#numActiveColumnsPerInhArea = -1, # Using percentage instead numActiveColumnsPerInhArea=1, # Only one feature active at a time # All input activity can contribute to feature output stimulusThreshold=0, synPermInactiveDec=0.3, synPermActiveInc=0.3, synPermConnected=0.3, # Connected threshold maxBoost=2, seed=1956, # The seed that Grok uses spVerbosity=1) # Instantiate the spatial pooler test bench. tb = VisionTestBench(sp) # Instantiate the classifier clf = exactMatch() # Train the spatial pooler on trainingVectors. numCycles = tb.train(trainingVectors, trainingTags, clf, maxTrainingCycles) # View the permanences and connections after training. tb.showPermsAndConns() #tb.savePermsAndConns('perms_and_conns.jpg') # Get testing images and convert them to vectors. testingImages, testingTags = data.getImagesAndTags(testingDataset) testingVectors = encoder.imagesToVectors(testingImages) # Test the spatial pooler on testingVectors. accuracy = tb.test(testingVectors, testingTags, clf, verbose=1)
#numActiveColumnsPerInhArea = -1, # Using percentage instead numActiveColumnsPerInhArea = 1, # Only one feature active at a time # All input activity can contribute to feature output stimulusThreshold = 0, synPermInactiveDec = 0.3, synPermActiveInc = 0.3, synPermConnected = 0.3, # Connected threshold maxBoost = 2, seed = 1956, # The seed that Grok uses spVerbosity = 1) # Instantiate the spatial pooler test bench. tb = VisionTestBench(sp) # Instantiate the classifier clf = exactMatch() # Train the spatial pooler on trainingVectors. numCycles = tb.train(trainingVectors, trainingTags, clf, maxTrainingCycles) # View the permanences and connections after training. tb.showPermsAndConns() #tb.savePermsAndConns('perms_and_conns.jpg') # Get testing images and convert them to vectors. testingImages, testingTags = data.getImagesAndTags(testingDataset) testingVectors = encoder.imagesToVectors(testingImages) # Test the spatial pooler on testingVectors. accuracy = tb.test(testingVectors, testingTags, clf, verbose=1)