def main(argv=None):
data.maybe_download_and_extract(FLAGS.data_dir)
#loop this
# for i in ["cnn", "small_cnn", "small_cnnact"]:
# FLAGS.model = i
print("THIS IS MODEL:", FLAGS.model)
train_loop()
def main(argv=None):
data.maybe_download_and_extract(FLAGS.data_dir)
# If cluster configuration flags were provided, save them
if FLAGS.ps_hosts != '' and FLAGS.worker_hosts != '':
ps_hosts = FLAGS.ps_hosts.split(",")
worker_hosts = FLAGS.worker_hosts.split(",")
cluster_config = {"ps": ps_hosts, "worker": worker_hosts}
# Save cluster configuration
with open('cluster.json', 'w') as f:
json.dump(cluster_config, f)
print('Cluster configuration saved.')
else:
try:
# Read cluster configuration
with open('cluster.json', 'r') as f:
cluster_config = json.load(f)
except (OSError, IOError) as e:
print("No cluster configuration found: you need to provide at " \
"least once the two lists of ps and worker hosts")
return
if FLAGS.job_name == '':
print('Pass this script a job name (ps or worker) to start a ' \
'training session.')
return
# Create a cluster
cluster = tf.train.ClusterSpec(cluster_config)
# Create and start a server for the local task.
server = tf.train.Server(cluster,
job_name=FLAGS.job_name,
task_index=FLAGS.task_index)
if FLAGS.job_name == "ps":
server.join()
elif FLAGS.job_name == "worker":
train.train_loop(cluster=cluster,
master=server.target,
task_index=FLAGS.task_index)
def main(argv=None):
data.maybe_download_and_extract(FLAGS.data_dir)
train_loop()
def main(argv=None):
data.maybe_download_and_extract(FLAGS.data_dir)
save_weights()
optimize_model_for_inference()
perform_inference()