from keras.layers import Activation, AvgPool2D, Conv2D, Dense, Dropout, Flatten, Input, MaxPool2D, merge
from keras.models import Model, Sequential
from utils import Maxout
def get_convnet_landslide_all(args) -> Model:
input_shape = (args.area_size, args.area_size, 14)
model = Sequential()
model.add(Conv2D(8, 3, 3, input_shape=input_shape, init='normal'))
model.add(Activation('relu'))
model.add(Conv2D(8, 3, 3, init='normal'))
model.add(Activation('relu'))
model.add(MaxPool2D((1, 1), strides=(1, 1)))
model.add(Dropout(0.25))
model.add(Flatten(name="flatten"))
#
model.add(Dense(512, activation='relu', name='dense', init='normal'))
model.add(Dropout(0.25))
model.add(Dense(1, name='last_layer'))
model.add(Activation('sigmoid'))
return model
def get_test_model(args):
y = x = Input(shape=(args.area_size, args.area_size, 14))
y = Conv2D(32, (5, 5))(y)
y = Activation('relu')(y)
y = AvgPool2D((3, 3), strides=(1, 1))(y)
y = Flatten(name="flatten")(y)
y = Dense(1, name='last_layer')(y)
y = Activation('sigmoid')(y)
return Model(x, y)
def get_model_1(args):
model = Sequential()
model.add(Conv2D(32, (5, 5), input_shape=(args.area_size, args.area_size, 14)))
model.add(Activation('relu'))
model.add(Conv2D(16, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPool2D((1, 1), strides=(1, 1)))
model.add(Dropout(0.25))
#
model.add(AvgPool2D((3, 3), strides=(1, 1)))
model.add(Flatten(name="flatten"))
#
model.add(Dense(1, name='last_layer'))
model.add(Activation('sigmoid'))
return model
def get_model_2(args):
model = Sequential()
model.add(Conv2D(32, (5, 1), padding="same", input_shape=(args.area_size, args.area_size, 14)))
model.add(Activation('relu'))
model.add(Conv2D(32, (1, 5), padding="same"))
model.add(Maxout())
model.add(Conv2D(32, (5, 1), padding="same"))
model.add(Activation('relu'))
model.add(Conv2D(32, (1, 5), padding="same"))
model.add(Maxout())
model.add(MaxPool2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
#
model.add(Conv2D(16, (3, 1), padding="same"))
model.add(Activation('relu'))
model.add(Conv2D(16, (1, 3), padding="same"))
model.add(Maxout())
model.add(Conv2D(16, (3, 1), padding="same"))
model.add(Activation('relu'))
model.add(Conv2D(16, (1, 3), padding="same"))
model.add(Maxout())
model.add(MaxPool2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
#
model.add(AvgPool2D((3, 3), strides=(1, 1)))
model.add(Flatten(name="flatten"))
#
model.add(Dense(1, name='last_layer'))
model.add(Activation('sigmoid'))
return model
def get_model_cifar(args):
model = Sequential()
model.add(Conv2D(32, (3, 3), padding='same', input_shape=(args.area_size, args.area_size, 14)))
model.add(Activation('relu'))
model.add(Conv2D(32, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPool2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(64, (3, 3), padding='same'))
model.add(Activation('relu'))
model.add(Conv2D(64, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPool2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(512))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(1))
model.add(Activation('sigmoid'))
return model
def get_model_3(args):
"""First inception network implementation"""
x = input_image = Input(shape=(args.area_size, args.area_size, 14))
tower_0 = Conv2D(64, (1, 1), border_mode='same', activation='relu')(x)
