def create_model(): """Factory method that creates model to be used by generic task.py.""" parser = argparse.ArgumentParser() # Label count needs to correspond to nubmer of labels in dictionary used # during preprocessing. parser.add_argument('--label_count', type=int, default=LABEL_COUNT) parser.add_argument('--dropout', type=float, default=0.5) parser.add_argument( '--inception_checkpoint_file', type=str, default=DEFAULT_INCEPTION_CHECKPOINT) args, task_args = parser.parse_known_args() # Adding the following 'hack' to make the example easier to run: use the # classifier_label count if it was specified as a task.py arg, to set the # model label_count. if '--classifier_label_count' in task_args: clabel_count = task_args[task_args.index('--classifier_label_count') + 1] logging.info("Found classifier_label_count task arg.") try: clabel_count = int(clabel_count) args.label_count = clabel_count except: logging.info("classifier label count was set, but is not an int.") logging.info("using label count: %s", args.label_count) override_if_not_in_args('--max_steps', '1000', task_args) override_if_not_in_args('--batch_size', '100', task_args) override_if_not_in_args('--eval_set_size', '370', task_args) override_if_not_in_args('--eval_interval_secs', '2', task_args) override_if_not_in_args('--log_interval_secs', '2', task_args) override_if_not_in_args('--min_train_eval_rate', '2', task_args) return Model(args.label_count, args.dropout, args.inception_checkpoint_file), task_args
def create_model(): """Factory method that creates model to be used by generic task.py.""" parser = argparse.ArgumentParser() parser.add_argument('--learning_rate', type=float, default=0.01) args, task_args = parser.parse_known_args() override_if_not_in_args('--max_steps', '5000', task_args) override_if_not_in_args('--batch_size', '100', task_args) override_if_not_in_args('--eval_set_size', '10000', task_args) override_if_not_in_args('--eval_interval_secs', '1', task_args) override_if_not_in_args('--log_interval_secs', '1', task_args) override_if_not_in_args('--min_train_eval_rate', '1', task_args) return Model(args.learning_rate, HIDDEN1, HIDDEN2), task_args
def create_model(): """Factory method that creates model to be used by generic task.py.""" parser = argparse.ArgumentParser() parser.add_argument('--learning_rate', type=float, default=0.002) args, task_args = parser.parse_known_args() override_if_not_in_args('--max_steps', '5000', task_args) override_if_not_in_args('--batch_size', '256', task_args) override_if_not_in_args('--eval_set_size', '10000', task_args) override_if_not_in_args('--eval_interval_secs', '1', task_args) override_if_not_in_args('--log_interval_secs', '1', task_args) override_if_not_in_args('--min_train_eval_rate', '1', task_args) return Model(args.learning_rate, HIDDEN1, HIDDEN2), task_args
def create_model(): """Factory method that creates model to be used by generic task.py.""" # HYPERPARAMETER TUNING: These flags are set by Cloud ML from the # hyperparameters defined in the API call. They will be passed in as normal # command line flags. parser = argparse.ArgumentParser() parser.add_argument('--learning_rate', type=float, default=0.01) parser.add_argument('--hidden1', type=int, default=128) parser.add_argument('--hidden2', type=int, default=32) args, task_args = parser.parse_known_args() override_if_not_in_args('--max_steps', '5000', task_args) override_if_not_in_args('--batch_size', '100', task_args) override_if_not_in_args('--eval_set_size', '10000', task_args) # HYPERPARAMETER TUNING: Do not write the objective value too frequently. override_if_not_in_args('--eval_interval_secs', '10', task_args) override_if_not_in_args('--log_interval_secs', '10', task_args) override_if_not_in_args('--min_train_eval_rate', '5', task_args) return Model(args.learning_rate, args.hidden1, args.hidden2), task_args
def create_model(): """Factory method that creates model to be used by generic task.py.""" parser = argparse.ArgumentParser() # Label count needs to correspond to nubmer of labels in dictionary used # during preprocessing. parser.add_argument('--label_count', type=int, default=5) parser.add_argument('--dropout', type=float, default=0.5) parser.add_argument( '--inception_checkpoint_file', type=str, default=DEFAULT_INCEPTION_CHECKPOINT) args, task_args = parser.parse_known_args() override_if_not_in_args('--max_steps', '1000', task_args) override_if_not_in_args('--batch_size', '100', task_args) override_if_not_in_args('--eval_set_size', '370', task_args) override_if_not_in_args('--eval_interval_secs', '2', task_args) override_if_not_in_args('--log_interval_secs', '2', task_args) override_if_not_in_args('--min_train_eval_rate', '2', task_args) return Model(args.label_count, args.dropout, args.inception_checkpoint_file), task_args