minimal_talos_example.py

import talos as ta
from keras.models import Sequential
from keras.layers import Dense

def minimal():

    x, y = ta.templates.datasets.iris()

    p = {'activation':['relu', 'elu'],
         'optimizer': ['Nadam', 'Adam'],
         'losses': ['logcosh'],
         'hidden_layers':[0, 1, 2],
         'batch_size': [20,30,40],
         'epochs': [10,20]}

    def iris_model(x_train, y_train, x_val, y_val, params):

        model = Sequential()
        model.add(Dense(32, input_dim=4, activation=params['activation']))
        model.add(Dense(3, activation='softmax'))
        model.compile(optimizer=params['optimizer'], loss=params['losses'])

        out = model.fit(x_train, y_train,
                         batch_size=params['batch_size'],
                         epochs=params['epochs'],
                         validation_data=[x_val, y_val],
                         verbose=0)

        return out, model

    scan_object = ta.Scan(x, y, params=p, model=iris_model, experiment_name="minimal_iris", fraction_limit=0.1)

    return scan_object