PyBrain - 示例

在本章中，列出了使用 PyBrain 執行的所有可能的示例。

示例 1

使用 NOR 真值表和測試其正確性。

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)

輸出

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)

示例 2

對於資料集，我們將使用如下所示的 sklearn 資料集中的資料集：從 sklearn 引用 load_digits 資料集： scikit-learn.org

它有 10 個類別，即從 0-9 預測的數字。

X 中的總輸入資料為 64。

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) ) 

# we are having inputs are 64 dim array and since the digits are from 0-9 
the classes considered is 10.

for i in range(len(X)):
ds.addSample(ravel(X[i]), y[i]) # adding sample to datasets
test_data_temp, training_data_temp = ds.splitWithProportion(0.25) 

#Splitting the datasets 25% as testdata and 75% as trained data
# Using splitWithProportion() method on dataset converts the dataset to 
#superviseddataset, so we will convert the dataset back to classificationdataset 
#as shown in above step.
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
)
#creating a network wherein the input and output are used from the training data.
trainer = BackpropTrainer(
   net, dataset=training_data, momentum=0.1,learningrate=0.01,verbose=True,weightdecay=0.01
)
#Training the Network
trnerr,valerr = trainer.trainUntilConvergence(dataset=training_data,maxEpochs=10)

#Visualizing the error and validation data
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']
))

輸出

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