def imagenet_model_fn(features, labels, mode, params):
"""Our model_fn for ResNet to be used with our Estimator."""
learning_rate_fn = resnet_run_loop.learning_rate_with_decay(
batch_size=params['batch_size'], batch_denom=256,
num_images=_NUM_IMAGES['train'], boundary_epochs=[30, 60, 80, 90],
decay_rates=[1, 0.1, 0.01, 0.001, 1e-4])
return resnet_model_fn(features, labels, mode,
Imagenet16Model,
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 cifar10_model_fn(features, labels, mode, params):
"""Model function for CIFAR-10."""
features = tf.reshape(features, [-1, HEIGHT, WIDTH, NUM_CHANNELS])
# Learning rate schedule follows arXiv:1512.03385 for ResNet-56 and under.
learning_rate_fn = resnet_run_loop.learning_rate_with_decay(
batch_size=params['batch_size'] * params.get('num_workers', 1),
batch_denom=128,
num_images=NUM_IMAGES['train'],
boundary_epochs=[91, 136, 182],
decay_rates=[1, 0.1, 0.01, 0.001])
# Weight decay of 2e-4 diverges from 1e-4 decay used in the ResNet paper
# and seems more stable in testing. The difference was nominal for ResNet-56.
weight_decay = 2e-4
# Empirical testing showed that including batch_normalization variables
# in the calculation of regularized loss helped validation accuracy
# for the CIFAR-10 dataset, perhaps because the regularization prevents
# overfitting on the small data set. We therefore include all vars when
# regularizing and computing loss during training.
def loss_filter_fn(_):
return True
return resnet_run_loop.resnet_model_fn(
features=features,
labels=labels,
mode=mode,
model_class=Cifar10Model,
resnet_size=params['resnet_size'],
weight_decay=weight_decay,
learning_rate_fn=learning_rate_fn,
momentum=0.9,
data_format=params['data_format'],
resnet_version=params['resnet_version'],
loss_scale=params['loss_scale'],
loss_filter_fn=loss_filter_fn,
dtype=params['dtype'],
fine_tune=params['fine_tune'])
def imagenet_model_fn(features, labels, mode, params):
"""Our model_fn for ResNet to be used with our Estimator."""
# Warmup and higher lr may not be valid for fine tuning with small batches
# and smaller numbers of training images.
if params['fine_tune']:
warmup = False
base_lr = .1
else:
warmup = True
base_lr = .128
learning_rate_fn = resnet_run_loop.learning_rate_with_decay(
batch_size=params['batch_size'] * params.get('num_workers', 1),
batch_denom=256,
num_images=NUM_IMAGES['train'],
boundary_epochs=[30, 60, 80, 90],
decay_rates=[1, 0.1, 0.01, 0.001, 1e-4],
warmup=warmup,
base_lr=base_lr)
return resnet_run_loop.resnet_model_fn(
features=features,
labels=labels,
mode=mode,
model_class=ImagenetModel,
resnet_size=params['resnet_size'],
weight_decay=flags.FLAGS.weight_decay,
learning_rate_fn=learning_rate_fn,
momentum=0.9,
data_format=params['data_format'],
resnet_version=params['resnet_version'],
loss_scale=params['loss_scale'],
loss_filter_fn=None,
dtype=params['dtype'],
fine_tune=params['fine_tune'],
label_smoothing=flags.FLAGS.label_smoothing)