tower_1 = Conv2D(64, (1, 1), border_mode='same', activation='relu')(x)
tower_1 = Conv2D(64, (3, 3), border_mode='same', activation='relu')(tower_1)
tower_2 = Conv2D(64, (1, 1), border_mode='same', activation='relu')(x)
tower_2 = Conv2D(64, (5, 5), border_mode='same', activation='relu')(tower_2)
tower_3 = MaxPool2D((3, 3), strides=(1, 1), border_mode='same')(x)
tower_3 = Conv2D(64, (1, 1), border_mode='same', activation='relu')(tower_3)
x = merge([tower_0, tower_1, tower_2, tower_3], mode='concat', concat_axis=3)
x = Dropout(0.5)(x)
tower_0 = Conv2D(32, (1, 1), border_mode='same', activation='relu')(x)
tower_1 = Conv2D(32, (1, 1), border_mode='same', activation='relu')(x)
tower_1 = Conv2D(32, (3, 3), border_mode='same', activation='relu')(tower_1)
tower_2 = Conv2D(32, (1, 1), border_mode='same', activation='relu')(x)
tower_2 = Conv2D(32, (5, 5), border_mode='same', activation='relu')(tower_2)
tower_3 = MaxPool2D((3, 3), strides=(1, 1), border_mode='same')(x)
tower_3 = Conv2D(32, (1, 1), border_mode='same', activation='relu')(tower_3)
x = merge([tower_0, tower_1, tower_2, tower_3], mode='concat', concat_axis=3)
x = Dropout(0.5)(x)
x = AvgPool2D((3, 3), strides=(1, 1))(x)
x = Flatten()(x)
# model.add(Dropout(0.5))
x = Dense(1)(x)
x = Activation('sigmoid')(x)
return Model(input_image, x)
def get_model_4(args):
"""First res network implementation"""
x = input_image = Input(shape=(args.area_size, args.area_size, 14))
x = Conv2D(filters=64, kernel_size=(1, 1), border_mode='same')(x)
y = Conv2D(filters=64, kernel_size=(3, 1), border_mode='same')(x)
y = Activation('relu')(y)
y = Conv2D(filters=64, kernel_size=(1, 3), border_mode='same')(y)
y = Activation('relu')(y)
y = Conv2D(filters=64, kernel_size=(3, 1), border_mode='same')(y)
y = Activation('relu')(y)
y = Conv2D(filters=64, kernel_size=(1, 3), border_mode='same')(y)
# this returns x + y.
x = merge([x, y], mode='sum')
x = Activation('relu')(x)
x = MaxPool2D(pool_size=(2, 2))(x)
y = Conv2D(filters=64, kernel_size=(3, 1), border_mode='same')(x)
y = Activation('relu')(y)
y = Conv2D(filters=64, kernel_size=(1, 3), border_mode='same')(y)
y = Activation('relu')(y)
y = Conv2D(filters=64, kernel_size=(3, 1), border_mode='same')(y)
y = Activation('relu')(y)
y = Conv2D(filters=64, kernel_size=(1, 3), border_mode='same')(y)
# this returns x + y.
x = merge([x, y], mode='sum')
x = Activation('relu')(x)
x = Conv2D(filters=32, kernel_size=(1, 1), border_mode='same')(x)
y = Conv2D(filters=32, kernel_size=(3, 1), border_mode='same')(x)
y = Activation('relu')(y)
y = Conv2D(filters=32, kernel_size=(1, 3), border_mode='same')(y)
y = Activation('relu')(y)
y = Conv2D(filters=32, kernel_size=(3, 1), border_mode='same')(y)
y = Activation('relu')(y)
y = Conv2D(filters=32, kernel_size=(1, 3), border_mode='same')(y)
# this returns x + y.
x = merge([x, y], mode='sum')
x = Activation('relu')(x)
x = MaxPool2D(pool_size=(2, 2))(x)
x = AvgPool2D((3, 3), strides=(1, 1))(x)
x = Flatten()(x)
# model.add(Dropout(0.5))
x = Dense(1)(x)
x = Activation('sigmoid')(x)
return Model(input_image, x)
model_pool = {
"test" : get_test_model,
"simple_conv" : get_model_1,
"medium_maxout_conv": get_model_2,
"inception_net" : get_model_3,
"resnet" : get_model_4,
"cifar" : get_model_cifar
}
def get_model_pool():
return model_pool
def get_model_by_name(name):
return model_pool[name]
