Hello,

I was working on C2_W2_Relu Optional Lab and I found that when I change w and b values for relu activation function it only changes the positions of the linear-pieces but they are still linear-pieces. So for bigger neural network lets assume that we have data that directly fits to -x^2 and we have relu activation function in hiden layers, linear activation function in the output layer. And lets assume that our input values in our dataset is vary from -100 to 100. My question is that for predicting really large values or really small values will neural network perform linear behavior instead of parabolic behavior? Because I think that small linear-pieces will fit to data and will output similar values to -x^2 for some values but for big or small values for x there will be only one linear-piece left to take decision and it will perform linear behavior. The idea can be seen more clearly in the image below.