overwrite=False, reuse=False, shrink_factor=4, dtype=tf.float32, model_fn=model_fn, n_samples_per_ref=5, lr_fn=lambda global_step: tf.train.exponential_decay( hyper['initial_lr'], global_step, 100000, hyper['decay_factor']), hyper=hyper, ) sigopt_params = [ sigopt_double('initial_lr', 1e-5, 1e-3), sigopt_double('decay_factor', 1e-3, 0.5), sigopt_int('num_layers', 10, 20), ] default_params = { 'initial_lr': 0.000965352400196344, 'decay_factor': 0.0017387361908150767, 'num_layers': 15, } def create_train_model(hyper, **kwargs): model_setup = model_setup_params(hyper) model_setup.update(kwargs) return Model(**model_setup)
queue_cap=300, overwrite=False, reuse=False, shrink_factor=4, dtype=tf.float32, model_fn=model_fn, lr_fn=lambda global_step: tf.train.exponential_decay( hyper['initial_lr'], global_step, 100000, hyper['decay_factor']), hyper=hyper, ) sigopt_params = [ sigopt_double('initial_lr', 1e-5, 1e-3), sigopt_double('decay_factor', 1e-3, 0.5), sigopt_int('num_layers', 10, 20), sigopt_int('num_sub_layers', 1, 2), ] default_params = { 'initial_lr': 0.000965352400196344, 'decay_factor': 0.0017387361908150767, 'num_layers': 20, 'num_sub_layers': 2 } def create_train_model(hyper, **kwargs): model_setup = model_setup_params(hyper) model_setup.update(kwargs) return Model(**model_setup)