def createPerceptronClassifier(img, samples, class_names, n_samples, ops=None, filepath=None, params={}):
mp = MultilayerPerceptron()
if "learning_rate" in params:
# In (0, 1]
mp.setLearningRate(params.get("learning_rate", mp.getLearningRate()))
# Number of nodes per layer: a set of comma-separated values (numbers), or:
# 'a' = (number of attributes + number of classes) / 2
# 'i' = number of attributes,
# 'o' = number of classes
# 't' = number of attributes + number of classes.
# See MultilayerPerceptron.setHiddenLayers
# https://weka.sourceforge.io/doc.dev/weka/classifiers/functions/MultilayerPerceptron.html#setHiddenLayers-java.lang.String-
mp.setHiddenLayers(params.get("hidden_layers", "10,5"))
return trainClassifier(mp, img, samples, class_names, n_samples, ops=ops, filepath=filepath)
for i in range(len(channels)):
stack.addSlice("channel" + str(i + 1), channels[i].getProcessor())
# create empty feature stack
features = FeatureStack(stack.getWidth(), stack.getHeight(), False)
# set my features to the feature stack
features.setStack(stack)
# put my feature stack into the array
featuresArray.set(features, 0)
featuresArray.setEnabledFeatures(features.getEnabledFeatures())
mp = MultilayerPerceptron()
hidden_layers = "%i,%i,%i" % (20, 14, 8)
mp.setHiddenLayers(hidden_layers)
mp.setLearningRate(0.7)
mp.setDecay(True)
mp.setTrainingTime(200)
mp.setMomentum(0.3)
wekaSegmentation = WekaSegmentation(image)
wekaSegmentation.setFeatureStackArray(featuresArray)
wekaSegmentation.setClassifier(mp)
wekaSegmentation.addExample(0, posroi, 1)
wekaSegmentation.addExample(1, negroi, 1)
wekaSegmentation.trainClassifier()
wekaSegmentation.saveClassifier(folder + "\\vessel-classifier_big.model")
output = wekaSegmentation.applyClassifier(image, featuresArray, 0, True)
