Defining Descrete Hyperprameters

Sat Apr 09 2022 14:22:03 GMT+0000 (Coordinated Universal Time)

Saved by @wessim

{    
        "learning_rate": normal(10, 3),
        "keep_probability": uniform(0.05, 0.1)
}

The Continuous hyperparameters are specified as a distribution over a continuous range of values: uniform(low, high) - Returns a value uniformly distributed between low and high loguniform(low, high) - Returns a value drawn according to exp(uniform(low, high)) so that the logarithm of the return value is uniformly distributed normal(mu, sigma) - Returns a real value that's normally distributed with mean mu and standard deviation sigma lognormal(mu, sigma) - Returns a value drawn according to exp(normal(mu, sigma)) so that the logarithm of the return value is normally distributed