If you use a squashing function on the output, then yes, it is impossible to get a result of 100 at an output. If you need to have outputs outside [0,1] or [-1,1], which are typical ranges for many squashing functions, I suggest using a linear transfer function on the output (or a rectified linear unit).
As for your main question, here is an example of how to calculate outputs manually if you have trained weights and biases. Suppose you had an input x that is 100-by-1 and 1000 hidden layer neurons (so a weight matrix w1 that is 100-by-1000 and bias b1 that is 1000-by-1).
Then, the input to the hidden layer is
z1 = w1'*x+b1;
and the output of the hidden layer is
h1 = f(z1); %where f is the hidden activation function (e.g. logistic, tanh, ReLU)
Next, if you have a single neuron in the output layer, you would have a second weight matrix w2 that is 1000-by-1 and a scalar bias b2. The output to the whole network is then given by
z2 = w2'*h+b2;
h2 = g(z2); %where g is the output activation function, not necessarily the same as f()
Hope this helps!
