configure_for_estimator.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

train_hooks:
- function: tensorflow.estimator.experimental.stop_if_no_increase_hook
  params:
    metric_name: f1
    min_steps: 1000
    run_every_secs: 120
    max_steps_without_increase: 1000

# 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