# Why are the values of my Y predicted the same and R-Squared Negative in SupervisedDBNRegression, Neural Networks

My model is not outputting the results I expected. I don't quite know my way around ANN. After learning how to use SupervisedDBNClassification from https://github.com/albertbup/deep-belief-network I decided to try SupervisedDBNRegression using the dataset located here https://raw.githubusercontent.com/Honeson/ANN-qos_qoe/main/QoS/qosqoeNew2. During pre-training stage, I get errors less than 0.05 but get losses up to 1468356 during the fine-tuning stage. My outputs are usually the same exact value. I tried changing the learning_rate, normalizing the date, e.t.c, according to the solution here Neural Network Always Produces Same/Similar Outputs for Any Input but to no avail, I keep getting from nan outputs, to all outputs being the same or in classes like in classification problem. A portion of the code to see what I have tried is here.

X = df[columns]
Y = df['StartUpDelay'].values

# Splitting data
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=1337)

# Data scaling
min_max_scaler = MinMaxScaler()
X_train = min_max_scaler.fit_transform(X_train)

# Training
regressor = SupervisedDBNRegression(hidden_layers_structure=[100],
learning_rate_rbm=0.05,
learning_rate=0.1,
n_epochs_rbm=20,
n_iter_backprop=100,
batch_size=16,
activation_function='relu')

regressor.fit(X_train, Y_train)

# Test
X_test = min_max_scaler.transform(X_test)
Y_pred = regressor.predict(X_test)
print(Y_pred)
print('Done.\nR-squared: %f\nMSE: %f' % (r2_score(Y_test, Y_pred), mean_squared_error(Y_test, Y_pred)))


I expect to get Y_pred such as 1002, 1653, 2764, 1121, 2112, 1452, 1324, 1331, etc but I rather got values like nan, nan, nan, ..., nan or values like 1456, 1456, 1456, 1456, ..., 1456, or values like 1111, 1222, 1333, 1111, 1222, 1333, ..., 1111, 1222, 1333.

I will appreciate detailed help as I don't quite know ANN very well. Thanks in advance.

Edit 1: I changed the learning rate again from 0.01 to 0.00001 and I got different values for the Y Predicted but quite bigger than expected result. I also got absurd metrics as

R-squared: -37862.875097 MSE: 56730616135.090736