I am struggling to understand why i am getting such a high loss/val_loss rate on my training. I am training a regression network. I've normalized the input data to range between -1 to 1, and left the output data unaltered, its range is approx. between  -100 to 100. I chose to normalize the input such that i could use tanh, as activation function as it outputs within this range. The neural network consist of 3 layers.   print Model definition!    model = Sequential()    #act = PReLU(init='normal', weights=None)    model.add(Dense(output_dim=400,input_dim=400, init=normal,activation=K.tanh))    #act1 = PReLU(init='normal', weights=None)    model.add(Dense(output_dim=400,input_dim=400, init=normal,activation=K.tanh))    #act2 = PReLU(init='normal', weights=None)    #model.add(Dense(output_dim=400, input_dim=400, init=normal,activation=K.tanh))    act4=ELU(100)    model.add(Dense(output_dim=13, input_dim=400, init=normal,activation=act4))The mapping between the input and output consist of mapping audio samples into MFCC features.  The samples are the ones i've normalized to the described range. Why am i getting these results?.. am I making anything that is unclear?Normalizing the output_range +-1:Why is the val_loss loss lower than the loss?
Why is my loss so high?
neural network;hyperparameter;loss function