Python tensorflow.python.ops.check_ops.assert_rank_at_least() Examples

def _dense_inner_flatten(inputs, new_rank):
  """Helper function for `inner_flatten`."""
  rank_assertion = check_ops.assert_rank_at_least(
      inputs, new_rank, message='inputs has rank less than new_rank')
  with ops.control_dependencies([rank_assertion]):
    outer_dimensions = array_ops.strided_slice(
        array_ops.shape(inputs), [0], [new_rank - 1])
    new_shape = array_ops.concat((outer_dimensions, [-1]), 0)
    reshaped = array_ops.reshape(inputs, new_shape)

  # if `new_rank` is an integer, try to calculate new shape.
  if isinstance(new_rank, six.integer_types):
    static_shape = inputs.get_shape()
    if static_shape is not None and static_shape.dims is not None:
      static_shape = static_shape.as_list()
      static_outer_dims = static_shape[:new_rank - 1]
      static_inner_dims = static_shape[new_rank - 1:]
      flattened_dimension = 1
      for inner_dim in static_inner_dims:
        if inner_dim is None:
          flattened_dimension = None
          break
        flattened_dimension *= inner_dim
      reshaped.set_shape(static_outer_dims + [flattened_dimension])
  return reshaped 

def _dense_inner_flatten(inputs, new_rank):
  """Helper function for `inner_flatten`."""
  rank_assertion = check_ops.assert_rank_at_least(
      inputs, new_rank, message='inputs has rank less than new_rank')
  with ops.control_dependencies([rank_assertion]):
    outer_dimensions = array_ops.strided_slice(
        array_ops.shape(inputs), [0], [new_rank - 1])
    new_shape = array_ops.concat((outer_dimensions, [-1]), 0)
    reshaped = array_ops.reshape(inputs, new_shape)

  # if `new_rank` is an integer, try to calculate new shape.
  if isinstance(new_rank, six.integer_types):
    static_shape = inputs.get_shape()
    if static_shape is not None and static_shape.dims is not None:
      static_shape = static_shape.as_list()
      static_outer_dims = static_shape[:new_rank - 1]
      static_inner_dims = static_shape[new_rank - 1:]
      flattened_dimension = 1
      for inner_dim in static_inner_dims:
        if inner_dim is None:
          flattened_dimension = None
          break
        flattened_dimension *= inner_dim
      reshaped.set_shape(static_outer_dims + [flattened_dimension])
  return reshaped 

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]
    diag = array_ops.matrix_diag_part(chol)
    deps.append(check_ops.assert_positive(diag))

    return control_flow_ops.with_dependencies(deps, chol) 

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]
    diag = array_ops.matrix_diag_part(chol)
    deps.append(check_ops.assert_positive(diag))

    return control_flow_ops.with_dependencies(deps, chol) 

def _dense_inner_flatten(inputs, new_rank):
  """Helper function for `inner_flatten`."""
  rank_assertion = check_ops.assert_rank_at_least(
      inputs, new_rank, message='inputs has rank less than new_rank')
  with ops.control_dependencies([rank_assertion]):
    outer_dimensions = array_ops.strided_slice(
        array_ops.shape(inputs), [0], [new_rank - 1])
    new_shape = array_ops.concat((outer_dimensions, [-1]), 0)
    reshaped = array_ops.reshape(inputs, new_shape)

  # if `new_rank` is an integer, try to calculate new shape.
  if isinstance(new_rank, six.integer_types):
    static_shape = inputs.get_shape()
    if static_shape is not None and static_shape.dims is not None:
      static_shape = static_shape.as_list()
      static_outer_dims = static_shape[:new_rank - 1]
      static_inner_dims = static_shape[new_rank - 1:]
      flattened_dimension = 1
      for inner_dim in static_inner_dims:
        if inner_dim is None:
          flattened_dimension = None
          break
        flattened_dimension *= inner_dim
      reshaped.set_shape(static_outer_dims + [flattened_dimension])
  return reshaped 

def _dense_inner_flatten(inputs, new_rank):
  """Helper function for `inner_flatten`."""
  rank_assertion = check_ops.assert_rank_at_least(
      inputs, new_rank, message='inputs has rank less than new_rank')
  with ops.control_dependencies([rank_assertion]):
    outer_dimensions = array_ops.strided_slice(
        array_ops.shape(inputs), [0], [new_rank - 1])
    new_shape = array_ops.concat((outer_dimensions, [-1]), 0)
    reshaped = array_ops.reshape(inputs, new_shape)

