if len(unique_row) == 0 or len(unique_col) == 0:
print("\n Finished! \n")
break
for index, row in increase_res.iterrows():
if row['row'] in unique_row and row['col'] in unique_col and row[
'row'] not in unique_col:
hidden_num -= 1
new_net = reduced_ann_net(old_net, int(row['row']),
int(row['col']), hidden_num)
print(
"\n======= Net hidden size: {}==========\n".format(hidden_num))
start_time = time.time()
acc, pred = test_model(new_net, x_test, y_test)
stop_time = time.time()
print("Execution time: %s ms" % ((stop_time - start_time) * 1000))
times.append((stop_time - start_time) * 1000)
mat = confusion(x_test.size(0), 3, pred, y_test)
F1_score(mat)
# Save the new network and evaluate its vector angle.
nets.append(new_net)
old_net = new_net
saveNNParas(new_net, x_test, hidden_num)
vectors = pd.read_excel('vector_angle.xls', header=None)
if (vectors.empty):
cnt = 10
#
#normalized = min_max_norm(raw_data, 14)
#normalized.to_excel('music-features-processed.xlsx')
X_train, Y_train, X_test, Y_test = load_data(
'music-affect_v1/music-features-processed.xlsx',
features_num,
label_loc,
features_selector=selector,
spliting_ratio=0.8)
net = Net(features_num, hidden_num, classes_num)
train_model(net, X_train, Y_train, lr=learning_rate, epochs=epochs_num)
start_time = time.time()
accuracy, Y_pred = test_model(net, X_test, Y_test)
print("Execution time: %s ms" % ((time.time() - start_time) * 1000))
#Save relevant parameter for analysis.
if accuracy > 40:
saveNNParas(net, X_test, hidden_num)
torch.save(net.state_dict(), 'net_model.pt')
saveDataset(X_train, Y_train, 'training')
saveDataset(X_test, Y_test, 'testing')
mat = confusion(X_test.size(0), classes_num, Y_pred, Y_test)
print("Confusion Matrix：")
print(mat)
F1_score(mat)
print("\n========================== END ==================================")