Python array_ops.matrix_diag_part方法代码示例

# 需要导入模块: from tensorflow.python.ops import array_ops [as 别名]
# 或者: from tensorflow.python.ops.array_ops import matrix_diag_part [as 别名]
def _MatrixSetDiagGrad(op, grad):
  """Gradient for MatrixSetDiag."""
  input_shape = op.inputs[0].get_shape().merge_with(grad.get_shape())
  diag_shape = op.inputs[1].get_shape()
  batch_shape = input_shape[:-2].merge_with(diag_shape[:-1])
  matrix_shape = input_shape[-2:]
  if batch_shape.is_fully_defined() and matrix_shape.is_fully_defined():
    diag_shape = batch_shape.as_list() + [min(matrix_shape.as_list())]
  else:
    with ops.colocate_with(grad):
      grad_shape = array_ops.shape(grad)
      grad_rank = array_ops.rank(grad)
      batch_shape = array_ops.slice(grad_shape, [0], [grad_rank - 2])
      matrix_shape = array_ops.slice(grad_shape, [grad_rank - 2], [2])
      min_dim = math_ops.reduce_min(matrix_shape)
      diag_shape = array_ops.concat([batch_shape, [min_dim]], 0)
  grad_input = array_ops.matrix_set_diag(
      grad, array_ops.zeros(
          diag_shape, dtype=grad.dtype))
  grad_diag = array_ops.matrix_diag_part(grad)
  return (grad_input, grad_diag) 

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

# 需要导入模块: from tensorflow.python.ops import array_ops [as 别名]
# 或者: from tensorflow.python.ops.array_ops import matrix_diag_part [as 别名]
def sqrt_log_abs_det(self):
    """Computes (log o abs o det)(X) for matrix X.

    Doesn't actually do the sqrt! Named as such to agree with API.

    To compute det(M + V D V.T), we use the matrix determinant lemma:
      det(Tril + V D V.T) = det(C) det(D) det(M)
    where C is defined as in `_inverse`, ie,
      C = inv(D) + V.T inv(M) V.

    See: https://en.wikipedia.org/wiki/Matrix_determinant_lemma

    Returns:
      log_abs_det: `Tensor`.
    """
    log_det_c = math_ops.log(math_ops.abs(
        linalg_ops.matrix_determinant(self._woodbury_sandwiched_term())))
    # Reduction is ok because we always prepad inputs to this class.
    log_det_m = math_ops.reduce_sum(math_ops.log(math_ops.abs(
        array_ops.matrix_diag_part(self._m))), axis=[-1])
    return log_det_c + 2. * self._d.sqrt_log_abs_det() + log_det_m 

# 需要导入模块: from tensorflow.python.ops import array_ops [as 别名]
# 或者: from tensorflow.python.ops.array_ops import matrix_diag_part [as 别名]
def test_diag_part(self):
    self._skip_if_tests_to_skip_contains("diag_part")
    for use_placeholder in False, True:
      for shape in self._shapes_to_test:
        for dtype in self._dtypes_to_test:
          with self.test_session(graph=ops.Graph()) as sess:
            sess.graph.seed = random_seed.DEFAULT_GRAPH_SEED
            operator, mat, feed_dict = self._operator_and_mat_and_feed_dict(
                shape, dtype, use_placeholder=use_placeholder)
            op_diag_part = operator.diag_part()
            mat_diag_part = array_ops.matrix_diag_part(mat)

            if not use_placeholder:
              self.assertAllEqual(
                  mat_diag_part.get_shape(), op_diag_part.get_shape())

            op_diag_part_, mat_diag_part_ = sess.run(
                [op_diag_part, mat_diag_part], feed_dict=feed_dict)

            self.assertAC(op_diag_part_, mat_diag_part_) 

# 需要导入模块: from tensorflow.python.ops import array_ops [as 别名]
# 或者: from tensorflow.python.ops.array_ops import matrix_diag_part [as 别名]
def add_to_tensor(self, mat, name="add_to_tensor"):
    """Add matrix represented by this operator to `mat`.  Equiv to `I + mat`.

