If I trained a multilayer percetron neural network manually, following exactly the backpropagation steps described in the article, with fixed initial weights (that is, without starting randomly), and then repeating this training again several more times just like I did the first time, ignoring the randomness and unforeseen events of the computation(because as I would be following everything manually, there would be no random initialization of the weights, no rounding problems, nor any variation in the process), if I do this when training the network, and I always use the same fixed initial weights( so that it always starts from the same starting point), using the same network structure (same number of layers, neurons, exactly the same network), with exactly the same parameters (such as number of epochs, learning rate, etc.), with the same dataset, with this network being trained with the dataset samples in the same order, following meticulously, very carefully, mathematically will I always get the same results? this is true ?
I are without considering of whether the predictions are correct or not. My specific doubt, whether mathematically the results will be the same in the circumstances described.