def run_with_crossvalidation(self, data, iterations=5):
"""
This function estimates the performance of the neural network using crossvalidation using a specified dataset.
Args:
:param ds (ClassificationData): the dataset used to crossvalidate the neural network.
iterations (int, optional): number of iterations for the crossvalidation.
:returns: error (float): the average percent error of the dataset, tested on the neural network using crossvalidation.
"""
x = data.X
y = data.y
n, m = x.shape
errors = np.zeros(iterations)
cv = cross_validation.KFold(n, iterations, shuffle=True)
i = 0
for train_index, test_index in cv:
x_train = x[train_index, :]
y_train = y[train_index]
x_test = x[test_index, :]
y_test = y[test_index]
ds_train = ClassificationData.conv2DS(x_train, y_train)
ds_test = ClassificationData.conv2DS(x_test, y_test)
trainer = BackpropTrainer(
self.network,
dataset=ds_train,
learningrate=self.learningrate,
momentum=self.momentum,
verbose=self.verbose,
batchlearning=self.batchlearning)
trainer.trainUntilConvergence(
dataset=ds_train,
maxEpochs=self.max_epochs,
continueEpochs=self.con_epochs)
errors[i] = percentError(
trainer.testOnClassData(dataset=ds_test),
ds_test['class'])
i += 1
print "Multi class NN cross-validation test errors: " % errors
return np.average(errors)
# Karol Dzitkowski # [email protected] # 10-10-2014 from pylab import * from csv_data import ClassificationData from classification import ClassificationNeuralNetwork from pybrain.utilities import percentError d_train = ClassificationData('data/class_2_train.csv') nClasses = d_train.DS.nClasses nn = ClassificationNeuralNetwork(2, nClasses, 6) # print nn.network nn.apply_custom_network([6,6]) # print nn.network #RUN NETWORK FOR CLASSIFICATION t = nn.run_with_crossvalidation(d_train) print 'train error', nn.test(d_train) d_test = ClassificationData('data/class_2_tst.csv') print 'test error', nn.test(d_test) # get error # result = t.testOnClassData(dataset=d_train.DS) # error = percentError(result, d_train.DS['class'])