  # if `new_rank` is an integer, try to calculate new shape.
  if isinstance(new_rank, six.integer_types):
    static_shape = inputs.get_shape()
    if static_shape is not None and static_shape.dims is not None:
      static_shape = static_shape.as_list()
      static_outer_dims = static_shape[:new_rank - 1]
      static_inner_dims = static_shape[new_rank - 1:]
      flattened_dimension = 1
      for inner_dim in static_inner_dims:
        if inner_dim is None:
          flattened_dimension = None
          break
        flattened_dimension *= inner_dim
      reshaped.set_shape(static_outer_dims + [flattened_dimension])
  return reshaped 

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]
    diag = array_ops.matrix_diag_part(chol)
    deps.append(check_ops.assert_positive(diag))

    return control_flow_ops.with_dependencies(deps, chol) 

def _dense_inner_flatten(inputs, new_rank):
  """Helper function for `inner_flatten`."""
  rank_assertion = check_ops.assert_rank_at_least(
      inputs, new_rank, message='inputs has rank less than new_rank')
  with ops.control_dependencies([rank_assertion]):
    outer_dimensions = array_ops.strided_slice(
        array_ops.shape(inputs), [0], [new_rank - 1])
    new_shape = array_ops.concat((outer_dimensions, [-1]), 0)
    reshaped = array_ops.reshape(inputs, new_shape)

  # if `new_rank` is an integer, try to calculate new shape.
  if isinstance(new_rank, six.integer_types):
    static_shape = inputs.get_shape()
    if static_shape is not None and static_shape.dims is not None:
      static_shape = static_shape.as_list()
      static_outer_dims = static_shape[:new_rank - 1]
      static_inner_dims = static_shape[new_rank - 1:]
      flattened_dimension = 1
      for inner_dim in static_inner_dims:
        if inner_dim is None:
          flattened_dimension = None
          break
        flattened_dimension *= inner_dim
      reshaped.set_shape(static_outer_dims + [flattened_dimension])
  return reshaped 

def _dense_inner_flatten(inputs, new_rank):
  """Helper function for `inner_flatten`."""
  rank_assertion = check_ops.assert_rank_at_least(
      inputs, new_rank, message='inputs has rank less than new_rank')
  with ops.control_dependencies([rank_assertion]):
    outer_dimensions = array_ops.slice(
        array_ops.shape(inputs), [0], [new_rank - 1])
    new_shape = array_ops.concat(0, (outer_dimensions, [-1]))
    reshaped = array_ops.reshape(inputs, new_shape)

  # if `new_rank` is an integer, try to calculate new shape.
  if isinstance(new_rank, six.integer_types):
    static_shape = inputs.get_shape()
    if static_shape is not None and static_shape.dims is not None:
      static_shape = static_shape.as_list()
      static_outer_dims = static_shape[:new_rank - 1]
      static_inner_dims = static_shape[new_rank - 1:]
      flattened_dimension = 1
      for inner_dim in static_inner_dims:
        if inner_dim is None:
          flattened_dimension = None
          break
        flattened_dimension *= inner_dim
      reshaped.set_shape(static_outer_dims + [flattened_dimension])
  return reshaped 

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]
    diag = array_ops.matrix_diag_part(chol)
    deps.append(check_ops.assert_positive(diag))

    return control_flow_ops.with_dependencies(deps, chol) 

def _forward(self, x):
    if self._static_event_ndims == 0:
      return math_ops.square(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 _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 _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 _forward(self, x):
    if self._static_event_ndims == 0:
      return math_ops.square(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.batch_matmul(x, x, adj_y=True) 

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 _forward(self, x):
    if self._static_event_ndims == 0:
      return math_ops.square(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 _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 _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 _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 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.name_scope(name, values=[x]):
    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():
      log_prod_gamma_x = math_ops.reduce_sum(
          math_ops.lgamma(x), reduction_indices=[-1])
      sum_x = math_ops.reduce_sum(x, reduction_indices=[-1])
      log_gamma_sum_x = math_ops.lgamma(sum_x)
      result = log_prod_gamma_x - log_gamma_sum_x
      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 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.name_scope(name, values=[x]):
    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():
      log_prod_gamma_x = math_ops.reduce_sum(
          math_ops.lgamma(x), reduction_indices=[-1])
      sum_x = math_ops.reduce_sum(x, reduction_indices=[-1])
      log_gamma_sum_x = math_ops.lgamma(sum_x)
      result = log_prod_gamma_x - log_gamma_sum_x
      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 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.name_scope(name, values=[x]):
    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():
      log_prod_gamma_x = math_ops.reduce_sum(
          math_ops.lgamma(x), reduction_indices=[-1])
      sum_x = math_ops.reduce_sum(x, reduction_indices=[-1])
      log_gamma_sum_x = math_ops.lgamma(sum_x)
      result = log_prod_gamma_x - log_gamma_sum_x
      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)