tf.contrib.distributions.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:
1 2 3 4 5 6 7 8 9 10 | # 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)# OperatorPDCholesky ignores the upper triangle.operator = OperatorPDCholesky(chol) |
Example of heteroskedastic 2-D linear regression.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | # 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 = 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: RankRTensor,R >= 2, where the last two dimensions are equal. -
transform: Element-wise function mappingTensorstoTensors. To be applied to the diagonal ofmatrix. IfNone,matrixis returned unchanged. Defaults toNone. -
name: A name to give created ops. Defaults to "matrix_diag_transform".
Returns:
A Tensor with same shape and dtype as matrix.
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