Switchable Normalization is a normalization technique that is able to learn different normalization operations for different normalization layers in a deep neural network in an end-to-end manner.
Keras port of the implementation of the paper Differentiable Learning-to-Normalize via Switchable Normalization.
Code ported from the switchnorm official repository.
Note
This only implements the moving average version of batch normalization component from the paper. The batch average technique cannot be easily implemented in Keras as a layer, and therefore it is not supported.
Simply import switchnorm.py and replace BatchNormalization layer with this layer.
from switchnorm import SwitchNormalization
ip = Input(...)
...
x = SwitchNormalization(axis=-1)(x)
...An example script for CIFAR 10 is provided in cifar.py, which trains a toy model for 25 epochs. It obtains an accuracy of 87%.
Below is the importance weights of the toy model trained on SwitchNormalization with momentum of 0.98.
Currently, when training this toy model, near the 22-25th epoch, training abruptly fluctuates and accuracy plummets when using a momentum of 0.99 or 0.997.
This is seen in Figure 8 from the paper, that Batch Normalization moving average is highly unstable.
Therefore the default momentum has been set to 0.98, which offers stability at the loss of performance. Higher momentum is recommended if using check pointing to restart training from best previous weights.
The tests require pytest to be installed and can be run using pytest . at the root of the directory. These tests are adapted from the Batch Normalization tests in Keras.
- Keras 2.1.6+
- Either Tensorflow, Theano or CNTK.