Which weights are the weights of parameters of Linear Regression? 
I have just started studying Linear Regression using Neural Network and I believe this is a naive question.
I have a neural network for a Linear Regression Model with 3 independent variables. I have 1 hidden layer with 3 neurons with linear activations for each neuron. The last neuron is just another linear activation function that sums the three outputs from the hidden layer and compare the predicted value with the actual value.
I am wondering which weights are the final weights between the weights before the hidden layer and the weights after the hidden layer that I can use for the parameters of the Linear Regression Model. Hope to hear some explanations.

This neural network does regression, but what it does is not equivalent to linear regression with the original features. So, you cannot just get the weights of either layer and build a linear regression model with those.
In order to emulate linear regression with a neural network, you should not have a hidden layer, but just one output layer.
