write_to_file = exp_dir + 'exp_stdout.txt' ########################################################################################### ########################################################################################### # LOAD DATA if args.dataset == 'clevr': # # CLEVR DATA dataset = load_clevr(batch_size=args.batch_size, vws=args.vws, quick=args.quick) elif args.dataset == 'cifar': # CIFAR DATA # train_image_dataset = load_cifar(data_dir=args.data_dir) dataset = load_cifar(data_dir=home + '/Documents/') print(len(dataset), dataset[0].shape) ########################################################################################### ########################################################################################### # Init Model # ------------------------------------------------------------------------------ sampling_batch_size = 64 shape = dataset[0].shape model = Glow_((sampling_batch_size, shape[0], shape[1], shape[2]), args).cuda() # print(model) print("number of model parameters:", sum([np.prod(p.size()) for p in model.parameters()])) # fasdfad # model = nn.DataParallel(model).cuda()
# # CLEVR DATA if args.machine in ['vws', 'vector', 'vaughn']: data_dir = home + "/vl_data/two_objects_large/" #vws else: data_dir = home + "/VL/data/two_objects_no_occ/" #boltz train_x, test_x = load_clevr(batch_size=args.batch_size, data_dir=data_dir, quick=args.quick) shape = train_x[0].shape elif args.dataset == 'cifar': # CIFAR DATA # train_image_dataset = load_cifar(data_dir=args.data_dir) train_x, test_x = load_cifar(data_dir=home + '/Documents/', dataset_size=args.dataset_size) shape = train_x[0].shape # print (len(test_x), 'test set len') svhn_test_x = load_svhn(data_dir=home + '/Documents/') # svhn_test_x = test_x # dataset = train_x elif args.dataset == 'flickr': train_x, test_x = load_flickr( data_dir='/scratch/gobi1/ccremer/Flickr_Faces/images1024x1024/', data_dir_test= '/scratch/gobi1/ccremer/Flickr_Faces/images1024x1024_test/', dataset_size=args.dataset_size)
else: result_list = average_weights_crossover(crossover, data, x_train, y_train, x_test, y_test, num_transplants, batch_size_activation, batch_size_sgd, work_id) return result_list if __name__ == "__main__": data = "cifar10" if data == "cifar10": x_train, x_test, y_train, y_test = load_cifar() elif data == "cifar100": x_train, x_test, y_train, y_test = load_cifar_100() elif data == "mnist": x_train, x_test, y_train, y_test = load_mnist() num_processes = 1 start = timer() pair_list = [pair for pair in range(num_processes)] results = crossover_offspring(data, x_train, y_train, x_test, y_test, pair_list) pickle.dump(results, open("crossover_results.pickle", "wb"))
print("**************** Training on MNIST *****************") model = Model() model.train(x_train_mnist, y_train_mnist) _ = model.test(x_test_mnist, y_test_mnist) # To empty the RAM del x_train_mnist del y_train_mnist del x_test_mnist del y_test_mnist del model time.sleep(5) print("Loading CIFAR-100 ...") x_train_cifar, y_train_cifar, x_test_cifar, y_test_cifar = load_cifar() print("Done") # Training for the cifar images. print("**************** Training on CIFAR-100 *****************") model = Model() model.train(x_train_cifar, y_train_cifar) _ = model.test(x_test_cifar, y_test_cifar) # To empty the RAM del x_train_cifar del y_train_cifar del x_test_cifar del y_test_cifar del model
# -*- coding: utf-8 -*- from __future__ import division import tensorflow as tf import numpy as np import os import shutil import time import load_data x_train, x_validation, x_test, y_train, y_validation, y_test \ = load_data.load_cifar('./data/cifar/', seed=0, as_image=True, scaling=True) BOARD_PATH = "./board/lab08-10_board" INPUT_DIM = np.size(x_train, 1) NCLASS = len(np.unique(y_train)) BATCH_SIZE = 32 TOTAL_EPOCH = 100 ALPHA = 0 INIT_LEARNING_RATE = 0.001 ntrain = len(x_train) nvalidation = len(x_validation) ntest = len(x_test) image_width = np.size(x_train, 1) image_height = np.size(x_train, 2) n_channels = np.size(x_train, 3)
import load_data import plot_helper x_train, x_validation, x_test, y_train, y_validation, y_test=load_data.load_cifar('./data/cifar/', seed=0, as_image=True, scaling=True) plot_helper.plot_cifar(x_train, 100) x_train, x_validation, x_test, y_train, y_validation, y_test=load_data.load_mnist('./data/cmnist/', seed=0, as_image=True, scaling=True) plot_helper.plot_mnist(x_train, 100)