tensorflow_probability/python/internal/distribution_util.py
def matrix_diag_transform(matrix, transform=None, name=None):
  """Transform diagonal of [batch-]matrix, leave rest of matrix unchanged.

  Create a trainable covariance defined by a Cholesky factor:

  ```python
  # Transform network layer into 2 x 2 array.
  matrix_values = tf.contrib.layers.fully_connected(activations, 4)
  matrix = tf.reshape(matrix_values, (batch_size, 2, 2))

  # Make the diagonal positive. If the upper triangle was zero, this would be a
  # valid Cholesky factor.
  chol = matrix_diag_transform(matrix, transform=tf.nn.softplus)

  # LinearOperatorLowerTriangular ignores the upper triangle.
  operator = LinearOperatorLowerTriangular(chol)
  ```

  Example of heteroskedastic 2-D linear regression.

  ```python
  tfd = tfp.distributions

  # Get a trainable Cholesky factor.
  matrix_values = tf.contrib.layers.fully_connected(activations, 4)
  matrix = tf.reshape(matrix_values, (batch_size, 2, 2))
  chol = matrix_diag_transform(matrix, transform=tf.nn.softplus)

  # Get a trainable mean.
  mu = tf.contrib.layers.fully_connected(activations, 2)

  # This is a fully trainable multivariate normal!
  dist = tfd.MultivariateNormalTriL(mu, chol)

  # Standard log loss. Minimizing this will "train" mu and chol, and then dist
  # will be a distribution predicting labels as multivariate Gaussians.
  loss = -1 * tf.reduce_mean(dist.log_prob(labels))
  ```

  Args:
    matrix:  Rank `R` `Tensor`, `R >= 2`, where the last two dimensions are
      equal.
    transform:  Element-wise function mapping `Tensors` to `Tensors`. To be
      applied to the diagonal of `matrix`. If `None`, `matrix` is returned
      unchanged. Defaults to `None`.
    name:  A name to give created ops. Defaults to "matrix_diag_transform".

  Returns:
    A `Tensor` with same shape and `dtype` as `matrix`.
  """
  with tf.name_scope(name or "matrix_diag_transform"):
    matrix = tf.convert_to_tensor(value=matrix, name="matrix")
    if transform is None:
      return matrix
    # Replace the diag with transformed diag.
    diag = tf.linalg.diag_part(matrix)
    transformed_diag = transform(diag)
    transformed_mat = tf.linalg.set_diag(matrix, transformed_diag)

  return transformed_mat
def matrix_diag_transform(matrix, transform=None, name=None):
  """Transform diagonal of [batch-]matrix, leave rest of matrix unchanged.

  Create a trainable covariance defined by a Cholesky factor:

  ```python
  # Transform network layer into 2 x 2 array.
  matrix_values = tf.contrib.layers.fully_connected(activations, 4)
  matrix = tf.reshape(matrix_values, (batch_size, 2, 2))

  # Make the diagonal positive. If the upper triangle was zero, this would be a
  # valid Cholesky factor.
  chol = matrix_diag_transform(matrix, transform=tf.nn.softplus)

  # LinearOperatorLowerTriangular ignores the upper triangle.
  operator = LinearOperatorLowerTriangular(chol)
  ```

  Example of heteroskedastic 2-D linear regression.

  ```python
  tfd = tfp.distributions

  # Get a trainable Cholesky factor.
  matrix_values = tf.contrib.layers.fully_connected(activations, 4)
  matrix = tf.reshape(matrix_values, (batch_size, 2, 2))
  chol = matrix_diag_transform(matrix, transform=tf.nn.softplus)

  # Get a trainable mean.
  mu = tf.contrib.layers.fully_connected(activations, 2)

  # This is a fully trainable multivariate normal!
  dist = tfd.MultivariateNormalTriL(mu, chol)

  # Standard log loss. Minimizing this will "train" mu and chol, and then dist
  # will be a distribution predicting labels as multivariate Gaussians.
  loss = -1 * tf.reduce_mean(dist.log_prob(labels))
  ```

  Args:
    matrix:  Rank `R` `Tensor`, `R >= 2`, where the last two dimensions are
      equal.
    transform:  Element-wise function mapping `Tensors` to `Tensors`. To be
      applied to the diagonal of `matrix`. If `None`, `matrix` is returned
      unchanged. Defaults to `None`.
    name:  A name to give created ops. Defaults to "matrix_diag_transform".

  Returns:
    A `Tensor` with same shape and `dtype` as `matrix`.
  """
  with tf.name_scope(name or "matrix_diag_transform"):
    matrix = tf.convert_to_tensor(value=matrix, name="matrix")
    if transform is None:
      return matrix
    # Replace the diag with transformed diag.
    diag = tf.linalg.diag_part(matrix)
    transformed_diag = transform(diag)
    transformed_mat = tf.linalg.set_diag(matrix, transformed_diag)

  return transformed_mat
def matrix_diag_transform ( matrix , transform = None , name = None ) : with tf . name_scope ( name or "matrix_diag_transform" ) : matrix = tf . convert_to_tensor ( value = matrix , name = "matrix" ) if transform is None : return matrix # Replace the diag with transformed diag. diag = tf . linalg . diag_part ( matrix ) transformed_diag = transform ( diag ) transformed_mat = tf . linalg . set_diag ( matrix , transformed_diag ) return transformed_mat
Transform diagonal of [batch-]matrix, leave rest of matrix unchanged.

  Create a trainable covariance defined by a Cholesky factor:

  ```python
  # Transform network layer into 2 x 2 array.
  matrix_values = tf.contrib.layers.fully_connected(activations, 4)
  matrix = tf.reshape(matrix_values, (batch_size, 2, 2))

  # Make the diagonal positive. If the upper triangle was zero, this would be a
  # valid Cholesky factor.
  chol = matrix_diag_transform(matrix, transform=tf.nn.softplus)

  # LinearOperatorLowerTriangular ignores the upper triangle.
  operator = LinearOperatorLowerTriangular(chol)
  ```

  Example of heteroskedastic 2-D linear regression.

  ```python
  tfd = tfp.distributions

  # Get a trainable Cholesky factor.
  matrix_values = tf.contrib.layers.fully_connected(activations, 4)
  matrix = tf.reshape(matrix_values, (batch_size, 2, 2))
  chol = matrix_diag_transform(matrix, transform=tf.nn.softplus)

  # Get a trainable mean.
  mu = tf.contrib.layers.fully_connected(activations, 2)

  # This is a fully trainable multivariate normal!
  dist = tfd.MultivariateNormalTriL(mu, chol)

  # Standard log loss. Minimizing this will "train" mu and chol, and then dist
  # will be a distribution predicting labels as multivariate Gaussians.
  loss = -1 * tf.reduce_mean(dist.log_prob(labels))
  ```

  Args:
    matrix:  Rank `R` `Tensor`, `R >= 2`, where the last two dimensions are
      equal.
    transform:  Element-wise function mapping `Tensors` to `Tensors`. To be
      applied to the diagonal of `matrix`. If `None`, `matrix` is returned
      unchanged. Defaults to `None`.
    name:  A name to give created ops. Defaults to "matrix_diag_transform".

  Returns:
    A `Tensor` with same shape and `dtype` as `matrix`.
Transform diagonal of [ batch - ] matrix leave rest of matrix unchanged .