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@@ -0,0 +1 @@ +/keras-swa-0.1.7.tar.gz diff --git a/python-keras-swa.spec b/python-keras-swa.spec new file mode 100644 index 0000000..a49ce0f --- /dev/null +++ b/python-keras-swa.spec @@ -0,0 +1,582 @@ +%global _empty_manifest_terminate_build 0 +Name: python-keras-swa +Version: 0.1.7 +Release: 1 +Summary: Simple stochastic weight averaging callback for Keras. +License: MIT +URL: https://github.com/simon-larsson/keras-swa +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/29/5d/487813b1983c777eeea282779ca6f9663570b721139034afa310e35de129/keras-swa-0.1.7.tar.gz +BuildArch: noarch + + +%description +# Keras SWA - Stochastic Weight Averaging + +[](https://pypi.python.org/pypi/keras-swa/) +[](https://github.com/simon-larsson/keras-swa/blob/master/LICENSE) + +This is an implemention of SWA for Keras and TF-Keras. + +## Introduction +Stochastic weight averaging (SWA) is build upon the same principle as [snapshot ensembling](https://arxiv.org/abs/1704.00109) and [fast geometric ensembling](https://arxiv.org/abs/1802.10026). The idea is that averaging select stages of training can lead to better models. Where as the two former methods average by sampling and ensembling models, SWA instead average weights. This has been shown to give comparable improvements confined into a single model. + +[](https://raw.githubusercontent.com/simon-larsson/keras-swa/master/swa_illustration.png) + +## Paper + - Title: Averaging Weights Leads to Wider Optima and Better Generalization + - Link: https://arxiv.org/abs/1803.05407 + - Authors: Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov, Dmitry Vetrov, Andrew Gordon Wilson + - Repo: https://github.com/timgaripov/swa (PyTorch) + +## Installation + + pip install keras-swa + +### SWA API + +Keras callback object for SWA. + +### Arguments +**start_epoch** - Starting epoch for SWA. + +**lr_schedule** - Learning rate schedule. `'manual'` , `'constant'` or `'cyclic'`. + +**swa_lr** - Learning rate used when averaging weights. + +**swa_lr2** - Upper bound of learning rate for the cyclic schedule. + +**swa_freq** - Frequency of weight averagining. Used with cyclic schedules. + +**batch_size** - Batch size model is being trained with (only when using batch normalization). + +**verbose** - Verbosity mode, 0 or 1. + +### Batch Normalization +Last epoch will be a forward pass, i.e. have learning rate set to zero, for models with batch normalization. This is due to the fact that batch normalization uses the running mean and variance of it's preceding layer to make a normalization. SWA will offset this normalization by suddenly changing the weights in the end of training. Therefore, it is necessary for the last epoch to be used to reset and recalculate batch normalization running mean and variance for the updated weights. Batch normalization gamma and beta values are preserved. + +**When using manual schedule:** The SWA callback will set learning rate to zero in the last epoch if batch normalization is used. This must not be undone by any external learning rate schedulers for SWA to work properly. + +### Learning Rate Schedules +The default schedule is `'manual'`, allowing the learning rate to be controlled by an external learning rate scheduler or the optimizer. Then SWA will only affect the final weights and the learning rate of the last epoch if batch normalization is used. The schedules for the two predefined, `'constant'` or `'cyclic'` can be observed below. + +[](https://raw.githubusercontent.com/simon-larsson/keras-swa/master/lr_schedules.png) + + +#### Example + +For Tensorflow Keras (with constant LR) +```python +from sklearn.datasets import make_blobs +from tensorflow.keras.utils import to_categorical +from tensorflow.keras.models import Sequential +from tensorflow.keras.layers import Dense +from