Pybrain 简明教程

PyBrain - Datasets Types

数据集是指提供给网络进行测试、验证和训练的数据。要使用的数据集类型取决于我们要使用机器学习执行的任务。我们将在本章中讨论各种数据集类型。

Datasets are data to be given to test, validate and train on networks. The type of dataset to be used depends on the tasks that we are going to do with machine learning. We are going to discuss the various dataset types in this chapter.

我们可以通过添加以下包来使用数据集 −

We can work with the dataset by adding the following package −

pybrain.dataset

SupervisedDataSet

SupervisedDataSet 包含 input 和 target 字段。它是数据集最简单的形式，主要用于监督式学习任务。

SupervisedDataSet consists of fields of input and target. It is the simplest form of a dataset and mainly used for supervised learning tasks.

以下是您如何在代码中使用它的方法 −

Below is how you can use it in the code −

from pybrain.datasets import SupervisedDataSet

SupervisedDataSet 中可用的方法如下 −

The methods available on SupervisedDataSet are as follows −

addSample(inp, target)

此方法将添加新的输入和目标样本。

This method will add a new sample of input and target.

splitWithProportion(proportion=0.10)

这将把数据集分为两部分。第一部分将占数据集输入部分的 %，即如果输入为 .10，那么它就是数据集的 10%，90% 的数据。您可以根据自己的选择决定比例。可以将已划分的数据集用于测试并训练您的网络。

This will divide the datasets into two parts. The first part will have the % of the dataset given as input, i.e., if the input is .10, then it is 10% of the dataset and 90% of data. You can decide the proportion as per your choice. The divided datasets can be used for testing and training your network.

copy() − 返回数据集的深度副本。

copy() − Returns a deep copy of the dataset.

clear() − 清除数据集。

clear() − Clear the dataset.

saveToFile(filename, format=None, **kwargs)

将对象保存到由 filename 给出的文件中。

Save the object to file given by filename.

Example

这里有一个使用 SupervisedDataset 的工作示例 −

Here is a working example using a SupervisedDataset −

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

以上程序的输出如下所示 −

The output for the above program is as follows −

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)

ClassificationDataSet

此数据集主要用于解决分类问题。它使用输入、目标字段以及一个名为“class”的附加字段，它是所给目标的自动化备份。例如，输出将是 1 或 0，或根据所给输入，输出将与值分组在一起，即它将属于特定类。

This dataset is mainly used to deal with classification problems. It takes in input, target field and also an extra field called "class" which is an automated backup of the targets given. For example, the output will be either 1 or 0 or the output will be grouped together with values based on input given., i.e., it will fall in one particular class.

以下是您如何在代码中使用它的方法 −

Here is how you can use it in the code −

from pybrain.datasets import ClassificationDataSet
Syntax
// ClassificationDataSet(inp, target=1, nb_classes=0, class_labels=None)

ClassificationDataSet 可用方法如下：

The methods available on ClassificationDataSet are as follows −

addSample(inp, target) − 此方法将添加一个新的输入和目标样本。

addSample(inp, target) − This method will add a new sample of input and target.

splitByClass() − 此方法将给出两个新的数据集，第一个数据集将拥有选中的类（0..nClasses-1），第二个数据集将拥有剩余的样本。

splitByClass() − This method will give two new datasets, the first dataset will have the class selected (0..nClasses-1), the second one will have remaining samples.

_convertToOneOfMany() − 此方法将目标类转换为 1 中 k 个表示形式，将旧目标作为字段类保留

_convertToOneOfMany() − This method will convert the target classes to a 1-of-k representation, retaining the old targets as a field class

下面是 ClassificationDataSet 的一个工作示例。

Here is a working example of ClassificationDataSet.

Example

from sklearn import datasets
import matplotlib.pyplot as plt
from pybrain.datasets import ClassificationDataSet
from pybrain.utilities import percentError
from pybrain.tools.shortcuts import buildNetwork
from pybrain.supervised.trainers import BackpropTrainer
from pybrain.structure.modules import SoftmaxLayer
from numpy import ravel
digits = datasets.load_digits()
X, y = digits.data, digits.target
ds = ClassificationDataSet(64, 1, nb_classes=10)

for i in range(len(X)):
ds.addSample(ravel(X[i]), y[i])
test_data_temp, training_data_temp = ds.splitWithProportion(0.25)
test_data = ClassificationDataSet(64, 1, nb_classes=10)

for n in range(0, test_data_temp.getLength()):
test_data.addSample( test_data_temp.getSample(n)[0], test_data_temp.getSample(n)[1] )
training_data = ClassificationDataSet(64, 1, nb_classes=10)

for n in range(0, training_data_temp.getLength()):
training_data.addSample( training_data_temp.getSample(n)[0], training_data_temp.getSample(n)[1] )
test_data._convertToOneOfMany()
training_data._convertToOneOfMany()
net = buildNetwork(training_data.indim, 64, training_data.outdim, outclass=SoftmaxLayer)
trainer = BackpropTrainer(
   net, dataset=training_data, momentum=0.1,learningrate=0.01,verbose=True,weightdecay=0.01
)
trnerr,valerr = trainer.trainUntilConvergence(dataset=training_data,maxEpochs=10)
plt.plot(trnerr,'b',valerr,'r')
plt.show()
trainer.trainEpochs(10)
print('Percent Error on testData:',percentError(trainer.testOnClassData(dataset=test_data), test_data['class']))

以上示例中使用的数据集是数字数据集，类为 0-9，因此有 10 个类。输入为 64，目标为 1，类为 10。

The dataset used in the above example is a digit dataset and the classes are from 0-9, so there are 10 classes. The input is 64, target is 1 and classes, 10.

该代码使用数据集训练网络，并输出训练误差和验证误差的图形。它还给出测试数据的百分比误差，如下所示：

The code trains the network with the dataset and outputs the graph for training error and validation error. It also gives the percent error on testdata which is as follows −

Output

Total error: 0.0432857814358
Total error: 0.0222276374185
Total error: 0.0149012052174
Total error: 0.011876985318
Total error: 0.00939854792853
Total error: 0.00782202445183
Total error: 0.00714707652044
Total error: 0.00606068893793
Total error: 0.00544257958975
Total error: 0.00463929281336
Total error: 0.00441275665294
('train-errors:', '[0.043286 , 0.022228 , 0.014901 , 0.011877 , 0.009399 , 0.007
   822 , 0.007147 , 0.006061 , 0.005443 , 0.004639 , 0.004413 ]')
('valid-errors:', '[0.074296 , 0.027332 , 0.016461 , 0.014298 , 0.012129 , 0.009
   248 , 0.008922 , 0.007917 , 0.006547 , 0.005883 , 0.006572 , 0.005811 ]')
Percent Error on testData: 3.34075723830735