configure.yaml

# Data source
data_source_scheme: local
train: "./data/train.conllx"
test: "./data/test.conllx"
tags: "./data/entity.txt"
vocabulary_file: "./data/unicode_char_list.txt"
shuffle_pool_size: 1000

# model configure
batch_size: 32
epochs: 4
intent_field: label
max_steps:
max_steps_without_increase: 15000
embedding_dim: 300
lstm_size: 100
use_attention_layer: false
max_sentence_len: 45
bilstm_stack_config: [
  {units: 100},
  # {units: 100, activation: 'seq2annotation.tf.python.keras.activations.relu6', recurrent_activation: 'seq2annotation.tf.python.keras.activations.relu6'},
]
use_batch_normalization_after_embedding: false
use_batch_normalization_after_bilstm: false
crf_params: {use_boundary: true}

optimizer_params:
  # static learning rate
  learning_rate: 0.01

  # learning rate deacy setting will override static learning rate
  learning_rate_exp_decay: 
    learning_rate: 0.001
    lr_decay_steps: 2000
    lr_decay_rate: 0.99
    staircase: true

# loading variable weights from checkpoint from specific dir
warm_start_dir: ""
# freeze the embedding layer
freeze_embedding: false


# Data output
result_dir: "./results"
params_log_file: "./results/params.json"
model_dir: "./results/model_dir"
h5_model_file: "./results/h5_model/model.h5"
saved_model_dir: "./results/saved_model"
deliverable_model_dir: "./results/deliverable_model"
summary_log_dir: "./results/summary_log_dir"

# checkpoint
save_checkpoints_secs: 60
throttle_secs: 60