def build_conv6_lottery(args):
return SequentialNetwork([
Conv2D(64, 3, kernel_initializer=glorot_normal, padding='same', kernel_regularizer=l2reg(args.l2), name='conv2D_1'),
Activation('relu'),
Conv2D(64, 3, kernel_initializer=glorot_normal, padding='same', kernel_regularizer=l2reg(args.l2), name='conv2D_2'),
Activation('relu'),
MaxPooling2D((2, 2), (2, 2)),
Conv2D(128, 3, kernel_initializer=glorot_normal, padding='same', kernel_regularizer=l2reg(args.l2), name='conv2D_3'),
Activation('relu'),
Conv2D(128, 3, kernel_initializer=glorot_normal, padding='same', kernel_regularizer=l2reg(args.l2), name='conv2D_4'),
Activation('relu'),
MaxPooling2D((2, 2), (2, 2)),
Conv2D(256, 3, kernel_initializer=glorot_normal, padding='same', kernel_regularizer=l2reg(args.l2), name='conv2D_5'),
Activation('relu'),
Conv2D(256, 3, kernel_initializer=glorot_normal, padding='same', kernel_regularizer=l2reg(args.l2), name='conv2D_6'),
Activation('relu'),
MaxPooling2D((2, 2), (2, 2)),
Flatten(),
# Dropout(0.5),
Dense(256, kernel_initializer=glorot_normal, activation=relu, kernel_regularizer=l2reg(args.l2), name='fc_1'),
# Dropout(0.5),
Dense(256, kernel_initializer=glorot_normal, activation=relu, kernel_regularizer=l2reg(args.l2), name='fc_2'),
# Dropout(0.5),
Dense(10, kernel_initializer=glorot_normal, activation=None, kernel_regularizer=l2reg(args.l2), name='fc_3')
])
def build_fc_lottery(args):
return SequentialNetwork([
Flatten(),
# BatchNormalization(momentum=0, name='batch_norm_1'),
Dense(300, kernel_initializer=glorot_normal, activation=relu, kernel_regularizer=l2reg(args.l2), name='fc_1'),
Dense(100, kernel_initializer=glorot_normal, activation=relu, kernel_regularizer=l2reg(args.l2), name='fc_2'),
Dense(10, kernel_initializer=glorot_normal, activation=None, kernel_regularizer=l2reg(args.l2), name='fc_3')
])
def build_network_fc(args):
return SequentialNetwork([
Flatten(),
Dense(100, kernel_initializer=he_normal, activation=relu, kernel_regularizer=l2reg(args.l2), name='fc_1'),
Dense(50, kernel_initializer=he_normal, activation=relu, kernel_regularizer=l2reg(args.l2), name='fc_2'),
Dense(5, kernel_initializer=he_normal, activation=None, kernel_regularizer=l2reg(args.l2), name='fc_3')
# can also try kernel_initializer=tfkeras.initializers.TruncatedNormal(mean=0.0, stddev=0.1)
])
def build_linknet_2(args):
layers = conv_bn_relu(32, 3, stride=1, name="block1_conv1")
for layer in conv_bn_relu(32, 3, stride=1, name="block1_conv2"):
layers.append(layer)
layers.append(MaxPooling2D((2, 2), strides=(2, 2), padding="same", name="block1_pool"))
layers.append(Activation('relu'))
layers.append(Flatten())
layers.append(Dense(400, kernel_initializer=he_normal, activation=relu, kernel_regularizer=l2reg(0), name='fc_1'))
layers.append(Dense(10, kernel_initializer=he_normal, activation=None, kernel_regularizer=l2reg(0), name='fc_2'))
return SequentialNetwork(layers)
def build_network_fc_special(args):
return SequentialNetwork([
Flatten(),
Dense(100, kernel_initializer=he_normal, activation=None, kernel_regularizer=l2reg(args.l2), name='fc_1'),
BatchNormalization(momentum=0, name='batch_norm_1'),
Activation('relu'),
Dense(50, kernel_initializer=he_normal, activation=None, kernel_regularizer=l2reg(args.l2), name='fc_2'),
BatchNormalization(momentum=0, name='batch_norm_1'),
Activation('relu'),
Dense(5, kernel_initializer=he_normal, activation=None, kernel_regularizer=l2reg(args.l2), name='fc_3')
])
def build_vgg_mini(args):
return SequentialNetwork([
Conv2D(64, (3, 3), kernel_initializer=he_normal, padding='same', activation=relu, kernel_regularizer=l2reg(args.l2), name='conv2D_1'),
MaxPooling2D((2, 2), (2, 2)),
Conv2D(128, (3, 3), kernel_initializer=he_normal, padding='same', activation=relu, kernel_regularizer=l2reg(args.l2), name='conv2D_2'),
MaxPooling2D((2, 2), (2, 2)),
Conv2D(256, (3, 3), kernel_initializer=he_normal, padding='same', activation=relu, kernel_regularizer=l2reg(args.l2), name='conv2D_3'),
MaxPooling2D((2, 2), (2, 2)),
Flatten(),
Dense(512, kernel_initializer=he_normal, activation=relu, kernel_regularizer=l2reg(args.l2), name='fc_1'),
Dropout(0.5),
Dense(10, kernel_initializer=he_normal, activation=None, kernel_regularizer=l2reg(args.l2), name='fc_2')
])
def build_lenet_conv(args): # ok this is a slightly modified lenet
return SequentialNetwork([
Conv2D(20, 5, kernel_initializer=he_normal, padding='same', kernel_regularizer=l2reg(args.l2), name='conv2D_1'),
