def main(args, defaults):
parameters = process_args(args, defaults)
os.environ['CUDA_VISIBLE_DEVICES'] = str(parameters.gpu_id)
logging.basicConfig(
level=logging.DEBUG,
format='%(asctime)-15s %(name)-5s %(levelname)-8s %(message)s',
filename=parameters.log_path)
console = logging.StreamHandler()
console.setLevel(logging.INFO)
formatter = logging.Formatter(
'%(asctime)-15s %(name)-5s %(levelname)-8s %(message)s')
console.setFormatter(formatter)
logging.getLogger('').addHandler(console)
gpu_options = tf.GPUOptions(allow_growth=True)
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True,
gpu_options=gpu_options)) as sess:
model = Model(phase=parameters.phase,
gpu_id=parameters.gpu_id,
channel=parameters.channel,
mean=parameters.mean,
visualize=parameters.visualize,
use_gru=parameters.use_gru,
load_model=parameters.load_model,
data_dir=parameters.data_dir,
label_path=parameters.label_path,
lexicon_file=parameters.lexicon_file,
model_dir=parameters.model_dir,
output_dir=parameters.output_dir,
steps_per_checkpoint=parameters.steps_per_checkpoint,
num_epoch=parameters.num_epoch,
batch_size=parameters.batch_size,
initial_learning_rate=parameters.initial_learning_rate,
clip_gradients=parameters.clip_gradients,
max_gradient_norm=parameters.max_gradient_norm,
target_embedding_size=parameters.target_embedding_size,
attn_num_hidden=parameters.attn_num_hidden,
attn_num_layers=parameters.attn_num_layers,
valid_target_length=float('inf'),
session=sess)
print('model init end, launch start...')
model.launch()
def test():
defaults = exp_config.ExpConfig
parameters = dict()
parameters['log_path'] = 'log.txt'
parameters['phase'] = 'train'
parameters['visualize'] = defaults.VISUALIZE
parameters['data_path'] = 'train.txt'
parameters['data_root_dir'] = '../data/date'
parameters['lexicon_file'] = 'lexicon.txt'
parameters['output_dir'] = defaults.OUTPUT_DIR
parameters['batch_size'] = 4
parameters['initial_learning_rate'] = 1.0
parameters['num_epoch'] = 30
parameters['steps_per_checkpoint'] = 200
parameters['target_vocab_size'] = defaults.TARGET_VOCAB_SIZE
parameters['model_dir'] = '../output'
parameters['target_embedding_size'] = 10
parameters['attn_num_hidden'] = defaults.ATTN_NUM_HIDDEN
parameters['attn_num_layers'] = defaults.ATTN_NUM_LAYERS
parameters['clip_gradients'] = defaults.CLIP_GRADIENTS
parameters['max_gradient_norm'] = defaults.MAX_GRADIENT_NORM
parameters['load_model'] = defaults.LOAD_MODEL
parameters['gpu_id'] = defaults.GPU_ID
parameters['use_gru'] = False
logging.basicConfig(
level=logging.DEBUG,
format='%(asctime)-15s %(name)-5s %(levelname)-8s %(message)s',
filename=parameters['log_path'])
console = logging.StreamHandler()
console.setLevel(logging.INFO)
formatter = logging.Formatter(
'%(asctime)-15s %(name)-5s %(levelname)-8s %(message)s')
console.setFormatter(formatter)
logging.getLogger('').addHandler(console)
gpu_options = tf.GPUOptions(allow_growth=True)
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True,
gpu_options=gpu_options)) as sess:
model = Model(
phase=parameters['phase'],
visualize=parameters['visualize'],
data_path=parameters['data_path'],
data_root_dir=parameters['data_root_dir'],
output_dir=parameters['output_dir'],
batch_size=parameters['batch_size'],
initial_learning_rate=parameters['initial_learning_rate'],
num_epoch=parameters['num_epoch'],
steps_per_checkpoint=parameters['steps_per_checkpoint'],
target_vocab_size=parameters['target_vocab_size'],
model_dir=parameters['model_dir'],
target_embedding_size=parameters['target_embedding_size'],
attn_num_hidden=parameters['attn_num_hidden'],
attn_num_layers=parameters['attn_num_layers'],
clip_gradients=parameters['clip_gradients'],
max_gradient_norm=parameters['max_gradient_norm'],
load_model=parameters['load_model'],
valid_target_length=float('inf'),
gpu_id=parameters['gpu_id'],
use_gru=parameters['use_gru'],
session=sess)
model.launch()