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diff --git a/python-kerastroke.spec b/python-kerastroke.spec new file mode 100644 index 0000000..5a5ee52 --- /dev/null +++ b/python-kerastroke.spec @@ -0,0 +1,326 @@ +%global _empty_manifest_terminate_build 0 +Name: python-kerastroke +Version: 2.1.1 +Release: 1 +Summary: A suite of the generalization-improvement techniques Stroke, Pruning, and NeuroPlast +License: MIT License +URL: https://github.com/CharlesAverill/kerastroke/ +Source0: https://mirrors.aliyun.com/pypi/web/packages/3c/4e/d74e798142e43eb3ec7129cce6704b7edaaded710e0dea8f1fb79e9d7175/kerastroke-2.1.1.tar.gz +BuildArch: noarch + +Requires: python3-tensorflow +Requires: python3-numpy + +%description +# KeraStroke + +KeraStroke is a [Python package](https://pypi.org/project/kerastroke/#description) that implements +"Post-Back-propagation Weight Operations", or "PBWOs"; generalization-improvement techniques for Keras models in the +form of custom Keras Callbacks. These techniques function +similarly but have different philosophies and results. The techniques are: +- Stroke: Re-initializaing random weight/bias values. +- Pruning: Reducing model size by setting weight/bias values that are close to 0, to 0. +- NeuroPlast: Re-initializing any weight/bias values that are 0 or close to 0. + +Stroke is modeled after seizures, which send random electrical signals throughout the brain, sometimes causing damage +to synapses. + +NeuroPlast is modeled after the concept of neuroplasticity, when neurons that no longer have a primary function begin +to rewire to improve another function. I started working on NeuroPlast after I read the work done by Blakemore and +Cooper on horizontal/vertical line receptor neurons in the brains of cats. + +If you'd like to see the tests I'm performing with KeraStroke, you can view my testing repository +[here](https://github.com/CharlesAverill/stroke-testing). + +KeraStroke 2.0.0 marks when I really started putting work into the project. I've made an effort to comment more, +clean my code up, and make the package easier to understand overall without sacrificing utility. + +# Limitations +KeraStroke is still in the development phase. Heavy testing has been done on +Dense nets, but little testing has been done on CNNs and no testing has been done on RNNs. As of 2.1.0, CNNs are +functioning properly in KeraStroke! The issue with previous versions had to do with the way the callback would retrieve + the weights from the models. The callbacks perform significantly better on DenseNets, but could still find use in CNNs. + I'm working on this, but will definitely need the help. Please see + [the github page](https://github.com/CharlesAverill/kerastroke/) or + [contact me](https://mail.google.com/mail/?view=cm&fs=1&to=charlesaverill20@gmail.com) to contribute to the project. + +# Stroke +The goal of the Stroke callback is to re-initialize weights/biases that have begun to contribute to overfitting. + +Parameters: + + - `set_value`: re-initialized weights will be set to this value, rather than a random one + - `low_bound`: low bound for weight re-initialization + - `high_bound`: high bound for weight re-initialization + - `volatility_ratio`: percentage of weights to be re-initialized + - `cutoff`: number of epochs to perform PBWOs + - `decay`: Every epoch, v_ratio is multiplied by this number. decay can be greater than 1.0, + but v_ratio will never exceed 1.0 + - `do_weights`: perform stroke on weights + - `do_biases`: perform stroke on biases + +# Pruning +The goal of the Pruning callback is to nullify weights/biases that are effectively 0. + +Parameters: + + - `set_value`: The value that pruned weights will be set to + - `min_value`: The lowest value a weight/bias can be to be oeprated on + - `max_value`: The highest value a weight/bias can be to be operated on + - `cutoff`: number of epochs to perform PBWOs + - `do_weights`: perform pruning on weights + - `do_biases`: perform pruning on biases + +# NeuroPlast +The goal of the NeuroPlast callback is to randomly re-initialize weights/biases that are effectively 0. + +Parameters: + + - `set_value`: re-initialized weights will be set to this value, rather than a random one + - `min_value`: lowest value a weight/bias can be to be operated on + - `max_value`: highest value a weight/bias can be to be operated on + - `low_bound`: low bound for weight re-initialization + - `high_bound`: high bound for weight re-initialization + - `cutoff`: number of epochs to perform PBWOs + - `do_weights`: perform neuroplast on weights + - `do_biases`: perform neuroplast on biases + +# Usage +KeraStroke Callbacks can be used like any other custom callback. Here's a basic example: + +```python +from kerastroke import Stroke +model.fit(X, y, + epochs=32, + callbacks=[Stroke()]) +``` + + + + +%package -n python3-kerastroke +Summary: A suite of the generalization-improvement techniques Stroke, Pruning, and NeuroPlast +Provides: python-kerastroke +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-kerastroke +# KeraStroke + +KeraStroke is a [Python package](https://pypi.org/project/kerastroke/#description) that implements +"Post-Back-propagation Weight Operations", or "PBWOs"; generalization-improvement techniques for Keras models in the +form of custom Keras Callbacks. These techniques function +similarly but have different philosophies and results. The techniques are: +- Stroke: Re-initializaing random weight/bias values. +- Pruning: Reducing model size by setting weight/bias values that are close to 0, to 0. +- NeuroPlast: Re-initializing any weight/bias values that are 0 or close to 0. + +Stroke is modeled after seizures, which send random electrical signals throughout the brain, sometimes causing damage +to synapses. + +NeuroPlast is modeled after the concept of neuroplasticity, when neurons that no longer have a primary function begin +to rewire to improve another function. I started working on NeuroPlast after I read the work done by Blakemore and +Cooper on horizontal/vertical line receptor neurons in the brains of cats. + +If you'd like to see the tests I'm performing with KeraStroke, you can view my testing repository +[here](https://github.com/CharlesAverill/stroke-testing). + +KeraStroke 2.0.0 marks when I really started putting work into the project. I've made an effort to comment more, +clean my code up, and make the package easier to understand overall without sacrificing utility. + +# Limitations +KeraStroke is still in the development phase. Heavy testing has been done on +Dense nets, but little testing has been done on CNNs and no testing has been done on RNNs. As of 2.1.0, CNNs are +functioning properly in KeraStroke! The issue with previous versions had to do with the way the callback would retrieve + the weights from the models. The callbacks perform significantly better on DenseNets, but could still find use in CNNs. + I'm working on this, but will definitely need the help. Please see + [the github page](https://github.com/CharlesAverill/kerastroke/) or + [contact me](https://mail.google.com/mail/?view=cm&fs=1&to=charlesaverill20@gmail.com) to contribute to the project. + +# Stroke +The goal of the Stroke callback is to re-initialize weights/biases that have begun to contribute to overfitting. + +Parameters: + + - `set_value`: re-initialized weights will be set to this value, rather than a random one + - `low_bound`: low bound for weight re-initialization + - `high_bound`: high bound for weight re-initialization + - `volatility_ratio`: percentage of weights to be re-initialized + - `cutoff`: number of epochs to perform PBWOs + - `decay`: Every epoch, v_ratio is multiplied by this number. decay can be greater than 1.0, + but v_ratio will never exceed 1.0 + - `do_weights`: perform stroke on weights + - `do_biases`: perform stroke on biases + +# Pruning +The goal of the Pruning callback is to nullify weights/biases that are effectively 0. + +Parameters: + + - `set_value`: The value that pruned weights will be set to + - `min_value`: The lowest value a weight/bias can be to be oeprated on + - `max_value`: The highest value a weight/bias can be to be operated on + - `cutoff`: number of epochs to perform PBWOs + - `do_weights`: perform pruning on weights + - `do_biases`: perform pruning on biases + +# NeuroPlast +The goal of the NeuroPlast callback is to randomly re-initialize weights/biases that are effectively 0. + +Parameters: + + - `set_value`: re-initialized weights will be set to this value, rather than a random one + - `min_value`: lowest value a weight/bias can be to be operated on + - `max_value`: highest value a weight/bias can be to be operated on + - `low_bound`: low bound for weight re-initialization + - `high_bound`: high bound for weight re-initialization + - `cutoff`: number of epochs to perform PBWOs + - `do_weights`: perform neuroplast on weights + - `do_biases`: perform neuroplast on biases + +# Usage +KeraStroke Callbacks can be used like any other custom callback. Here's a basic example: + +```python +from kerastroke import Stroke +model.fit(X, y, + epochs=32, + callbacks=[Stroke()]) +``` + + + + +%package help +Summary: Development documents and examples for kerastroke +Provides: python3-kerastroke-doc +%description help +# KeraStroke + +KeraStroke is a [Python package](https://pypi.org/project/kerastroke/#description) that implements +"Post-Back-propagation Weight Operations", or "PBWOs"; generalization-improvement techniques for Keras models in the +form of custom Keras Callbacks. These techniques function +similarly but have different philosophies and results. The techniques are: +- Stroke: Re-initializaing