def build_network(num_outputs,
architecture,
classification=False,
no_softmax=False,
name=None):
""" Constructs a CNN.
# Arguments:
- num_outputs: number of final output units.
- architecture: name of the architecture. See ARCHITECTURES for a list of possible values and README.md for descriptions.
- classification: If `True`, the final layer will have a softmax activation, otherwise no activation at all.
- no_softmax: Usually, the last layer will have a softmax activation if `classification` is True. However, if `no_softmax` is set
to True as well, the last layer will not have any activation.
- name: The name of the network.
# Returns:
keras.models.Model
"""
if architecture.lower().endswith('-selu'):
activation = 'selu'
architecture = architecture[:-5]
else:
activation = 'relu'
# CIFAR-100 architectures
if architecture == 'resnet-32':
return cifar_resnet.SmallResNet(
5,
filters=[16, 32, 64],
activation=activation,
include_top=classification,
top_activation=None if no_softmax else 'softmax',
classes=num_outputs,
name=name)
elif architecture == 'resnet-110':
return cifar_resnet.SmallResNet(
18,
filters=[16, 32, 64],
activation=activation,
include_top=classification,
top_activation=None if no_softmax else 'softmax',
classes=num_outputs,
name=name)
elif architecture == 'resnet-110-fc':
return cifar_resnet.SmallResNet(
18,
filters=[16, 32, 64],
activation=activation,
include_top=True,
top_activation='softmax' if classification and
(not no_softmax) else None,
classes=num_outputs,
name=name)
elif architecture == 'resnet-110-wfc':
return cifar_resnet.SmallResNet(
18,
filters=[32, 64, 128],
activation=activation,
include_top=True,
top_activation='softmax' if classification and
(not no_softmax) else None,
classes=num_outputs,
name=name)
elif architecture == 'wrn-28-10':
return wrn.create_wide_residual_network(
(32, 32, 3),
nb_classes=num_outputs,
N=4,
k=10,
verbose=0,
final_activation='softmax' if classification and
(not no_softmax) else None,
name=name)
elif architecture == 'densenet-100-12':
return densenet.DenseNet(growth_rate=12,
depth=100,
nb_dense_block=3,
bottleneck=False,
nb_filter=16,
reduction=0.0,
classes=num_outputs,
activation='softmax' if classification and
(not no_softmax) else None,
name=name)
elif architecture == 'densenet-100-24':
return densenet.DenseNet(growth_rate=24,
depth=100,
nb_dense_block=3,
bottleneck=False,
nb_filter=16,
reduction=0.0,
classes=num_outputs,
activation='softmax' if classification and
(not no_softmax) else None,
name=name)
elif architecture == 'densenet-bc-190-40':
return densenet.DenseNet(growth_rate=40,
depth=190,
nb_dense_block=3,
bottleneck=True,
nb_filter=-1,
reduction=0.5,
classes=num_outputs,
activation='softmax' if classification and
(not no_softmax) else None,
name=name)
elif architecture == 'pyramidnet-272-200':
return cifar_pyramidnet.PyramidNet(
272,
200,
bottleneck=True,
activation=activation,
classes=num_outputs,
top_activation='softmax' if classification and
(not no_softmax) else None,
name=name)
elif architecture == 'pyramidnet-110-270':
return cifar_pyramidnet.PyramidNet(
110,
270,
bottleneck=False,
activation=activation,
classes=num_outputs,
top_activation='softmax' if classification and
(not no_softmax) else None,
name=name)
elif architecture == 'simple':
return plainnet.PlainNet(
num_outputs,
activation=activation,
final_activation='softmax' if classification and
(not no_softmax) else None,
name=name)
# ImageNet architectures
elif architecture in ('resnet-50', 'resnet-101', 'resnet-152'):
if architecture == 'resnet-101':
factory = keras_applications.resnet.ResNet101
elif architecture == 'resnet-152':
factory = keras_applications.resnet.ResNet152
else:
# ResNet50 has been available from the beginning, while the other two were added in keras-applications 1.0.7.
# Thus, we use the initial implementation of ResNet50 for compatibility's sake.
factory = keras.applications.ResNet50
rn = factory(include_top=False, weights=None)