tensorflow.keras.optimizers import SGD + +from swa.tfkeras import SWA + +# make dataset +X, y = make_blobs(n_samples=1000, + centers=3, + n_features=2, + cluster_std=2, + random_state=2) + +y = to_categorical(y) + +# build model +model = Sequential() +model.add(Dense(50, input_dim=2, activation='relu')) +model.add(Dense(3, activation='softmax')) + +model.compile(loss='categorical_crossentropy', + optimizer=SGD(lr=0.1)) + +epochs = 100 +start_epoch = 75 + +# define swa callback +swa = SWA(start_epoch=start_epoch, + lr_schedule='constant', + swa_lr=0.01, + verbose=1) + +# train +model.fit(X, y, epochs=epochs, verbose=1, callbacks=[swa]) +``` + +Or for Keras (with Cyclic LR) +```python +from sklearn.datasets import make_blobs +from keras.utils import to_categorical +from keras.models import Sequential +from keras.layers import Dense, BatchNormalization +from keras.optimizers import SGD + +from swa.keras import SWA + +# make dataset +X, y = make_blobs(n_samples=1000, + centers=3, + n_features=2, + cluster_std=2, + random_state=2) + +y = to_categorical(y) + +# build model +model = Sequential() +model.add(Dense(50, input_dim=2, activation='relu')) +model.add(BatchNormalization()) +model.add(Dense(3, activation='softmax')) + +model.compile(loss='categorical_crossentropy', + optimizer=SGD(learning_rate=0.1)) + +epochs = 100 +start_epoch = 75 + +# define swa callback +swa = SWA(start_epoch=start_epoch, + lr_schedule='cyclic', + swa_lr=0.001, + swa_lr2=0.003, + swa_freq=3, + batch_size=32, # needed when using batch norm + verbose=1) + +# train +model.fit(X, y, batch_size=32, epochs=epochs, verbose=1, callbacks=[swa]) +``` + +Output +``` +Model uses batch normalization. SWA will require last epoch to be a forward pass and will run with no learning rate +Epoch 1/100 +1000/1000 [==============================] - 1s 547us/sample - loss: 0.5529 +Epoch 2/100 +1000/1000 [==============================] - 0s 160us/sample - loss: 0.4720 +... +Epoch 74/100 +1000/1000 [==============================] - 0s 160us/sample - loss: 0.4249 + +Epoch 00075: starting stochastic weight averaging +Epoch 75/100 +1000/1000 [==============================] - 0s 164us/sample - loss: 0.4357 +Epoch 76/100 +1000/1000 [==============================] - 0s 165us/sample - loss: 0.4209 +... +Epoch 99/100 +1000/1000 [==============================] - 0s 167us/sample - loss: 0.4263 + +Epoch 00100: final model weights set to stochastic weight average + +Epoch 00100: reinitializing batch normalization layers + +Epoch 00100: running forward pass to adjust batch normalization +Epoch 100/100 +1000/1000 [==============================] - 0s 166us/sample - loss: 0.4408 +``` + +### Collaborators + +- [Simon Larsson](https://github.com/simon-larsson "Github") +- [Alex Stoken](https://github.com/alexstoken "Github") + +%package -n python3-keras-swa +Summary: Simple stochastic weight averaging callback for Keras. +Provides: python-keras-swa +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-keras-swa +# Keras SWA - Stochastic Weight Averaging + +[](https://pypi.python.org/pypi/keras-swa/) +[](https://github.com/simon-larsson/keras-swa/blob/master/LICENSE) + +This is an implemention of SWA for Keras and TF-Keras. + +## Introduction +Stochastic weight averaging (SWA) is build upon the same principle as [snapshot ensembling](https://arxiv.org/abs/1704.00109) and [fast geometric ensembling](https://arxiv.org/abs/1802.10026). The idea is that averaging select stages of training can lead to better models. Where as the two former methods average by sampling and ensembling models, SWA instead average weights. This has been shown to give comparable improvements confined into a single model. + +[](https://raw.githubusercontent.com/simon-larsson/keras-swa/master/swa_illustration.png) + +## Paper + - Title: Averaging Weights Leads to Wider Optima