keras_gradient_noise

Simple way to add gradient noise to any Keras optimizer.

Install via: pip install keras_gradient_noise

Gradient Noise

Introduced by "Adding Gradient Noise Improves Learning for Very Deep Networks" (Neelakantan et al 2015), the idea is to add a bit of decaying Gaussian noise to your gradients before each update step. This is shown to reduce overfitting and training loss.

Equation 1 of the paper defines two parameters for the method:

• η defines the total amount of noise (recommended to be one of {0.01, 0.3, 1.0})
• γ defines the decay rate of the noise (recommended to be 0.55)

How to use in your code

Simply wrap your optimizer class with the provided add_gradient_noise() function:

from keras.optimizers import Adam
from keras_gradient_noise import add_gradient_noise

# ...

NoisyAdam = add_gradient_noise(Adam)

model.compile(optimizer=NoisyAdam())

Note the use of brackets. add_gradient_noise() expects a Keras-compatible optimizer class, not an instance of one.

You can adjust the two parameters η and γ via initialization arguments. They have the following default values:

NoisyOptimizer(noise_eta=0.3, noise_gamma=0.55)

Feedback, contributions, etc.

Please don't hesitate to reach out via GitHub issues or a quick email! Thanks!