The following has to do with implementing a neural network in python:
def update_mini_batch(self, mini_batch): nabla_b = [np.zeros(b.shape) for b in self.biases] nabla_w = [np.zeros(w.shape) for w in self.weights] for x, y in mini_batch: delta_nabla_b, delta_nabla_w = self.backprop(x, y) nabla_b = [nb+dnb for nb, dnb in zip(nabla_b, delta_nabla_b)] nabla_w = [nw+dnw for nw, dnw in zip(nabla_w, delta_nabla_w)]
In the first two lines it initializes two tensors with zeros. Then in the next for loop it updates them by adding each element within (which is a zero) to another element in another similar tensor that is due to the backprop function.
For me this should be equivalent to
for x, y in mini_batch: nabla_b, nabla_w = self.backprop(x, y)
But I can’t really be sure. Both run successfully and the code depends on randomness.
They are not equivalent. The top one will sum nablas over minibatch. The bottom one will only keep values from the last sample.