def batch_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 = batch_matrix_diag_transform(matrix, transform=tf.nn.softplus)
# OperatorPDCholesky ignores the upper triangle.
operator = OperatorPDCholesky(chol)
```
Example of heteroskedastic 2-D linear regression.
```python
# Get a trainable Cholesky factor.
matrix_values = tf.contrib.layers.fully_connected(activations, 4)
matrix = tf.reshape(matrix_values, (batch_size, 2, 2))
chol = batch_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 = tf.contrib.distributions.MVNCholesky(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_pdf(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 "batch_matrix_diag_transform".
Returns:
A `Tensor` with same shape and `dtype` as `matrix`.
"""
with ops.op_scope([matrix], name, 'batch_matrix_diag_transform'):
matrix = ops.convert_to_tensor(matrix, name='matrix')
if transform is None:
return matrix
# Replace the diag with transformed diag.
diag = array_ops.batch_matrix_diag_part(matrix)
transformed_diag = transform(diag)
matrix += array_ops.batch_matrix_diag(transformed_diag - diag)
return matrix
def batch_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 = batch_matrix_diag_transform(matrix, transform=tf.nn.softplus)
# OperatorPDCholesky ignores the upper triangle.
operator = OperatorPDCholesky(chol)
```
Example of heteroskedastic 2-D linear regression.
```python
# Get a trainable Cholesky factor.
matrix_values = tf.contrib.layers.fully_connected(activations, 4)
matrix = tf.reshape(matrix_values, (batch_size, 2, 2))
chol = batch_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 = tf.contrib.distributions.MVNCholesky(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_pdf(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 "batch_matrix_diag_transform".
Returns:
A `Tensor` with same shape and `dtype` as `matrix`.
"""
with ops.op_scope([matrix], name, 'batch_matrix_diag_transform'):
matrix = ops.convert_to_tensor(matrix, name='matrix')
if transform is None:
return matrix
# Replace the diag with transformed diag.
diag = array_ops.batch_matrix_diag_part(matrix)
transformed_diag = transform(diag)
matrix += array_ops.batch_matrix_diag(transformed_diag - diag)
return matrix
def variance(self, name="variance"):
"""Variance of the distribution."""
with ops.name_scope(self.name):
with ops.op_scope([self._n, self._p, self._mean], name):
p = array_ops.expand_dims(
self._p * array_ops.expand_dims(
array_ops.ones_like(self._n), -1), -1)
variance = -math_ops.batch_matmul(
array_ops.expand_dims(self._mean, -1), p, adj_y=True)
variance += array_ops.batch_matrix_diag(self._mean)
return variance
def variance(self, name="variance"):
"""Variance of the distribution."""
with ops.name_scope(self.name):
with ops.name_scope(name, values=[self._n, self._p, self._mean]):
p = array_ops.expand_dims(
self._p * array_ops.expand_dims(
array_ops.ones_like(self._n), -1), -1)
variance = -math_ops.batch_matmul(
array_ops.expand_dims(self._mean, -1), p, adj_y=True)
variance += array_ops.batch_matrix_diag(self._mean)
return variance
def variance(self, name="variance"):
"""Variance of the distribution."""
with ops.name_scope(self.name):
with ops.op_scope([self._alpha, self._alpha_0], name):
alpha = array_ops.expand_dims(self._alpha, -1)
alpha_0 = array_ops.expand_dims(self._alpha_0, -1)
expanded_alpha_0 = array_ops.expand_dims(alpha_0, -1)
variance = -math_ops.batch_matmul(alpha, alpha, adj_y=True) / (
expanded_alpha_0 ** 2 * (expanded_alpha_0 + 1))
diagonal = self._alpha / (alpha_0 * (alpha_0 + 1))
variance += array_ops.batch_matrix_diag(diagonal)
return variance
def variance(self, name="variance"):
"""Variance of the distribution."""
with ops.name_scope(self.name):
with ops.name_scope(name, values=[self._alpha, self._alpha_0]):
alpha = array_ops.expand_dims(self._alpha, -1)
alpha_0 = array_ops.expand_dims(self._alpha_0, -1)
expanded_alpha_0 = array_ops.expand_dims(alpha_0, -1)
variance = -math_ops.batch_matmul(alpha, alpha, adj_y=True) / (
expanded_alpha_0 ** 2 * (expanded_alpha_0 + 1))
diagonal = self._alpha / (alpha_0 * (alpha_0 + 1))
variance += array_ops.batch_matrix_diag(diagonal)
return variance
def variance(self, name="mean"):
"""Class variances for every batch member.
