Quite possibly the simplest neural network you can build...
The network has 1 input node, 1 output node and 1 connection between the two, like so:
- To see this network with a hidden layer, see the
withHiddenLayer
branch. - To see this network used to predict the value of a car, see the
predictCarValue
branch.
The goal of this network is to recognise and calculate the required function of an input based on the provided output. For example, when provided with variables A and B, the network will calculate X when B = AX. The network is currently set up to calculate X when provided with A = 1.5 and B = 0.5. These variables map to the nodes in the network where A is the input node, B is the output node and X is the weight of the edge connecting them.
Run the network using python simplenet.py
The network will train 100 times and the output for each run will be displayed in the terminal.