# This example uses text input fields import deeplearn # Reads a number of data samples from a CSV file # where the expected output value is the second field (index 1) noOfSamples = deeplearn.readCsvFile("xor.data", 4, 1, [2], 0) print str(noOfSamples) + " samples loaded" # The error threshold (percent) for each layer of the network. # After going below the threshold the pre-training will move # on to the next layer deeplearn.setErrorThresholds([0.8, 0.8]) # The learning rate in the range 0.0-1.0 deeplearn.setLearningRate(0.2) # The percentage of dropouts in the range 0-100 deeplearn.setDropoutsPercent(0.001) # Title of the training error image deeplearn.setPlotTitle("Xor Training") # The number of time steps after which the training error image is redrawn deeplearn.setHistoryPlotInterval(50000) print "Training started" timeStep = 0 while (deeplearn.training() != 0): timeStep = timeStep + 1
#!/usr/bin/env python3 import deeplearn # Reads a number of data samples from a CSV file # where the expected output value is the second field (index 1) noOfSamples = deeplearn.readCsvFile("xor.data", 4, 1, [2], 0) print(str(noOfSamples) + " samples loaded") # The error threshold (percent) for each layer of the network. # After going below the threshold the pre-training will move # on to the next layer deeplearn.setErrorThresholds([0.8, 0.8]) # The learning rate in the range 0.0-1.0 deeplearn.setLearningRate(0.2) # The percentage of dropouts in the range 0-100 deeplearn.setDropoutsPercent(0.001) # Title of the training error image deeplearn.setPlotTitle("Xor Training") # The number of time steps after which the training error image is redrawn deeplearn.setHistoryPlotInterval(50000) print("Training started") timeStep = 0 while (deeplearn.training() != 0): timeStep = timeStep + 1