Python array_ops.matrix_band_part函数代码示例

  def benchmarkMatrixBandPartOp(self):
    for shape_ in self.shapes:
      for limits in (-1, -1), (-1, 0), (0, -1), (2, 2):
        with ops.Graph().as_default(), \
            session.Session() as sess, \
            ops.device("/cpu:0"):
          matrix = variables.Variable(array_ops.ones(shape_))
          band = array_ops.matrix_band_part(matrix, limits[0], limits[1])
          variables.global_variables_initializer().run()
          self.run_op_benchmark(
              sess,
              control_flow_ops.group(band),
              min_iters=10,
              name="matrix_band_part_cpu_{shape}_{limits}".format(
                  shape=shape_, limits=limits))

        if test_lib.is_gpu_available(True):
          with ops.Graph().as_default(), \
              session.Session() as sess, \
              ops.device("/gpu:0"):
            matrix = variables.Variable(array_ops.ones(shape_))
            band = array_ops.matrix_band_part(matrix, limits[0], limits[1])
            variables.global_variables_initializer().run()
            self.run_op_benchmark(
                sess,
                control_flow_ops.group(band),
                min_iters=10,
                name="matrix_band_part_gpu_{shape}_{limits}".format(
                    shape=shape_, limits=limits))

  def _verifyLu(self, x, output_idx_type=dtypes.int64):
    # Verify that Px = LU.
    lu, perm = linalg_ops.lu(x, output_idx_type=output_idx_type)

    # Prepare the lower factor of shape num_rows x num_rows
    lu_shape = np.array(lu.shape.as_list())
    batch_shape = lu_shape[:-2]
    num_rows = lu_shape[-2]
    num_cols = lu_shape[-1]

    lower = array_ops.matrix_band_part(lu, -1, 0)

    if num_rows > num_cols:
      eye = linalg_ops.eye(
          num_rows, batch_shape=batch_shape, dtype=lower.dtype)
      lower = array_ops.concat([lower, eye[..., num_cols:]], axis=-1)
    elif num_rows < num_cols:
      lower = lower[..., :num_rows]

    # Fill the diagonal with ones.
    ones_diag = array_ops.ones(
        np.append(batch_shape, num_rows), dtype=lower.dtype)
    lower = array_ops.matrix_set_diag(lower, ones_diag)

    # Prepare the upper factor.
    upper = array_ops.matrix_band_part(lu, 0, -1)

    verification = math_ops.matmul(lower, upper)

    # Permute the rows of product of the Cholesky factors.
    if num_rows > 0:
      # Reshape the product of the triangular factors and permutation indices
      # to a single batch dimension. This makes it easy to apply
      # invert_permutation and gather_nd ops.
      perm_reshaped = array_ops.reshape(perm, [-1, num_rows])
      verification_reshaped = array_ops.reshape(verification,
                                                [-1, num_rows, num_cols])
      # Invert the permutation in each batch.
      inv_perm_reshaped = map_fn.map_fn(array_ops.invert_permutation,
                                        perm_reshaped)
      batch_size = perm_reshaped.shape.as_list()[0]
      # Prepare the batch indices with the same shape as the permutation.
      # The corresponding batch index is paired with each of the `num_rows`
      # permutation indices.
      batch_indices = math_ops.cast(
          array_ops.broadcast_to(
              math_ops.range(batch_size)[:, None], perm_reshaped.shape),
          dtype=output_idx_type)
      permuted_verification_reshaped = array_ops.gather_nd(
          verification_reshaped,
          array_ops.stack([batch_indices, inv_perm_reshaped], axis=-1))

      # Reshape the verification matrix back to the original shape.
      verification = array_ops.reshape(permuted_verification_reshaped,
                                       lu_shape)

    self._verifyLuBase(x, lower, upper, perm, verification,
                       output_idx_type)

def random_tril_matrix(shape,
                       dtype,
                       force_well_conditioned=False,
                       remove_upper=True):
  """[batch] lower triangular matrix.

