In a Dense NN,
- Input to a Layer depends on the output of the previous layer and its Neuron count
- Previous Layer for the first layer is the Input Data Features
- Output of a Layer is equal to the Layer's neuron count. A copy of each goes to all the Neuron of the Next Layer
It means for your case,
- First Layer will have *M(Input features)3 inputs going in i.e. M to each Neuron and 3 coming out.
- The Second layer will have *3(Previous Layer)5 inputs going in and 5 coming out
- The Last layer will have 5(Previous Layer)*2 inputs going in and 2 coming out
A Neural Network is a complex Tensor operation. Arrow and Circle are logical representations. Each arrow got a weight and each Circle got the Activation function and the Bias term. So, you may count this way too.
Validate this with Keras
Model.summary result which shows the parameter count.