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📜  PyBrain-图层

📅  最后修改于: 2020-12-10 05:17:37             🧑  作者: Mango

层基本上是在网络的隐藏层上使用的一组功能。

我们将在本章中详细介绍有关图层的以下内容-

  • 理解层
  • 使用Pybrain创建图层

了解图层

我们之前已经看到了使用以下图层的示例-

  • TanhLayer
  • SoftmaxLayer

使用TanhLayer的示例

以下是我们使用TanhLayer建立网络的一个示例-

testnetwork.py

from pybrain.tools.shortcuts import buildNetwork
from pybrain.structure import TanhLayer
from pybrain.datasets import SupervisedDataSet
from pybrain.supervised.trainers import BackpropTrainer

# Create a network with two inputs, three hidden, and one output
nn = buildNetwork(2, 3, 1, bias=True, hiddenclass=TanhLayer)

# Create a dataset that matches network input and output sizes:
norgate = SupervisedDataSet(2, 1)

# Create a dataset to be used for testing.
nortrain = SupervisedDataSet(2, 1)

# Add input and target values to dataset
# Values for NOR truth table
norgate.addSample((0, 0), (1,))
norgate.addSample((0, 1), (0,))
norgate.addSample((1, 0), (0,))
norgate.addSample((1, 1), (0,))

# Add input and target values to dataset
# Values for NOR truth table
nortrain.addSample((0, 0), (1,))
nortrain.addSample((0, 1), (0,))
nortrain.addSample((1, 0), (0,))
nortrain.addSample((1, 1), (0,))

#Training the network with dataset norgate.
trainer = BackpropTrainer(nn, norgate)

# will run the loop 1000 times to train it.
for epoch in range(1000):
   trainer.train()
trainer.testOnData(dataset=nortrain, verbose = True)

输出

上面代码的输出如下-

Python testnetwork.py

C:\pybrain\pybrain\src>python testnetwork.py
Testing on data:
('out: ', '[0.887 ]')
('correct:', '[1 ]')
error: 0.00637334
('out: ', '[0.149 ]')
('correct:', '[0 ]')
error: 0.01110338
('out: ', '[0.102 ]')
('correct:', '[0 ]')
error: 0.00522736
('out: ', '[-0.163]')
('correct:', '[0 ]')
error: 0.01328650
('All errors:', [0.006373344564625953, 0.01110338071737218, 
   0.005227359234093431, 0.01328649974219942])
('Average error:', 0.008997646064572746)
('Max error:', 0.01328649974219942, 'Median error:', 0.01110338071737218)

使用SoftMaxLayer的示例

以下是我们使用SoftmaxLayer建立网络的一个示例-

from pybrain.tools.shortcuts import buildNetwork
from pybrain.structure.modules import SoftmaxLayer
from pybrain.datasets import SupervisedDataSet
from pybrain.supervised.trainers import BackpropTrainer

# Create a network with two inputs, three hidden, and one output
nn = buildNetwork(2, 3, 1, bias=True, hiddenclass=SoftmaxLayer)

# Create a dataset that matches network input and output sizes:
norgate = SupervisedDataSet(2, 1)

# Create a dataset to be used for testing.
nortrain = SupervisedDataSet(2, 1)

# Add input and target values to dataset
# Values for NOR truth table
norgate.addSample((0, 0), (1,))
norgate.addSample((0, 1), (0,))
norgate.addSample((1, 0), (0,))
norgate.addSample((1, 1), (0,))

# Add input and target values to dataset
# Values for NOR truth table
nortrain.addSample((0, 0), (1,))
nortrain.addSample((0, 1), (0,))
nortrain.addSample((1, 0), (0,))
nortrain.addSample((1, 1), (0,))

#Training the network with dataset norgate.
trainer = BackpropTrainer(nn, norgate)

# will run the loop 1000 times to train it.
for epoch in range(1000):
trainer.train()
trainer.testOnData(dataset=nortrain, verbose = True)

输出

输出如下-

C:\pybrain\pybrain\src>python example16.py
Testing on data:
('out: ', '[0.918 ]')
('correct:', '[1 ]')
error: 0.00333524
('out: ', '[0.082 ]')
('correct:', '[0 ]')
error: 0.00333484
('out: ', '[0.078 ]')
('correct:', '[0 ]')
error: 0.00303433
('out: ', '[-0.082]')
('correct:', '[0 ]')
error: 0.00340005
('All errors:', [0.0033352368788838365, 0.003334842961037291, 
   0.003034328685718761, 0.0034000458892589056])
('Average error:', 0.0032761136037246985)
('Max error:', 0.0034000458892589056, 'Median error:', 0.0033352368788838365)

在Pybrain中创建图层

在Pybrain中，您可以如下创建自己的图层-

要创建图层，您需要使用NeuronLayer类作为基类来创建所有类型的图层。

例

from pybrain.structure.modules.neuronlayer import NeuronLayer
class LinearLayer(NeuronLayer):
   def _forwardImplementation(self, inbuf, outbuf):
      outbuf[:] = inbuf
   def _backwardImplementation(self, outerr, inerr, outbuf, inbuf):
      inerr[:] = outer

要创建图层，我们需要实现两种方法： _forwardImplementation()和_backwardImplementation() 。

_forwardImplementation()接受2个参数inbuf和outbuf，它们是Scipy数组。其大小取决于图层的输入和输出尺寸。

_backwardImplementation()用于计算输出相对于给定输入的导数。

因此，要在Pybrain中实现层，这是层类的骨架-

from pybrain.structure.modules.neuronlayer import NeuronLayer
class NewLayer(NeuronLayer):
   def _forwardImplementation(self, inbuf, outbuf):
      pass
   def _backwardImplementation(self, outerr, inerr, outbuf, inbuf):
      pass

如果您想将二次多项式函数为一个层，我们可以如下进行操作：

考虑我们有一个多项式函数为-

f(x) = 3x2

上述多项式函数的导数如下-

f(x) = 6 x

上述多项式函数的最终层类别如下：

testlayer.py

from pybrain.structure.modules.neuronlayer import NeuronLayer
class PolynomialLayer(NeuronLayer):
   def _forwardImplementation(self, inbuf, outbuf):
      outbuf[:] = 3*inbuf**2
   def _backwardImplementation(self, outerr, inerr, outbuf, inbuf):
      inerr[:] = 6*inbuf*outerr

现在让我们利用如下所示创建的图层-

testlayer1.py

from testlayer import PolynomialLayer
from pybrain.tools.shortcuts import buildNetwork
from pybrain.tests.helpers import gradientCheck

n = buildNetwork(2, 3, 1, hiddenclass=PolynomialLayer)
n.randomize()

gradientCheck(n)

GradientCheck()将测试该图层是否工作正常。我们需要通过用于图层的网络进行渐变检查(n)。如果该图层工作正常，则将输出为“ Perfect Gradient”。

输出

C:\pybrain\pybrain\src>python testlayer1.py
Perfect gradient