- PyBrain 教程
- PyBrain - 主页
- PyBrain - 概述
- PyBrain - 环境设置
- PyBrain - PyBrain 网络简介
- PyBrain - 使用网络
- PyBrain - 使用数据集
- PyBrain - 数据集类型
- PyBrain - 导入数据集的数据
- PyBrain - 网络训练数据集
- PyBrain - 测试网络
- 使用前馈网络
- PyBrain - 使用循环网络
- 使用优化算法训练网络
- PyBrain - 层
- PyBrain - 连接
- PyBrain - 强化学习模块
- PyBrain - API 和工具
- PyBrain - 示例
- PyBrain 有用资源
- PyBrain - 快速指南
- PyBrain - 有用的资源
- PyBrain - 讨论
PyBrain - 使用前馈网络
前馈网络是一种神经网络,节点之间的信息向前移动,永远不会向后传播。前馈网络是人工神经网络中第一个也是最简单的网络。信息从输入节点传递到隐藏节点,然后传递到输出节点。
在本章中,我们将讨论如何 -
- 创建前馈网络
- 将连接和模块添加到 FFN
创建前馈网络
您可以使用您选择的 python IDE,即 PyCharm。在此,我们使用 Visual Studio Code 编写代码并将在终端中执行相同的代码。
要创建前馈网络,我们需要从pybrain.struct导入它,如下所示 -
ffn.py
from pybrain.structure import FeedForwardNetwork network = FeedForwardNetwork() print(network)
执行 ffn.py 如下所示 -
C:\pybrain\pybrain\src>python ffn.py FeedForwardNetwork-0 Modules: [] Connections: []
我们尚未向前馈网络添加任何模块和连接。因此,网络显示模块和连接的空数组。
添加模块和连接
首先,我们将创建输入层、隐藏层、输出层并将其添加到模块中,如下所示 -
ffy.py
from pybrain.structure import FeedForwardNetwork from pybrain.structure import LinearLayer, SigmoidLayer network = FeedForwardNetwork() #creating layer for input => 2 , hidden=> 3 and output=>1 inputLayer = LinearLayer(2) hiddenLayer = SigmoidLayer(3) outputLayer = LinearLayer(1) #adding the layer to feedforward network network.addInputModule(inputLayer) network.addModule(hiddenLayer) network.addOutputModule(outputLayer) print(network)
输出
C:\pybrain\pybrain\src>python ffn.py FeedForwardNetwork-3 Modules: [] Connections: []
我们仍然将模块和连接视为空。我们需要提供与创建的模块的连接,如下所示 -
在下面的代码中,我们创建了输入层、隐藏层和输出层之间的连接,并将该连接添加到网络中。
ffy.py
from pybrain.structure import FeedForwardNetwork from pybrain.structure import LinearLayer, SigmoidLayer from pybrain.structure import FullConnection network = FeedForwardNetwork() #creating layer for input => 2 , hidden=> 3 and output=>1 inputLayer = LinearLayer(2) hiddenLayer = SigmoidLayer(3) outputLayer = LinearLayer(1) #adding the layer to feedforward network network.addInputModule(inputLayer) network.addModule(hiddenLayer) network.addOutputModule(outputLayer) #Create connection between input ,hidden and output input_to_hidden = FullConnection(inputLayer, hiddenLayer) hidden_to_output = FullConnection(hiddenLayer, outputLayer) #add connection to the network network.addConnection(input_to_hidden) network.addConnection(hidden_to_output) print(network)
输出
C:\pybrain\pybrain\src>python ffn.py FeedForwardNetwork-3 Modules: [] Connections: []
我们仍然无法获得模块和连接。现在让我们添加最后一步,即我们需要添加 sortModules() 方法,如下所示 -
ffy.py
from pybrain.structure import FeedForwardNetwork from pybrain.structure import LinearLayer, SigmoidLayer from pybrain.structure import FullConnection network = FeedForwardNetwork() #creating layer for input => 2 , hidden=> 3 and output=>1 inputLayer = LinearLayer(2) hiddenLayer = SigmoidLayer(3) outputLayer = LinearLayer(1) #adding the layer to feedforward network network.addInputModule(inputLayer) network.addModule(hiddenLayer) network.addOutputModule(outputLayer) #Create connection between input ,hidden and output input_to_hidden = FullConnection(inputLayer, hiddenLayer) hidden_to_output = FullConnection(hiddenLayer, outputLayer) #add connection to the network network.addConnection(input_to_hidden) network.addConnection(hidden_to_output) network.sortModules() print(network)
输出
C:\pybrain\pybrain\src>python ffn.py FeedForwardNetwork-6 Modules: [<LinearLayer 'LinearLayer-3'gt;, <SigmoidLayer 'SigmoidLayer-7'>, <LinearLayer 'LinearLayer-8'>] Connections: [<FullConnection 'FullConnection-4': 'SigmoidLayer-7' -> 'LinearLayer-8'>, <FullConnection 'FullConnection-5': 'LinearLayer-3' -> 'SigmoidLayer-7'>]
我们现在可以看到前馈网络的模块和连接详细信息。