Python tensorflow.python.ops.linalg_ops.matrix_inverse() Examples

The following are 10 code examples of tensorflow.python.ops.linalg_ops.matrix_inverse(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module tensorflow.python.ops.linalg_ops , or try the search function .
Example #1
Source File: linalg_grad.py    From lambda-packs with MIT License 5 votes vote down vote up 
def _MatrixDeterminantGrad(op, grad):
  """Gradient for MatrixDeterminant."""
  a = op.inputs[0]
  c = op.outputs[0]
  a_adj_inv = linalg_ops.matrix_inverse(a, adjoint=True)
  multipliers = array_ops.reshape(
      grad * c, array_ops.concat([array_ops.shape(c), [1, 1]], 0))
  return multipliers * a_adj_inv
Example #2
Source File: gmm_ops.py    From lambda-packs with MIT License 5 votes vote down vote up 
def _define_distance_to_clusters(self, data):
    """Defines the Mahalanobis distance to the assigned Gaussian."""
    # TODO(xavigonzalvo): reuse (input - mean) * cov^-1 * (input -
    # mean) from log probability function.
    self._all_scores = []
    for shard in data:
      all_scores = []
      shard = array_ops.expand_dims(shard, 0)
      for c in xrange(self._num_classes):
        if self._covariance_type == FULL_COVARIANCE:
          cov = self._covs[c, :, :]
        elif self._covariance_type == DIAG_COVARIANCE:
          cov = array_ops.diag(self._covs[c, :])
        inverse = linalg_ops.matrix_inverse(cov + self._min_var)
        inv_cov = array_ops.tile(
            array_ops.expand_dims(inverse, 0),
            array_ops.stack([self._num_examples, 1, 1]))
        diff = array_ops.transpose(shard - self._means[c, :, :], perm=[1, 0, 2])
        m_left = math_ops.matmul(diff, inv_cov)
        all_scores.append(
            math_ops.sqrt(
                math_ops.matmul(
                    m_left, array_ops.transpose(
                        diff, perm=[0, 2, 1]))))
      self._all_scores.append(
          array_ops.reshape(
              array_ops.concat(all_scores, 1),
              array_ops.stack([self._num_examples, self._num_classes])))

    # Distance to the associated class.
    self._all_scores = array_ops.concat(self._all_scores, 0)
    assignments = array_ops.concat(self.assignments(), 0)
    rows = math_ops.to_int64(math_ops.range(0, self._num_examples))
    indices = array_ops.concat(
        [array_ops.expand_dims(rows, 1), array_ops.expand_dims(assignments, 1)],
        1)
    self._scores = array_ops.gather_nd(self._all_scores, indices)
Example #3
Source File: image_ops.py    From lambda-packs with MIT License 5 votes vote down vote up 
def _image_projective_transform_grad(op, grad):
  """Computes the gradient for ImageProjectiveTransform."""
  images = op.inputs[0]
  transforms = op.inputs[1]
  interpolation = op.get_attr("interpolation")

  image_or_images = ops.convert_to_tensor(images, name="images")
  transform_or_transforms = ops.convert_to_tensor(
      transforms, name="transforms", dtype=dtypes.float32)

  if image_or_images.dtype.base_dtype not in _IMAGE_DTYPES:
    raise TypeError("Invalid dtype %s." % image_or_images.dtype)
  if len(image_or_images.get_shape()) == 2:
    images = image_or_images[None, :, :, None]
  elif len(image_or_images.get_shape()) == 3:
    images = image_or_images[None, :, :, :]
  elif len(image_or_images.get_shape()) == 4:
    images = image_or_images
  else:
    raise TypeError("Images should have rank between 2 and 4")
  if len(transform_or_transforms.get_shape()) == 1:
    transforms = transform_or_transforms[None]
  elif len(transform_or_transforms.get_shape()) == 2:
    transforms = transform_or_transforms
  else:
    raise TypeError("Transforms should have rank 1 or 2.")

  # Invert transformations
  transforms = _flat_transforms_to_matrices(transforms=transforms)
  inverse = linalg_ops.matrix_inverse(transforms)
  transforms = _transform_matrices_to_flat(inverse)
  output = gen_image_ops.image_projective_transform(
      grad, transforms, interpolation=interpolation)
  if len(image_or_images.get_shape()) == 2:
    return [output[0, :, :, 0], None]
  elif len(image_or_images.get_shape()) == 3:
    return [output[0, :, :, :], None]
  else:
    return [output, None]
Example #4
Source File: linalg_grad.py    From auto-alt-text-lambda-api with MIT License 5 votes vote down vote up 
def _MatrixDeterminantGrad(op, grad):
  """Gradient for MatrixDeterminant."""
  a = op.inputs[0]
  c = op.outputs[0]
  a_adj_inv = linalg_ops.matrix_inverse(a, adjoint=True)
  multipliers = array_ops.reshape(
      grad * c, array_ops.concat([array_ops.shape(c), [1, 1]], 0))
  return multipliers * a_adj_inv
Example #5
Source File: gmm_ops.py    From auto-alt-text-lambda-api with MIT License 5 votes vote down vote up 
def _define_distance_to_clusters(self, data):
    """Defines the Mahalanobis distance to the assigned Gaussian."""
    # TODO(xavigonzalvo): reuse (input - mean) * cov^-1 * (input -
    # mean) from log probability function.
    self._all_scores = []
    for shard in data:
      all_scores = []
      shard = array_ops.expand_dims(shard, 0)
      for c in xrange(self._num_classes):
        if self._covariance_type == FULL_COVARIANCE:
          cov = self._covs[c, :, :]
        elif self._covariance_type == DIAG_COVARIANCE:
          cov = array_ops.diag(self._covs[c, :])
        inverse = linalg_ops.matrix_inverse(cov + self._min_var)
        inv_cov = array_ops.tile(
            array_ops.expand_dims(inverse, 0),
            array_ops.stack([self._num_examples, 1, 1]))
        diff = array_ops.transpose(shard - self._means[c, :, :], perm=[1, 0, 2])
        m_left = math_ops.matmul(diff, inv_cov)
        all_scores.append(
            math_ops.sqrt(
                math_ops.matmul(
                    m_left, array_ops.transpose(
                        diff, perm=[0, 2, 1]))))
      self._all_scores.append(
          array_ops.reshape(
              array_ops.concat(all_scores, 1),
              array_ops.stack([self._num_examples, self._num_classes])))