and Better Generalization + - Link: https://arxiv.org/abs/1803.05407 + - Authors: Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov, Dmitry Vetrov, Andrew Gordon Wilson + - Repo: https://github.com/timgaripov/swa (PyTorch) + +## Installation + + pip install keras-swa + +### SWA API + +Keras callback object for SWA. + +### Arguments +**start_epoch** - Starting epoch for SWA. + +**lr_schedule** - Learning rate schedule. `'manual'` , `'constant'` or `'cyclic'`. + +**swa_lr** - Learning rate used when averaging weights. + +**swa_lr2** - Upper bound of learning rate for the cyclic schedule. + +**swa_freq** - Frequency of weight averagining. Used with cyclic schedules. + +**batch_size** - Batch size model is being trained with (only when using batch normalization). + +**verbose** - Verbosity mode, 0 or 1. + +### Batch Normalization +Last epoch will be a forward pass, i.e. have learning rate set to zero, for models with batch normalization. This is due to the fact that batch normalization uses the running mean and variance of it's preceding layer to make a normalization. SWA will offset this normalization by suddenly changing the weights in the end of training. Therefore, it is necessary for the last epoch to be used to reset and recalculate batch normalization running mean and variance for the updated weights. Batch normalization gamma and beta values are preserved. + +**When using manual schedule:** The SWA callback will set learning rate to zero in the last epoch if batch normalization is used. This must not be undone by any external learning rate schedulers for SWA to work properly. + +### Learning Rate Schedules +The default schedule is `'manual'`, allowing the learning rate to be controlled by an external learning rate scheduler or the optimizer. Then SWA will only affect the final weights and the learning rate of the last epoch if batch normalization is used. The schedules for the two predefined, `'constant'` or `'cyclic'` can be observed below. + +[](https://raw.githubusercontent.com/simon-larsson/keras-swa/master/lr_schedules.png) + + +#### Example + +For Tensorflow Keras (with constant LR) +```python +from sklearn.datasets import make_blobs +from tensorflow.keras.utils import to_categorical +from tensorflow.keras.models import Sequential +from tensorflow.keras.layers import Dense +from tensorflow.keras.optimizers import SGD + +from swa.tfkeras import SWA + +# make dataset +X, y = make_blobs(n_samples=1000, + centers=3, + n_features=2, + cluster_std=2, + random_state=2) + +y = to_categorical(y) + +# build model +model = Sequential() +model.add(Dense(50, input_dim=2, activation='relu')) +model.add(Dense(3, activation='softmax')) + +model.compile(loss='categorical_crossentropy', + optimizer=SGD(lr=0.1)) + +epochs = 100 +start_epoch = 75 + +# define swa callback +swa = SWA(start_epoch=start_epoch, + lr_schedule='constant', + swa_lr=0.01, + verbose=1) + +# train +model.fit(X, y, epochs=epochs, verbose=1, callbacks=[swa]) +``` + +Or for Keras (with Cyclic LR) +```python +from sklearn.datasets import make_blobs +from keras.utils import to_categorical +from keras.models import Sequential +from keras.layers import Dense, BatchNormalization +from keras.optimizers import SGD + +from swa.keras import SWA + +# make dataset +X, y = make_blobs(n_samples=1000, + centers=3, + n_features=2, + cluster_std=2, + random_state=2) + +y = to_categorical(y) + +# build model +model = Sequential() +model.add(Dense(50, input_dim=2, activation='relu')) +model.add(BatchNormalization()) +model.add(Dense(3, activation='softmax')) + +model.compile(loss='categorical_crossentropy', + optimizer=SGD(learning_rate=0.1)) + +epochs = 100 +start_epoch = 75 + +# define swa callback +swa = SWA(start_epoch=start_epoch, + lr_schedule='cyclic', + swa_lr=0.001, + swa_lr2=0.003, + swa_freq=3, + batch_size=32, # needed