The variance for each batch member is defined as the following:
```
Var(X_j) = n * alpha_j / alpha_0 * (1 - alpha_j / alpha_0) *
(n + alpha_0) / (1 + alpha_0)
```
where `alpha_0 = sum_j alpha_j`.
The covariance between elements in a batch is defined as:
```
Cov(X_i, X_j) = -n * alpha_i * alpha_j / alpha_0 ** 2 *
(n + alpha_0) / (1 + alpha_0)
```
Args:
name: The name for this op.
Returns:
A `Tensor` representing the variances for each batch member.
"""
alpha = self._alpha
alpha_sum = self._alpha_sum
n = self._n
with ops.name_scope(self.name):
with ops.name_scope(name, values=[alpha, alpha_sum, n]):
expanded_alpha_sum = array_ops.expand_dims(alpha_sum, -1)
shared_factor = n * (expanded_alpha_sum + n) / (
expanded_alpha_sum + 1) * array_ops.ones_like(alpha)
mean_no_n = alpha / expanded_alpha_sum
expanded_mean_no_n = array_ops.expand_dims(mean_no_n, -1)
variance = -math_ops.batch_matmul(
expanded_mean_no_n, expanded_mean_no_n, adj_y=True)
variance += array_ops.batch_matrix_diag(mean_no_n)
variance *= array_ops.expand_dims(shared_factor, -1)
return variance
def variance(self, name='mean'):
"""Class variances for every batch member.
The variance for each batch member is defined as the following:
```
Var(X_j) = n * alpha_j / alpha_0 * (1 - alpha_j / alpha_0) *
(n + alpha_0) / (1 + alpha_0)
```
where `alpha_0 = sum_j alpha_j`.
The covariance between elements in a batch is defined as:
```
Cov(X_i, X_j) = -n * alpha_i * alpha_j / alpha_0 ** 2 *
(n + alpha_0) / (1 + alpha_0)
```
Args:
name: The name for this op.
Returns:
A `Tensor` representing the variances for each batch member.
"""
alpha = self._alpha
alpha_sum = self._alpha_sum
n = self._n
with ops.name_scope(self.name):
with ops.op_scope([alpha, alpha_sum, n], name):
expanded_alpha_sum = array_ops.expand_dims(alpha_sum, -1)
shared_factor = n * (expanded_alpha_sum + n) / (
expanded_alpha_sum + 1) * array_ops.ones_like(alpha)
mean_no_n = alpha / expanded_alpha_sum
expanded_mean_no_n = array_ops.expand_dims(mean_no_n, -1)
variance = -math_ops.batch_matmul(
expanded_mean_no_n, expanded_mean_no_n, adj_y=True)
variance += array_ops.batch_matrix_diag(mean_no_n)
variance *= array_ops.expand_dims(shared_factor, -1)
return variance
def _BatchMatrixDiagPartGrad(_, grad): return array_ops.batch_matrix_diag(grad)
def _BatchMatrixDiagPartGrad(_, grad):
return array_ops.batch_matrix_diag(grad)
def _sqrt_to_dense(self): return array_ops.batch_matrix_diag(self._diag)
def _to_dense(self): return array_ops.batch_matrix_diag(math_ops.square(self._diag))
def _sqrt_to_dense(self):
return array_ops.batch_matrix_diag(self._diag)
def _to_dense(self):
return array_ops.batch_matrix_diag(math_ops.square(self._diag))
def _to_dense(self): diag = array_ops.ones(self.vector_shape(), dtype=self.dtype) dense = array_ops.batch_matrix_diag(diag) dense.set_shape(self.get_shape()) return dense
def _to_dense(self):
diag = array_ops.ones(self.vector_shape(), dtype=self.dtype)
dense = array_ops.batch_matrix_diag(diag)
dense.set_shape(self.get_shape())
return dense