KeyErrors when running pipeline test about seq2seq

[minglu2019]

I met same problem, then I downgraded tensorflow 1.14.0 to1.12.0, it works

[akhilkishore]

I am facing the same problem , any solution for this ?

[jverce]

Same here, after applying suggestions from #316

[freecui]

I am facing the same problem , any solution for this ?

[kuhanw]

Is there a fix for this aside from downgrading tensorflow? I am having the same issue.

[YoshDesu]

Any news on this issue ?

[HowieYang0]

I added the following lines of code:

if not hasattr(flags.FLAGS, 'showprefixforinfo'):
    flags.DEFINE_string(
        'showprefixforinfo', 'true',
        'If False, do not prepend prefix to info messages when it is logged to stderr'
        )

Please check if it works for you?

[Marmar202]

I'm getting this error: return self._flags()[name]
KeyError: 'verbosity'

Here is the code:

def del_all_flags(FLAGS):
flags_dict = FLAGS._flags()
keys_list = [keys for keys in flags_dict]
for keys in keys_list:
FLAGS.delattr(keys)

del_all_flags(tf.flags.FLAGS)
flags.DEFINE_string(
'model_dir', None, 'Path to output model directory '
'where event and checkpoint files will be written.')
flags.DEFINE_string('pipeline_config_path', None, 'Path to pipeline config '
'file.')
flags.DEFINE_integer('num_train_steps', None, 'Number of train steps.')
flags.DEFINE_boolean('eval_training_data', False,
'If training data should be evaluated for this job. Note '
'that one call only use this in eval-only mode, and '
'checkpoint_dir must be supplied.')
flags.DEFINE_integer('sample_1_of_n_eval_examples', 1, 'Will sample one of '
'every n eval input examples, where n is provided.')
flags.DEFINE_integer('sample_1_of_n_eval_on_train_examples', 5, 'Will sample '
'one of every n train input examples for evaluation, '
'where n is provided. This is only used if '
'eval_training_data is True.')
flags.DEFINE_string(
'checkpoint_dir', None, 'Path to directory holding a checkpoint. If '
'checkpoint_dir is provided, this binary operates in eval-only mode, '
'writing resulting metrics to model_dir.')
flags.DEFINE_boolean(
'run_once', False, 'If running in eval-only mode, whether to run just '
'one round of eval vs running continuously (default).'
)
flags.DEFINE_integer(
'max_eval_retries', 0, 'If running continuous eval, the maximum number of '
'retries upon encountering tf.errors.InvalidArgumentError. If negative, '
'will always retry the evaluation.'
)
FLAGS = flags.FLAGS

def main(unused_argv):
flags.mark_flag_as_required('model_dir')
flags.mark_flag_as_required('pipeline_config_path')
config = tf.estimator.RunConfig(model_dir=FLAGS.model_dir)

train_and_eval_dict = model_lib.create_estimator_and_inputs(
run_config=config,
pipeline_config_path=FLAGS.pipeline_config_path,
train_steps=FLAGS.num_train_steps,
sample_1_of_n_eval_examples=FLAGS.sample_1_of_n_eval_examples,
sample_1_of_n_eval_on_train_examples=(
FLAGS.sample_1_of_n_eval_on_train_examples))
estimator = train_and_eval_dict['estimator']
train_input_fn = train_and_eval_dict['train_input_fn']
eval_input_fns = train_and_eval_dict['eval_input_fns']
eval_on_train_input_fn = train_and_eval_dict['eval_on_train_input_fn']
predict_input_fn = train_and_eval_dict['predict_input_fn']
train_steps = train_and_eval_dict['train_steps']

if FLAGS.checkpoint_dir:
if FLAGS.eval_training_data:
name = 'training_data'
input_fn = eval_on_train_input_fn
else:
name = 'validation_data'
# The first eval input will be evaluated.
input_fn = eval_input_fns[0]
if FLAGS.run_once:
estimator.evaluate(input_fn,
steps=None,
checkpoint_path=tf.train.latest_checkpoint(
FLAGS.checkpoint_dir))
else:
model_lib.continuous_eval(estimator, FLAGS.checkpoint_dir, input_fn,
train_steps, name, FLAGS.max_eval_retries)
else:
train_spec, eval_specs = model_lib.create_train_and_eval_specs(
train_input_fn,
eval_input_fns,
eval_on_train_input_fn,
predict_input_fn,
train_steps,
eval_on_train_data=False)

# Currently only a single Eval Spec is allowed.
tf.estimator.train_and_evaluate(estimator, train_spec, eval_specs[0])

if name == 'main':
tf.app.run()

ANY HELP WOULD BE APPRECIATED!