Python check_ops.assert_rank_at_least函数代码示例

 def test_rank_one_tensor_doesnt_raise_if_rank_just_right_static_rank(self):
   with self.test_session():
     tensor = constant_op.constant([1, 2], name="my_tensor")
     desired_rank = 1
     with ops.control_dependencies(
         [check_ops.assert_rank_at_least(tensor, desired_rank)]):
       array_ops.identity(tensor).eval()

 def _assert_valid_alpha(self, alpha, validate_args):
   alpha = ops.convert_to_tensor(alpha, name="alpha")
   if not validate_args:
     return alpha
   return control_flow_ops.with_dependencies(
       [check_ops.assert_rank_at_least(alpha, 1),
        check_ops.assert_positive(alpha)], alpha)

 def test_rank_one_tensor_doesnt_raise_if_rank_just_right_dynamic_rank(self):
   with self.test_session():
     tensor = array_ops.placeholder(dtypes.float32, name="my_tensor")
     desired_rank = 1
     with ops.control_dependencies(
         [check_ops.assert_rank_at_least(tensor, desired_rank)]):
       array_ops.identity(tensor).eval(feed_dict={tensor: [1, 2]})

 def test_rank_one_tensor_raises_if_rank_too_small_static_rank(self):
   tensor = constant_op.constant([1, 2], name="my_tensor")
   desired_rank = 2
   with self.assertRaisesRegexp(ValueError, "rank at least 2"):
     with ops.control_dependencies(
         [check_ops.assert_rank_at_least(tensor, desired_rank)]):
       self.evaluate(array_ops.identity(tensor))

def maybe_check_quadrature_param(param, name, validate_args):
  """Helper which checks validity of `loc` and `scale` init args."""
  with ops.name_scope(name="check_" + name, values=[param]):
    assertions = []
    if param.shape.ndims is not None:
      if param.shape.ndims == 0:
        raise ValueError("Mixing params must be a (batch of) vector; "
                         "{}.rank={} is not at least one.".format(
                             name, param.shape.ndims))
    elif validate_args:
      assertions.append(check_ops.assert_rank_at_least(
          param, 1,
          message=("Mixing params must be a (batch of) vector; "
                   "{}.rank is not at least one.".format(
                       name))))

    # TODO(jvdillon): Remove once we support k-mixtures.
    if param.shape.with_rank_at_least(1)[-1] is not None:
      if param.shape[-1].value != 1:
        raise NotImplementedError("Currently only bimixtures are supported; "
                                  "{}.shape[-1]={} is not 1.".format(
                                      name, param.shape[-1].value))
    elif validate_args:
      assertions.append(check_ops.assert_equal(
          array_ops.shape(param)[-1], 1,
          message=("Currently only bimixtures are supported; "
                   "{}.shape[-1] is not 1.".format(name))))

    if assertions:
      return control_flow_ops.with_dependencies(assertions, param)
    return param

 def _check_alpha(self, alpha):
   alpha = ops.convert_to_tensor(alpha, name='alpha')
   if not self.strict:
     return alpha
   return control_flow_ops.with_dependencies(
       [check_ops.assert_rank_at_least(alpha, 1),
        check_ops.assert_positive(alpha)], alpha)

 def test_rank_zero_tensor_raises_if_rank_too_small_static_rank(self):
   with self.test_session():
     tensor = constant_op.constant(1, name="my_tensor")
     desired_rank = 1
     with self.assertRaisesRegexp(ValueError, "my_tensor.*rank at least 1"):
       with ops.control_dependencies(
           [check_ops.assert_rank_at_least(tensor, desired_rank)]):
         array_ops.identity(tensor).eval()

 def test_rank_one_tensor_raises_if_rank_too_small_dynamic_rank(self):
   with self.test_session():
     tensor = array_ops.placeholder(dtypes.float32, name="my_tensor")
     desired_rank = 2
     with ops.control_dependencies(
         [check_ops.assert_rank_at_least(tensor, desired_rank)]):
       with self.assertRaisesOpError("my_tensor.*rank"):
         array_ops.identity(tensor).eval(feed_dict={tensor: [1, 2]})

def lbeta(x, name='lbeta'):
  r"""Computes `ln(|Beta(x)|)`, reducing along the last dimension.

