# Restricting the output of a model didn't improve the loss value of the model evaluation

There is a deep model for prediction.

The outputs are some numbers between 0 and 80. (In the dataset the outputs are 0-80)

The model Loss value is 70 and I would like to reduce it.

I printed the outputs after evaluating the model by test values and some of the predicted values are more than 80 or less than 0.

I decided to set up the final layer to predict just in 0-80 in the training step, therefore I set a lambda layer after final Dense layer to clip output values.

The codes:

def relu_advanced(x):
return K.relu(x, max_value=80)

def createModel4():
model = models.Sequential()
activation='relu',

model.summary()
return model


I tested the model with and without the relu_advanced and unfortunately, the Loss value is increased with advanced_relu!

While there is no value much than 80 or less than zero, I don't know what may happen that the Loss is increased?

Thank you