XRNN 1.1.1: Minor enhancements

This release introduced the following minor enhancements:

1. BatchNormalization.backward method is now 35% faster and consumes 20-40% less memory.
2. Optimized build.yml efficiency. It only runs the full build on releases.
3. Introduced a logo for project.
4. Clearer and more consistent formatting across different files.

XRNN v1.1.0: Enhanced Performance, Security, and Cross-Platform Compatibility

XRNN 1.1.0 Release Notes

The aim of 1.1.0 release is to refine the already exposed API, by improving performance, security, documentation, and test coverage. A big focus also was to streamline the build process, making it happen automatically when the project is being built using standard tools with no need for any extra steps.

New Features:

• Losses and Optimizers can now be passed as their string representation, which will automatically construct the respective class with its default parameters.
• Model.add will automatically build the newly added layers if the model has already been built.
• Model.build works correctly with layers that have been built then added to the model.
• Model.output_shape: Retrun the output shape of the model if it's built.
• Dense.build doesn't require input_shape to be passed if input_dim is passed to the constructor.
• Metrics.Accuracy works with 2 dimensional lables.
• Serialization and Saving API Rewrite:
  • Implement JSON serialization and deserialization for objects through get_config and from_config functions.
  • Rename (get/set/save/load)_parameters to (get/set/save/load)_params.
  • Improve security for load and save methods by getting rid of pickle usage.
  • Remove set_params_from_keras_model. The same functionality can be achieved by passing keras=True to set_params.

Bug Fixes:

• Flatten.compute_output_shape return np.int32
• Model.backward wouldn't call Layer.backward when Layer.training is set to False.
• Model.train didn't increment Optimizer.epoch.
• Model.train would print even if print_every is turned off
• Casting bool arrays to float when changing the data type of the layer.
• CategoricalCrossentropy didn't handle 1 dimensional labels correctly.
• MSE performed labels clipping which caused incorrect results for any label with a value > 1 or < -1.
• Dropout used the same mask (random array to "turn off" neurons) for all batches.
• Calculating accuracy for MSE would cause an error because of the use of np.abs, which isn't implemented in all Numpy versions.
• ReLU uses results cached when it was in training mode during inference which would raise an error if it wasn't trained or give the wrong results if it was trained.

Improved tests:

• Implement tests for all the modules.
• 98% coverage rate.

Cross-Platform Compatibility:

• Package is now cross-platform, supporting various architectures.

Automatic Build Process:

• Integrated automatic build process using standard tools.
• Developers no longer need to perform special actions for building.

Pre-built Binaries:

• Provided pre-built binaries (wheels) for almost all platforms and architectures.

Compiler Agnostic Build Process:

• Ensured compatibility with different compilers for seamless integration.
• Any version of GCC, Clang, MSVC can be used to compile the package on any platform where they are available.
• Set the compiler to be used in pyproject.toml under the xrnn tool table.

Enhanced Documentation:

• Improved documentation with better code examples for easier usage.
• Rewritten README to reflect the major changes.

Improved External Data Handling and Validation:

• Strengthened security measures for handling external data.
• Downloaded datasets are now saved at ~/.xrnn directory not inside the package's root.

GitHub Actions Workflow Integration:

• build.yml: Builds the package on Windows (32+64), macOS (Intel+Arm) and many/musl linux (x86_64, i386, aarch64).
• tests.yml: Tests the built package on all supported Python versions (3.6-3.12).
• publish.yml: Deploys the artifacts (wheels + sdist) to PyPI on release creation.

Performance Critical Part Rewrite:

• The code had to be written in C++ even though it's intended to be C code because of a limitation in MSVC caused by its ancient openmp library, which I was able to get around and the code is written in C directly how it's supposed to be.

This release marks a significant milestone, bringing improved performance, security, and convenience to users.

XRNN 1.0.2 release, minor changes.

1. Changed the license link in README.me to link to this GitHub repo.
2. Capitalized the web pages names.

XRNN 1.0.1 release, minor changes and bug fixes.

1. Changed how the license is described in the project metadata section in both setup.py and pyproject.toml so it's rendered correctly on PyPI.
2. Removed the trailing '' after "Simply run the following command:" in README.md.
3. Increment the version number to stay consistent with the package version on PyPI.

XRNN 1.0.0 release, the first stable release.

This is the first production ready release for this project.