def main(argv):
parser = resnet_run_loop.ResnetArgParser(
resnet_size_choices=[18, 34, 50, 101, 152, 200])
parser.set_defaults(
train_epochs=90,
version=1
)
flags = parser.parse_args(args=argv[2:])
seed = int(argv[1])
print('Setting random seed = ', seed)
print('special seeding')
mlperf_log.resnet_print(key=mlperf_log.RUN_SET_RANDOM_SEED, value=seed)
random.seed(seed)
tf.set_random_seed(seed)
numpy.random.seed(seed)
mlperf_log.resnet_print(key=mlperf_log.PREPROC_NUM_TRAIN_EXAMPLES,
value=_NUM_IMAGES['train'])
mlperf_log.resnet_print(key=mlperf_log.PREPROC_NUM_EVAL_EXAMPLES,
value=_NUM_IMAGES['validation'])
input_function = flags.use_synthetic_data and get_synth_input_fn() or input_fn
resnet_run_loop.resnet_main(seed,
flags, imagenet_model_fn, input_function,
shape=[_DEFAULT_IMAGE_SIZE, _DEFAULT_IMAGE_SIZE, _NUM_CHANNELS])
def main(argv):
parser = resnet_run_loop.ResnetArgParser(
resnet_size_choices=[18, 34, 50, 101, 152, 200])
flags = parser.parse_args(args=argv[1:])
input_function = flags.use_synthetic_data and get_synth_input_fn(
) or input_fn
resnet_run_loop.resnet_main(flags, imagenet_model_fn, input_function)
def main(argv):
parser = resnet_run_loop.ResnetArgParser()
# Set defaults that are reasonable for this model.
parser.set_defaults(data_dir='/tmp/cifar10_data',
model_dir='/tmp/cifar10_model',
resnet_size=32,
train_epochs=250,
epochs_between_evals=10,
batch_size=128)
flags = parser.parse_args(args=argv[1:])
input_function = flags.use_synthetic_data and get_synth_input_fn() or input_fn
resnet_run_loop.resnet_main(flags, cifar10_model_fn, input_function)
def main(argv):
parser = resnet_run_loop.ResnetArgParser(
resnet_size_choices=[18, 34, 50, 101, 152, 200])
parser.set_defaults(train_epochs=100)
flags = parser.parse_args(args=argv[1:])
input_function = flags.use_synthetic_data and get_synth_input_fn(
) or input_fn
resnet_run_loop.resnet_main(
flags,
imagenet_model_fn,
input_function,
shape=[_DEFAULT_IMAGE_SIZE, _DEFAULT_IMAGE_SIZE, _NUM_CHANNELS])
def main(argv):
parser = resnet_run_loop.ResnetArgParser(
resnet_size_choices=[18, 34, 50, 101, 152, 200])
parser.set_defaults(train_epochs=100)
flags = parser.parse_args(args=argv[1:])
#procid = os.environ['SLURM_PROCID']
#procid = os.environ['ALPS_APP_PE']
#flags.model_dir = flags.model_dir + '/' + procid
#flags.benchmark_log_dir = flags.benchmark_log_dir + '/' + procid
#flags.export_dir = flags.export_dir + '/' + procid
input_function = flags.use_synthetic_data and get_synth_input_fn(
) or input_fn
resnet_run_loop.resnet_main(
flags,
imagenet_model_fn,
input_function,
_NUM_IMAGES['train'],
_NUM_IMAGES['validation'],
shape=[_DEFAULT_IMAGE_SIZE, _DEFAULT_IMAGE_SIZE, _NUM_CHANNELS])
def main(argv):
parser = resnet_run_loop.ResnetArgParser(
resnet_size_choices=[18, 34, 50, 101, 152, 200])
parser.set_defaults(train_epochs=100)
flags = parser.parse_args(args=argv[2:])
seed = int(argv[1])
print('Setting random seed = ', seed)
print('special seeding')
random.seed(seed)
tf.set_random_seed(seed)
numpy.random.seed(seed)
input_function = flags.use_synthetic_data and get_synth_input_fn(
) or input_fn
resnet_run_loop.resnet_main(
seed,
flags,
imagenet_model_fn,
input_function,
shape=[_DEFAULT_IMAGE_SIZE, _DEFAULT_IMAGE_SIZE, _NUM_CHANNELS])
boundary_epochs=[30, 60, 80, 90],
decay_rates=[1, 0.1, 0.01, 0.001, 1e-4])
return resnet_run_loop.resnet_model_fn(features,
labels,
mode,
ImagenetModel,
resnet_size=params['resnet_size'],
weight_decay=1e-4,
learning_rate_fn=learning_rate_fn,
momentum=0.9,
data_format=params['data_format'],
version=params['version'],
loss_filter_fn=None,
multi_gpu=params['multi_gpu'])
def main(unused_argv):
input_function = FLAGS.use_synthetic_data and get_synth_input_fn(
) or input_fn
resnet_run_loop.resnet_main(FLAGS, imagenet_model_fn, input_function)
if __name__ == '__main__':
tf.logging.set_verbosity(tf.logging.INFO)
parser = resnet_run_loop.ResnetArgParser(
resnet_size_choices=[18, 34, 50, 101, 152, 200])
FLAGS, unparsed = parser.parse_known_args()
tf.app.run(argv=[sys.argv[0]] + unparsed)
resnet_size=params['resnet_size'],
weight_decay=weight_decay,
learning_rate_fn=learning_rate_fn,
momentum=0.9,
data_format=params['data_format'],
version=params['version'],
loss_filter_fn=loss_filter_fn,
multi_gpu=params['multi_gpu'])
def main(unused_argv):
input_function = FLAGS.use_synthetic_data and get_synth_input_fn(
) or input_fn
resnet_run_loop.resnet_main(FLAGS, cifar10_model_fn, input_function)
if __name__ == '__main__':
tf.logging.set_verbosity(tf.logging.INFO)
parser = resnet_run_loop.ResnetArgParser()
# Set defaults that are reasonable for this model.
parser.set_defaults(data_dir='/tmp/cifar10_data',
model_dir='/tmp/cifar10_model',
resnet_size=32,
train_epochs=250,
epochs_per_eval=10,
batch_size=128)
FLAGS, unparsed = parser.parse_known_args()
tf.app.run(argv=[sys.argv[0]] + unparsed)
