I have just been testing out some machine learning as I am new to it and have a simple dataset currently 500k rows the target value is literally the sum of each row. I was using model = Sequential()model.add(Dense(4, input_dim=4, activation='ELU'))model.add(Dense(1, activation='linear'))# Compile the modelmodel.compile(loss='mean_squared_error', optimizer='adam') model.fit(X, y, epochs=4000, verbose=1, batch_size=120). I then fed the model some unseen data to see if it could make the new calculations again literally just sum up the values.
22000 - 11000 - 6000 - 1500 should equal 3500 but instead, i got 3499.9915. The results for the other new unseen data was all similar I was wondering how i can fix this I know ai models need a lot of data but surely for something so trivial I would have expected it to get the values perfectly correct. My long term goal is to build data validation through calculations which is why i am practicing such a basic model.
tradegreek t1_ja336tf wrote
Reply to [D] Simple Questions Thread by AutoModerator
I have just been testing out some machine learning as I am new to it and have a simple dataset currently 500k rows the target value is literally the sum of each row. I was using model = Sequential()model.add(Dense(4, input_dim=4, activation='ELU'))model.add(Dense(1, activation='linear'))# Compile the modelmodel.compile(loss='mean_squared_error', optimizer='adam') model.fit(X, y, epochs=4000, verbose=1, batch_size=120). I then fed the model some unseen data to see if it could make the new calculations again literally just sum up the values.
22000 - 11000 - 6000 - 1500 should equal 3500 but instead, i got 3499.9915. The results for the other new unseen data was all similar I was wondering how i can fix this I know ai models need a lot of data but surely for something so trivial I would have expected it to get the values perfectly correct. My long term goal is to build data validation through calculations which is why i am practicing such a basic model.