Metadata-Version: 2.1
Name: keras-swa
Version: 0.0.6
Summary: Simple stochastic weight averaging callback for Keras.
Home-page: https://github.com/simon-larsson/keras-swa
Author: Simon Larsson
Author-email: simonlarsson0@gmail.com
License: MIT
Description: # Keras SWA - Stochastic Weight Averaging
        
        [![PyPI version](https://badge.fury.io/py/keras-swa.svg)](https://pypi.python.org/pypi/keras-swa/) 
        [![License](https://img.shields.io/badge/license-MIT-blue.svg)](https://github.com/simon-larsson/keras-swa/blob/master/LICENSE)
        
        This is an implemention of SWA for Keras and TF-Keras. It currently only implements the constant learning rate scheduler, the cyclic learning rate described in the paper will come soon.
        
        ## 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.
        
        [![Illustration](https://raw.githubusercontent.com/simon-larsson/keras-swa/master/swa_illustration.png)](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
        
        
        ## 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 for the updated weights.
        
        ### SWA
        
        Keras callback object for SWA.  
        
        #### Arguments
        **start_epoch** - Starting epoch for SWA.
        
        **lr_schedule** - Learning rate scheduler (optional),  `'constant'` for the non-cyclic scheduler from the paper.
        
        **swa_lr** - Minimum learning rate for scheduler.
        
        **batch_size** - Batch size (Keras API only, automatic in TF-Keras).
        
        **verbose** - Verbosity mode, 0 or 1.
        
        #### Example
        
        For Keras
        ```python
        from sklearn.datasets.samples_generator import make_blobs
        from keras.utils import to_categorical
        from keras.models import Sequential
        from keras.layers import Dense
        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(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='constant', 
                  swa_lr=0.01, 
                  verbose=1)
        
        # train
        model.fit(X, y, epochs=epochs, verbose=1, callbacks=[swa])
        ```
        
        Or for Keras in Tensorflow
        
        ```python
        from sklearn.datasets.samples_generator 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(learning_rate=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])
        ```
        
        Output
        ```
        Epoch 1/100
        1000/1000 [==============================] - 1s 703us/step - loss: 0.7518
        Epoch 2/100
        1000/1000 [==============================] - 0s 47us/step - loss: 0.5997
        ...
        Epoch 74/100
        1000/1000 [==============================] - 0s 31us/step - loss: 0.3913
        Epoch 75/100
        Epoch 00075: starting stochastic weight averaging
        1000/1000 [==============================] - 0s 202us/step - loss: 0.3907
        Epoch 76/100
        1000/1000 [==============================] - 0s 47us/step - loss: 0.3911
        ...
        Epoch 99/100
        1000/1000 [==============================] - 0s 31us/step - loss: 0.3910
        Epoch 100/100
        1000/1000 [==============================] - 0s 47us/step - loss: 0.3905
        
        Epoch 00100: final model weights set to stochastic weight average
        ```
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Topic :: Scientific/Engineering
Description-Content-Type: text/markdown
