vocabulary=vocabulary, with_meta_optimizer=True, restore_path=the_only_pupil_restore_path, restore_optimizer_path=os.path.join(training_path, 'checkpoints', 'final'), save_path=os.path.join(base, 'loss_best', 'test', 'pupil_training'), allow_growth=True, batch_size=BATCH_SIZE, checkpoint_steps=None, result_types=['perplexity', 'loss', 'bpc', 'accuracy'], printed_result_types=['perplexity', 'loss', 'bpc', 'accuracy'], stop=OPTIMIZER_RANGE, # stop=4000, train_dataset_text=train_text, validation_dataset_texts=[valid_text], results_collect_interval=1, additions_to_feed_dict=opt_inf_add_feed, validation_additions_to_feed_dict=valid_add_feed, no_validation=False, ) env.test( restore_path=os.path.join(base, 'loss_best', 'test', 'pupil_training', 'checkpoints/final'), save_path=os.path.join(base, 'loss_best', 'test', 'testing'), additions_to_feed_dict=valid_add_feed, validation_dataset_texts=[test_text], valid_batch_kwargs=dict( vocabulary=vocabulary ), printed_result_types=['perplexity', 'loss', 'bpc', 'accuracy'] )
results_collect_interval=config['results_collect_interval'], no_validation=False, validation_batch_size=BATCH_SIZE, valid_batch_kwargs=dict(num_unrollings=config['num_unrollings'], ), log_launch=False, ) if config['train']: restore_path = os.path.join(os.path.expanduser(config['save_path']), 'checkpoints/all_vars/best') if config['test']: env.test( restore_path=restore_path, save_path=os.path.expanduser(config['save_path']) + '/testing', vocabulary=vocabulary, validation_datasets={'test': test_text}, printed_result_types=['perplexity', 'loss', 'bpc', 'accuracy'], validation_batch_size=BATCH_SIZE, valid_batch_kwargs=dict(num_unrollings=config['num_unrollings'], ), log_launch=False, ) if config['file_dialog']: env.file_dialog( restore_path=restore_path, vocabulary=vocabulary, input_replica_file=os.path.expanduser(config['input_replica_file']), result_file=os.path.expanduser(config['file_dialog_answers_file']), character_positions_in_vocabulary=cpiv, batch_generator_class=BatchGenerator, reset_state_after_model_answer=args. reset_state_after_model_answer, # if True after bot answer hidden state is reset answer_len_limit=500., # max number of characters in bot answer
# pupil_restore_paths=['debug_empty_meta_optimizer/not_learning_issue_es20_nn20/checkpoints/0'], # stop=stop_specs, restore_path=RESTORE_PATH, save_path=training_path, stop=1000, results_collect_interval=500, summary=False, add_graph_to_summary=False, train_dataset=dict(train='train'), train_batch_kwargs=dict(data_dir=data_dir), valid_batch_kwargs=dict(data_dir=data_dir), # train_dataset_text='abc', validation_datasets=dict(valid='validation'), learning_rate=dict( init=best_conf['learning_rate/init'], decay=1., period=1e+6, type='exponential_decay', ), batch_size=BATCH_SIZE, no_validation=True, ) env.test( restore_path=os.path.join(training_path, 'checkpoints/final'), save_path=os.path.join(base, metric + '_best', 'test', 'testing'), additions_to_feed_dict=valid_add_feed, validation_datasets=dict(test='test'), valid_batch_kwargs=dict(data_dir=data_dir), printed_result_types=['perplexity', 'loss', 'bpc', 'accuracy'])
save_path=path, # restore_path='lstm_sample_test/scipop3_1000_bs256_11.12/checkpoints/2000', learning_rate=learning_rate, batch_size=BATCH_SIZE, checkpoint_steps=None, result_types=['perplexity', 'loss', 'bpc', 'accuracy'], printed_result_types=['perplexity', 'loss', 'bpc', 'accuracy'], stop=stop_specs, # stop=1000, train_dataset=dict(train='train'), train_batch_kwargs=dict(valid_size=VALID_SIZE), valid_batch_kwargs=dict(valid_size=VALID_SIZE), # train_dataset_text='abc', validation_datasets=dict(valid='validation'), results_collect_interval=100, additions_to_feed_dict=train_add_feed, validation_additions_to_feed_dict=valid_add_feed, no_validation=False, summary=False, add_graph_to_summary=False, ) env.test(restore_path=os.path.join(path, 'checkpoints/best'), save_path=os.path.join(path, 'test'), additions_to_feed_dict=valid_add_feed, validation_datasets=dict(test='test'), valid_batch_kwargs=dict(valid_size=VALID_SIZE), printed_result_types=['perplexity', 'loss', 'bpc', 'accuracy']) # log_device_placement=True)
valid_add_feed = [ # {'placeholder': 'sampling_prob', 'value': 1.}, { 'placeholder': 'dropout', 'value': 1. } ] add_metrics = ['bpc', 'perplexity', 'accuracy'] tf.set_random_seed(1) env.build_pupil(batch_size=32, num_layers=1, num_nodes=[100], num_output_layers=1, num_output_nodes=[], vocabulary_size=vocabulary_size, embedding_size=150, num_unrollings=4, init_parameter=3., num_gpus=1, regime='training_with_meta_optimizer', additional_metrics=add_metrics, going_to_limit_memory=True) env.test(restore_path='lstm/text8_pretrain/checkpoints/200', save_path='lstm/text8_pretrain/validation200', vocabulary=vocabulary, additions_to_feed_dict=valid_add_feed, validation_dataset_texts=[valid_text], printed_result_types=['perplexity', 'loss', 'bpc', 'accuracy'])