I'm trying to develop a multi regression model to predict energy consumption during one day period. X-set dimension is (10178, 52) and consist of 52-feature and Y-set dimension is (10178, 48) as output. I have used the following code:
xtrain, xtest, ytrain, ytest=train_test_split(X, Y, test_size=0.1) in_dim = X.shape out_dim = Y.shape model = Sequential() model.add(Dense(48*4, input_dim=in_dim, activation="relu")) model.add(Dense(86, activation="relu")) model.add(Dense(out_dim)) model.summary() model.compile(loss="mean_absolute_error", optimizer="adam") model.fit(xtrain,ytrain, epochs=100, batch_size=12,)
after compiling my model although my model's loss is very low but when I visualize my output the result is unsatisfying as follow:
any idea what I'm doing wrong?!? my initial guess is that since output dimension is high(48-dimension) compared to input dimension I need a lot more Data. or maybe I'm using wrong loss function or the model is too shallow. also it is noticeable that model's output at spark point is very poor.