why the sigmoid function will be 1 and 0 if we use a fully connected layer that produce a big enough positive(res negative )output I am using a fully connected neural network that uses sigmoid activation function. If we feed a big enough weights the sigmoid function will finally become 1 or 0, is there any solution to avoid this?
Will this lead to classical sigmoid problems vanishing gradient or exploding gradient?
In general it's better to not use sigmoid function in any hidden layer. There are many other great options such as ReLU and ELU. However, if for any reason you have to use sigmoid-like function, then go with Tanh function, at least it has ~0 mean.