def main(_):
tf.logging.set_verbosity(tf.logging.INFO)
random.seed(FLAGS.random_seed)
params = contrib_training.HParams(
num_steps=FLAGS.num_steps,
val_freq=FLAGS.val_freq,
seq_len=FLAGS.seq_len,
batch_size=FLAGS.batch_size,
emb_variable=FLAGS.emb_variable,
emb_size=FLAGS.emb_size,
vocab_size=4,
hidden_lstm_size=FLAGS.hidden_lstm_size,
norm_lstm=FLAGS.norm_lstm,
dropout_rate=FLAGS.dropout_rate,
learning_rate=FLAGS.learning_rate,
reg_type=FLAGS.reg_type,
reg_weight=FLAGS.reg_weight,
out_dir=FLAGS.out_dir,
in_tr_data_dir=FLAGS.in_tr_data_dir,
in_val_data_dir=FLAGS.in_val_data_dir,
ood_val_data_dir=FLAGS.ood_val_data_dir,
master=FLAGS.master,
save_meta=FLAGS.save_meta,
filter_label=FLAGS.filter_label,
mutation_rate=FLAGS.mutation_rate,
)
# setup output directory
create_out_dir(params)
# load datasets
params.add_hparam('in_tr_file_pattern', 'in_tr')
params.add_hparam('in_val_file_pattern', 'in_val')
params.add_hparam('ood_val_file_pattern', 'ood_val')
(in_tr_dataset, in_val_dataset, ood_val_dataset) = load_datasets(params)
# print parameter settings
tf.logging.info(params)
with tf.gfile.GFile(os.path.join(params.model_dir, 'params.json'),
mode='w') as f:
f.write(json.dumps(params.to_json(), sort_keys=True))
# construct model
model = SeqModel(params)
model.reset()
## if previous model ckpt exists, restore the model from there
tf.logging.info('model dir=%s', os.path.join(params.out_dir,
'*.ckpt.index'))
prev_steps, ckpt_file = utils.get_latest_ckpt(params.model_dir)
if ckpt_file:
tf.logging.info('previous ckpt exist, prev_steps=%s', prev_steps)
model.restore_from_ckpt(ckpt_file)
# training
model.train(in_tr_dataset, in_val_dataset, ood_val_dataset, prev_steps)
def main(_):
tf.logging.set_verbosity(tf.logging.INFO)
random.seed(FLAGS.random_seed)
params = contrib_training.HParams(
embedding=FLAGS.embedding,
num_steps=FLAGS.num_steps,
val_freq=FLAGS.val_freq,
seq_len=FLAGS.seq_len,
batch_size=FLAGS.batch_size,
emb_size=FLAGS.emb_size,
vocab_size=4,
hidden_lstm_size=FLAGS.hidden_lstm_size,
hidden_dense_size=FLAGS.hidden_dense_size,
dropout_rate=FLAGS.dropout_rate,
learning_rate=FLAGS.learning_rate,
num_motifs=FLAGS.num_motifs,
len_motifs=FLAGS.len_motifs,
temperature=FLAGS.temperature,
reweight_sample=FLAGS.reweight_sample,
l2_reg=FLAGS.l2_reg,
out_dir=FLAGS.out_dir,
in_tr_data_dir=FLAGS.in_tr_data_dir,
in_val_data_dir=FLAGS.in_val_data_dir,
ood_val_data_dir=FLAGS.ood_val_data_dir,
master=FLAGS.master,
save_meta=FLAGS.save_meta,
label_dict_file=FLAGS.label_dict_file,
mutation_rate=FLAGS.mutation_rate,
epsilon=FLAGS.epsilon,
)
# create output directories
create_out_dir(params)
# load datasets and labels for training
params.add_hparam('in_tr_file_pattern', 'in_tr')
params.add_hparam('in_val_file_pattern', 'in_val')
params.add_hparam('ood_val_file_pattern', 'ood_val')
label_sample_size, in_tr_dataset, in_val_dataset, ood_val_dataset = load_datasets_and_labels(
params)
params.add_hparam('n_class', len(label_sample_size))
tf.logging.info('label_sample_size=%s', label_sample_size)
# compute weights for labels
# load the dictionary for class labels.
# Key: class name (string), values: encoded class label (int)
with tf.gfile.GFile(os.path.join(params.label_dict_file),
'rb') as f_label_code:
# label_dict_after_2016_new_species0 = json.load(f)
params.add_hparam('label_dict', yaml.safe_load(f_label_code))
tf.logging.info('# of label_dict=%s', len(params.label_dict))
label_weights = utils.compute_label_weights_using_sample_size(
params.label_dict, label_sample_size)
params.add_hparam('label_weights', label_weights)
# print parameter settings
tf.logging.info(params)
with tf.gfile.GFile(os.path.join(params.model_dir, 'params.json'),
mode='w') as f:
f.write(json.dumps(params.to_json(), sort_keys=True))
# construct model
tf.logging.info('create model')
model = SeqPredModel(params)
model.reset()
## if previous model ckpt exists, restore the model from there
tf.logging.info('model dir=%s',
os.path.join(params.model_dir, '*.ckpt.index'))
prev_steps, ckpt_file = utils.get_latest_ckpt(params.model_dir)
if ckpt_file:
tf.logging.info('previous ckpt exist, prev_steps=%s', prev_steps)
model.restore_from_ckpt(ckpt_file)
# training
tf.logging.info('strart training')
model.train(in_tr_dataset, in_val_dataset, ood_val_dataset, prev_steps)