    Args:
      mat:  `Tensor` with same `dtype` and shape broadcastable to `self`.
      name:  A name to give this `Op`.

    Returns:
      A `Tensor` with broadcast shape and same `dtype` as `self`.
    """
    with self._name_scope(name, values=[mat]):
      # Shape [B1,...,Bb, 1]
      multiplier_vector = array_ops.expand_dims(self.multiplier, -1)

      # Shape [C1,...,Cc, M, M]
      mat = ops.convert_to_tensor(mat, name="mat")

      # Shape [C1,...,Cc, M]
      mat_diag = array_ops.matrix_diag_part(mat)

      # multiplier_vector broadcasts here.
      new_diag = multiplier_vector + mat_diag

      return array_ops.matrix_set_diag(mat, new_diag) 

# 需要导入模块: from tensorflow.python.ops import array_ops [as 别名]
# 或者: from tensorflow.python.ops.array_ops import matrix_diag_part [as 别名]
def _log_abs_determinant(self):
    # Recall
    #   det(L + UDV^H) = det(D^{-1} + V^H L^{-1} U) det(D) det(L)
    #                  = det(C) det(D) det(L)
    log_abs_det_d = self.diag_operator.log_abs_determinant()
    log_abs_det_l = self.base_operator.log_abs_determinant()

    if self._use_cholesky:
      chol_cap_diag = array_ops.matrix_diag_part(self._chol_capacitance)
      log_abs_det_c = 2 * math_ops.reduce_sum(
          math_ops.log(chol_cap_diag), reduction_indices=[-1])
    else:
      det_c = linalg_ops.matrix_determinant(self._capacitance)
      log_abs_det_c = math_ops.log(math_ops.abs(det_c))

    return log_abs_det_c + log_abs_det_d + log_abs_det_l 

# 需要导入模块: from tensorflow.python.ops import array_ops [as 别名]
# 或者: from tensorflow.python.ops.array_ops import matrix_diag_part [as 别名]
def _MatrixSetDiagGrad(op, grad):
  input_shape = op.inputs[0].get_shape().merge_with(grad.get_shape())
  diag_shape = op.inputs[1].get_shape()
  batch_shape = input_shape[:-2].merge_with(diag_shape[:-1])
  matrix_shape = input_shape[-2:]
  if batch_shape.is_fully_defined() and matrix_shape.is_fully_defined():
    diag_shape = batch_shape.as_list() + [min(matrix_shape.as_list())]
  else:
    with ops.colocate_with(grad):
      grad_shape = array_ops.shape(grad)
      grad_rank = array_ops.rank(grad)
      batch_shape = array_ops.slice(grad_shape, [0], [grad_rank - 2])
      matrix_shape = array_ops.slice(grad_shape, [grad_rank - 2], [2])
      min_dim = math_ops.reduce_min(matrix_shape)
      diag_shape = array_ops.concat([batch_shape, [min_dim]], 0)
  grad_input = array_ops.matrix_set_diag(
      grad, array_ops.zeros(
          diag_shape, dtype=grad.dtype))
  grad_diag = array_ops.matrix_diag_part(grad)
  return (grad_input, grad_diag) 

# 需要导入模块: from tensorflow.python.ops import array_ops [as 别名]
# 或者: from tensorflow.python.ops.array_ops import matrix_diag_part [as 别名]
def _define_full_covariance_probs(self, shard_id, shard):
    """Defines the full covariance probabilties per example in a class.

    Updates a matrix with dimension num_examples X num_classes.

    Args:
      shard_id: id of the current shard.
      shard: current data shard, 1 X num_examples X dimensions.
    """
    diff = shard - self._means
    cholesky = linalg_ops.cholesky(self._covs + self._min_var)
    log_det_covs = 2.0 * math_ops.reduce_sum(
        math_ops.log(array_ops.matrix_diag_part(cholesky)), 1)
    x_mu_cov = math_ops.square(
        linalg_ops.matrix_triangular_solve(
            cholesky, array_ops.transpose(
                diff, perm=[0, 2, 1]), lower=True))
    diag_m = array_ops.transpose(math_ops.reduce_sum(x_mu_cov, 1))
    self._probs[shard_id] = -0.5 * (diag_m + math_ops.to_float(self._dimensions)
                                    * math_ops.log(2 * np.pi) + log_det_covs) 

# 需要导入模块: from tensorflow.python.ops import array_ops [as 别名]
# 或者: from tensorflow.python.ops.array_ops import matrix_diag_part [as 别名]
def sqrt_log_abs_det(self):
    """Computes (log o abs o det)(X) for matrix X.

