def main():
args_parser = argparse.ArgumentParser()
args = parameters.initialise_arguments(args_parser)
parameters.HYPER_PARAMS = hparam.HParams(**args.__dict__)
# Set python level verbosity
tf.logging.set_verbosity(args.verbosity)
# Set C++ Graph Execution level verbosity
os.environ['TF_CPP_MIN_LOG_LEVEL'] = str(
tf.logging.__dict__[args.verbosity] / 10)
# Directory to store output model and checkpoints
output_dir = args.job_dir
# Run the training job
learn_runner.run(experiment.generate_experiment_fn(
min_eval_frequency=args.min_eval_frequency,
eval_delay_secs=args.eval_delay_secs,
train_steps=args.train_steps,
eval_steps=args.eval_steps,
export_strategies=[
saved_model_export_utils.make_export_strategy(
serving.SERVING_FUNCTIONS[args.export_format],
exports_to_keep=1,
default_output_alternative_key=None,
)
]),
run_config=run_config.RunConfig(model_dir=output_dir),
hparams=parameters.HYPER_PARAMS)
def main():
args_parser = argparse.ArgumentParser()
args = parameters.initialise_arguments(args_parser)
parameters.HYPER_PARAMS = args
print('')
print('Hyper-parameters:')
print(parameters.HYPER_PARAMS)
print('')
# Set python level verbosity
tf.logging.set_verbosity(args.verbosity)
# Set C++ Graph Execution level verbosity
os.environ['TF_CPP_MIN_LOG_LEVEL'] = str(
tf.logging.__dict__[args.verbosity] / 10)
# Directory to store output model and checkpoints
model_dir = args.job_dir
# If job_dir_reuse is False then remove the job_dir if it exists
print("Resume training:", args.reuse_job_dir)
if not args.reuse_job_dir:
if tf.gfile.Exists(args.job_dir):
tf.gfile.DeleteRecursively(args.job_dir)
print("Deleted job_dir {} to avoid re-use".format(args.job_dir))
else:
print("No job_dir available to delete")
else:
print("Reusing job_dir {} if it exists".format(args.job_dir))
run_config = tf.estimator.RunConfig(
tf_random_seed=19830610,
log_step_count_steps=1000,
save_checkpoints_secs=
120, # change if you want to change frequency of saving checkpoints
keep_checkpoint_max=3,
model_dir=model_dir)
run_config = run_config.replace(model_dir=model_dir)
print("Model Directory:", run_config.model_dir)
# Run the train and evaluate experiment
time_start = datetime.utcnow()
print("")
print("Experiment started at {}".format(time_start.strftime("%H:%M:%S")))
print(".......................................")
run_experiment(run_config)
time_end = datetime.utcnow()
print(".......................................")
print("Experiment finished at {}".format(time_end.strftime("%H:%M:%S")))
print("")
time_elapsed = time_end - time_start
print("Experiment elapsed time: {} seconds".format(
time_elapsed.total_seconds()))
print("")
def main():
args_parser = argparse.ArgumentParser()
args = parameters.initialise_arguments(args_parser)
parameters.HYPER_PARAMS = hparam.HParams(**args.__dict__)
# Set python level verbosity
tf.logging.set_verbosity(args.verbosity)
# Set C++ Graph Execution level verbosity
os.environ['TF_CPP_MIN_LOG_LEVEL'] = str(tf.logging.__dict__[args.verbosity] / 10)
# Directory to store output model and checkpoints
output_dir = args.job_dir
# If job_dir_reuse is False then remove the job_dir if it exists
if not args.reuse_job_dir:
if tf.gfile.Exists(args.job_dir):
tf.gfile.DeleteRecursively(args.job_dir)
tf.logging.info("Deleted job_dir {} to avoid re-use".format(args.job_dir))
else:
tf.logging.info("No job_dir available to delete")
else:
tf.logging.info("Reusing job_dir {} if it exists".format(args.job_dir))
# Run the training experiment
learn_runner.run(
experiment.generate_experiment_fn(
min_eval_frequency=args.min_eval_frequency,
eval_delay_secs=args.eval_delay_secs,
train_steps=args.train_steps,
eval_steps=args.eval_steps,
export_strategies=[saved_model_export_utils.make_export_strategy(
serving.SERVING_FUNCTIONS[args.export_format],
exports_to_keep=1,
default_output_alternative_key=None,
)]
),
run_config=tf.contrib.learn.RunConfig(
model_dir=output_dir,
log_device_placement=True
),
schedule="train_and_evaluate",
hparams=parameters.HYPER_PARAMS
)
