Python check_ops.assert_less方法代码示例

# 需要导入模块: from tensorflow.python.ops import check_ops [as 别名]
# 或者: from tensorflow.python.ops.check_ops import assert_less [as 别名]
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) 

# 需要导入模块: from tensorflow.python.ops import check_ops [as 别名]
# 或者: from tensorflow.python.ops.check_ops import assert_less [as 别名]
def _mean(self):
    mean = self.loc * array_ops.ones(self.batch_shape_tensor(),
                                     dtype=self.dtype)
    if self.allow_nan_stats:
      nan = np.array(np.nan, dtype=self.dtype.as_numpy_dtype())
      return array_ops.where(
          math_ops.greater(
              self.df,
              array_ops.ones(self.batch_shape_tensor(), dtype=self.dtype)),
          mean,
          array_ops.fill(self.batch_shape_tensor(), nan, name="nan"))
    else:
      return control_flow_ops.with_dependencies(
          [
              check_ops.assert_less(
                  array_ops.ones([], dtype=self.dtype),
                  self.df,
                  message="mean not defined for components of df <= 1"),
          ],
          mean) 

# 需要导入模块: from tensorflow.python.ops import check_ops [as 别名]
# 或者: from tensorflow.python.ops.check_ops import assert_less [as 别名]
def _mode(self):
    k = math_ops.cast(self.event_shape_tensor()[0], self.dtype)
    mode = (self.concentration - 1.) / (
        self.total_concentration[..., array_ops.newaxis] - k)
    if self.allow_nan_stats:
      nan = array_ops.fill(
          array_ops.shape(mode),
          np.array(np.nan, dtype=self.dtype.as_numpy_dtype()),
          name="nan")
      return array_ops.where(
          math_ops.reduce_all(self.concentration > 1., axis=-1),
          mode, nan)
    return control_flow_ops.with_dependencies([
        check_ops.assert_less(
            array_ops.ones([], self.dtype),
            self.concentration,
            message="Mode undefined when any concentration <= 1"),
    ], mode) 

# 需要导入模块: from tensorflow.python.ops import check_ops [as 别名]
# 或者: from tensorflow.python.ops.check_ops import assert_less [as 别名]
def _variance(self):
    var = (math_ops.square(self.rate)
           / math_ops.square(self.concentration - 1.)
           / (self.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")
      return array_ops.where(self.concentration > 2., var, nan)
    else:
      return control_flow_ops.with_dependencies([
          check_ops.assert_less(
              constant_op.constant(2., dtype=self.dtype),
              self.concentration,
              message="variance undefined when any concentration <= 2"),
      ], var) 

# 需要导入模块: from tensorflow.python.ops import check_ops [as 别名]
# 或者: from tensorflow.python.ops.check_ops import assert_less [as 别名]
def _assert_non_singular(self):
    """Private default implementation of _assert_non_singular."""
    logging.warn(
        "Using (possibly slow) default implementation of assert_non_singular."
        "  Requires conversion to a dense matrix and O(N^3) operations.")
    if self._can_use_cholesky():
      return self.assert_positive_definite()
    else:
      singular_values = linalg_ops.svd(
          self._get_cached_dense_matrix(), compute_uv=False)
      # TODO(langmore) Add .eig and .cond as methods.
      cond = (math_ops.reduce_max(singular_values, axis=-1) /
              math_ops.reduce_min(singular_values, axis=-1))
      return check_ops.assert_less(
          cond,
          self._max_condition_number_to_be_non_singular(),
          message="Singular matrix up to precision epsilon.")
    raise NotImplementedError("assert_non_singular is not implemented.") 

# 需要导入模块: from tensorflow.python.ops import check_ops [as 别名]
# 或者: from tensorflow.python.ops.check_ops import assert_less [as 别名]
def assert_no_entries_with_modulus_zero(
    x, message=None, name="assert_no_entries_with_modulus_zero"):
  """Returns `Op` that asserts Tensor `x` has no entries with modulus zero.

  Args:
    x:  Numeric `Tensor`, real, integer, or complex.
    message:  A string message to prepend to failure message.
    name:  A name to give this `Op`.

  Returns:
    An `Op` that asserts `x` has no entries with modulus zero.
  """
  with ops.name_scope(name, values=[x]):
    x = ops.convert_to_tensor(x, name="x")
    dtype = x.dtype.base_dtype
    should_be_nonzero = math_ops.abs(x)
    zero = ops.convert_to_tensor(0, dtype=dtype.real_dtype)
    return check_ops.assert_less(zero, should_be_nonzero, message=message) 

# 需要导入模块: from tensorflow.python.ops import check_ops [as 别名]
# 或者: from tensorflow.python.ops.check_ops import assert_less [as 别名]
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) 

# 需要导入模块: from tensorflow.python.ops import check_ops [as 别名]
# 或者: from tensorflow.python.ops.check_ops import assert_less [as 别名]
def _mean(self):
    mean = self.mu * array_ops.ones(self.batch_shape(), dtype=self.dtype)
    if self.allow_nan_stats:
      nan = np.array(np.nan, dtype=self.dtype.as_numpy_dtype())
      return array_ops.where(
          math_ops.greater(
              self.df,
              array_ops.ones(self.batch_shape(), dtype=self.dtype)),
          mean,
          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.df,
                  message="mean not defined for components of df <= 1"),
          ],
          mean) 

