def OptNN(d1, h1, d2, h2, d3, start, stop, max_epochs):
params2 = params.copy()
on_epoch = [AdjustVariable('update_learning_rate',
start = start, stop = stop),
AdjustVariable('update_momentum', start = .9, stop = .999)]
params2['dropout1_p'] = d1
params2['dropout2_p'] = d2
params2['dropout3_p'] = d3
params2['dropout4_p'] = d4
params2['hidden1_num_units'] = h1
params2['hidden2_num_units'] = h2
params2['hidden3_num_units'] = h3
params2['max_epochs'] = max_epochs
params2['on_epoch_finished'] = on_epoch
kcv = StratifiedKFold(Y, 5, shuffle = True)
res = np.empty((len(Y), len(np.unique(Y)))); i = 1
CVScores = []
for train_idx, valid_idx in kcv:
logger.info("Running fold %d...", i); i += 1
net = NeuralNet(**params2)
net.set_params(eval_size = None)
net.fit(X[train_idx], Y[train_idx])
res[valid_idx, :] = net.predict_proba(X[valid_idx])
CVScores.append(log_loss(Y[valid_idx], res[valid_idx]))
return -np.mean(CVScores)
def OptNN2(d0, d1,d2, d3, h1, h2, h3, me, ls, le):
h1, h2, h3 = int(h1), int(h2), int(h3);
me = int(me)
params = dict(
layers = [
('input', layers.InputLayer),
('dropout1', layers.DropoutLayer),
('hidden1', layers.DenseLayer),
('dropout2', layers.DropoutLayer),
('hidden2', layers.DenseLayer),
('dropout3', layers.DropoutLayer),
('hidden3', layers.DenseLayer),
('dropout4', layers.DropoutLayer),
('output', layers.DenseLayer),
],
input_shape = (None, 93),
dropout1_p = d0,
hidden1_num_units = h1,
dropout2_p = d1,
hidden2_num_units = h2,
dropout3_p = d2,
hidden3_num_units = h3,
dropout4_p = d3,
output_nonlinearity = softmax,
output_num_units = 9,
update = nesterov_momentum,
update_learning_rate = theano.shared(float32(l_start)),
update_momentum = theano.shared(float32(m_start)),
regression = False,
on_epoch_finished = [
AdjustVariable('update_learning_rate', start = ls,
stop = le, is_log = True),
AdjustVariable('update_momentum', start = m_start,
stop = m_stop, is_log = False),
],
max_epochs = me,
verbose = 1,
)
CVScores = []
res = np.empty((len(Y), len(np.unique(Y))))
kcv = StratifiedKFold(Y, 5, shuffle = True); i = 1
for train_idx, valid_idx in kcv:
logger.info("Running fold %d...", i); i += 1
net = NeuralNet(**params)
net.set_params(eval_size = None)
net.fit(X[train_idx], Y[train_idx])
res[valid_idx, :] = net.predict_proba(X[valid_idx])
CVScores.append(log_loss(Y[valid_idx], res[valid_idx]))
return -np.mean(CVScores)
