I have some questions about the sequential model from Keras. Please let me know if there is anything wrong with how I presented my questions and I will be more than happy to fix it to make it align with community guidelines.
I am working with a data set of about 6000 points. I have three input variables (all real numbers) and I have one output (also a real number). It is essentially a regression problem.
- I found here that the sequential model is not a good idea to use if you have multiple inputs. Is this true? I also found many examples of people using this network for multiple input values so I'm confused.
- My three data inputs represent three different parameters for a structure design. This is considered sequential data is that correct?
The current code I have is giving me rather good $R^2$ values (~.85) so I am wondering if this is a fluke or if what I have done is valid even though I have a multi-dimensional input.
This is more of a conceptual question but in case it helps, here is the code I wrote:
model = Sequential() model.add(Dense(3, input_shape=(3,), activation='relu')) model.add(Dense(13, activation='relu'))#hiddn layer 1 model.add(Dense(9, activation='relu'))#hidden layer 2 model.add(Dense(9, activation='relu'))#hidden layer 3 model.add(Dense(9, activation='relu'))#hidden layer 4 model.add(Dense(1,)) model.compile(Adam(learning_rate = 0.001), 'mean_squared_error') earlystopper = EarlyStopping(monitor='val_loss', min_delta=0, patience=15, verbose=1, mode='auto') model.fit(X_trainL, y_trainL, epochs = 5000, validation_split = 0.1,shuffle = True, verbose = 0,callbacks=[earlystopper])