when using batch norm + verbose=1) + +# train +model.fit(X, y, batch_size=32, epochs=epochs, verbose=1, callbacks=[swa]) +``` + +Output +``` +Model uses batch normalization. SWA will require last epoch to be a forward pass and will run with no learning rate +Epoch 1/100 +1000/1000 [==============================] - 1s 547us/sample - loss: 0.5529 +Epoch 2/100 +1000/1000 [==============================] - 0s 160us/sample - loss: 0.4720 +... +Epoch 74/100 +1000/1000 [==============================] - 0s 160us/sample - loss: 0.4249 + +Epoch 00075: starting stochastic weight averaging +Epoch 75/100 +1000/1000 [==============================] - 0s 164us/sample - loss: 0.4357 +Epoch 76/100 +1000/1000 [==============================] - 0s 165us/sample - loss: 0.4209 +... +Epoch 99/100 +1000/1000 [==============================] - 0s 167us/sample - loss: 0.4263 + +Epoch 00100: final model weights set to stochastic weight average + +Epoch 00100: reinitializing batch normalization layers + +Epoch 00100: running forward pass to adjust batch normalization +Epoch 100/100 +1000/1000 [==============================] - 0s 166us/sample - loss: 0.4408 +``` + +### Collaborators + +- [Simon Larsson](https://github.com/simon-larsson "Github") +- [Alex Stoken](https://github.com/alexstoken "Github") + +%package help +Summary: Development documents and examples for keras-swa +Provides: python3-keras-swa-doc +%description help +# Keras SWA - Stochastic Weight Averaging + +[](https://pypi.python.org/pypi/keras-swa/) +[](https://github.com/simon-larsson/keras-swa/blob/master/LICENSE) + +This is an implemention of SWA for Keras and TF-Keras. + +## Introduction +Stochastic weight averaging (SWA) is build upon the same principle as [snapshot ensembling](https://arxiv.org/abs/1704.00109) and [fast geometric ensembling](https://arxiv.org/abs/1802.10026). The idea is that averaging select stages of training can lead to better models. Where as the two former methods average by sampling and ensembling models, SWA instead average weights. This has been shown to give comparable improvements confined into a single model. + +[](https://raw.githubusercontent.com/simon-larsson/keras-swa/master/swa_illustration.png) + +## Paper + - Title: Averaging Weights Leads to Wider Optima and Better Generalization + - Link: https://arxiv.org/abs/1803.05407 + - Authors: Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov, Dmitry Vetrov, Andrew Gordon Wilson + - Repo: https://github.com/timgaripov/swa (PyTorch) + +## Installation + + pip install keras-swa + +### SWA API + +Keras callback object for SWA. + +### Arguments +**start_epoch** - Starting epoch for SWA. + +**lr_schedule** - Learning rate schedule. `'manual'` , `'constant'` or `'cyclic'`. + +**swa_lr** - Learning rate used when averaging weights. + +**swa_lr2** - Upper bound of learning rate for the cyclic schedule. + +**swa_freq** - Frequency of weight averagining. Used with cyclic schedules. + +**batch_size** - Batch size model is being trained with (only when using batch normalization). + +**verbose** - Verbosity mode, 0 or 1. + +### Batch Normalization +Last epoch will be a forward pass, i.e. have learning rate set to zero, for models with batch normalization. This is due to the fact that batch normalization uses the running mean and variance of it's preceding layer to make a normalization. SWA will offset this normalization by suddenly changing the weights in the end of training. Therefore, it is necessary for the last epoch to be used to reset and recalculate batch normalization running mean and variance for the updated weights. Batch normalization gamma and beta values are preserved. + +**When using manual schedule:** The SWA callback will set learning rate to zero in the last epoch if batch normalization is used. This must not be undone by any external learning