Sisy_^phus

Install sisy:

Sisy is still in development, to install it you would need to clone the repository and at the sisy/-root install it locally as a pip package wtih pip install -e . See https://pip.pypa.io/en/stable/reference/pip_install/#install-editable

Using Minos to do the heavy lifting, Sisy uses genetic algorithms to find the best topology and hyper parameters for your keras neural networks.

Examples

The examples directory tries to mimic the keras examples with added parameter searches.

This is based on reuters_mlp.py from keras examples.

          # Our Input size is the number of words in our reuters data we want to examine
layout = [('Input', {'units': 10000}),
          # 'units' : a range to try for the number of inputs 
          # 'activation' we specify a list of the activation types we want to try
          ('Dense', {'units': range(400, 600), 'activation': ['relu','tanh']}),
          # 'rate' is a f(loat)range from 0.2 to 0.8 , forced into a list
          ('Dropout', {'rate': list(frange(0.2,0.8))}),
          ('Output', {'units': 42, 'activation': 'softmax'})]

run_sisy_experiment(layout, 'sisy_reuters_mlp', (x_train, y_train), (x_test, y_test),
                    optimizer='adam',
                    metric='acc',
                    epochs=10,
                    batch_size=32,
                    n_jobs=8,
                    # 'devices' : Lets run this on the gpus 0 and 1
                    devices=['/gpu:0','/gpu:1'],
                    # 'population_size' : The number of different blueprints to try per generation.
                    population_size=10,
                    # 'generations' : The number of times to evolve the population
                    # ( evolving here means taking the best blueprints and
                    # combining them to create ${population_size} more new blueprints)
                    generations=10,
                    loss='categorical_crossentropy',
                    # 'shuffle' : Defaults to true
                    shuffle=False)

Which will produce log files that are viewable with the UI.

python -m sisy

Will open Sisy log viewer in your browser

The end result is an optimal network you can load with sisy_load_model("your_experiment_label")