config.yaml

---
dataset_dir: inputs/dataset  # dataset location
label_from: directory # other options "file, dataframe"
# use build in keras flow_from_*. This doesn't provide much flexibility. And this is pretty slow
data_preparation: custom
# Dataset is cached in memory/ disk+memory
# Most of the time in a epoch wasted on reading the file from disk.
# if the data is in the cache then each epoch will be much faster
# If the dataset is bigger than your memory then use disk
data_set_cache: memory   # "memory", "disk"
# Directory where all the model outputs will be saved
output_dir: output
# How much log information we want
log_level: info # "info", "debug", "warning", "error"
log_to: console # "console", "file"
# Percentage of dataset that will be used for validation purpose
validation_rate: .3  # float value
# Shape of the input image
input_shape: [64, 64, 3]  # first two element is image size and last one is channel
# Number of output class to predict
num_output_class: 6 # int
# All the callbacks for our training
callbacks:  # tensorboard, checkpoint, reduce_lr_plateau
#- tensorboard
- checkpoint
#- custom
transfer_learning:  # Find link of all pretrained model here https://tfhub.dev/
  hub_link: "https://tfhub.dev/google/imagenet/resnet_v1_101/classification/4"
  do_fine_tuning: False
# Values to search in the region. For now it will use a grid search.
hparam_tuning:
  learning_rate:
  - .1
  - .01
  - .001
  dropout:
  - .1
  - .2
  - .3
  optimizer:
  - adam
  - sgd
  epochs:
  - 2
  activation:
  - relu
  loss:
  - sparse_categorical_crossentropy
  batch_size:
  - 16
  - 32
# Hyper parameter that will be used for training purpose
hparam:
  activation: relu
  learning_rate: .001
  dropout: .3
  optimizer: adam
  epochs: 3
  loss: sparse_categorical_crossentropy
  batch_size: 32