default='cv',
type=str,
help='Dataset for evaluation')
def main():
def eval_input_fn():
return input_fn(*data[config['data']],
batch_size=config['batch_size'],
shuffle=False)
data = inputs.load_data(config['n_examples_for_cv'])
estimator = tf.estimator.Estimator(model_fn=model_fn,
params=config,
model_dir=config['model_dir'])
for ckpt in tf.train.get_checkpoint_state(
config['model_dir']).all_model_checkpoint_paths:
with mu.Timer() as timer:
result = estimator.evaluate(eval_input_fn, checkpoint_path=ckpt)
result['data'] = config['data']
logger.info('Done in %.fs', timer.eclipsed)
logger.info('\n%s\n%s%s%s\n', data, '*' * 10, result, '*' * 10)
if __name__ == '__main__':
tf.logging.set_verbosity(tf.logging.INFO)
FLAGS = parser.parse_args()
config = mu.load_config(path=None, **FLAGS.__dict__)
logger.info('\n%s\n', mu.json_out(config))
main()
def main():
logger.info('\n%s\n', mu.json_out(config.state))
experiment = Experiment(config)
experiment.train()