3.2. Generate a configuration file

You can generate your configuration file interactively by running blueoil init command.

$ ./blueoil.sh init

blueoil init command generates a configuration file to train your new model.

With the blueoil init command, you can configure your training configuration interactively.

This is an example of configuration.

#### Generate config ####
  your model name ():  test
  choose task type  classification
  choose network  LmnetV1Quantize
  choose dataset format  Caltech101
  training dataset path:  {dataset_dir}/train/
  set validataion dataset? (if answer no, the dataset will be separated for training and validation by 9:1 ratio.)  yes
  validataion dataset path:  {dataset_dir}/test/
  batch size (integer):  64
  image size (integer x integer):  32x32
  how many epochs do you run training (integer):  100
  select optimizer: MomentumOptimizer
  initial learning rate: 0.001
  choose learning rate schedule ({epochs} is the number of training epochs you entered before):  '2-step-decay' -> learning rate decrease by 1/10 on {epochs}/2 and {epochs}-1.
  enable data augmentation?  No
  apply quantization at the first layer: yes

#### how can I change small setting? Or I need to re-run `blueoil init` again?
You don't need to re-run `bluoil init` again.
`blueoil init` just generates a config file in YAML format. You can change some settings, according to comments.

#### data augmentation
You can use various augmentation methods in generated YAML, also you can change augmentation methods's parameter.
Under `commmon.data_augmentation` in generated yaml, augmentation methods are listed and the parameters are nested in each methods.

generated yaml:

    - Blur:
        - value: (0, 1)
    - Color:
        - value: (0.75, 1.25)
    - Contrast:
        - value: (0.75, 1.25)
    - FlipLeftRight:
        - probability: 0.5

Please see the data augmentor reference page, when you want to know about augmentation methods. the all of augmentation methods and parameter are explained, methods name in generated yaml correspond to class name under lmnet.data_augmentor in the reference.

3.2.1. optimizer

You can choose optimizer from Adam or Momentum. Each optimizer uses TensorFlow implementation. Please see TensorFlow documentation, AdamOptimizer and MomentumOptimizer.

generated yaml:

  # supported 'optimizer' is 'Momentum', 'Adam' currently.
  # Momentum
  #    https://www.tensorflow.org/api_docs/python/tf/train/MomentumOptimizer
  # Adam
  #    https://www.tensorflow.org/api_docs/python/tf/train/AdamOptimizer
  optimizer: Adam

Blueoil use optimizers with your input learning rate for both Adam and Momentum, and momentum=0.9 for Momentum. Other values are TensorFlow default described in TensorFlow documentation.