https://gcc.gnu.org/bugzilla/show_bug.cgi?id=122741
Paul Thomas <pault at gcc dot gnu.org> changed:
What |Removed |Added
----------------------------------------------------------------------------
Last reconfirmed| |2025-11-18
Status|UNCONFIRMED |NEW
Ever confirmed|0 |1
CC| |pault at gcc dot gnu.org
Assignee|unassigned at gcc dot gnu.org |pault at gcc dot gnu.org
--- Comment #1 from Paul Thomas <pault at gcc dot gnu.org> ---
Created attachment 62829
--> https://gcc.gnu.org/bugzilla/attachment.cgi?id=62829&action=edit
"Fix" for this PR
Fiats now builds and fails in one of the tests:
[100%] Project compiled successfully.
Append '-- --help' or '-- -h' to your `fpm test` command to display usage
information.
Running all tests.
(Add '-- --contains <string>' to run only tests with subjects or descriptions
containing the specified string.)
An neural_network_t object encoding an asymmetric XOR-AND-the-2nd-input network
passes on learning the truth table for XOR-AND-the-2nd-input logic.
1 of 1 tests passed. 0 tests were skipped.
A hyperparameters_t object
passes on component-wise construction followed by conversion to and from
JSON.
1 of 1 tests passed. 0 tests were skipped.
A metadata_t object
passes on component-wise construction followed by conversion to and from
JSON.
1 of 1 tests passed. 0 tests were skipped.
A network_configuration_t object
passes on component-wise construction and then conversion to and from JSON.
1 of 1 tests passed. 0 tests were skipped.
An neural_network_t that encodes an XOR gate
passes on performing elemental inference with 1 hidden layer.
passes on performing elemental inference with 2 hidden layers.
passes on converting a network with 2 hidden layers to and from JSON format.
passes on converting a network with varying-width hidden layers to/from
JSON.
passes on performing inference with a network with hidden layers of varying
width.
passes on double-precision inference.
6 of 6 tests passed. 0 tests were skipped.
A tensor_map_t object
passes on component-wise construction followed by conversion to and from
JSON.
passes on mapping to and from the unit interval as an identity
transformation.
2 of 2 tests passed. 0 tests were skipped.
A tensor_names_t object
passes on component-wise construction followed by conversion to and from
JSON.
1 of 1 tests passed. 0 tests were skipped.
An tensor_t object
passes on double-precision construction and value extraction.
1 of 1 tests passed. 0 tests were skipped.
A trainable_network_t object
munmap_chunk(): invalid pointer
Program received signal SIGABRT: Process abort signal.