  Given one-dimensional `z = [z_0,...,z_{K-1}]`, we define

  ```Beta(z) = \prod_j Gamma(z_j) / Gamma(\sum_j z_j)```

  And for `n + 1` dimensional `x` with shape `[N1, ..., Nn, K]`, we define
  `lbeta(x)[i1, ..., in] = Log(|Beta(x[i1, ..., in, :])|)`.  In other words,
  the last dimension is treated as the `z` vector.

  Note that if `z = [u, v]`, then
  `Beta(z) = int_0^1 t^{u-1} (1 - t)^{v-1} dt`, which defines the traditional
  bivariate beta function.

  Args:
    x: A rank `n + 1` `Tensor` with type `float`, or `double`.
    name: A name for the operation (optional).

  Returns:
    The logarithm of `|Beta(x)|` reducing along the last dimension.

  Raises:
    ValueError:  If `x` is empty with rank one or less.
  """
  with ops.op_scope([x], name):
    x = ops.convert_to_tensor(x, name='x')
    x = control_flow_ops.with_dependencies(
        [check_ops.assert_rank_at_least(x, 1)], x)

    is_empty = math_ops.equal(0, array_ops.size(x))

    def nonempty_lbeta():
      last_index = array_ops.size(array_ops.shape(x)) - 1
      log_prod_gamma_x = math_ops.reduce_sum(
          math_ops.lgamma(x),
          reduction_indices=last_index)
      sum_x = math_ops.reduce_sum(x, reduction_indices=last_index)
      log_gamma_sum_x = math_ops.lgamma(sum_x)
      result = log_prod_gamma_x - log_gamma_sum_x
      result.set_shape(x.get_shape()[:-1])
      return result

    def empty_lbeta():
      # If x is empty, return version with one less dimension.
      # Can only do this if rank >= 2.
      assertion = check_ops.assert_rank_at_least(x, 2)
      with ops.control_dependencies([assertion]):
        return array_ops.squeeze(x, squeeze_dims=[0])

    static_size = x.get_shape().num_elements()
    if static_size is not None:
      if static_size > 0:
        return nonempty_lbeta()
      else:
        return empty_lbeta()
    else:
      return control_flow_ops.cond(is_empty, empty_lbeta, nonempty_lbeta)

 def _forward(self, x):
   if self.validate_args:
     is_matrix = check_ops.assert_rank_at_least(x, 2)
     shape = array_ops.shape(x)
     is_square = check_ops.assert_equal(shape[-2], shape[-1])
     x = control_flow_ops.with_dependencies([is_matrix, is_square], x)
   # For safety, explicitly zero-out the upper triangular part.
   x = array_ops.matrix_band_part(x, -1, 0)
   return math_ops.matmul(x, x, adjoint_b=True)

  def __init__(self,
               alpha,
               validate_args=False,
               allow_nan_stats=True,
               name="Dirichlet"):
    """Initialize a batch of Dirichlet distributions.

    Args:
      alpha:  Positive floating point tensor with shape broadcastable to
        `[N1,..., Nm, k]` `m >= 0`.  Defines this as a batch of `N1 x ... x Nm`
         different `k` class Dirichlet distributions.
      validate_args: `Boolean`, default `False`.  Whether to assert valid values
        for parameters `alpha` and `x` in `prob` and `log_prob`.  If `False`,
        correct behavior is not guaranteed.
      allow_nan_stats: `Boolean`, default `True`.  If `False`, raise an
        exception if a statistic (e.g. mean/mode/etc...) is undefined for any
        batch member.  If `True`, batch members with valid parameters leading to
        undefined statistics will return NaN for this statistic.
      name: The name to prefix Ops created by this distribution class.

    Examples:

    ```python
    # Define 1-batch of 2-class Dirichlet distributions,
    # also known as a Beta distribution.
    dist = Dirichlet([1.1, 2.0])

    # Define a 2-batch of 3-class distributions.
    dist = Dirichlet([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
    ```

    """
    parameters = locals()
    parameters.pop("self")
    with ops.name_scope(name, values=[alpha]) as ns:
      alpha = ops.convert_to_tensor(alpha, name="alpha")
      with ops.control_dependencies([
          check_ops.assert_positive(alpha),
          check_ops.assert_rank_at_least(alpha, 1)
