def imagenet_model_fn(features, labels, mode, params):
"""Our model_fn for ResNet to be used with our Estimator."""
if params['flag_bias'] == True:
## lr for bias correction learning
learning_rate_fn = resnet.learning_rate_with_decay(
batch_size=params['batch_size'], batch_denom=256,
num_images=params['num_train_images'],
boundary_epochs=[30*_BIAS_EPOCHS, 60*_BIAS_EPOCHS, 80*_BIAS_EPOCHS, 90*_BIAS_EPOCHS],
decay_rates=[1, 0.1, 0.01, 0.001, 1e-4])
else:
## normal train lr
learning_rate_fn = resnet.learning_rate_with_decay(
batch_size=params['batch_size'], batch_denom=256,
num_images=params['num_train_images'], boundary_epochs=[30, 60, 80, 90],
decay_rates=[1, 0.1, 0.01, 0.001, 1e-4])
## increase the weight decay in initial stages
## This line of code is here and left unchanged for a long time.
weight_decay = 1e-4 * nb_groups / (params['itera'] + 1)
return resnet.resnet_model_fn(features, labels, mode, ImagenetModel,
resnet_size=params['resnet_size'],
weight_decay=weight_decay,
learning_rate_fn=learning_rate_fn,
momentum=0.9,
data_format=params['data_format'],
itera=params['itera'],
nb_groups=params['nb_groups'],
restore_model_dir=params['restore_model_dir'],
flag_bias=params['flag_bias'],
loss_filter_fn=None)
def cifar10_model_fn(features, labels, mode, params):
"""Model function for CIFAR-10."""
features = tf.reshape(features, [-1, _HEIGHT, _WIDTH, _NUM_CHANNELS])
learning_rate_fn = resnet.learning_rate_with_decay(
batch_size=params['batch_size'], batch_denom=128,
num_images=_NUM_IMAGES['train'], boundary_epochs=[100, 150, 200],
decay_rates=[1, 0.1, 0.01, 0.001])
# We use a weight decay of 0.0002, which performs better
# than the 0.0001 that was originally suggested.
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(name):
return True
return resnet.resnet_model_fn(features, labels, mode, Cifar10Model,
resnet_size=params['resnet_size'],
weight_decay=weight_decay,
learning_rate_fn=learning_rate_fn,
momentum=0.9,
data_format=params['data_format'],
loss_filter_fn=loss_filter_fn)
def cifar10_model_fn(features, labels, mode, params):
"""Model function for CIFAR-10."""
features = tf.reshape(features, [-1, _HEIGHT, _WIDTH, _NUM_CHANNELS])
learning_rate_fn = resnet.learning_rate_with_decay(
batch_size=params['batch_size'],
batch_denom=128,
num_images=_NUM_IMAGES['train'],
boundary_epochs=[100, 150, 200],
decay_rates=[1, 0.1, 0.01, 0.001])
# We use a weight decay of 0.0002, which performs better
# than the 0.0001 that was originally suggested.
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(name):
return True
return resnet.resnet_model_fn(features,
labels,
mode,
Cifar10Model,
resnet_size=params['resnet_size'],
weight_decay=weight_decay,
learning_rate_fn=learning_rate_fn,
momentum=0.9,
data_format=params['data_format'],
loss_filter_fn=loss_filter_fn,
multi_gpu=params['multi_gpu'])
def imagenet_model_fn(features, labels, mode, params):
"""Our model_fn for ResNet to be used with our Estimator."""
learning_rate_fn = resnet.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.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'],
loss_filter_fn=None)
def imagenet_model_fn(features, labels, mode, params):
"""Our model_fn for ResNet to be used with our Estimator."""
learning_rate_fn = resnet.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.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'],
loss_filter_fn=None)
def cifar_model_fn(features, labels, mode, params):
"""
Returns the cifar-10 residual network model
:param features: the image data
:param labels: the labels of images
:param mode: the mode, training, evaluation or prediction
:param params: the parameters to be passed to the model
:return: a resnet model fn
"""
features = tf.reshape(features, [-1, _WIDTH, _HEIGHT, _CHANNELS])
# the function that decays learning rate overtime
learning_rate_fn = resnet.learning_rate_with_decay(
num_images=_NUM_IMAGES['train'],
batch_size=params['batch_size'],
boundary_epochs=[100, 150, 200],
decay_rates=[1, 0.1, 0.01, 0.001])
weight_decay = 2e-4
return resnet_model.resnet_model_fn(features, labels, mode, Cifar10Model,
params['resnet_size'], weight_decay,
learning_rate_fn, 0.9,
params['data_format'],
params['multi_gpu'])