文件名称:myBackPropagation
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- 上传时间：2012-11-16
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The principle of back propagation is actually quite easy to understand, even though the maths behind it can look rather daunting. The basic steps are:
Initialise the network with small random weights.
Present an input pattern to the input layer of the network.
Feed the input pattern forward through the network to calculate its activation value.
Take the difference between desired output and the activation value to calculate the network’s activation error.
Adjust the weights feeding the output neuron to reduce its activation error for this input pattern.
Propagate an error value back to each hidden neuron that is proportional to their contribution of the network’s activation error.
Adjust the weights feeding each hidden neuron to reduce their contribution of error for this input pattern.
Repeat steps 2 to 7 for each input pattern in the input collection.
Repeat step 8 until the network is suitably trained.-The principle of back propagation is actually quite easy to understand, even though the maths behind it can look rather daunting. The basic steps are:
Initialise the network with small random weights.
Present an input pattern to the input layer of the network.
Feed the input pattern forward through the network to calculate its activation value.
Take the difference between desired output and the activation value to calculate the network’s activation error.
Adjust the weights feeding the output neuron to reduce its activation error for this input pattern.
Propagate an error value back to each hidden neuron that is proportional to their contribution of the network’s activation error.
Adjust the weights feeding each hidden neuron to reduce their contribution of error for this input pattern.
Repeat steps 2 to 7 for each input pattern in the input collection.
Repeat step 8 until the network is suitably trained.
Initialise the network with small random weights.
Present an input pattern to the input layer of the network.
Feed the input pattern forward through the network to calculate its activation value.
Take the difference between desired output and the activation value to calculate the network’s activation error.
Adjust the weights feeding the output neuron to reduce its activation error for this input pattern.
Propagate an error value back to each hidden neuron that is proportional to their contribution of the network’s activation error.
Adjust the weights feeding each hidden neuron to reduce their contribution of error for this input pattern.
Repeat steps 2 to 7 for each input pattern in the input collection.
Repeat step 8 until the network is suitably trained.-The principle of back propagation is actually quite easy to understand, even though the maths behind it can look rather daunting. The basic steps are:
Initialise the network with small random weights.
Present an input pattern to the input layer of the network.
Feed the input pattern forward through the network to calculate its activation value.
Take the difference between desired output and the activation value to calculate the network’s activation error.
Adjust the weights feeding the output neuron to reduce its activation error for this input pattern.
Propagate an error value back to each hidden neuron that is proportional to their contribution of the network’s activation error.
Adjust the weights feeding each hidden neuron to reduce their contribution of error for this input pattern.
Repeat steps 2 to 7 for each input pattern in the input collection.
Repeat step 8 until the network is suitably trained.
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下载文件列表
myBackPropagation/myBackPropagation.sln
myBackPropagation/myBackPropagation.suo
myBackPropagation/myBackPropagation/
myBackPropagation/myBackPropagation/BACKPROPAGATION.cs
myBackPropagation/myBackPropagation/bin/
myBackPropagation/myBackPropagation/bin/Debug/
myBackPropagation/myBackPropagation/bin/Debug/myBackPropagation.exe
myBackPropagation/myBackPropagation/bin/Debug/myBackPropagation.pdb
myBackPropagation/myBackPropagation/bin/Debug/myBackPropagation.vshost.exe
myBackPropagation/myBackPropagation/bin/Debug/myBackPropagation.vshost.exe.manifest
myBackPropagation/myBackPropagation/bin/Debug/Patterns.csv
myBackPropagation/myBackPropagation/bin/Debug/Patterns1.csv
myBackPropagation/myBackPropagation/bin/Debug/Patterns2.csv
myBackPropagation/myBackPropagation/ClassDiagram1.cd
myBackPropagation/myBackPropagation/GRADIENTDESCENT.cs
myBackPropagation/myBackPropagation/Layer.cs
myBackPropagation/myBackPropagation/myBackPropagation.csproj
myBackPropagation/myBackPropagation/Network.cs
myBackPropagation/myBackPropagation/NeuralNetwork.cs
myBackPropagation/myBackPropagation/Neuron.cs
myBackPropagation/myBackPropagation/obj/
myBackPropagation/myBackPropagation/obj/x86/
myBackPropagation/myBackPropagation/obj/x86/Debug/
myBackPropagation/myBackPropagation/obj/x86/Debug/DesignTimeResolveAssemblyReferencesInput.cache
myBackPropagation/myBackPropagation/obj/x86/Debug/myBackPropagation.csproj.FileListAbsolute.txt
myBackPropagation/myBackPropagation/obj/x86/Debug/myBackPropagation.exe
myBackPropagation/myBackPropagation/obj/x86/Debug/myBackPropagation.pdb
myBackPropagation/myBackPropagation/obj/x86/Debug/ResolveAssemblyReference.cache
myBackPropagation/myBackPropagation/obj/x86/Debug/TempPE/
myBackPropagation/myBackPropagation/Program.cs
myBackPropagation/myBackPropagation/Properties/
myBackPropagation/myBackPropagation/Properties/AssemblyInfo.cs
myBackPropagation/myBackPropagation/trainningExamples.cs
myBackPropagation/myBackPropagation/Weight.cs
myBackPropagation/myBackPropagation.suo
myBackPropagation/myBackPropagation/
myBackPropagation/myBackPropagation/BACKPROPAGATION.cs
myBackPropagation/myBackPropagation/bin/
myBackPropagation/myBackPropagation/bin/Debug/
myBackPropagation/myBackPropagation/bin/Debug/myBackPropagation.exe
myBackPropagation/myBackPropagation/bin/Debug/myBackPropagation.pdb
myBackPropagation/myBackPropagation/bin/Debug/myBackPropagation.vshost.exe
myBackPropagation/myBackPropagation/bin/Debug/myBackPropagation.vshost.exe.manifest
myBackPropagation/myBackPropagation/bin/Debug/Patterns.csv
myBackPropagation/myBackPropagation/bin/Debug/Patterns1.csv
myBackPropagation/myBackPropagation/bin/Debug/Patterns2.csv
myBackPropagation/myBackPropagation/ClassDiagram1.cd
myBackPropagation/myBackPropagation/GRADIENTDESCENT.cs
myBackPropagation/myBackPropagation/Layer.cs
myBackPropagation/myBackPropagation/myBackPropagation.csproj
myBackPropagation/myBackPropagation/Network.cs
myBackPropagation/myBackPropagation/NeuralNetwork.cs
myBackPropagation/myBackPropagation/Neuron.cs
myBackPropagation/myBackPropagation/obj/
myBackPropagation/myBackPropagation/obj/x86/
myBackPropagation/myBackPropagation/obj/x86/Debug/
myBackPropagation/myBackPropagation/obj/x86/Debug/DesignTimeResolveAssemblyReferencesInput.cache
myBackPropagation/myBackPropagation/obj/x86/Debug/myBackPropagation.csproj.FileListAbsolute.txt
myBackPropagation/myBackPropagation/obj/x86/Debug/myBackPropagation.exe
myBackPropagation/myBackPropagation/obj/x86/Debug/myBackPropagation.pdb
myBackPropagation/myBackPropagation/obj/x86/Debug/ResolveAssemblyReference.cache
myBackPropagation/myBackPropagation/obj/x86/Debug/TempPE/
myBackPropagation/myBackPropagation/Program.cs
myBackPropagation/myBackPropagation/Properties/
myBackPropagation/myBackPropagation/Properties/AssemblyInfo.cs
myBackPropagation/myBackPropagation/trainningExamples.cs
myBackPropagation/myBackPropagation/Weight.cs
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