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I am thinking of an application where I want to teach a Neural Network to adjust some parameters on another device for a specific purpose. I say "adjust some parameters" because I need the output to "turn the knobs" of something else, not only binary "push buttons"

What kind of neural network do I need? is it a specific architecture?

The NNs I have used so far were sequential and outputting vectors like [0] or [1] and classifiers with outputs like [1,0,0,0] [0,1,0,0] [0,0,1,0] [0,0,0,1] for example

But now I need outputs like [0.25, 0.99, 0.14, 0.54],

  • 0.25 for knob 1
  • 0.99 for knob 2
  • 0.14 for knob 3
  • 0.54 for knob 4

Thank you for your answers

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  • $\begingroup$ You should probably be implementing a multi-layered perceptron with 2 or more hidden layers and one output layer with identity as the activation function. The error can be calculated using least-mean squares. You can do it using scikit-learn library of Python. See this link. $\endgroup$ – kiner_shah Dec 13 '17 at 15:17
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This is a regression problem instead of a classification problem. For both, you can use a feedforward neural network. These are the typical ones you've probably seen where each neuron in a layer is connected to each neuron in the next.

In a classification problem, you want an output like you mentioned above, a one-hot vector corresponding to the correct class. The way you get this vector is by applying a soft-max activation to the outputs of your last layers. If you don't apply this activation, your output vector might be something like [-2.5, 0.6, 10.2, 5.1]. You can see that this looks a lot like the vector you want. So, you can probably use a feedforward architecture (with non-linear activations on the hidden layers if there are non-linearities in your data) with a final layer of 4 neurons. Then, when you train, that should (hopefully) converge to the correct data outputs. If it doesn't, you will need to mess with the architecture/get more training data/lots of other levers to play with.

This link goes to a tutorial for regression using Keras. Keras is a nice package written to take powerful libraries like TensorFlow and Theano and give them a simple interface. TensorFlow can be quite complex (I've never used Theano so I don't know) when you're starting and trying to understand the syntax and the ML concepts. Keras helps take away the confusing syntax.

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