type='exponential_decay'
)
)
tf.set_random_seed(1)
_, biggest_idx, _ = get_num_exps_and_res_files(save_path)
if biggest_idx is None:
initial_experiment_counter_value = 0
else:
initial_experiment_counter_value = biggest_idx + 1
env.grid_search_for_meta(
evaluation,
kwargs_for_pupil_building,
kwargs_for_optimizer_building,
build_pupil_hyperparameters=build_pupil_hyperparameters,
build_optimizer_hyperparameters=build_optimizer_hyperparameters,
other_hyperparameters=other_hyperparameters,
initial_experiment_counter_value=initial_experiment_counter_value,
**launch_kwargs
)
hp_names = get_hp_names_from_conf_file(parameter_set_file_name)
for_plotting = get_optimizer_evaluation_results(save_path, hp_names, AVERAGING_NUMBER)
best = get_best(for_plotting, 'optimizer')
metric_res = best['adam_prep']['loss']
best_on_valid = metric_res['validation']
print(' ' * 2 + 'loss' + ':', best_on_valid[1])
# opt_inf_pupil_restore_paths={
# 'prelearn2000': 'lstm/test_res_net_1000_emb150_nl1_nn100_bs32_nu10/checkpoints/2000'
# },
# opt_inf_additions_to_feed_dict=opt_inf_add_feed,
# opt_inf_validation_dataset_texts=[valid_text],
# opt_inf_train_dataset_texts=[train_text],
# validation_additions_to_feed_dict=valid_add_feed,
vocabulary=vocabulary,
batch_size=32,
num_unrollings=4,
learning_rate={
'type': 'exponential_decay',
'init': .002,
'decay': .5,
'period': 400
},
results_collect_interval=10,
opt_inf_results_collect_interval=1,
permute=False,
summary=True,
add_graph_to_summary=True)
env.grid_search_for_meta(
evaluation,
kwargs_for_pupil_building,
kwargs_for_optimizer_building,
build_pupil_hyperparameters=build_pupil_hyperparameters,
build_optimizer_hyperparameters=build_optimizer_hyperparameters,
other_hyperparameters=other_hyperparameters,
**launch_kwargs)
