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%%HTML
<style>
.container { width:100% } 
</style>

Visualize the Architecture of a Neural Network



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import graphviz as gv

The function $\texttt{generateNN}(\texttt{name}, \texttt{Topology})$ takes a filename name and a network topology Topology as its argument and draws a graph of the resulting neural net. A network topology is a list of numbers specifying the number of neurons of each layer. For example, the network topology [3, 8, 6, 2] specifies a neural network with three layers of neurons. The network has $3$ input nodes, the first hidden layer has $8$ neurons, the second hidden layer has $6$ neurons, and the output layer has $2$ neurons.



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def generateNN(name, Topology):
    L = len(Topology)
    input_layer   = ['i' + str(i) for i in range(1, Topology[0]+1)]
    hidden_layers = [['h' + str(k+1) + ',' + str(i) for i in range(1, s+1)] 
                                                    for (k, s) in enumerate(Topology[1:-1])]
    output_layer  = ['o' + str(i) for i in range(1, Topology[-1]+1)]
    nng = gv.Graph(name)
    nng.attr(rankdir='LR', splines='false')
    # create nodes for input layer
    for n in input_layer:
        nng.node(n, label='', shape='point', width='0.05')
    # create nodes for hidden layers
    for NodeList in hidden_layers:
        for n in NodeList:
            nng.node(n, label='', shape='circle', width='0.1')
    # create nodes for output layer
    for n in output_layer:
        nng.node(n, label='', shape='circle', width='0.1')
    # connect input layer to first hidden layer
    for n1 in input_layer:
        for n2 in hidden_layers[0]:
            nng.edge(n1, n2)
    # connect hidden layers d to hidden layer d+1
    for d in range(0, L-3):
        for n1 in hidden_layers[d]:
            for n2 in hidden_layers[d+1]:
                nng.edge(n1, n2)
    # connect output layer
    for n1 in hidden_layers[L-3]:
        for n2 in output_layer:
            nng.edge(n1, n2)
    return nng



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Topology = [3, 6, 4, 2]



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nn1 = generateNN('nn1', Topology)
nn1









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Topology = [8, 12, 8, 6, 3]



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nn2 = generateNN('nn2', Topology)
nn2









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