import time
import keras
from keras.layers import Dense, BatchNormalization, Activation
import config
from util import data_loader
from util import keras_util
from util.keras_util import KerasModelConfig
model_config = KerasModelConfig(k_fold_file="1.txt",
model_path=os.path.abspath(__file__),
image_resolution=224,
data_type=[config.DATA_TYPE_ORIGINAL],
label_position=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12],
train_batch_size=[32, 32, 32],
val_batch_size=256,
predict_batch_size=256,
epoch=[1, 4, 10],
lr=[0.001, 0.0001, 0.00001],
freeze_layers=[-1, 0.6, 5])
def get_model(freeze_layers=-1, lr=0.01, output_dim=1, weights="imagenet"):
base_model = keras.applications.DenseNet169(include_top=False, weights=weights,
input_shape=model_config.image_shape, pooling="avg")
x = base_model.output
x = Dense(256, use_bias=False)(x)
x = BatchNormalization()(x)
x = Activation("relu")(x)
import keras
from keras.layers import Dense, BatchNormalization, Activation
import config
from util import data_loader
from util import keras_util
from util.keras_util import KerasModelConfig
model_config = KerasModelConfig(
k_fold_file="1.txt",
model_path=os.path.abspath(__file__),
image_resolution=264,
data_type=[config.DATA_TYPE_ORIGINAL],
label_position=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12],
train_batch_size=[16, 16],
data_visualization=True,
val_batch_size=256,
predict_batch_size=256,
epoch=[2, 6],
lr=[0.0001, 0.00001],
clr=False,
freeze_layers=[0, 0],
input_norm=False)
def get_model(freeze_layers=-1, lr=0.01, output_dim=1, weights="imagenet"):
base_model = keras.applications.DenseNet169(
include_top=False,
weights=weights,
input_shape=model_config.image_shape,
pooling="avg")
import config
from util import data_loader
from util import keras_util
from util import metrics
from util.keras_util import KerasModelConfig
model_config = KerasModelConfig(k_fold_file="1.txt",
model_path=os.path.abspath(__file__),
image_resolution=324,
data_type=[config.DATA_TYPE_ORIGINAL],
label_position=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12],
label_color_augment=[0, 1, 3, 5, 6, 7, 9, 10, 11, 12],
label_up_sampling=[0, 0, 0, 0, 0, 0, 50, 0, 0, 0, 0, 0, 0],
downsampling=0.8,
initial_epoch=1,
train_batch_size=[16, 16],
val_batch_size=128,
predict_batch_size=128,
epoch=[5, 10],
lr=[0.0001, 0.00005],
freeze_layers=[0, 0],
tta_flip=True,
data_visualization=True)
def get_model(freeze_layers=-1, lr=0.01, output_dim=1, weights="imagenet"):
base_model = keras.applications.Xception(include_top=False, weights=weights,
input_shape=model_config.image_shape, pooling="avg")
x = base_model.output
from keras.layers import Dense, BatchNormalization, Activation
import config
from util import data_loader
from util import keras_util
from util.keras_util import KerasModelConfig
model_config = KerasModelConfig(k_fold_file="1.txt",
model_path=os.path.abspath(__file__),
image_resolution=224,
data_type=[config.DATA_TYPE_ORIGINAL],
label_position=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12],
label_color_augment=[0, 1, 3, 5, 6, 7, 9, 10, 11, 12],
label_up_sampling=[15, 0, 0, 15, 0, 0, 15, 0, 0, 0, 0, 0, 15],
train_batch_size=[32, 32, 32],
val_batch_size=256,
predict_batch_size=256,
downsampling=0.5,
epoch=[1, 4, 8],
lr=[0.001, 0.0001, 0.00005],
freeze_layers=[-1, 0.6, 5],
input_norm=False,
tta_crop=True,
tta_flip=True,
data_visualization=True)
def get_model(freeze_layers=-1, lr=0.01, output_dim=1, weights="imagenet"):
base_model = keras.applications.NASNetMobile(include_top=False, weights=weights,
input_shape=model_config.image_shape, pooling="avg")
x = base_model.output
import keras
from keras.layers import Dense, BatchNormalization, Activation
import config
from util import data_loader
from util import keras_util
from util.keras_util import KerasModelConfig
model_config = KerasModelConfig(
k_fold_file="1.txt",
model_path=os.path.abspath(__file__),
image_resolution=400,
data_type=[config.DATA_TYPE_ORIGINAL],
label_position=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12],
label_color_augment=[0, 1, 3, 5, 6, 7, 9, 10, 11, 12],
