The second parameter 0 represents the correct output given the input. The backpropagation is done with this line of code: myNetwork.propagate(learningRate, ), where the learningRate is a constant that tells the network how much it should adjust its weights each time. After each forward propagation we need to do a backpropagation, where the network updates it’s own weights and biases. This is the forward propagation, also called activating the network. We start by doing myNetwork.activate(), where is the data point we’re sending into the network. Each time we propagate forward and backwards four times, passing in the four possible inputs for this network. Here we’re running the network 20,000 times. If you’re confused of what a layer is, check out the screencast above. The number passed to the function dictates how many neurons each layer should have. We do this with the new Layer() function in synaptic. The first thing we need to do is to create the layers.
Now that you’ve gotten a basic intro, let’s jump into the code. NeuralNetworksAndDeepLarning - by Michael Nielsen.Hackers Guide to Neural Nets - by Andrej Karpathy.A Step by Step Backpropagation Example - by Matt Mazur.How backpropagation works technically is outside the scope of this tutorial, but here’s the three best sources I’ve found for understanding it: Do this thousands of times and your network will soon become good at generalizing. This learning process is called backpropagation. When training the network, you’re simply showing it loads of examples such as hand written digits, and getting the network to predict the right answer.Īfter each prediction, you’ll calculate how wrong the prediction was, and adjust the weights and bias values so that the network will guess a little bit more correct the next time around. Like with the blue and brown numbers in our example above. And being a good at generalizing is a matter of having the right weights and bias values across the network. The goal of a neural network is to train it to do generalizations, such as recognize hand written digits or email spam.