Simple stochastic weight averaging callback for Keras.
Keras SWA - Stochastic Weight Averaging
This is an implemention of SWA for Keras and TF-Keras.
Stochastic weight averaging (SWA) is build upon the same principle as snapshot ensembling and fast geometric ensembling. 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.
- 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)
pip install keras-swa
Keras callback object for SWA.
start_epoch - Starting epoch for SWA.
lr_schedule - Learning rate schedule.
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. Only needed in the Keras API when using both batch normalization and a data generator.
verbose - Verbosity mode, 0 or 1.
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,
'cyclic' can be observed below.
For Keras (with constant LR)
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(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 in Tensorflow (with Cyclic LR)
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='cyclic', swa_lr=0.001, swa_lr2=0.003, swa_freq=3, verbose=1) # train model.fit(X, y, epochs=epochs, verbose=1, callbacks=[swa])
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
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