Pybrain 简明教程
PyBrain - Layers
图层基本上是用于网络隐藏层的一组函数。
我们将在本章中了解图层的以下详细信息：
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Understanding layer
-
Creating Layer using Pybrain
Understanding layers
我们之前已经看到使用图层的示例，如下所示：
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TanhLayer
-
SoftmaxLayer
Example using TanhLayer
下面是一个我们使用TanhLayer构建网络的示例：
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)Output
以上代码的输出如下 −
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)Example using SoftMaxLayer
下面是一个我们使用 SoftmaxLayer 构建网络的示例：
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)Output
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)Creating Layer in Pybrain
在 Pybrain 中，您可以按照如下方式创建自己的层：
要创建层，您需要使用 NeuronLayer class 作为基类来创建所有类型的层。
Example
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要创建层，我们需要实现两种方法：_forwardImplementation() 和 _backwardImplementation()。
The _forwardImplementation() takes in 2 arguments inbuf 和 outbuf，它们是 Scipy 数组。其大小取决于层的输入和输出维度。
_backwardImplementation() 用于计算输出相对于给定输入的导数。
因此，要在 Pybrain 中实现一个层，这个层类的框架就是：
from pybrain.structure.modules.neuronlayer import NeuronLayer
class NewLayer(NeuronLayer):
def _forwardImplementation(self, inbuf, outbuf):
pass
def _backwardImplementation(self, outerr, inerr, outbuf, inbuf):
pass如果您想实现一个二次多项式函数作为层，我们可以按照如下方式进行：
考虑我们有一个多项式函数：
f(x) = 3x2以上多项式函数的导数为：
f(x) = 6 x以上多项式函数的最终层类为：
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现在让我们利用创建的层，如下所示：
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() 将测试层运行是否良好。我们需要将层使用到的网络传递到 gradientCheck(n)。如果层运行良好，它将输出“Perfect Gradient”。