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The constructor of the Helper class requires 2 arguments. A simple invocation might look something like this: Helper('data.txt', ) \ The Helper class has many configuration options, which are documented below. It is recommended that you use the Helper class to do this, which will simplify the use of the network by handling the wiring and instantiation of the other classes, and by providing reasonable defaults for many of the required configuration parameters.
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The number of input and output values in each training example must match the number of input and output neurons in the network.Ī data file representing the XOR function might look like this: # XOR dataĪs well as supplying a training set, you will need to write a few lines of Python code to configure how the network will run. Each line must consist of a comma-separated list of output values, followed by a ':', followed by a comma-separated list of input values. To use the utility with a training set, the data must be saved in a correctly formatted text file, with each line in the file containing the data for a single training example. Using an iterative process, the network gradually alters these weights so that eventually a given input will yield an output consistent with those contained in the training set. The network is initialised by setting the weights of all the axons to random values. The input values from the training set are used as inputs to the first layer of neurons in the network, and the outputs from the last layer of neurons are compared with the training example output values. Each training example must contain one or more input values, and one or more output values. The network analyses a set of data that you supply, known as the training set, which consists of multiple data items or training examples.
Axons modify the values that pass through them, the degree to which values are modified is determined by the axon's weight - a numeric value that can change over time. Neurons are connected together by axons which pass values from one neuron to the next. Neurons accept one or more input values and use those inputs to calculate a single output value, which is then passed on to all the neurons in the next layer.
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The network consists of many nodes or neurons arranged in a series of layers, with each neuron in a given layer being connected to all the neurons in the following layer. However, if you interested in learning how neural networks work, or want a very simple implementation to adapt for your own purposes, then this utility may be useful.
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If you need a neural network for an application where performance is important, then you should use one of the well maintained open source libraries that are available for free online. This Python utility provides a simple implementation of a Neural Network, and was written mostly as a learning exercise. Enter pswkd6 in the Promotional Code box when you check out.
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