def testing(self, trial, seed):
"""
Test the network on the test set for a particular trial and store the
results
@param trial (int) trial count
"""
if self.verbosity > 0:
i = sum(self.partitions[trial][0])
indices = []
for numTokens in self.partitions[trial][1]:
indices.append(i)
i += numTokens
print(
"\tRunner selects to test on sequences starting at indices "
"{}".format(indices))
results = ([], [])
testIndex = len(self.partitions[trial][0])
for numTokens in self.partitions[trial][1]:
predictions = []
activations = []
for _ in xrange(numTokens):
predicted, active = self.model.testModel(seed)
activations.append(active)
predictions.append(predicted)
winningPredictions = self._selectWinners(predictions, activations)
# TODO: switch to standard (expected, actual) format
results[0].append(winningPredictions)
results[1].append(self.actualLabels[trial][testIndex])
testIndex += 1
# Prepare data for writeOutClassifications
trainIdx = range(len(self.partitions[trial][0]))
testIdx = range(
len(self.partitions[trial][0]),
len(self.partitions[trial][0]) + len(self.partitions[trial][1]))
self.partitions[trial] = (trainIdx, testIdx)
self.samples = NetworkDataGenerator.getSamples(self.dataFiles[trial])
self.results.append(results)
def testing(self, trial, seed):
"""
Test the network on the test set for a particular trial and store the
results
@param trial (int) trial count
"""
if self.verbosity > 0:
i = sum(self.partitions[trial][0])
indices = []
for numTokens in self.partitions[trial][1]:
indices.append(i)
i += numTokens
print ("\tRunner selects to test on sequences starting at indices "
"{}".format(indices))
results = ([], [])
testIndex = len(self.partitions[trial][0])
for numTokens in self.partitions[trial][1]:
predictions = []
activations = []
for _ in xrange(numTokens):
predicted, active = self.model.testModel(seed)
activations.append(active)
predictions.append(predicted)
winningPredictions = self._selectWinners(predictions, activations)
# TODO: switch to standard (expected, actual) format
results[0].append(winningPredictions)
results[1].append(self.actualLabels[trial][testIndex])
testIndex += 1
# Prepare data for writeOutClassifications
trainIdx = range(len(self.partitions[trial][0]))
testIdx = range(len(self.partitions[trial][0]),
len(self.partitions[trial][0]) + len(self.partitions[trial][1]))
self.partitions[trial] = (trainIdx, testIdx)
self.samples = NetworkDataGenerator.getSamples(self.dataFiles[trial])
self.results.append(results)
