PyBrain - 前馈网络的使用

前馈网络是一种神经网络，其中节点之间信息流动方向向前，永远不会向后流动。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，然后再传递到输出节点。

在本章中，我们将讨论如何：

• 创建前馈网络
• 向 FFN 添加连接和模块

创建前馈网络

您可以使用您选择的 Python IDE，例如 PyCharm。在这里，我们使用 Visual Studio Code 编写代码，并在终端中执行相同的代码。

要创建前馈网络，我们需要从 pybrain.structure 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:
[]

我们还没有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、隐藏、输出层，并将它们添加到模块中，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:
[]

我们仍然得到空模块和连接。我们需要为创建的模块提供连接，如下所示：

这是我们创建了输入、隐藏和输出层之间的连接并将连接添加到网络的代码。

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:
[]

我们仍然无法获取模块和连接。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'>]

现在我们可以看到前馈网络的模块和连接详细信息。