  Args:
    shape:  `TensorShape` or Python `list`.  Shape of the returned matrix.
    dtype:  `TensorFlow` `dtype` or Python dtype
    force_well_conditioned:  Python `bool`. If `True`, returned matrix will have
      eigenvalues with modulus in `(1, 2)`.  Otherwise, eigenvalues are unit
      normal random variables.
    remove_upper:  Python `bool`.
      If `True`, zero out the strictly upper triangle.
      If `False`, the lower triangle of returned matrix will have desired
      properties, but will not have the strictly upper triangle zero'd out.

  Returns:
    `Tensor` with desired shape and dtype.
  """
  with ops.name_scope("random_tril_matrix"):
    # Totally random matrix.  Has no nice properties.
    tril = random_normal(shape, dtype=dtype)
    if remove_upper:
      tril = array_ops.matrix_band_part(tril, -1, 0)

    # Create a diagonal with entries having modulus in [1, 2].
    if force_well_conditioned:
      maxval = ops.convert_to_tensor(np.sqrt(2.), dtype=dtype.real_dtype)
      diag = random_sign_uniform(
          shape[:-1], dtype=dtype, minval=1., maxval=maxval)
      tril = array_ops.matrix_set_diag(tril, diag)

    return tril

  def _sample_n(self, n, seed):
    batch_shape = self.batch_shape_tensor()
    event_shape = self.event_shape_tensor()
    batch_ndims = array_ops.shape(batch_shape)[0]

    ndims = batch_ndims + 3  # sample_ndims=1, event_ndims=2
    shape = array_ops.concat([[n], batch_shape, event_shape], 0)

    # Complexity: O(nbk**2)
    x = random_ops.random_normal(shape=shape,
                                 mean=0.,
                                 stddev=1.,
                                 dtype=self.dtype,
                                 seed=seed)

    # Complexity: O(nbk)
    # This parametrization is equivalent to Chi2, i.e.,
    # ChiSquared(k) == Gamma(alpha=k/2, beta=1/2)
    expanded_df = self.df * array_ops.ones(
        self.scale_operator.batch_shape_tensor(),
        dtype=self.df.dtype.base_dtype)
    g = random_ops.random_gamma(shape=[n],
                                alpha=self._multi_gamma_sequence(
                                    0.5 * expanded_df, self.dimension),
                                beta=0.5,
                                dtype=self.dtype,
                                seed=distribution_util.gen_new_seed(
                                    seed, "wishart"))

    # Complexity: O(nbk**2)
    x = array_ops.matrix_band_part(x, -1, 0)  # Tri-lower.

    # Complexity: O(nbk)
    x = array_ops.matrix_set_diag(x, math_ops.sqrt(g))

    # Make batch-op ready.
    # Complexity: O(nbk**2)
    perm = array_ops.concat([math_ops.range(1, ndims), [0]], 0)
    x = array_ops.transpose(x, perm)
    shape = array_ops.concat([batch_shape, [event_shape[0]], [-1]], 0)
    x = array_ops.reshape(x, shape)

    # Complexity: O(nbM) where M is the complexity of the operator solving a
    # vector system. E.g., for LinearOperatorDiag, each matmul is O(k**2), so
    # this complexity is O(nbk**2). For LinearOperatorLowerTriangular,
    # each matmul is O(k^3) so this step has complexity O(nbk^3).
    x = self.scale_operator.matmul(x)

    # Undo make batch-op ready.
    # Complexity: O(nbk**2)
    shape = array_ops.concat([batch_shape, event_shape, [n]], 0)
    x = array_ops.reshape(x, shape)
    perm = array_ops.concat([[ndims - 1], math_ops.range(0, ndims - 1)], 0)
    x = array_ops.transpose(x, perm)

    if not self.cholesky_input_output_matrices:
      # Complexity: O(nbk^3)
      x = math_ops.matmul(x, x, adjoint_b=True)

    return x

def _QrGrad(op, dq, dr):
  """Gradient for Qr."""
  q, r = op.outputs
  if q.dtype.is_complex:
    raise NotImplementedError("QrGrad not implemented for dtype: %s" % q.dtype)
  if (r.shape.ndims is None or r.shape.as_list()[-2] is None or
      r.shape.as_list()[-1] is None):
    raise NotImplementedError("QrGrad not implemented with dynamic shapes.")
  if r.shape.dims[-2].value != r.shape.dims[-1].value:
    raise NotImplementedError("QrGrad not implemented when ncols > nrows "
                              "or full_matrices is true and ncols != nrows.")

