# Operations on the GPU if available if torch.cuda.is_available(): DEVICE = 'cuda' else: DEVICE = 'cpu' device = torch.device(DEVICE) set_seed_everywhere(SEED) # %% Data params['t_span'] = (params['t_min'], params['t_max']) params['p_span'] = (params['p_min'], params['p_max']) data = create_dataset(params) train, trainc, test = init_dataset(data, params, transformation=None) # %% Train model_nn = NN(nn_params) results_NN = train_NN(model_nn, train, test, nn_params, params, args, noise=0.01) # model_pinn = PINN(nn_params, params)
np.mean(mse_u_loss), np.mean(mse_f_loss)]) # Save models and data torch.save(model.state_dict(), PATH_MODELS + NAME + f'_{n_epochs}_{n_data}_{n_coll}.pth') np.savez(PATH_DATA + NAME + f'_{n_epochs}_{batch_size}_{n_data}_{n_coll}', train_loss=train_loss) return train_loss #%% Testing if __name__ == '__main__': args, general, params, nn_params = cli() params['t_span'] = (params['t_min'], params['t_max']) params['p_span'] = (params['p_min'], params['p_max']) data = create_dataset(params) train, trainc, test = init_dataset(data, params) #model_nn = NN(nn_params) #results_nn = train_NN(model_nn, train, test, nn_params, data_params) model_pinn = PINN(nn_params, params) results_pinn = train_PINN(model_pinn, trainc, test, nn_params, params)
import argparse import numpy as np import tensorflow as tf from model import ANN import data parser = argparse.ArgumentParser(description='Visualize ANN') parser.add_argument('-d', '--dataset', type=str, default='mnist', choices=data.get_names()) parser.add_argument('--num_iter', type=int, default=5000) args = parser.parse_args() dataset = data.init_dataset(name=args.dataset) model = ANN(dataset.shape) model.train(dataset.tr_data, dataset.tr_labels, num_iter=args.num_iter)
if __name__ == '__main__': DATA_DIR = 'data/' from cli import cli from data import create_dataset, init_dataset args, general, params, nn_params = cli() params['t_span'] = (params['t_min'], params['t_max']) params['p_span'] = (params['p_min'], params['p_max']) n_data = params["n_data"] n_coll = params['n_collocation'] data = create_dataset(params) X_u, X_f, y_delta, y_omega = data train, trainc, test = init_dataset(data, params) train_idx, trainc_idx, test_idx = init_dataset(data, params, sample=False, transformation=None) X_train, y_delta_train, y_omega_train, trf_params_train = train X_test, y_delta_test, y_omega_test, trf_params_test = test X_train_idx, y_delta_train_idx, y_omega_train_idx, trf_params_train_idx = train_idx X_test_idx, y_delta_test_idx, y_omega_test_idx, trf_params_test_idx = test_idx #%% Data for BNN idx = 0 X_selected = torch.tensor(X_train_idx[idx * n_data:idx * n_data + n_data, :2], dtype=torch.float32) y_selected = torch.tensor(y_delta_train_idx[idx * n_data:idx * n_data +
dataset_path = '../dataset/train2017' style_image_path = '../style/kandinsky.jpg' content_image_path = '../content/tubingen.jpg' output_path = '../output/' batch_size = 1 batch_shape = (batch_size, 256, 256, 3) content_loss_weight = 1e0 style_loss_weight = 1e3 tv_loss_weight = 2e2 learning_rate = 1 epoches = 2 data.init_dataset(dataset_path, batch_shape) content_img = data.get_img(content_image_path) content_img = data.img_fit_to(content_img) content_input = np.expand_dims(content_img, axis=0) content_input = models.vgg_preprocess(content_input) content_input_vgg = tf.constant(content_input, dtype=tf.float32, name='content_input') style_input = np.expand_dims(data.get_img(style_image_path), axis=0) style_input = models.vgg_preprocess(style_input) style_input_vgg = tf.Variable(style_input, dtype=tf.float32, name='style_input') #mixer_net = models.load_mixer_net(batch_input) #image_input = mixer_net
}, resolver=lambda root, args, *_: save_obj_detect_image( args.get('id'), args.get('project'), args.get('annotations')) ), 'allPost': GraphQLField( ObjAllPostType, args={ 'name': GraphQLArgument(GraphQLString), 'description': GraphQLArgument(GraphQLString), 'tissue': GraphQLArgument(GraphQLString), 'dataset': GraphQLArgument(GraphQLString) }, resolver=lambda root, args, *_: save_model_post( args.get('name'), args.get('description'), args.get('tissue'), args.get("dataset"), ), ) } ) Schema = GraphQLSchema(QueryRootType, MutationRootType) # Init test project fold_fpath = data.get_fpath(cfg.PROJECT_NAME, cfg.FOLD_FNAME) if not os.path.exists(fold_fpath): _ = data.init_dataset(cfg.PROJECT_NAME, cfg.MEDIA_PATH, cfg.IMG_EXT, cfg.PROJECT_LABELS)
import os, sys, numpy as np import config os.chdir('src/') # fix for data.init_dataset() np.random.seed(config.seed) import data, tfidf, models, sentimentanalysis from utils import utils, io # info = pandas.read_csv(config.dataset_dir + 'final_data.csv') dataset = data.init_dataset() # load model m = config.dataset_dir + 'models/default_model.json' w = config.dataset_dir + 'models/default_model_w.h5' model = models.load_model(m, w) if __name__ == '__main__': args = sys.argv if len(args) > 1: filename = '../' + args[1] else: filename = config.dataset_dir + '1118.txt' print('\n filename:', filename) tokens, lines = io.read_book3(filename) # build feature vector v1 = data.tokenlist_to_vector(tokens, dataset.sentiment_dataset) v2 = np.array(sentimentanalysis.per_book(lines))