#!/usr/bin/python

import sys
import deeplearn


noOfSamples = deeplearn.readCsvFile("wdbc.data", 16, 3, [1])
print str(noOfSamples) + " samples loaded"

deeplearn.setErrorThresholds([1.6, 0.6, 0.6, 3.0])
deeplearn.setLearningRate(0.2)
deeplearn.setDropoutsPercent(0.001)
deeplearn.setHistoryPlotInterval(500000)
deeplearn.setPlotTitle("Cancer Classification Training")

print "Training started"

timeStep = 0
while (deeplearn.training() != 0):
    timeStep = timeStep + 1

print "Training Completed"
print "Test data set performance is " + str(deeplearn.getPerformance()) + "%";

deeplearn.export("result.c")
print "Exported trained network"

#!/usr/bin/python

import sys
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("wdbc.data", 16, 3, [1])
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([1.6, 0.6, 0.6, 3.0])

# 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("Cancer Classification Training")

# The number of time steps after which the training error image is redrawn
deeplearn.setHistoryPlotInterval(500000)

print "Training started"

timeStep = 0
while (deeplearn.training() != 0):
    timeStep = timeStep + 1

print "Training Completed"
print "Test data set performance is " + str(deeplearn.getPerformance()) + "%";

deeplearn.export("result.c")
print "Exported trained network"

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