  qdq = math_ops.matmul(q, dq, adjoint_a=True)
  qdq_ = qdq - _linalg.adjoint(qdq)
  rdr = math_ops.matmul(r, dr, adjoint_b=True)
  rdr_ = rdr - _linalg.adjoint(rdr)
  tril = array_ops.matrix_band_part(qdq_ + rdr_, -1, 0)

  def _TriangularSolve(x, r):
    """Equiv to matmul(x, adjoint(matrix_inverse(r))) if r is upper-tri."""
    return _linalg.adjoint(
        linalg_ops.matrix_triangular_solve(
            r, _linalg.adjoint(x), lower=False, adjoint=False))

  grad_a = math_ops.matmul(q, dr + _TriangularSolve(tril, r))
  grad_b = _TriangularSolve(dq - math_ops.matmul(q, qdq), r)
  return grad_a + grad_b

def _MatrixTriangularSolveGrad(op, grad):
    """Gradient for MatrixTriangularSolve."""
    a = op.inputs[0]
    adjoint_a = op.get_attr("adjoint")
    lower_a = op.get_attr("lower")
    c = op.outputs[0]
    grad_b = linalg_ops.matrix_triangular_solve(a, grad, lower=lower_a, adjoint=not adjoint_a)
    if adjoint_a:
        grad_a = -math_ops.batch_matmul(c, grad_b, adj_y=True)
    else:
        grad_a = -math_ops.batch_matmul(grad_b, c, adj_y=True)
    if lower_a:
        grad_a = array_ops.matrix_band_part(grad_a, -1, 0)
    else:
        grad_a = array_ops.matrix_band_part(grad_a, 0, -1)
    return (grad_a, grad_b)

 def _random_cholesky_array(self, shape):
   mat = self._rng.rand(*shape)
   chol = distribution_util.matrix_diag_transform(
       mat, transform=nn_ops.softplus)
   # Zero the upper triangle because we're using this as a true Cholesky factor
   # in our tests.
   return array_ops.matrix_band_part(chol, -1, 0).eval()

  def testMatrixBandPart(self, batch_shape, rows, cols):
    # TODO(b/125505881): Disabled due to LLVM backend crash.
    if self.device == 'XLA_CPU' and cols == 7 and rows == 1 and batch_shape == [
        1, 3, 2
    ]:
      pass
    for dtype in self.float_types:
      with self.cached_session():
        mat = np.ones(batch_shape + [rows, cols]).astype(dtype)
        batch_mat = np.tile(mat, batch_shape + [1, 1])
        for lower in -1, 0, 1, rows - 1:
          for upper in -1, 0, 1, cols - 1:
            band_np = mat
            if lower >= 0:
              band_np = np.triu(band_np, -lower)
            if upper >= 0:
              band_np = np.tril(band_np, upper)
            if batch_shape:
              band_np = np.tile(band_np, batch_shape + [1, 1])

            placeholder = array_ops.placeholder(dtype)
            with self.test_scope():
              band = array_ops.matrix_band_part(
                  placeholder, constant_op.constant(lower, dtype=dtypes.int32),
                  constant_op.constant(upper, dtype=dtypes.int32))
              feed_dict = {placeholder: batch_mat}
              self.assertAllEqual(band_np, band.eval(feed_dict=feed_dict))

def sign_magnitude_positive_definite(
    raw, off_diagonal_scale=0., overall_scale=0.):
  """Constructs a positive definite matrix from an unconstrained input matrix.

  We want to keep the whole matrix on a log scale, but also allow off-diagonal
  elements to be negative, so the sign of off-diagonal elements is modeled
  separately from their magnitude (using the lower and upper triangles
  respectively). Specifically:

  for i < j, we have:
    output_cholesky[i, j] = raw[j, i] / (abs(raw[j, i]) + 1) *
        exp((off_diagonal_scale + overall_scale + raw[i, j]) / 2)

  output_cholesky[i, i] = exp((raw[i, i] + overall_scale) / 2)

  output = output_cholesky^T * output_cholesky

  where raw, off_diagonal_scale, and overall_scale are
  un-constrained real-valued variables. The resulting values are stable
  around zero due to the exponential (and the softsign keeps the function
  smooth).

