* Add remainder_scalar op to numeric trait and associated int/float functions
* Update burn-tch crate
* Update ndarray crate
* Update jit crate
* Update candle crate
* Update fusion crate
* Update autodiff crate
* Forgot float.rs for fusion
* Add burn-tensor tests
* Redirect to the pre-existing modulus op
* Fix sign
* Remove mut from burn-tch
* Use sign trick to make wgpu backend work
* Add more unit tests in to cover bases
* Naming fix for burn-fusion
* Update tests w/PyTorch link
* Use different WGSL instructions for remainder
* Redirect to remainder Operator instead of modulo
* Revert Modulo in instruction.rs
* Update links to latest commit off main
* Some pedantry
* Update links and add jit
* Update instructions for burn-jit and wgpu
* Updated import section with more recent links
* Some grammar/typo/styling fixes
* Code added to burn-wgpu too
* Add training report summary
* Fix LossMetric batch size state
* Add NumericEntry de/serialize
* Fix clippy suggestion
* Compact recorder does not use compression (anymore)
* Add learner summary expected results tests
* Add summary to learner builder and automatically display in fit
- Add LearnerSummaryConfig
- Keep track of summary metrics names
- Add model field when displaying from learner.fit()
* Add multilabel classification dataset
- Add MultiLabel annotation support
- Refactor de/serialize annotation with AnnotationRaw
- Add ImageFolderDataset::with_items methods
* Fix custom-image-classification example deps
* Add image_folder_dataset_multilabel test
* Do not change class names order when provided
* Add hamming score and multi-label classification output
* Add new_classification_with_items test
* Fix clippy suggestions
* Implement default trait for hamming score
* Remove de/serialization and use AnnotationRaw as type
* Fix clippy
* Fix metric backend phantom data
* refactor execute_dynamic into Execution
* minor change
* extension cfg
* jitkernel and sourcekernel
* add todo statement
* cleanup and docs
* update book
* fix server dependancy on compiler
* refactor into shader information
* refactor to compile shader once
* clippy
* clippy
* clippy
* fix doc
* fix doc
* fmt
* rename feature flag
* refactor
* All broked
* compile at the right time
* todo done
* all dynamic
* all dynamic in template too
* fmt
* fix ci
---------
Co-authored-by: nathaniel <nathaniel.simard.42@gmail.com>
* Implement LeakyReLu
* Cargo fmt
* Apply suggestions
* cargo fmt
* Use float_mul_scalar
* Should be grad
* Add to books module
* Move test files
* Update leaky relu to use activation function
* Update tensor.md
* Fix failing test due to approx
* Add back the function comment
* Fix comment per PR feedback
---------
Co-authored-by: Dilshod Tadjibaev <939125+antimora@users.noreply.github.com>
* Initial padding approach
Create padding implementation for the last two dimensions of Float and Int Tensors.
Create PadMode Enum, allowing Constant padding.
Create Padding Struct with Uniform, Asymmetric, height, and width implementations.
Create tests for the padding implementation.
* Update padding.rs
remove unneeded import
* Update from Merge
Use crate Element
Swap from old from_data() to new from_data_devauto()
* Formatting Changes
Formatting changes from cargo fmt --all
* Additional Format Change
One more format change that cargo fmt didn't get the first time.
* Changes to Example
Modify Example to ensure it works.
* modify naming
better names for impl / input variables.
* Modify API
- Change Padding to PadSize.
- integrate padding value into PadMode.
- update tests and examples.
* Comments and print
Improve comments+naming and remove println
* Pad Fixes
Moved pad to numeric
Simplified PadMode Element
updated tensor creations
fixed doc example
* Fix test location
* Simplified pad API
* Fix for failed unit tests
* Remove bool_full
* Rename `pads` to `padding`
---------
Co-authored-by: Dilshod Tadjibaev <939125+antimora@users.noreply.github.com>
* separate forward backward
* refactor with pool strategy
* refactor further
* pooling refactored
* refactoring for adaptive wip
* wip adaptive
* adaptive
* delete some wgsl
* avg pool backward
* clippy
* minor refactor
* works
* delete wgsl
* separate forward backward
* refactor with pool strategy
* refactor further
* pooling refactored
* refactoring for adaptive wip
* wip adaptive
* adaptive
* delete some wgsl
* avg pool backward
* clippy
* minor refactor
* Bump sysinfo crate to 0.30.7
* [backend-comparison] Add CPUs and GPUs system info to results
* [backend-comparison] Add integrated GPUs to gathered system info
* [backend-comparison] Use AutoGraphicsApi wgpu backend selection
Guillaume Lagrange