    Doesn't actually do the sqrt! Named as such to agree with API.

    To compute det(M + V D V.T), we use the matrix determinant lemma:
      det(Tril + V D V.T) = det(C) det(D) det(M)
    where C is defined as in `_inverse`, ie,
      C = inv(D) + V.T inv(M) V.

    See: https://en.wikipedia.org/wiki/Matrix_determinant_lemma

    Returns:
      log_abs_det: `Tensor`.
    """
    log_det_c = math_ops.log(math_ops.abs(
        linalg_ops.matrix_determinant(self._woodbury_sandwiched_term())))
    # Reduction is ok because we always prepad inputs to this class.
    log_det_m = math_ops.reduce_sum(math_ops.log(math_ops.abs(
        array_ops.matrix_diag_part(self._m))), reduction_indices=[-1])
    return log_det_c + 2. * self._d.sqrt_log_abs_det() + log_det_m 

# 需要导入模块: from tensorflow.python.ops import array_ops [as 别名]
# 或者: from tensorflow.python.ops.array_ops import matrix_diag_part [as 别名]
def _pairwise_squared_distance_matrix(x):
  """Pairwise squared distance among a (batch) matrix's rows (2nd dim).

  This saves a bit of computation vs. using _cross_squared_distance_matrix(x,x)

  Args:
    x: `[batch_size, n, d]` float `Tensor`

  Returns:
    squared_dists: `[batch_size, n, n]` float `Tensor`, where
    squared_dists[b,i,j] = ||x[b,i,:] - x[b,j,:]||^2
  """

  x_x_transpose = math_ops.matmul(x, x, adjoint_b=True)
  x_norm_squared = array_ops.matrix_diag_part(x_x_transpose)
  x_norm_squared_tile = array_ops.expand_dims(x_norm_squared, 2)

  # squared_dists[b,i,j] = ||x_bi - x_bj||^2 = x_bi'x_bi- 2x_bi'x_bj + x_bj'x_bj
  squared_dists = x_norm_squared_tile - 2 * x_x_transpose + array_ops.transpose(
      x_norm_squared_tile, [0, 2, 1])

  return squared_dists 

# 需要导入模块: from tensorflow.python.ops import array_ops [as 别名]
# 或者: from tensorflow.python.ops.array_ops import matrix_diag_part [as 别名]
def _MatrixSetDiagGrad(op, grad):
  input_shape = op.inputs[0].get_shape().merge_with(grad.get_shape())
  diag_shape = op.inputs[1].get_shape()
  batch_shape = input_shape[:-2].merge_with(diag_shape[:-1])
  matrix_shape = input_shape[-2:]
  if batch_shape.is_fully_defined() and matrix_shape.is_fully_defined():
    diag_shape = batch_shape.as_list() + [min(matrix_shape.as_list())]
  else:
    with ops.colocate_with(grad):
      grad_shape = array_ops.shape(grad)
      grad_rank = array_ops.rank(grad)
      batch_shape = array_ops.slice(grad_shape, [0], [grad_rank - 2])
      matrix_shape = array_ops.slice(grad_shape, [grad_rank - 2], [2])
      min_dim = math_ops.reduce_min(matrix_shape)
      diag_shape = array_ops.concat(0, [batch_shape, [min_dim]])
  grad_input = array_ops.matrix_set_diag(
      grad, array_ops.zeros(
          diag_shape, dtype=grad.dtype))
  grad_diag = array_ops.matrix_diag_part(grad)
  return (grad_input, grad_diag) 

# 需要导入模块: from tensorflow.python.ops import array_ops [as 别名]
# 或者: from tensorflow.python.ops.array_ops import matrix_diag_part [as 别名]
def trace(x, name=None):
  """Compute the trace of a tensor `x`.

