function that executed forward prop, backprop and loss calculation

train.py

# training neural net
def train(self):

    # dynamically calculating layers and their respective z
    for i in range(len(self.input)):

        self.z0 = self.input[i].reshape([-1, 1])
        
        # forward step
        output = self.forwardprop()

        self.y_hat[i] = output
        
        # backward step
        self.backprop(self.y_hat[i], self.y[i])
        
        # loss calculation step
        self.NN_loss(self.y_hat[i], self.y[i])