Neural Networks (Python) - Implement a backpropagation algorithm from scratch (can use numpy, pandas, matplotlib) with the following data given in a CSV file:
Notes –
• Treat weights as matrices and hidden nodes as vectors
• Add 1 bias node to input and hidden layer
• Please use 4 input nodes, 1 hidden layer with 5 or 6 hidden nodes and output layer with 4 nodes ( i = 4, j = 5, k = 4)
1) Set all weights to random numbers between 0 and 1 (random # generator seed should be constant) and use sigmoid activation.
2) Back propagation error calculation using (For all weights jk (middle & output layer):
Where the weighted sum is taken of the δ values of all units that receive output from unit j.
f '(S) = the derivative of the sigmoid function f
f '(S) = f (Sj)(1-f (Sj))
Sj = weighted sum of inputs to j
3) Use the Delta rule to update weights: (with a learning coefficient = 0.5)
Use ij for weights between input & middle layer. And jk for weights between middle & output layer
4) Next Training Pattern (3 in total):
• Rinse, repeat until you have moved through the entire training set (ideally randomly*).
• This is one iteration, (or epoch)
• Calculate (& plot) one RMS error value for each iteration.
*Initially, move sequentially through all training patterns, and when it works, move to making it random
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