def build_all_cnn(args):
return SequentialNetwork([
#Dropout(0.1),
Conv2D(96, (3, 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'),
Conv2D(96, (3, 3), kernel_initializer=he_normal, padding='same', kernel_regularizer=l2reg(args.l2), name='conv2D_2'),
BatchNormalization(momentum=0.0, name='batch_norm_2'),
Activation('relu'),
Conv2D(96, (3, 3), kernel_initializer=he_normal, padding='same', strides=(2, 2), name='conv2D_strided_1'),
Dropout(0.5),
Conv2D(192, (3, 3), kernel_initializer=he_normal, padding='same', kernel_regularizer=l2reg(args.l2), name='conv2D_3'),
BatchNormalization(momentum=0.0, name='batch_norm_3'),
Activation('relu'),
Conv2D(192, (3, 3), kernel_initializer=he_normal, padding='same', kernel_regularizer=l2reg(args.l2), name='conv2D_4'),
BatchNormalization(momentum=0.0, name='batch_norm_4'),
Activation('relu'),
Conv2D(192, (3, 3), kernel_initializer=he_normal, padding='same', strides=(2, 2), name='conv2D_strided_2'),
Dropout(0.5),
Conv2D(192, (3, 3), kernel_initializer=he_normal, padding='same', kernel_regularizer=l2reg(args.l2), name='conv2D_5'),
BatchNormalization(momentum=0.0, name='batch_norm_5'),
Activation('relu'),
Conv2D(192, (1, 1), kernel_initializer=he_normal, padding='same', kernel_regularizer=l2reg(args.l2), name='conv2D_6'),
BatchNormalization(momentum=0.0, name='batch_norm_6'),
Activation('relu'),
Conv2D(10, (1, 1), kernel_initializer=he_normal, padding='same', kernel_regularizer=l2reg(args.l2), name='conv2D_7'),
GlobalAveragePooling2D()
])
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_vgg_half(args):
return SequentialNetwork([
Conv2D(32, (3, 3), kernel_initializer=he_normal, padding='same', activation=relu, kernel_regularizer=l2reg(args.l2), name='conv2D_1'),
MaxPooling2D((2, 2), (2, 2)),
Conv2D(64, (3, 3), kernel_initializer=he_normal, padding='same', activation=relu, kernel_regularizer=l2reg(args.l2), name='conv2D_2'),
MaxPooling2D((2, 2), (2, 2)),
Conv2D(128, (3, 3), kernel_initializer=he_normal, padding='same', activation=relu, kernel_regularizer=l2reg(args.l2), name='conv2D_3'),
Conv2D(128, (3, 3), kernel_initializer=he_normal, padding='same', activation=relu, kernel_regularizer=l2reg(args.l2), name='conv2D_4'),
MaxPooling2D((2, 2), (2, 2)),
Conv2D(256, (3, 3), kernel_initializer=he_normal, padding='same', activation=relu, kernel_regularizer=l2reg(args.l2), name='conv2D_5'),
Conv2D(256, (3, 3), kernel_initializer=he_normal, padding='same', activation=relu, kernel_regularizer=l2reg(args.l2), name='conv2D_6'),
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(512, kernel_initializer=he_normal, activation=relu, kernel_regularizer=l2reg(args.l2), name='fc_2'),
Dropout(0.5),
Dense(10, kernel_initializer=he_normal, activation=None, kernel_regularizer=l2reg(args.l2), name='fc_3')
])
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')
])