def __init__(self): self.num_classes = 10 self.batch_size = 128 self.model_path = args.path_cifar10_vgg_model create_dir_if_needed(self.model_path) self.model_pef_path = args.path_cifar10_vgg_model_perf create_dir_if_needed(self.model_pef_path) self.epochs = 150 self.img_rows, self.img_cols, self.img_chns = 32, 32, 3 (self.x_train, self.y_train), (self.x_test, self.y_test) = cifar10.load_data() self.pre_reshape() self.weight_decay = 0.0005 self.x_shape = [32, 32, 3] self.model = self.get_model_archi() #self.weights = self.get_model_weights() print(self.x_train.shape) print(self.x_test.shape)
def __init__(self): self.num_classes = 10 self.batch_size = 32 self.model_path = args.path_cifar10_resnet_model create_dir_if_needed(self.model_path) self.model_pef_path = args.path_cifar10_resnet_model_perf create_dir_if_needed(self.model_pef_path) self.epochs = 200 self.img_rows, self.img_cols, self.img_chns = 32, 32, 3 (self.x_train, self.y_train), (self.x_test, self.y_test) = cifar10.load_data() self.pre_reshape() self.weight_decay = 0.0005 self.x_shape = [32, 32, 3] self.model = self.get_model_archi() # self.weights = self.get_model_weights() self.lr_reducer = ReduceLROnPlateau(factor=np.sqrt(0.1), cooldown=0, patience=5, min_lr=0.5e-6) self.early_stopper = EarlyStopping(min_delta=0.001, patience=10) self.csv_logger = CSVLogger('resnet18_cifar10.csv') print(self.x_train.shape) print(self.x_test.shape)
def __init__(self): self.num_classes = 10 self.batch_size = 128 self.model_path = args.path_cifar10_vgg_model create_dir_if_needed(self.model_path) self.model_pef_path = args.path_cifar10_vgg_model_perf create_dir_if_needed(self.model_pef_path) self.epochs = 200 self.img_rows, self.img_cols, self.img_chns = 32, 32, 3 (self.x_train, self.y_train), (self.x_test, self.y_test) = cifar10.load_data() self.weight_decay = 0.0005 self.x_shape = [32, 32, 3] self.mean = 120.707 self.std = 64.15 self.model = self.get_model_archi() #self.weights = self.get_model_weights() #training parameters self.lr = 0.1 self.lr_decay = 1e-6 self.lr_drop = 20 print(self.x_train.shape) print(self.x_test.shape)
def __init__(self): self.num_classes = 10 self.img_rows, self.img_cols, self.img_chns = 28, 28, 1 self.model = self.get_model_archi() self.model_path = args.path_mnist_model create_dir_if_needed(self.model_path) self.model_pef_path = args.path_mnist_model_perf #not necessary!! create_dir_if_needed(self.model_pef_path) self.batch_size = 128 self.epochs = 3 (self.x_train, self.y_train), (self.x_test, self.y_test) = mnist.load_data()
def __init__(self): self.model_path = args.path_cifar10_2c2d_model create_dir_if_needed(self.model_path) self.model_pef_path = args.path_cifar10_2c2d_model_perf create_dir_if_needed(self.model_pef_path) self.num_classes = 10 self.batch_size = 128 self.epochs = 25 self.img_rows, self.img_cols, self.img_chns = 32, 32, 3 (self.x_train, self.y_train), (self.x_test, self.y_test) = cifar10.load_data() self.pre_reshape() self.model = self.get_model_archi()
def __init__(self): self.num_classes = 10 self.batch_size = 128 self.model_path = args.path_cifar10_2c1d_model create_dir_if_needed(self.model_path) self.model_pef_path = args.path_cifar10_2c1d_model_perf create_dir_if_needed(self.model_pef_path) self.epochs = 25 self.img_rows, self.img_cols, self.img_chns = 32, 32, 3 (self.x_train, self.y_train), (self.x_test, self.y_test) = cifar10.load_data() #shape: (50000, 32, 32, 3) (50000, 1) (10000, 32, 32, 3) (10000, 1) self.mean = 120.707 self.std = 64.15 self.model = self.get_model_archi()
import logging import keras from keras.datasets import mnist, cifar10 from keras.models import Sequential, model_from_json, load_model from keras.layers import Dense, Dropout, Flatten from keras.layers import Conv2D, MaxPooling2D from keras import backend as K from my_funs import print_acc_per_class, create_dir_if_needed from my_models import Cifar10_2c2d, Mnist_2c1d, Cifar10_vgg, Cifar10_resnet from parameters import * # create the needed directories for i in [model_dir, log_dir, img_dir]: create_dir_if_needed(i) # global variables # create log at terminal and disk at the same time logging.basicConfig( level=logging.INFO, format="%(asctime)s [%(levelname)-5.5s] %(message)s", handlers=[logging.FileHandler(args.log_file), logging.StreamHandler()]) def create_dir_if_needed(dest_directory): """ Create directory if doesn't exist :param dest_directory: :return: True if everything went well
def set_saliency_path(self, path): self.saliency_path = path create_dir_if_needed(path)
def set_model_path(self, path): self.model_path = path create_dir_if_needed(path)