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config_demo.yaml 1.65 KB
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# - 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
Python
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