# Effect of large activations of hidden layers

The example is trying to predict wether coffe is well roasted or badly. 1 is good roasted and 0 is bad. The architecture is:

Now I try to visualize the model. Unit 1 has higher values when the duration of roasting is too little. Unit 2 has higher values for bad combinations of temperature and time. The blue-hatched regions show the layer outputted higher activations for the respective unit.

I used a threshold of 0.5 So I can conclude higher activations from layer 1 give an output class of 0. But as I used the sigmoid activation function I thought that large values give an output class of 1. And very small values give a 0, due to the S-shape.

• Can you please highlight the question? Now sure what to answer. Jul 14, 2023 at 17:27
• Maybe you have negative weights in your output layer. What are the 3 weights that compute a2 from a1 ? Jul 14, 2023 at 18:37
• if it's a 2 layer network or more, you cannot reason anymore like this Jul 14, 2023 at 19:56

The sigmoid activation is defined as follows: $$\sigma(x)=\frac{1}{1+e^{-x}}$$, this means that for $$x\to\infty$$ the activation saturates to one whereas for $$x\to-\infty$$ it saturates to zero.
The output layer can sum these activation and rescale such that a further sigmoid, returns values close to $$1$$ for points that fall outside the triangle.