break tdp = preprocess_batch_for_training(preprocessor, batch, action_names) edp = EvaluationDataPage.create_from_tdp(tdp, trainer) if accumulated_edp is None: accumulated_edp = edp else: accumulated_edp = accumulated_edp.append(edp) accumulated_edp = accumulated_edp.compute_values(trainer.gamma) cpe_start_time = time.time() details = evaluator.evaluate_post_training(accumulated_edp) details.log() logger.info("CPE evaluation took {} seconds.".format(time.time() - cpe_start_time)) through_put = (len(dataset) * int(params["epochs"])) / (time.time() - start_time) logger.info("Training finished. Processed ~{} examples / s.".format( round(through_put))) if writer is not None: writer.close() return export_trainer_and_predictor(trainer, params["model_output_path"]) if __name__ == "__main__": params = parse_args(sys.argv) train_network(params)
num_batches = int(len(dataset) / training_parameters.minibatch_size) logger.info("Read in batch data set {} of size {} examples. Data split " "into {} batches of size {}.".format( params["training_data_path"], len(dataset), num_batches, training_parameters.minibatch_size, )) trainer = DQNTrainer(trainer_params, state_normalization, params["use_gpu"]) for epoch in range(params["epochs"]): for batch_idx in range(num_batches): helpers.report_training_status(batch_idx, num_batches, epoch, params["epochs"]) batch = dataset.read_batch(batch_idx) tdp = preprocess_batch_for_training(action_names, batch, state_normalization) trainer.train(tdp) logger.info("Training finished. Saving PyTorch model to {}".format( params["pytorch_output_path"])) helpers.save_model_to_file(trainer, params["pytorch_output_path"]) if __name__ == "__main__": params = helpers.parse_args(sys.argv) train_network(params)
tdp = preprocess_batch_for_training(preprocessor, batch, action_names) tdp.set_type(trainer.dtype) edp = EvaluationDataPage.create_from_tdp(tdp, trainer) if accumulated_edp is None: accumulated_edp = edp else: accumulated_edp = accumulated_edp.append(edp) accumulated_edp = accumulated_edp.compute_values(trainer.gamma) cpe_start_time = time.time() details = evaluator.evaluate_post_training(accumulated_edp) details.log() logger.info( "CPE evaluation took {} seconds.".format(time.time() - cpe_start_time) ) through_put = (len(dataset) * int(params["epochs"])) / (time.time() - start_time) logger.info( "Training finished. Processed ~{} examples / s.".format(round(through_put)) ) if writer is not None: writer.close() return export_trainer_and_predictor(trainer, params["model_output_path"]) if __name__ == "__main__": params = parse_args(sys.argv) train_network(params)