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.

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