pylops.PyTensorOperator

class pylops.PyTensorOperator(LOp)

PyTensor Op which applies a PyLops Linear Operator, including gradient support.

This class “converts” a PyLops LinearOperator class into a PyTensor Op. This applies the LinearOperator in “forward-mode” in self.perform, and applies its adjoint when computing the vector-Jacobian product (self.grad), as that is the analytically correct gradient for linear operators. This class should pass pytensor.gradient.verify_grad.

Parameters:

LOppylops.LinearOperator

PyLops Linear operator

Methods

L_op(inputs, outputs, output_grads)	Construct a graph for the L-operator.
R_op(inputs, eval_points)	Construct a graph for the R-operator.
__init__(LOp)
add_tag_trace(thing[, user_line])	Add tag.trace to a node or variable.
do_constant_folding(fgraph, node)	Determine whether or not constant folding should be performed for the given node.
grad(inputs, output_grads)	Construct a graph for the gradient with respect to each input variable.
inplace_on_inputs(allowed_inplace_inputs)	Try to return a version of self that tries to inplace in as many as allowed_inplace_inputs.
make_node(x)	Construct an Apply node that represent the application of this operation to the given inputs.
make_py_thunk(node, storage_map, ...[, debug])	Make a Python thunk.
make_thunk(node, storage_map, compute_map, ...)	Create a thunk.
perform(node, inputs, output_storage)	Calculate the function on the inputs and put the variables in the output storage.
prepare_node(node, storage_map, compute_map, ...)	Make any special modifications that the Op needs before doing Op.make_thunk.

Examples using pylops.PyTensorOperator

Bayesian Linear Regression

Bayesian Linear Regression