  `trace(x)` returns the sum along the main diagonal of each inner-most matrix
  in x. If x is of rank `k` with shape `[I, J, K, ..., L, M, N]`, then output
  is a tensor of rank `k-2` with dimensions `[I, J, K, ..., L]` where

  `output[i, j, k, ..., l] = trace(x[i, j, i, ..., l, :, :])`

  For example:

  ```python
  # 'x' is [[1, 2],
  #         [3, 4]]
  tf.trace(x) ==> 5

  # 'x' is [[1,2,3],
  #         [4,5,6],
  #         [7,8,9]]
  tf.trace(x) ==> 15

  # 'x' is [[[1,2,3],
  #          [4,5,6],
  #          [7,8,9]],
  #         [[-1,-2,-3],
  #          [-4,-5,-6],
  #          [-7,-8,-9]]]
  tf.trace(x) ==> [15,-15]
  ```

  Args:
    x: tensor.
    name: A name for the operation (optional).

  Returns:
    The trace of input tensor.
  """
  with ops.name_scope(name, "Trace", [x]) as name:
    x = ops.convert_to_tensor(x, name="x")
    return reduce_sum(array_ops.matrix_diag_part(x), [-1], name=name) 

# 需要导入模块: from tensorflow.python.ops import array_ops [as 别名]
# 或者: from tensorflow.python.ops.array_ops import matrix_diag_part [as 别名]
def _MatrixDiagGrad(_, grad):
  return array_ops.matrix_diag_part(grad) 

# 需要导入模块: from tensorflow.python.ops import array_ops [as 别名]
# 或者: from tensorflow.python.ops.array_ops import matrix_diag_part [as 别名]
def _SelfAdjointEigV2Grad(op, grad_e, grad_v):
  """Gradient for SelfAdjointEigV2."""
  e = op.outputs[0]
  v = op.outputs[1]
  # a = op.inputs[0], which satisfies
  # a[...,:,:] * v[...,:,i] = e[...,i] * v[...,i]
  with ops.control_dependencies([grad_e.op, grad_v.op]):
    if grad_v is not None:
      # Construct the matrix f(i,j) = (i != j ? 1 / (e_i - e_j) : 0).
      # Notice that because of the term involving f, the gradient becomes
      # infinite (or NaN in practice) when eigenvalues are not unique.
      # Mathematically this should not be surprising, since for (k-fold)
      # degenerate eigenvalues, the corresponding eigenvectors are only defined
      # up to arbitrary rotation in a (k-dimensional) subspace.
      f = array_ops.matrix_set_diag(
          math_ops.reciprocal(
              array_ops.expand_dims(e, -2) - array_ops.expand_dims(e, -1)),
          array_ops.zeros_like(e))
      grad_a = math_ops.matmul(
          v,
          math_ops.matmul(
              array_ops.matrix_diag(grad_e) + f * math_ops.matmul(
                  v, grad_v, adjoint_a=True),
              v,
              adjoint_b=True))
    else:
      grad_a = math_ops.matmul(
          v, math_ops.matmul(
              array_ops.matrix_diag(grad_e), v, adjoint_b=True))
    # The forward op only depends on the lower triangular part of a, so here we
    # symmetrize and take the lower triangle
    grad_a = array_ops.matrix_band_part(
        grad_a + array_ops.matrix_transpose(grad_a), -1, 0)
    grad_a = array_ops.matrix_set_diag(grad_a,
                                       0.5 * array_ops.matrix_diag_part(grad_a))
    return grad_a 

# 需要导入模块: from tensorflow.python.ops import array_ops [as 别名]
# 或者: from tensorflow.python.ops.array_ops import matrix_diag_part [as 别名]
def _add_to_tensor(self, mat):
    # Add to a tensor in O(k) time!
    mat_diag = array_ops.matrix_diag_part(mat)
    new_diag = self._scale + mat_diag
    return array_ops.matrix_set_diag(mat, new_diag)