def build_network():
network = resnet_model.resnet(
resnet_depth=params['resnet_depth'],
num_classes=params['num_label_classes'],
dropblock_size=params['dropblock_size'],
dropblock_keep_probs=dropblock_keep_probs,
pre_activation=params['pre_activation'],
data_format=params['data_format'])
return network(inputs=features,
is_training=(mode == tf.estimator.ModeKeys.TRAIN))
def build_network():
network = resnet_model.resnet(
resnet_depth=params['resnet_depth'],
num_classes=params['num_label_classes'],
dropblock_size=params['dropblock_size'],
dropblock_keep_probs=dropblock_keep_probs,
pre_activation=params['pre_activation'],
norm_act_layer=params['norm_act_layer'],
data_format=params['data_format'],
se_ratio=params['se_ratio'],
drop_connect_rate=params['drop_connect_rate'],
use_resnetd_stem=params['use_resnetd_stem'],
resnetd_shortcut=params['resnetd_shortcut'],
replace_stem_max_pool=params['replace_stem_max_pool'],
dropout_rate=params['dropout_rate'],
bn_momentum=params['bn_momentum'])
return network(
inputs=features, is_training=(mode == tf.estimator.ModeKeys.TRAIN))