  Args:
    raw: A [..., M, M] Tensor.
    off_diagonal_scale: A scalar or [...] shaped Tensor controlling the relative
        scale of off-diagonal values in the output matrix.
    overall_scale: A scalar or [...] shaped Tensor controlling the overall scale
        of the output matrix.
  Returns:
    The `output` matrix described above, a [..., M, M] positive definite matrix.

  """
  raw = ops.convert_to_tensor(raw)
  diagonal = array_ops.matrix_diag_part(raw)
  def _right_pad_with_ones(tensor, target_rank):
    # Allow broadcasting even if overall_scale and off_diagonal_scale have batch
    # dimensions
    tensor = ops.convert_to_tensor(tensor, dtype=raw.dtype.base_dtype)
    return array_ops.reshape(tensor,
                             array_ops.concat(
                                 [
                                     array_ops.shape(tensor), array_ops.ones(
                                         [target_rank - array_ops.rank(tensor)],
                                         dtype=target_rank.dtype)
                                 ],
                                 axis=0))
  # We divide the log values by 2 to compensate for the squaring that happens
  # when transforming Cholesky factors into positive definite matrices.
  sign_magnitude = (gen_math_ops.exp(
      (raw + _right_pad_with_ones(off_diagonal_scale, array_ops.rank(raw)) +
       _right_pad_with_ones(overall_scale, array_ops.rank(raw))) / 2.) *
                    nn.softsign(array_ops.matrix_transpose(raw)))
  sign_magnitude.set_shape(raw.get_shape())
  cholesky_factor = array_ops.matrix_set_diag(
      input=array_ops.matrix_band_part(sign_magnitude, 0, -1),
      diagonal=gen_math_ops.exp((diagonal + _right_pad_with_ones(
          overall_scale, array_ops.rank(diagonal))) / 2.))
  return math_ops.matmul(cholesky_factor, cholesky_factor, transpose_a=True)

 def CheckUnitary(self, x):
   # Tests that x[...,:,:]^H * x[...,:,:] is close to the identity.
   xx = math_ops.matmul(x, x, adjoint_a=True)
   identity = array_ops.matrix_band_part(array_ops.ones_like(xx), 0, 0)
   if is_single:
     tol = 1e-5
   else:
     tol = 1e-14
   self.assertAllClose(identity.eval(), self.evaluate(xx), atol=tol)

 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 _sample_n(self, n, seed):
    batch_shape = self.batch_shape()
    event_shape = self.event_shape()
    batch_ndims = array_ops.shape(batch_shape)[0]

    ndims = batch_ndims + 3  # sample_ndims=1, event_ndims=2
    shape = array_ops.concat(((n,), batch_shape, event_shape), 0)

    # Complexity: O(nbk^2)
    x = random_ops.random_normal(shape=shape,
                                 mean=0.,
                                 stddev=1.,
                                 dtype=self.dtype,
                                 seed=seed)

    # Complexity: O(nbk)
    # This parametrization is equivalent to Chi2, i.e.,
    # ChiSquared(k) == Gamma(alpha=k/2, beta=1/2)
    g = random_ops.random_gamma(shape=(n,),
                                alpha=self._multi_gamma_sequence(
                                    0.5 * self.df, self.dimension),
                                beta=0.5,
                                dtype=self.dtype,
                                seed=distribution_util.gen_new_seed(
                                    seed, "wishart"))

    # Complexity: O(nbk^2)
    x = array_ops.matrix_band_part(x, -1, 0)  # Tri-lower.

    # Complexity: O(nbk)
    x = array_ops.matrix_set_diag(x, math_ops.sqrt(g))

    # Make batch-op ready.
    # Complexity: O(nbk^2)
    perm = array_ops.concat((math_ops.range(1, ndims), (0,)), 0)
    x = array_ops.transpose(x, perm)
    shape = array_ops.concat((batch_shape, (event_shape[0], -1)), 0)
    x = array_ops.reshape(x, shape)

