# 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
