def build_network():
if model == 'resnet':
network = resnet_v1(resnet_depth=model_depth,
num_classes=n_classes,
data_format=data_format)
return network(inputs=features,
is_training=(mode == tf.estimator.ModeKeys.TRAIN))
elif model == 'densenet':
if model_depth == 121:
return densenet_imagenet_121(
features,
is_training=(mode == tf.estimator.ModeKeys.TRAIN),
num_classes=n_classes)
elif model_depth == 169:
return densenet_imagenet_169(
features,
is_training=(mode == tf.estimator.ModeKeys.TRAIN),
num_classes=n_classes)
elif model_depth == 201:
return densenet_imagenet_201(
features,
is_training=(mode == tf.estimator.ModeKeys.TRAIN),
num_classes=n_classes)
else:
raise Exception(f'Unkown densenet model depth: {model_depth}')
else:
raise Exception(f'Unknown model: {model}')
def build_network():
network = resnet_v1(
resnet_depth=resnet_depth,
num_classes=n_classes,
data_format=data_format)
return network(
inputs=features, is_training=(mode == tf.estimator.ModeKeys.TRAIN))
def build_network():
network = resnet_v1(resnet_depth=resnet_depth,
num_classes=n_classes,
data_format=data_format)
return network(inputs=image,
cell=one_hot_cell,
well_type=one_hot_well,
is_training=(mode == tf.estimator.ModeKeys.TRAIN))