rate schedulers for SWA to work properly. + +### Learning Rate Schedules +The default schedule is `'manual'`, allowing the learning rate to be controlled by an external learning rate scheduler or the optimizer. Then SWA will only affect the final weights and the learning rate of the last epoch if batch normalization is used. The schedules for the two predefined, `'constant'` or `'cyclic'` can be observed below. + +[](https://raw.githubusercontent.com/simon-larsson/keras-swa/master/lr_schedules.png) + + +#### Example + +For Tensorflow Keras (with constant LR) +```python +from sklearn.datasets import make_blobs +from tensorflow.keras.utils import to_categorical +from tensorflow.keras.models import Sequential +from tensorflow.keras.layers import Dense +from tensorflow.keras.optimizers import SGD + +from swa.tfkeras import SWA + +# make dataset +X, y = make_blobs(n_samples=1000, + centers=3, + n_features=2, + cluster_std=2, + random_state=2) + +y = to_categorical(y) + +# build model +model = Sequential() +model.add(Dense(50, input_dim=2, activation='relu')) +model.add(Dense(3, activation='softmax')) + +model.compile(loss='categorical_crossentropy', + optimizer=SGD(lr=0.1)) + +epochs = 100 +start_epoch = 75 + +# define swa callback +swa = SWA(start_epoch=start_epoch, + lr_schedule='constant', + swa_lr=0.01, + verbose=1) + +# train +model.fit(X, y, epochs=epochs, verbose=1, callbacks=[swa]) +``` + +Or for Keras (with Cyclic LR) +```python +from sklearn.datasets import make_blobs +from keras.utils import to_categorical +from keras.models import Sequential +from keras.layers import Dense, BatchNormalization +from keras.optimizers import SGD + +from swa.keras import SWA + +# make dataset +X, y = make_blobs(n_samples=1000, + centers=3, + n_features=2, + cluster_std=2, + random_state=2) + +y = to_categorical(y) + +# build model +model = Sequential() +model.add(Dense(50, input_dim=2, activation='relu')) +model.add(BatchNormalization()) +model.add(Dense(3, activation='softmax')) + +model.compile(loss='categorical_crossentropy', + optimizer=SGD(learning_rate=0.1)) + +epochs = 100 +start_epoch = 75 + +# define swa callback +swa = SWA(start_epoch=start_epoch, + lr_schedule='cyclic', + swa_lr=0.001, + swa_lr2=0.003, + swa_freq=3, + batch_size=32, # needed when using batch norm + verbose=1) + +# train +model.fit(X, y, batch_size=32, epochs=epochs, verbose=1, callbacks=[swa]) +``` + +Output +``` +Model uses batch normalization. SWA will require last epoch to be a forward pass and will run with no learning rate +Epoch 1/100 +1000/1000 [==============================] - 1s 547us/sample - loss: 0.5529 +Epoch 2/100 +1000/1000 [==============================] - 0s 160us/sample - loss: 0.4720 +... +Epoch 74/100 +1000/1000 [==============================] - 0s 160us/sample - loss: 0.4249 + +Epoch 00075: starting stochastic weight averaging +Epoch 75/100 +1000/1000 [==============================] - 0s 164us/sample - loss: 0.4357 +Epoch 76/100 +1000/1000 [==============================] - 0s 165us/sample - loss: 0.4209 +... +Epoch 99/100 +1000/1000 [==============================] - 0s 167us/sample - loss: 0.4263 + +Epoch 00100: final model weights set to stochastic weight average + +Epoch 00100: reinitializing batch normalization layers + +Epoch 00100: running forward pass to adjust batch normalization +Epoch 100/100 +1000/1000 [==============================] - 0s 166us/sample - loss: 0.4408 +``` + +### Collaborators + +- [Simon Larsson](https://github.com/simon-larsson "Github") +- [Alex Stoken](https://github.com/alexstoken "Github") + +%prep +%autosetup -n keras-swa-0.1.7 + +%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-keras-swa -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 0.1.7-1 +- Package Spec generated @@ -0,0 +1 @@ +42a54964ba532105a4e708be09cebc2d keras-swa-0.1.7.tar.gz |