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I am working on creating a NN with the following architecture:

Input layer (180 neurons)
Hidden Layer 1 (18 neurons)
Hidden Layer 2 (4 neurons)
Output Layer (1 neuron)

I am trying to figure out the best tool for assembling this NN such that it is not fully connected.

I would like to have the input layer NOT fully connected to Hidden Layer 1, and Hidden Layer 1 NOT fully connected to Hidden Layer 2. I believe these would be considered convolutional layers, but I am having trouble finding a python tool that allows me to specify connections.

Does anyone know of a python tool appropriate for this?

EDITS:

Example of selective connections structure I'm looking to achieve

enter image description here

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  • $\begingroup$ If you want to create a neural network with convolutional layers (i.e. a convolutional neural network) you can use any of the popular machine learning/neural network libraries (e.g. tensorflow, keras, or pytorch). However, you do specify the number of neurons in the hidden layers, which you later mention would likely be convolutional layers, so I am not exactly sure if that's what you're looking for. $\endgroup$
    – Oxbowerce
    Jan 6, 2023 at 14:51
  • $\begingroup$ I've been mostly in Sklearn and starting to look at keras right now, but I've added an example to my question of the architecture I'm looking to achieve. I think this might help clarify what I'm after $\endgroup$
    – jros
    Jan 6, 2023 at 15:00
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    $\begingroup$ Based on the image you added this would not be a convolutional layer, but a non-fully connected linear layer. You should be able to achieve something like this in the pytorch framework, a quick search provided me the following answer on stackoverflow. $\endgroup$
    – Oxbowerce
    Jan 6, 2023 at 18:55

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