# Learn make your own neural network record (ii)

Source: Internet
Author: User

Through the previous theoretical study, as well as the analysis of the relationship between error and weight, derive the formula to practice doing a own neural network through Python3.5:

Follow the python introduction in the book and introduce the Zeros () in the NumPy:

`Import= Numpy.zeros ([3,2= 1a[] = 2a[2,1] = 5print(a)`

The result is:

[1.0.]
[0.2.]
[0.5.]

You can use this method to generate a matrix.

Next, use Python to build the skeleton of the neural network:

Build a basic model:

`ImportNumPyclassneuralnetwork:def __init__(self, inputnodes, hiddennodes, Outputnodes, learningrate): Self.inodes=inputnodes self.hnodes=hiddennodes self.onodes=outputnodes SELF.LR=learningrate#generate the link weights between the range of-1 to +1Self.wih = numpy.random.normal (0.0, pow (self.hnodes, 0.5) , (Self.hnodes, self.inodes)) self.who= Numpy.random.normal (0.0, pow (self.onodes, 0.5), (Self.onodes, self.hnodes))Pass    defTrain (self):Pass    defquery (self):Pass#TestInput_nodes = 3Hidden_nodes= 3Output_nodes= 3learning_rate= 0.5#Create instance of neural networkn = neuralnetwork (Input_nodes, Hidden_nodes, Output_nodes, Learning_rate)`

The Cunstructor has a start node, a hidden node, and an output node, including learning rate.

Each link weight uses the form of a numpy random number to initialize a series of weight and then training data to find the error and reverse modify

Learn make your own neural network record (ii)

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