.v2 import bool [as 别名]
def call(self, inputs=None, training=None, mask=None):
    """Defines the forward pass for ranking model.

    Args:
      inputs: (dict) with a mix of context (2D) and example features (3D).
      training: (bool) whether in train or inference mode.
      mask: (tf.Tensor) Mask is a tensor of shape [batch_size, list_size], which
        is True for a valid example and False for invalid one.

    Returns:
      (tf.Tensor) A score tensor of shape [batch_size, list_size].
    """
    context_features, example_features = self.transform(
        features=inputs, training=training, mask=mask)
    logits = self.compute_logits(
        context_features=context_features,
        example_features=example_features,
        training=training,
        mask=mask)
    return logits 

# 需要导入模块: from tensorflow.compat import v2 [as 别名]
# 或者: from tensorflow.compat.v2 import bool [as 别名]
def _should_stop(state, stopping_policy_fn):
  """Indicates whether the overall Brent search should continue.

  Args:
    state: A Python `_BrentSearchState` namedtuple.
    stopping_policy_fn: Python `callable` controlling the algorithm termination.

  Returns:
    A boolean value indicating whether the overall search should continue.
  """
  return tf.convert_to_tensor(
      stopping_policy_fn(state.finished), name="should_stop", dtype=tf.bool)


# This is a direct translation of the Brent root-finding method.
# Each operation is guarded by a call to `tf.where` to avoid performing
# unnecessary calculations. 

# 需要导入模块: from tensorflow.compat import v2 [as 别名]
# 或者: from tensorflow.compat.v2 import bool [as 别名]
def __init__(self, weekend_mask=None, holidays=None):
    """Initializer.

    Args:
      weekend_mask: Boolean `Tensor` of 7 elements one for each day of the week
        starting with Monday at index 0. A `True` value indicates the day is
        considered a weekend day and a `False` value implies a week day.
        Default value: None which means no weekends are applied.
      holidays: Defines the holidays that are added to the weekends defined by
        `weekend_mask`. An instance of `dates.DateTensor` or an object
        convertible to `DateTensor`.
        Default value: None which means no holidays other than those implied by
          the weekends (if any).
    """
    if weekend_mask is not None:
      weekend_mask = tf.cast(weekend_mask, dtype=tf.bool)
    if holidays is not None:
      holidays = dt.convert_to_date_tensor(holidays).ordinal()
    self._to_biz_space, self._from_biz_space = hol.business_day_mappers(
        weekend_mask=weekend_mask, holidays=holidays) 

# 需要导入模块: from tensorflow.compat import v2 [as 别名]
# 或者: from tensorflow.compat.v2 import bool [as 别名]
def _info(self):
    return tfds.core.DatasetInfo(
        builder=self,
        description=_DESCRIPTION,
        features=tfds.features.FeaturesDict({
            'ID': tfds.features.Text(),
            'Text': tfds.features.Text(),
            'Pronoun': tfds.features.Text(),
            'Pronoun-offset': tf.int32,
            'A': tfds.features.Text(),
            'A-offset': tf.int32,
            'A-coref': tf.bool,
            'B': tfds.features.Text(),
            'B-offset': tf.int32,
            'B-coref': tf.bool,
            'URL': tfds.features.Text()
        }),
        supervised_keys=None,
        homepage='https://github.com/google-research-datasets/gap-coreference',
        citation=_CITATION,
    ) 

# 需要导入模块: from tensorflow.compat import v2 [as 别名]
# 或者: from tensorflow.compat.v2 import bool [as 别名]
def _info(self):
    return tfds.core.DatasetInfo(
        builder=self,
        description=_DESCRIPTION,
        features=tfds.features.FeaturesDict({
            'sentence_good': tfds.features.Text(),
            'sentence_bad': tfds.features.Text(),
            'field': tfds.features.Text(),
            'linguistics_term': tfds.features.Text(),
            'UID': tfds.features.Text(),
            'simple_LM_method': tf.bool,
            'one_prefix_method': tf.bool,
            'two_prefix_method': tf.bool,
            'lexically_identical': tf.bool,
            'pair_id': tf.int32,
        }),
        supervised_keys=None,
        # Homepage of the dataset for documentation
        homepage=_PROJECT_URL,
        citation=_CITATION,
    ) 

# 需要导入模块: from tensorflow.compat import v2 [as 别名]
# 或者: from tensorflow.compat.v2 import bool [as 别名]
def _info(self):
    features = {
        'image':
            tfds.features.Image(encoding_format='jpeg'),
        'image/filename':
            tfds.features.Text(),
        'faces':
            tfds.features.Sequence({
                'bbox': tfds.features.BBoxFeature(),
                'blur': tf.uint8,
                'expression': tf.bool,
                'illumination': tf.bool,
                'occlusion': tf.uint8,
                'pose': tf.bool,
                'invalid': tf.bool,
            }),
    }
    return tfds.core.DatasetInfo(
        builder=self,
        description=_DESCRIPTION,
        features=tfds.features.FeaturesDict(features),
        homepage=_PROJECT_URL,
        citation=_CITATION,
    ) 

# 需要导入模块: from tensorflow.compat import v2 [as 别名]
# 或者: from tensorflow.compat.v2 import bool [as 别名]
def __init__(self, state_space_size, unroll_length=1):
    self._state_space_size = state_space_size
    # Creates simple dynamics (T stands for transition):
    #   states = [0, 1, ... len(state_space_size - 1)] + [STOP]
    #   actions = [-1, 1]
    #   T(s, a) = s + a  iff (s + a) is a valid state
    #           = STOP   otherwise
    self._action_space = [-1, 1]
    self._current_state = None
    self._env_spec = common.EnvOutput(
        reward=tf.TensorSpec(shape=[unroll_length + 1], dtype=tf.float32),
        done=tf.TensorSpec(shape=[unroll_length + 1], dtype=tf.bool),
        observation={
            'f1':
                tf.TensorSpec(
                    shape=[unroll_length + 1, 4, 10], dtype=tf.float32),
            'f2':
                tf.TensorSpec(
                    shape=[unroll_length + 1, 7, 10, 2], dtype=tf.float32)
        },
        info=tf.TensorSpec(shape=[unroll_length + 1], dtype=tf.string))