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)