    # Complexity: O(nbM) where M is the complexity of the operator solving a
    # vector system.  E.g., for OperatorPDDiag, each matmul is O(k^2), so
    # this complexity is O(nbk^2). For OperatorPDCholesky, each matmul is
    # O(k^3) so this step has complexity O(nbk^3).
    x = self.scale_operator_pd.sqrt_matmul(x)

    # Undo make batch-op ready.
    # Complexity: O(nbk^2)
    shape = array_ops.concat((batch_shape, event_shape, (n,)), 0)
    x = array_ops.reshape(x, shape)
    perm = array_ops.concat(((ndims - 1,), math_ops.range(0, ndims - 1)), 0)
    x = array_ops.transpose(x, perm)

    if not self.cholesky_input_output_matrices:
      # Complexity: O(nbk^3)
      x = math_ops.matmul(x, x, adjoint_b=True)

    return x

def _GradWithInverseL(l, l_inverse, grad):
  middle = math_ops.matmul(l, grad, adjoint_a=True)
  middle = array_ops.matrix_set_diag(middle,
                                     0.5 * array_ops.matrix_diag_part(middle))
  middle = array_ops.matrix_band_part(middle, -1, 0)
  grad_a = math_ops.matmul(
      math_ops.matmul(l_inverse, middle, adjoint_a=True), l_inverse)
  grad_a += math_ops.conj(array_ops.matrix_transpose(grad_a))
  return grad_a * 0.5

 def Test(self):
   shape = batch_shape_ + shape_
   x = constant_op.constant(np.random.rand(*shape), dtype=dtype_)
   with self.test_session(use_gpu=True):
     for lower in -1, 0, 1, shape_[-2] - 1:
       for upper in -1, 0, 1, shape_[-1] - 1:
         y = array_ops.matrix_band_part(x, lower, upper)
         error = gradient_checker.compute_gradient_error(
             x, x.get_shape().as_list(), y, y.get_shape().as_list())
         self.assertLess(error, 1e-4)

  def __init__(self,
               tril,
               is_non_singular=None,
               is_self_adjoint=None,
               is_positive_definite=None,
               is_square=None,
               name="LinearOperatorLowerTriangular"):
    r"""Initialize a `LinearOperatorLowerTriangular`.

    Args:
      tril:  Shape `[B1,...,Bb, N, N]` with `b >= 0`, `N >= 0`.
        The lower triangular part of `tril` defines this operator.  The strictly
        upper triangle is ignored.  Allowed dtypes: `float16`, `float32`,
        `float64`.
      is_non_singular:  Expect that this operator is non-singular.
        This operator is non-singular if and only if its diagonal elements are
        all non-zero.
      is_self_adjoint:  Expect that this operator is equal to its hermitian
        transpose.  This operator is self-adjoint only if it is diagonal with
        real-valued diagonal entries.  In this case it is advised to use
        `LinearOperatorDiag`.
      is_positive_definite:  Expect that this operator is positive definite,
        meaning the quadratic form `x^H A x` has positive real part for all
        nonzero `x`.  Note that we do not require the operator to be
        self-adjoint to be positive-definite.  See:
        https://en.wikipedia.org/wiki/Positive-definite_matrix\
            #Extension_for_non_symmetric_matrices
      is_square:  Expect that this operator acts like square [batch] matrices.
      name: A name for this `LinearOperator`.

    Raises:
      TypeError:  If `diag.dtype` is not an allowed type.
      ValueError:  If `is_square` is `False`.
    """

    if is_square is False:
      raise ValueError(
          "Only square lower triangular operators supported at this time.")
    is_square = True

    with ops.name_scope(name, values=[tril]):
      self._tril = ops.convert_to_tensor(tril, name="tril")
      self._check_tril(self._tril)
      self._tril = array_ops.matrix_band_part(tril, -1, 0)
      self._diag = array_ops.matrix_diag_part(self._tril)

      super(LinearOperatorLowerTriangular, self).__init__(
          dtype=self._tril.dtype,
          graph_parents=[self._tril],
          is_non_singular=is_non_singular,
          is_self_adjoint=is_self_adjoint,
          is_positive_definite=is_positive_definite,
          is_square=is_square,
          name=name)