train.py<pre style="height: 150px; line-height: 10px;"># The MIT license:
#
# Copyright 2017 Andre Netzeband
#
# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated 
# documentation files (the "Software"), to deal in the Software without restriction, including without limitation 
# the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and 
# to permit persons to whom the Software is furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all copies or substantial portions of 
# the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO 
# THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, 
# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 
# SOFTWARE.
#
# Note: The DeepDriving project on this repository is derived from the DeepDriving project devloped by the princeton 
# university (http://deepdriving.cs.princeton.edu/). The above license only applies to the parts of the code, which 
# were not a derivative of the original DeepDriving project. For the derived parts, the original license and 
# copyright is still valid. Keep this in mind, when using code from this project.

import misc.settings
import deep_learning as dl
import tensorflow as tf
import phases
import os
import shutil

SettingFile = "run.cfg"
IsRestore   = True

def main():
  Settings = misc.settings.CSettings(SettingFile)

  Runner = dl.CPhaseRunner()
  Runner.add(phases.doTrainClassifier)
  Runner.add(phases.doEvalClassifier)

  if IsRestore:
    Runner.restoreLast(Settings["Runner"]["CheckpointPath"])

  Arguments = {
    'ResultPath': os.path.join("Results", "multi_task_gb_inheritance_full_desc"),
    'TrainSummaryPath': os.path.join("Summary", "multi_task_gb_inheritance_full_desc"),
    #'ValueToChange': ['Optimizer', 'StartingLearningRate']
  }
  if os.path.exists(Arguments['ResultPath']):
    shutil.rmtree(Arguments['ResultPath'])
  os.makedirs(Arguments['ResultPath'])

  Runner.run(Arguments={'Arguments': Arguments, 'Iteration': 0, 'Value': 0})
  print("Training took ({})".format(misc.time.getStringFromTime(Runner.LastRuntime)))

if __name__ == "__main__":
  main()</pre>