Hi Srinivas,
I understand from the description that you are manually calculating the forward pass of your neural network and comparing the output with the output of the trained model.
If the difference in the outputs is not very significant, the discrepancy of the output can be attributed to the numerical precision issues. MATLAB uses double-precision floating-point numbers, which can lead to small differences in calculations due to rounding errors.
There can also be other reasons for the discrepancy in outputs. Here are some things to check:
Activation Functions: You can verify that you are using the correct activation functions for each layer using “bestNetwork.layers{1}.transferFcn” and “bestNetwork.layers{2}.transferFcn“ functions.
Network Architecture: You can verify that your manual calculation matches the architecture of your trained network. You can check the number of layers, the number of neurons in each layer, the connections between the layers, etc.
You can refer to the following link for more information about the properties and functions for the network https://www.mathworks.com/help/deeplearning/ug/neural-network-subobject-properties.html#bss4hk6-99.
I hope this helps!