    # Distance to the associated class.
    self._all_scores = array_ops.concat(self._all_scores, 0)
    assignments = array_ops.concat(self.assignments(), 0)
    rows = math_ops.to_int64(math_ops.range(0, self._num_examples))
    indices = array_ops.concat(
        [array_ops.expand_dims(rows, 1), array_ops.expand_dims(assignments, 1)],
        1)
    self._scores = array_ops.gather_nd(self._all_scores, indices)
Example #6
Source File: linalg_grad.py    From deep_image_model with Apache License 2.0 5 votes vote down vote up 
def _MatrixDeterminantGrad(op, grad):
  """Gradient for MatrixDeterminant."""
  a = op.inputs[0]
  c = op.outputs[0]
  a_adj_inv = linalg_ops.matrix_inverse(a, adjoint=True)
  multipliers = array_ops.reshape(
      grad * c, array_ops.concat(0, [array_ops.shape(c), [1, 1]]))
  return multipliers * a_adj_inv
Example #7
Source File: utils.py    From noisy-K-FAC with Apache License 2.0 5 votes vote down vote up 
def posdef_inv_matrix_inverse(tensor, identity, damping):
    """Computes inverse(tensor + damping * identity) directly."""
    return linalg_ops.matrix_inverse(tensor + damping * identity)
Example #8
Source File: linalg_grad.py    From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License 5 votes vote down vote up 
def _MatrixDeterminantGrad(op, grad):
  """Gradient for MatrixDeterminant."""
  a = op.inputs[0]
  c = op.outputs[0]
  a_adj_inv = linalg_ops.matrix_inverse(a, adjoint=True)
  multipliers = array_ops.reshape(grad * c,
                                  array_ops.concat([array_ops.shape(c), [1, 1]],
                                                   0))
  return multipliers * a_adj_inv
Example #9
Source File: linalg_grad.py    From keras-lambda with MIT License 5 votes vote down vote up 
def _MatrixDeterminantGrad(op, grad):
  """Gradient for MatrixDeterminant."""
  a = op.inputs[0]
  c = op.outputs[0]
  a_adj_inv = linalg_ops.matrix_inverse(a, adjoint=True)
  multipliers = array_ops.reshape(
      grad * c, array_ops.concat([array_ops.shape(c), [1, 1]], 0))
  return multipliers * a_adj_inv
Example #10
Source File: gmm_ops.py    From keras-lambda with MIT License 5 votes vote down vote up 
def _define_distance_to_clusters(self, data):
    """Defines the Mahalanobis distance to the assigned Gaussian."""
    # TODO(xavigonzalvo): reuse (input - mean) * cov^-1 * (input -
    # mean) from log probability function.
    self._all_scores = []
    for shard in data:
      all_scores = []
      shard = array_ops.expand_dims(shard, 0)
      for c in xrange(self._num_classes):
        if self._covariance_type == FULL_COVARIANCE:
          cov = self._covs[c, :, :]
        elif self._covariance_type == DIAG_COVARIANCE:
          cov = array_ops.diag(self._covs[c, :])
        inverse = linalg_ops.matrix_inverse(cov + self._min_var)
        inv_cov = array_ops.tile(
            array_ops.expand_dims(inverse, 0),
            array_ops.stack([self._num_examples, 1, 1]))
        diff = array_ops.transpose(shard - self._means[c, :, :], perm=[1, 0, 2])
        m_left = math_ops.matmul(diff, inv_cov)
        all_scores.append(
            math_ops.sqrt(
                math_ops.matmul(
                    m_left, array_ops.transpose(
                        diff, perm=[0, 2, 1]))))
      self._all_scores.append(
          array_ops.reshape(
              array_ops.concat(all_scores, 1),
              array_ops.stack([self._num_examples, self._num_classes])))

    # Distance to the associated class.
    self._all_scores = array_ops.concat(self._all_scores, 0)
    assignments = array_ops.concat(self.assignments(), 0)
    rows = math_ops.to_int64(math_ops.range(0, self._num_examples))
    indices = array_ops.concat(
        [array_ops.expand_dims(rows, 1), array_ops.expand_dims(assignments, 1)],
        1)
    self._scores = array_ops.gather_nd(self._all_scores, indices)