label_up_sampling=[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 50],
downsampling=0.8,
train_batch_size=[16, 16, 16],
val_batch_size=128,
predict_batch_size=128,
epoch=[1, 4, 8],
lr=[0.001, 0.0001, 0.00001],
freeze_layers=[-1, 0.6, 5])
def get_model(freeze_layers=-1, lr=0.01, output_dim=1, weights="imagenet"):
base_model = keras.applications.ResNet50(
include_top=False,
weights=weights,
input_shape=model_config.image_shape,
pooling="avg")
import keras
from keras.layers import Dense, BatchNormalization, Activation
import config
from util import data_loader
from util import keras_util
from util.keras_util import KerasModelConfig
model_config = KerasModelConfig(
k_fold_file="1.txt",
model_path=os.path.abspath(__file__),
image_resolution=224,
data_type=[config.DATA_TYPE_ORIGINAL],
label_position=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12],
label_up_sampling=[50, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
train_batch_size=[16, 16, 16],
initial_epoch=4,
val_batch_size=256,
predict_batch_size=256,
epoch=[1, 4, 8],
lr=[0.0005, 0.00005, 0.000005],
data_visualization=True,
freeze_layers=[-1, 0.6, 5])
def get_model(freeze_layers=-1, lr=0.01, output_dim=1, weights="imagenet"):
base_model = keras.applications.DenseNet201(
include_top=False,
weights=weights,
input_shape=model_config.image_shape,
pooling="avg")
import keras
from keras.layers import Dense, BatchNormalization, Activation
import config
from util import data_loader
from util import keras_util
from util import metrics
from util.keras_util import KerasModelConfig
model_config = KerasModelConfig(
k_fold_file="1.txt",
image_resolution=224,
model_path=os.path.abspath(__file__),
data_type=[config.DATA_TYPE_SEGMENTED],
label_position=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12],
train_batch_size=[32, 32, 32],
val_batch_size=64,
predict_batch_size=128,
epoch=[1, 6, 12],
lr=[0.001, 0.0001, 0.00001],
freeze_layers=[-1, 0.2, 0])
def get_model(freeze_layers=-1, lr=0.01, output_dim=1, weights="imagenet"):
base_model = keras.applications.InceptionV3(
weights=weights,
include_top=False,
input_shape=model_config.image_shape,
pooling="max")
x = base_model.output
x = Dense(256, use_bias=False)(x)
from keras.layers import Dense, BatchNormalization, Activation
import config
from util import data_loader
from util import keras_util
from util.keras_util import KerasModelConfig
model_config = KerasModelConfig(
k_fold_file="1.txt",
model_path=os.path.abspath(__file__),
image_resolution=224,
data_type=[config.DATA_TYPE_SEGMENTED],
label_position=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12],
label_up_sampling=[0, 0, 0, 20, 0, 0, 0, 0, 0, 0, 0, 0, 50],
downsampling=0.8,
train_batch_size=[16, 16, 16],
data_visualization=True,
val_batch_size=256,
predict_batch_size=256,
epoch=[1, 3, 8],
lr=[0.0005, 0.00005, 0.000005],
freeze_layers=[-1, 0.6, 5],
tta_flip=True,
input_norm=False)
def get_model(freeze_layers=-1, lr=0.01, output_dim=1, weights="imagenet"):
base_model = keras.applications.DenseNet201(
include_top=False,
weights=weights,
input_shape=model_config.image_shape,
from keras.layers import Dense, BatchNormalization, Activation
import config
from util import data_loader
from util import keras_util
from util import metrics
from util.keras_util import KerasModelConfig
model_config = KerasModelConfig(k_fold_file="1.txt",
model_path=os.path.abspath(__file__),
image_resolution=224,
data_type=[config.DATA_TYPE_SEGMENTED],
label_position=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12],
label_color_augment=[0, 1, 3, 5, 6, 7, 9, 10, 11, 12],
train_batch_size=[32, 32, 32],
initial_epoch=1,
val_batch_size=256,
predict_batch_size=256,
epoch=[1, 4, 10],
lr=[0.001, 0.0001, 0.00001],
freeze_layers=[-1, 0.6, 5],
data_visualization=True,
tta_flip=True)
def get_model(freeze_layers=-1, lr=0.01, output_dim=1, weights="imagenet"):
base_model = keras.applications.InceptionV3(weights=weights, include_top=False,
input_shape=model_config.image_shape, pooling="avg")
x = base_model.output
x = Dense(512, use_bias=False)(x)
x = BatchNormalization()(x)