# BatchNormalization(momentum=0.0, name='batch_norm_1'),
Activation('relu'),
MaxPooling2D((2, 2), (2, 2)),
Conv2D(40, 5, kernel_initializer=he_normal, padding='same', kernel_regularizer=l2reg(args.l2), name='conv2D_2'),
# BatchNormalization(momentum=0.0, name='batch_norm_2'),
Activation('relu'),
MaxPooling2D((2, 2), (2, 2)),
Flatten(),
Dropout(0.25),
Dense(400, kernel_initializer=he_normal, activation=relu, kernel_regularizer=l2reg(args.l2), name='fc_1'),
Dropout(0.5),
Dense(10, kernel_initializer=he_normal, activation=None, kernel_regularizer=l2reg(args.l2), name='fc_2')
])
def build_basic_model(args):
return SequentialNetwork([
Conv2D(16, 2, padding='same', name='conv2D_1'),
# BatchNormalization(momentum=0.0, name='batch_norm_1'),
Activation('relu'),
MaxPooling2D((2, 2), (2, 2)),
Conv2D(32, 2, padding='same', name='conv2D_2'),
# BatchNormalization(momentum=0.0, name='batch_norm_2'),
Activation('relu'),
MaxPooling2D((2, 2), (2, 2)),
Conv2D(64, 2, padding='same', name='conv2D_3'),
Activation('relu'),
MaxPooling2D((2,2), (2,2)),
GlobalAveragePooling2D(),
Dense(1000, activation=relu),
Dropout(0.2),
Dense(1000, activation=relu, name='fc_1'),
Dropout(0.2),
Dense(5, activation= None, name='fc_2')
])
def build_resnet(args):
return SequentialNetwork([
# pre-blocks
Conv2D(16, 3, kernel_initializer=he_normal, padding='same', kernel_regularizer=l2reg(args.l2), name='conv2D_1'),
BatchNormalization(momentum=0.0, name='batch_norm_1'),
Activation('relu'),
# set 1
ResidualBlock(3, 16, first_stride=(1, 1), name_prefix='1A_', identity=True, resize=args.resize_more, l2=args.l2, l2_shortcut=args.l2),
ResidualBlock(3, 16, first_stride=(1, 1), name_prefix='1B_', identity=True, resize=args.resize_more, l2=args.l2, l2_shortcut=args.l2),
ResidualBlock(3, 16, first_stride=(1, 1), name_prefix='1C_', identity=True, resize=args.resize_more, l2=args.l2, l2_shortcut=args.l2),
# set 2
ResidualBlock(3, 32, first_stride=(2, 2), name_prefix='2A_', identity=False, resize=args.resize_less, l2=args.l2, l2_shortcut=args.l2),
ResidualBlock(3, 32, first_stride=(1, 1), name_prefix='2B_', identity=True, resize=args.resize_less, l2=args.l2, l2_shortcut=args.l2),
ResidualBlock(3, 32, first_stride=(1, 1), name_prefix='2C_', identity=True, resize=args.resize_less, l2=args.l2, l2_shortcut=args.l2),
# set 3
ResidualBlock(3, 64, first_stride=(2, 2), name_prefix='3A_', identity=False, resize=args.resize_less, l2=args.l2, l2_shortcut=args.l2),
ResidualBlock(3, 64, first_stride=(1, 1), name_prefix='3B_', identity=True, resize=args.resize_more, l2=args.l2, l2_shortcut=args.l2),
ResidualBlock(3, 64, first_stride=(1, 1), name_prefix='3C_', identity=True, resize=args.resize_more, l2=args.l2, l2_shortcut=args.l2),
# post-blocks
GlobalAveragePooling2D(),
Dense(10, kernel_initializer=he_normal, activation=None, kernel_regularizer=l2reg(args.l2_special), name='fc_last')
])
def build_fc_adjustable(args):
if args.num_layers == 3:
return SequentialNetwork([
Flatten(),
Dense(455, kernel_initializer=he_normal, activation=relu, kernel_regularizer=l2reg(args.l2), name='fc_1'),
Dense(67, kernel_initializer=he_normal, activation=relu, kernel_regularizer=l2reg(args.l2), name='fc_2'),
Dense(10, kernel_initializer=he_normal, activation=None, kernel_regularizer=l2reg(args.l2), name='fc_3')
])
elif args.num_layers == 4:
return SequentialNetwork([
Flatten(),
Dense(734, kernel_initializer=he_normal, activation=relu, kernel_regularizer=l2reg(args.l2), name='fc_1'),
Dense(175, kernel_initializer=he_normal, activation=relu, kernel_regularizer=l2reg(args.l2), name='fc_2'),
Dense(42, kernel_initializer=he_normal, activation=relu, kernel_regularizer=l2reg(args.l2), name='fc_3'),
Dense(10, kernel_initializer=he_normal, activation=None, kernel_regularizer=l2reg(args.l2), name='fc_4')
])
elif args.num_layers == 5:
return SequentialNetwork([
Flatten(),
Dense(977, kernel_initializer=he_normal, activation=relu, kernel_regularizer=l2reg(args.l2), name='fc_1'),
Dense(311, kernel_initializer=he_normal, activation=relu, kernel_regularizer=l2reg(args.l2), name='fc_2'),
Dense(99, kernel_initializer=he_normal, activation=relu, kernel_regularizer=l2reg(args.l2), name='fc_3'),
Dense(31, kernel_initializer=he_normal, activation=relu, kernel_regularizer=l2reg(args.l2), name='fc_4'),
Dense(10, kernel_initializer=he_normal, activation=None, kernel_regularizer=l2reg(args.l2), name='fc_5')
])