# Depending on the Keras version, the ResNet50 model may or may not contain a final average pooling layer.
rn_out = rn.layers[-2].output if isinstance(
rn.layers[-1],
keras.layers.AveragePooling2D) else rn.layers[-1].output
x = keras.layers.GlobalAvgPool2D(name='avg_pool')(rn_out)
x = keras.layers.Dense(
num_outputs,
activation='softmax' if classification and
(not no_softmax) else None,
name='prob' if classification else 'embedding')(x)
return keras.models.Model(rn.inputs, x, name=name)
elif architecture.startswith('rn'):
import keras_resnet.models
factories = {
'rn18': keras_resnet.models.ResNet18,
'rn34': keras_resnet.models.ResNet34,
'rn50': keras_resnet.models.ResNet50,
'rn101': keras_resnet.models.ResNet101,
'rn152': keras_resnet.models.ResNet152,
'rn200': keras_resnet.models.ResNet200
}
input_ = keras.layers.Input((3, None, None)) if K.image_data_format(
) == 'channels_first' else keras.layers.Input((None, None, 3))
rn = factories[architecture](input_,
include_top=classification
and (not no_softmax),
classes=num_outputs,
freeze_bn=False,
name=name)
if (not classification) or no_softmax:
x = keras.layers.GlobalAvgPool2D(name='avg_pool')(rn.outputs[-1])
x = keras.layers.Dense(
num_outputs,
name='prob' if classification else 'embedding',
activation=None if no_softmax else 'softmax')(x)
rn = keras.models.Model(input_, x, name=name)
return rn
elif architecture == 'nasnet-a':
nasnet = keras.applications.NASNetLarge(include_top=False,
input_shape=(224, 224, 3),
weights=None,
pooling='avg')
x = keras.layers.Dense(num_outputs,
activation='softmax' if classification and
(not no_softmax) else None,
name='prob' if classification else 'embedding')(
nasnet.output)
return keras.models.Model(nasnet.inputs, x, name=name)
else:
raise ValueError(
'Unknown network architecture: {}'.format(architecture))
def build_network(num_outputs, architecture, classification = False, name = None):
""" Constructs a CNN.
# Arguments:
- num_outputs: number of final output units.
- architecture: name of the architecture. See ARCHITECTURES for a list of possible values and README.md for descriptions.
- classification: If `True`, the final layer will have a softmax activation, otherwise no activation at all.
- name: The name of the network.
# Returns:
keras.models.Model
"""
if architecture.lower().endswith('-selu'):
activation = 'selu'
architecture = architecture[:-5]
else:
activation = 'relu'
# CIFAR-100 architectures
if architecture == 'resnet-32':
return cifar_resnet.SmallResNet(5, filters = [16, 32, 64] if classification else [32, 64, num_outputs], activation = activation,
include_top = classification, classes = num_outputs, name = name)
elif architecture == 'resnet-110':
return cifar_resnet.SmallResNet(18, filters = [16, 32, 64] if classification else [32, 64, num_outputs], activation = activation,
include_top = classification, classes = num_outputs, name = name)
elif architecture == 'resnet-110-fc':
return cifar_resnet.SmallResNet(18, filters = [32, 64, 128], activation = activation,
include_top = True, top_activation = 'softmax' if classification else None,
classes = num_outputs, name = name)
elif architecture == 'wrn-28-10':
return wrn.create_wide_residual_network((32, 32, 3), nb_classes = num_outputs, N = 4, k = 10, verbose = 0,
final_activation = 'softmax' if classification else None, name = name)
elif architecture == 'densenet-100-12':
return densenet.DenseNet(growth_rate = 12, depth = 100, bottleneck = False,
classes = num_outputs, activation = 'softmax' if classification else None, name = name)
elif architecture == 'pyramidnet-272-200':
return cifar_pyramidnet.PyramidNet(272, 200, bottleneck = True, activation = activation,
classes = num_outputs, top_activation = 'softmax' if classification else None, name = name)
elif architecture == 'pyramidnet-110-270':
return cifar_pyramidnet.PyramidNet(110, 270, bottleneck = False, activation = activation,
classes = num_outputs, top_activation = 'softmax' if classification else None, name = name)
elif architecture == 'simple':
return plainnet.PlainNet(num_outputs,
activation = activation,
final_activation = 'softmax' if classification else None,
name = name)
# ImageNet architectures
elif architecture == 'resnet-50':
rn50 = keras.applications.ResNet50(include_top=False, weights=None)
rn50_out = rn50.layers[-2].output if isinstance(rn50.layers[-1], keras.layers.AveragePooling2D) else rn50.layers[-1].output
x = keras.layers.GlobalAvgPool2D(name='avg_pool')(rn50_out)
x = keras.layers.Dense(num_outputs, activation = 'softmax' if classification else None, name = 'prob' if classification else 'embedding')(x)
return keras.models.Model(rn50.inputs, x, name=name)
elif architecture.startswith('rn'):
import keras_resnet.models
factories = {
'rn18' : keras_resnet.models.ResNet18,
'rn34' : keras_resnet.models.ResNet34,
'rn50' : keras_resnet.models.ResNet50,
'rn101' : keras_resnet.models.ResNet101,
'rn152' : keras_resnet.models.ResNet152,
'rn200' : keras_resnet.models.ResNet200
}
input_ = keras.layers.Input((3, None, None)) if K.image_data_format() == 'channels_first' else keras.layers.Input((None, None, 3))
rn = factories[architecture](input_, include_top = classification, classes = num_outputs, freeze_bn = False, name = name)
if not classification:
x = keras.layers.GlobalAvgPool2D(name = 'avg_pool')(rn.outputs[-1])
x = keras.layers.Dense(num_outputs, name = 'embedding')(x)
rn = keras.models.Model(input_, x, name = name)
return rn
elif architecture == 'nasnet-a':
nasnet = keras.applications.NASNetLarge(include_top=False, input_shape=(224,224,3), weights=None, pooling='avg')
x = keras.layers.Dense(num_outputs, activation = 'softmax' if classification else None, name = 'prob' if classification else 'embedding')(nasnet.output)
return keras.models.Model(nasnet.inputs, x, name=name)
else:
raise ValueError('Unknown network architecture: {}'.format(architecture))