      ] if validate_args else []):
        self._alpha = array_ops.identity(alpha, name="alpha")
        self._alpha_sum = math_ops.reduce_sum(alpha,
                                              reduction_indices=[-1],
                                              keep_dims=False)
    super(Dirichlet, self).__init__(
        dtype=self._alpha.dtype,
        validate_args=validate_args,
        allow_nan_stats=allow_nan_stats,
        is_continuous=True,
        is_reparameterized=False,
        parameters=parameters,
        graph_parents=[self._alpha, self._alpha_sum],
        name=ns)

  def __init__(self,
               alpha,
               validate_args=True,
               allow_nan_stats=False,
               name="Dirichlet"):
    """Initialize a batch of Dirichlet distributions.

    Args:
      alpha:  Positive floating point tensor with shape broadcastable to
        `[N1,..., Nm, k]` `m >= 0`.  Defines this as a batch of `N1 x ... x Nm`
         different `k` class Dirichlet distributions.
      validate_args: Whether to assert valid values for parameters `alpha` and
        `x` in `prob` and `log_prob`.  If `False`, correct behavior is not
        guaranteed.
      allow_nan_stats:  Boolean, default `False`.  If `False`, raise an
        exception if a statistic (e.g. mean/mode/etc...) is undefined for any
        batch member.  If `True`, batch members with valid parameters leading to
        undefined statistics will return NaN for this statistic.
      name: The name to prefix Ops created by this distribution class.

    Examples:

    ```python
    # Define 1-batch of 2-class Dirichlet distributions,
    # also known as a Beta distribution.
    dist = Dirichlet([1.1, 2.0])

    # Define a 2-batch of 3-class distributions.
    dist = Dirichlet([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
    ```

    """
    with ops.op_scope([alpha], name):
      alpha = ops.convert_to_tensor(alpha, name="alpha_before_deps")
      with ops.control_dependencies([
          check_ops.assert_positive(alpha), check_ops.assert_rank_at_least(
              alpha, 1)
      ] if validate_args else []):
        alpha = array_ops.identity(alpha, name="alpha")

      self._alpha = alpha
      self._name = name

      # Used for mean/mode/variance/entropy computations
      self._alpha_0 = math_ops.reduce_sum(alpha,
                                          reduction_indices=[-1],
                                          keep_dims=False)

      self._get_batch_shape = self._alpha_0.get_shape()
      self._get_event_shape = self._alpha.get_shape().with_rank_at_least(1)[-1:]
      self._validate_args = validate_args
      self._allow_nan_stats = allow_nan_stats

 def _prob(self, x):
   if self.validate_args:
     is_vector_check = check_ops.assert_rank_at_least(x, 1)
     right_vec_space_check = check_ops.assert_equal(
         self.event_shape_tensor(),
         array_ops.gather(array_ops.shape(x), array_ops.rank(x) - 1),
         message=
         "Argument 'x' not defined in the same space R^k as this distribution")
     with ops.control_dependencies([is_vector_check]):
       with ops.control_dependencies([right_vec_space_check]):
         x = array_ops.identity(x)
   return math_ops.cast(
       math_ops.reduce_all(math_ops.abs(x - self.loc) <= self._slack, axis=-1),
       dtype=self.dtype)

 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)

  def _check_chol(self, chol):
    """Verify that `chol` is proper."""
    chol = ops.convert_to_tensor(chol, name='chol')
    if not self.verify_pd:
      return chol

    shape = array_ops.shape(chol)
    rank = array_ops.rank(chol)

    is_matrix = check_ops.assert_rank_at_least(chol, 2)
    is_square = check_ops.assert_equal(
        array_ops.gather(shape, rank - 2), array_ops.gather(shape, rank - 1))

    deps = [is_matrix, is_square]
    deps.append(check_ops.assert_positive(self._diag))

    return control_flow_ops.with_dependencies(deps, chol)