# - requirement.txt  -  GPU: tensorflow-gpu, CPU: tensorflow
# - If you use the GPU version, you need to install some additional applications.
# TrainRegex and TestRegex: Default matching apple_20181010121212.jpg file.
# - The Default is .*?(?=_.*\.)
# TrainsPath and TestPath: The local absolute path of your training and testing set.
# TestSetNum: This is an optional parameter that is used when you want to extract some of the test set
# - from the training set when you are not preparing the test set separately.
System:
  DeviceUsage: 0.7
  TrainRegex: '.*?(?=_)'
  TestRegex: '.*?(?=_)'
  TrainsPath: './dataset/mnist-CNN5BLSTM-H64-28x28_trains.tfrecords'
  TestPath: './dataset/mnist-CNN5BLSTM-H64-28x28_test.tfrecords'
  TestSetNum: 300

# CNNNetwork: [CNN5, DenseNet]
# RecurrentNetwork: [BLSTM, LSTM]
# - The recommended configuration is CNN5+BLSTM / DenseNet+BLSTM
# HiddenNum: [64, 128, 256]
# - This parameter indicates the number of nodes used to remember and store past states.
NeuralNet:
  CNNNetwork: CNN5
  RecurrentNetwork: BLSTM
  HiddenNum: 64
  KeepProb: 0.98

# SavedSteps: A Session.run() execution is called a Epochs,
# - Used to save training progress, Default value is 100.
# ValidationSteps: Used to calculate accuracy, Default value is 100.
# TestNum: The number of samples for each test batch.
# - A test for every saved steps.
# EndAcc: Finish the training when the accuracy reaches [EndAcc*100]%.
# EndEpochs: Finish the training when the epoch is greater than the defined epoch.
Trains:
  SavedSteps: 100
  ValidationSteps: 500
  EndAcc: 0.975
  EndEpochs: 1
  BatchSize: 64
  TestBatchSize: 300
  LearningRate: 0.01
  DecayRate: 0.98
  DecaySteps: 10000