random weight/bias values. +- Pruning: Reducing model size by setting weight/bias values that are close to 0, to 0. +- NeuroPlast: Re-initializing any weight/bias values that are 0 or close to 0. + +Stroke is modeled after seizures, which send random electrical signals throughout the brain, sometimes causing damage +to synapses. + +NeuroPlast is modeled after the concept of neuroplasticity, when neurons that no longer have a primary function begin +to rewire to improve another function. I started working on NeuroPlast after I read the work done by Blakemore and +Cooper on horizontal/vertical line receptor neurons in the brains of cats. + +If you'd like to see the tests I'm performing with KeraStroke, you can view my testing repository +[here](https://github.com/CharlesAverill/stroke-testing). + +KeraStroke 2.0.0 marks when I really started putting work into the project. I've made an effort to comment more, +clean my code up, and make the package easier to understand overall without sacrificing utility. + +# Limitations +KeraStroke is still in the development phase. Heavy testing has been done on +Dense nets, but little testing has been done on CNNs and no testing has been done on RNNs. As of 2.1.0, CNNs are +functioning properly in KeraStroke! The issue with previous versions had to do with the way the callback would retrieve + the weights from the models. The callbacks perform significantly better on DenseNets, but could still find use in CNNs. + I'm working on this, but will definitely need the help. Please see + [the github page](https://github.com/CharlesAverill/kerastroke/) or + [contact me](https://mail.google.com/mail/?view=cm&fs=1&to=charlesaverill20@gmail.com) to contribute to the project. + +# Stroke +The goal of the Stroke callback is to re-initialize weights/biases that have begun to contribute to overfitting. + +Parameters: + + - `set_value`: re-initialized weights will be set to this value, rather than a random one + - `low_bound`: low bound for weight re-initialization + - `high_bound`: high bound for weight re-initialization + - `volatility_ratio`: percentage of weights to be re-initialized + - `cutoff`: number of epochs to perform PBWOs + - `decay`: Every epoch, v_ratio is multiplied by this number. decay can be greater than 1.0, + but v_ratio will never exceed 1.0 + - `do_weights`: perform stroke on weights + - `do_biases`: perform stroke on biases + +# Pruning +The goal of the Pruning callback is to nullify weights/biases that are effectively 0. + +Parameters: + + - `set_value`: The value that pruned weights will be set to + - `min_value`: The lowest value a weight/bias can be to be oeprated on + - `max_value`: The highest value a weight/bias can be to be operated on + - `cutoff`: number of epochs to perform PBWOs + - `do_weights`: perform pruning on weights + - `do_biases`: perform pruning on biases + +# NeuroPlast +The goal of the NeuroPlast callback is to randomly re-initialize weights/biases that are effectively 0. + +Parameters: + + - `set_value`: re-initialized weights will be set to this value, rather than a random one + - `min_value`: lowest value a weight/bias can be to be operated on + - `max_value`: highest value a weight/bias can be to be operated on + - `low_bound`: low bound for weight re-initialization + - `high_bound`: high bound for weight re-initialization + - `cutoff`: number of epochs to perform PBWOs + - `do_weights`: perform neuroplast on weights + - `do_biases`: perform neuroplast on biases + +# Usage +KeraStroke Callbacks can be used like any other custom callback. Here's a basic example: + +```python +from kerastroke import Stroke +model.fit(X, y, + epochs=32, + callbacks=[Stroke()]) +``` + + + + +%prep +%autosetup -n kerastroke-2.1.1 + +%build +%py3_build + +%install +%py3_install +install -d -m755 %{buildroot}/%{_pkgdocdir} +if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi +if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi +if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi +if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi +pushd %{buildroot} +if [ -d usr/lib ]; then + find usr/lib -type f -printf "\"/%h/%f\"\n" >> filelist.lst +fi +if [ -d usr/lib64 ]; then + find usr/lib64 -type f -printf "\"/%h/%f\"\n" >> filelist.lst +fi +if [ -d usr/bin ]; then + find usr/bin -type f -printf "\"/%h/%f\"\n" >> filelist.lst +fi +if [ -d usr/sbin ]; then + find usr/sbin -type f -printf "\"/%h/%f\"\n" >> filelist.lst +fi +touch doclist.lst +if [ -d usr/share/man ]; then + find usr/share/man -type f -printf "\"/%h/%f.gz\"\n" >> doclist.lst +fi +popd +mv %{buildroot}/filelist.lst . +mv %{buildroot}/doclist.lst . + +%files -n python3-kerastroke -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Tue Jun 20 2023 Python_Bot <Python_Bot@openeuler.org> - 2.1.1-1 +- Package Spec generated |