# 需要导入模块: from tensorflow.python.ops import check_ops [as 别名]
# 或者: from tensorflow.python.ops.check_ops import assert_less [as 别名]
def _mode(self):
    mode = ((self.alpha - 1.) /
            (array_ops.expand_dims(self.alpha_sum, dim=-1) -
             math_ops.cast(self.event_shape()[0], self.dtype)))
    if self.allow_nan_stats:
      nan = np.array(np.nan, dtype=self.dtype.as_numpy_dtype())
      shape = array_ops.concat((self.batch_shape(), self.event_shape()), 0)
      return array_ops.where(
          math_ops.greater(self.alpha, 1.),
          mode,
          array_ops.fill(shape, nan, name="nan"))
    else:
      return control_flow_ops.with_dependencies([
          check_ops.assert_less(
              array_ops.ones((), dtype=self.dtype), self.alpha,
              message="mode not defined for components of alpha <= 1")
      ], mode) 

# 需要导入模块: from tensorflow.python.ops import check_ops [as 别名]
# 或者: from tensorflow.python.ops.check_ops import assert_less [as 别名]
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 math_ops.select(
          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) 

# 需要导入模块: from tensorflow.python.ops import check_ops [as 别名]
# 或者: from tensorflow.python.ops.check_ops import assert_less [as 别名]
def _variance(self):
    var = (self._ones() *
           math_ops.square(self.sigma) * self.df / (self.df - 2))
    # When 1 < df <= 2, variance is infinite.
    inf = np.array(np.inf, dtype=self.dtype.as_numpy_dtype())
    result_where_defined = math_ops.select(
        math_ops.greater(self.df, array_ops.fill(self.batch_shape(), 2.)),
        var,
        array_ops.fill(self.batch_shape(), inf, name="inf"))

    if self.allow_nan_stats:
      nan = np.array(np.nan, dtype=self.dtype.as_numpy_dtype())
      return math_ops.select(
          math_ops.greater(self.df, self._ones()),
          result_where_defined,
          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.df,
              message="variance not defined for components of df <= 1"),
      ], result_where_defined) 

# 需要导入模块: from tensorflow.python.ops import check_ops [as 别名]
# 或者: from tensorflow.python.ops.check_ops import assert_less [as 别名]
def _mode(self):
    mode = ((self.alpha - 1.) /
            (array_ops.expand_dims(self.alpha_sum, dim=-1) -
             math_ops.cast(self.event_shape()[0], self.dtype)))
    if self.allow_nan_stats:
      nan = np.array(np.nan, dtype=self.dtype.as_numpy_dtype())
      shape = array_ops.concat(0, (self.batch_shape(), self.event_shape()))
      return math_ops.select(
          math_ops.greater(self.alpha, 1.),
          mode,
          array_ops.fill(shape, nan, name="nan"))
    else:
      return control_flow_ops.with_dependencies([
          check_ops.assert_less(
              array_ops.ones((), dtype=self.dtype), self.alpha,
              message="mode not defined for components of alpha <= 1")
      ], mode) 

# 需要导入模块: from tensorflow.python.ops import check_ops [as 别名]
# 或者: from tensorflow.python.ops.check_ops import assert_less [as 别名]
def _mode(self):
    mode = (self.concentration - 1.) / self.rate
    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")
      return array_ops.where(self.concentration > 1., mode, nan)
    else:
      return control_flow_ops.with_dependencies([
          check_ops.assert_less(
              array_ops.ones([], self.dtype),
              self.concentration,
              message="mode not defined when any concentration <= 1"),
          ], mode) 

# 需要导入模块: from tensorflow.python.ops import check_ops [as 别名]
# 或者: from tensorflow.python.ops.check_ops import assert_less [as 别名]
def _maybe_assert_valid_concentration(self, concentration, validate_args):
    """Checks the validity of the concentration parameter."""
    if not validate_args:
      return concentration
    return control_flow_ops.with_dependencies([
        check_ops.assert_positive(
            concentration,
            message="Concentration parameter must be positive."),
        check_ops.assert_rank_at_least(
            concentration, 1,
            message="Concentration parameter must have >=1 dimensions."),
        check_ops.assert_less(
            1, array_ops.shape(concentration)[-1],
            message="Concentration parameter must have event_size >= 2."),
    ], concentration) 

# 需要导入模块: from tensorflow.python.ops import check_ops [as 别名]
# 或者: from tensorflow.python.ops.check_ops import assert_less [as 别名]
def _maybe_assert_valid_sample(self, x):
    """Checks the validity of a sample."""
    if not self.validate_args:
      return x
    return control_flow_ops.with_dependencies([
        check_ops.assert_positive(
            x,
            message="sample must be positive"),
        check_ops.assert_less(
            x, array_ops.ones([], self.dtype),
            message="sample must be no larger than `1`."),
    ], x)