C++ Library for Neural Networks — Use libneuron to design neural networks with back propagation and evolution.
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114 lines
3.5 KiB
114 lines
3.5 KiB
8 years ago
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/*! @file
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@id $Id$
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*/
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// 1 2 3 4 5 6 7 8
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// 45678901234567890123456789012345678901234567890123456789012345678901234567890
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/** @page neronet Neural Network with Hidden Layers
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@section neuro-intro Overview
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A complex neural network can be imitiated as a vector @c I of @c i
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input values, a vector @c O of @c o output values and any number
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@c l of hidden layers, where each of them contains @c h
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neurons.
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A neural network with double precision is inistialized as:
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@code
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NeuroNet<double, i, o, l+1, h> net;
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@endcode
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@dot
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digraph g {
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rankdir=LR;
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ranksep=1.5;
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subgraph clusterInput {
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label="Input Layer";
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I1 [label=<I<SUB>1</SUB>>];
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I2 [label=<I<SUB>2</SUB>>];
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Ix [label=<I<SUB>…</SUB>>];
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Ii [label=<I<SUB>i</SUB>>];
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}
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subgraph clusterHidden1 {
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label="First Hidden Layer";
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H11 [label=<H<SUB>11</SUB>>];
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H12 [label=<H<SUB>12</SUB>>];
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H1x [label=<H<SUB>1…</SUB>>];
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H1h [label=<H<SUB>1h</SUB>>];
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}
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subgraph clusterHidden2 {
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label="Second Hidden Layer";
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H21 [label=<H<SUB>21</SUB>>];
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H22 [label=<H<SUB>22</SUB>>];
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H2x [label=<H<SUB>2…</SUB>>];
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H2h [label=<H<SUB>2h</SUB>>];
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}
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subgraph clusterHiddenx {
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label="More Hidden Layers";
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Hx1 [label=<H<SUB>…1</SUB>>];
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Hx2 [label=<H<SUB>…2</SUB>>];
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Hxx [label=<H<SUB>……</SUB>>];
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Hxh [label=<H<SUB>…h</SUB>>];
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}
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subgraph clusterHiddenl {
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label="Last Hidden Layer";
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Hl1 [label=<H<SUB>l1</SUB>>];
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Hl2 [label=<H<SUB>l2</SUB>>];
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Hlx [label=<H<SUB>l…</SUB>>];
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Hlh [label=<H<SUB>lh</SUB>>];
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}
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subgraph clusterOutput {
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label="Output Layer";
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O1 [label=<O<SUB>1</SUB>>];
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O2 [label=<O<SUB>2</SUB>>];
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Ox [label=<O<SUB>…</SUB>>];
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Oo [label=<O<SUB>o</SUB>>];
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}
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I1 -> { H11; H12; H1x; H1h; }
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I2 -> { H11; H12; H1x; H1h; }
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Ix -> { H11; H12; H1x; H1h; }
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Ii -> { H11; H12; H1x; H1h; }
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H11 -> { H21; H22; H2x; H2h; }
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H12 -> { H21; H22; H2x; H2h; }
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H1x -> { H21; H22; H2x; H2h; }
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H1h -> { H21; H22; H2x; H2h; }
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H21 -> { Hx1; Hx2; Hxx; Hxh; }
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H22 -> { Hx1; Hx2; Hxx; Hxh; }
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H2x -> { Hx1; Hx2; Hxx; Hxh; }
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H2h -> { Hx1; Hx2; Hxx; Hxh; }
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Hx1 -> { Hl1; Hl2; Hlx; Hlh; }
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Hx2 -> { Hl1; Hl2; Hlx; Hlh; }
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Hxx -> { Hl1; Hl2; Hlx; Hlh; }
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Hxh -> { Hl1; Hl2; Hlx; Hlh; }
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Hl1 -> { O1; O2; Ox; Oo; }
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Hl2 -> { O1; O2; Ox; Oo; }
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Hlx -> { O1; O2; Ox; Oo; }
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Hlh -> { O1; O2; Ox; Oo; }
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}
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@enddot
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@section neuro-forward Forward Propagation
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The connections between two layers can be modelled as a
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Matrix. Then Matrix H<sub>1</sub> contains the weights from @c I
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to the first hidden layer, @c H<sub>2</sub> from the first to the
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second, and so on, until @c H<sub>l+1</sub> contains the weights
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from layer @c l to the output @c O.
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The output vector is then calculatd as:
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O = I × H<sub>1</sub> × H<sub>2</sub> × H<sub>…</sub> × H<sub>l+1</sub>
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@section beuro-backward Back Propagation
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*/
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template
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<typename TYPE,
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size_t INPUT_LAYERS,
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size_t OUTPUT_LAYERS,
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size_t HIDDEN_LAYERS = INPUT_LAYERS+OUTPUT_LAYERS,
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size_t HIDDEN_LAYER_SIZE = INPUT_LAYERS+OUTPUT_LAYERS>
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class NeuroNet {
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};
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