How to build a network using PyBrain

Recipe Objective - How to build a network using PyBrain?

For more related projects -

https://www.dezyre.com/projects/data-science-projects/tensorflow-projects
https://www.dezyre.com/projects/data-science-projects/keras-deep-learning-projects

Let's try to build a network on the Iris dataset-

# Importing all the necessary libraries
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

# Loading iris dataset from sklearn datasets
iris = datasets.load_iris()

# Defining feature variables and target variable
X_data = iris.data
y_data = iris.target

# Defining classification dataset model
classification_dataset = ClassificationDataSet(4, 1, nb_classes=3)

# Adding sample into classification dataset
for i in range(len(X_data)):
  classification_dataset.addSample(ravel(X_data[i]), y_data[i])

# Spilling data into testing and training data with the ratio 7:3
testing_data, training_data = classification_dataset.splitWithProportion(0.3)

# Classification dataset for test data
test_data = ClassificationDataSet(4, 1, nb_classes=3)

# Adding sample into testing classification dataset
for n in range(0, testing_data.getLength()):
   test_data.addSample( testing_data.getSample(n)[0], testing_data.getSample(n)[1] )

# Classification dataset for train data
train_data = ClassificationDataSet(4, 1, nb_classes=3)

# Adding sample into training classification dataset
for n in range(0, training_data.getLength()):
   train_data.addSample( training_data.getSample(n)[0], training_data.getSample(n)[1] )

test_data._convertToOneOfMany()
train_data._convertToOneOfMany()

# Building network with outclass as SoftmaxLayer on training data
build_network = buildNetwork(train_data.indim, 4, train_data.outdim, outclass=SoftmaxLayer)

# Building a backproptrainer on training data
trainer = BackpropTrainer(build_network, dataset=train_data, learningrate=0.01, verbose=True)

# 20 iterations on training data
trainer.trainEpochs(20)

# Testing data
print('Error percentage on testing data=>',percentError(trainer.testOnClassData(dataset=test_data), test_data['class']))


Output -
Total error:  0.116173324197
Total error:  0.108456354663
Total error:  0.10245801191
Total error:  0.0977106095499
Total error:  0.0929660056728
Total error:  0.0883331491649
Total error:  0.0848566302306
Total error:  0.0811810786595
Total error:  0.0781061808762
Total error:  0.0758569722131
Total error:  0.0735541564513
Total error:  0.0714176535205
Total error:  0.0695884320282
Total error:  0.0680868021349
Total error:  0.0665663288663
Total error:  0.0654873247161
Total error:  0.0642748540764
Total error:  0.0634026381591
Total error:  0.0625583220368
Total error:  0.0618042876573
Error percentage on testing data=> 42.22222222222222

In this way, we can build a network using pybrain.

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