Returns:
The neuron activation based on the summed output.
"""
return z if z >= 0 else 0.01 * z
if __name__ == '__main__':
# if executed from automation script
if len(argv) == 3:
AUTO = bool(int(argv[2]))
else:
AUTO = False
MSE, TRP, TEP = [], [], [] # set up variables to store testing data
# load data to train and test network on
TRAIN, TEST = io.load_data(f'../data/{argv[1]}.csv', par.get_holdout())
# network-specific parameters
FEATURES = len(TRAIN[0][:-1]) # number of attributes of data
CLASSES = len({c[-1] for c in TRAIN + TEST}) # distinct classifications
HIDDEN_SIZE = par.get_hidden_size(argv[1])
DIMENSIONS = (HIDDEN_SIZE * (FEATURES + 1)) + (CLASSES * (HIDDEN_SIZE + 1))
EPOCHS, AXIS_RANGE = par.get_epochs(), par.get_rand_range()
# de-specific parameters
POP_SIZE = par.get_de_population_size()
CROSS_RATE, DIFF_WEIGHT = par.get_de_params(argv[1])
# run the de-nn
differential_evolution(DIMENSIONS, EPOCHS, POP_SIZE, AXIS_RANGE, \
CROSS_RATE, DIFF_WEIGHT)
if not AUTO:
io.plot_data(EPOCHS, MSE, TRP, TEP)
exit(0)
z : summing output.
Returns:
The differential of the neural output.
"""
return z * (1 - z)
if __name__ == '__main__':
# if executed from automation script
if len(argv) == 3:
AUTO = bool(int(argv[2]))
else:
AUTO = False
TRAIN, TEST = io.load_data(f'../data/{argv[1]}.csv')
FEATURES = len(TRAIN[0][:-1])
CLASSES = len({c[-1] for c in TRAIN + TEST})
HIDDEN_SIZE = par.get_hidden_size(argv[1])
DIMENSIONS = (HIDDEN_SIZE * (FEATURES+1)) + \
(CLASSES * (HIDDEN_SIZE+1))
WEIGHTS = [random.uniform(par.get_rand_range()[0], par.get_rand_range()[1])\
for _ in range(DIMENSIONS)]
NETWORK = net.initialize_network(WEIGHTS, FEATURES, HIDDEN_SIZE, CLASSES)
LEARNING_RATE, MOMENTUM_RATE = par.get_bp_params(argv[1])
EPOCHS = par.get_epochs()
MSE, TRP, TEP = [], [], []
stochastic_gradient_descent(NETWORK, CLASSES, TRAIN)
if not AUTO:
io.plot_data(EPOCHS, MSE, TRP, TEP)
exit(0)