def neural_net_helper(data,
label=None,
eta=0.01,
iterations=100,
layer_structure=[],
regression=False,
tuning=False):
if label == None:
label = class_label
f = 5 # fold-value
tune = data['tune']
perf = []
start_time = time.time()
for i in range(f):
# set variable to determine if extra printing is required for network
# since we're doing cross-validation, print for first fold only
print_on = False
all_folds = data['folds'].copy()
if not tuning:
holdout = all_folds[i]
else:
holdout = tune
folds = all_folds
folds.pop(i)
training = pd.concat(folds)
nn = NeuralNet(training, label, eta, iterations, layer_structure,
regression, print_on)
nn.build()
result = nn.test(holdout)
if print_on:
nn.pretty_print()
perf.append(result)
elapsed = time.time() - start_time
print('\n======== avg. summary of performance ========')
if regression:
print_helper_regressor(perf, f)
else:
print_helper_classifier(perf, f)
print('total execution time:\t{:.2f}s'.format(elapsed))
