when i debug,a error have found, who can help me what's wrong,thanks tf.flags.DEFINE_string("config_paths", "
../example_configs/conv_seq2seq.yml,
../example_configs/train_seq2seq.yml,
../example_configs/text_metrics_bpe.yml", """Path to a YAML configuration files defining FLAG values. Multiple files can be separated by commas. Files are merged recursively. Setting a key in these files is equivalent to setting the FLAG value with the same name.""") tf.flags.DEFINE_string("hooks", "[]", """YAML configuration string for the training hooks to use.""") tf.flags.DEFINE_string("metrics", "[]", """YAML configuration string for the training metrics to use.""") tf.flags.DEFINE_string("model", "", """Name of the model class. Can be either a fully-qualified name, or the name of a class defined in seq2seq.models.""") tf.flags.DEFINE_string("model_params", '{"vocab_source": "/root/nmt_data/toy_reverse/train/vocab.sources.txt","vocab_target": "/root/nmt_data/toy_reverse/train/vocab.targets.txt"}', """YAML configuration string for the model parameters.""")

tf.flags.DEFINE_string("input_pipeline_train", '{"class": "ParallelTextInputPipelineFairseq", "params": {"source_files": "/root/nmt_data/toy_reverse/train/sources.txt", "target_files": "/root/nmt_data/toy_reverse/train/targets.txt"}}', """YAML configuration string for the training data input pipeline.""") tf.flags.DEFINE_string("input_pipeline_dev", '{"class": "ParallelTextInputPipelineFairseq", "params": {"source_files": "/root/nmt_data/toy_reverse/dev/sources.txt", "target_files": "/root/nmt_data/toy_reverse/dev/targets.txt"}}', """YAML configuration string for the development data input pipeline.""") tf.flags.DEFINE_string("buckets", None, """Buckets input sequences according to these length. A comma-separated list of sequence length buckets, e.g. "10,20,30" would result in 4 buckets: <10, 10-20, 20-30, >30. None disabled bucketing. """) tf.flags.DEFINE_integer("batch_size", 32, """Batch size used for training and evaluation.""") tf.flags.DEFINE_string("output_dir", None, """The directory to write model checkpoints and summaries to. If None, a local temporary directory is created.""")

Training parameters

tf.flags.DEFINE_string("schedule", "continuous_train_and_eval", """Estimator function to call, defaults to continuous_train_and_eval for local run""") tf.flags.DEFINE_integer("train_steps", None, """Maximum number of training steps to run. If None, train forever.""") tf.flags.DEFINE_integer("eval_every_n_steps", 1000, "Run evaluation on validation data every N steps.")

RunConfig Flags

tf.flags.DEFINE_integer("tf_random_seed", None, """Random seed for TensorFlow initializers. Setting this value allows consistency between reruns.""") tf.flags.DEFINE_integer("save_checkpoints_secs", None, """Save checkpoints every this many seconds. Can not be specified with save_checkpoints_steps.""") tf.flags.DEFINE_integer("save_checkpoints_steps", None, """Save checkpoints every this many steps. Can not be specified with save_checkpoints_secs.""") tf.flags.DEFINE_integer("keep_checkpoint_max", 5, """Maximum number of recent checkpoint files to keep. As new files are created, older files are deleted. If None or 0, all checkpoint files are kept.""") tf.flags.DEFINE_integer("keep_checkpoint_every_n_hours", 4, """In addition to keeping the most recent checkpoint files, keep one checkpoint file for every N hours of training.""") tf.flags.DEFINE_float("gpu_memory_fraction", 1.0, """Fraction of GPU memory used by the process on each GPU uniformly on the same machine.""") tf.flags.DEFINE_boolean("gpu_allow_growth", False, """Allow GPU memory allocation to grow dynamically.""") tf.flags.DEFINE_boolean("log_device_placement", False, """Log the op placement to devices""")