From 44363bdf89bf78a62776129c4a5f97ad6a360293 Mon Sep 17 00:00:00 2001 From: Francois Fleuret Date: Thu, 15 Jun 2017 14:26:54 +0200 Subject: [PATCH] Update. --- cnn-svrt.py | 31 ++++++++++++++++++++++++++----- 1 file changed, 26 insertions(+), 5 deletions(-) diff --git a/cnn-svrt.py b/cnn-svrt.py index 755d1c7..79d3ff4 100755 --- a/cnn-svrt.py +++ b/cnn-svrt.py @@ -23,6 +23,7 @@ import time import argparse +from colorama import Fore, Back, Style import torch @@ -54,14 +55,21 @@ parser.add_argument('--nb_epochs', type = int, default = 25, help = 'How many training epochs') +parser.add_argument('--log_file', + type = str, default = 'cnn-svrt.log', + help = 'Log file name') + args = parser.parse_args() ###################################################################### -log_file = open('cnn-svrt.log', 'w') +log_file = open(args.log_file, 'w') + +print('Logging into ' + args.log_file) def log_string(s): - s = time.ctime() + ' ' + str(problem_number) + ' | ' + s + s = Fore.GREEN + time.ctime() + Style.RESET_ALL + ' ' + \ + str(problem_number) + ' ' + s log_file.write(s + '\n') log_file.flush() print(s) @@ -70,7 +78,10 @@ def log_string(s): def generate_set(p, n): target = torch.LongTensor(n).bernoulli_(0.5) + t = time.time() input = svrt.generate_vignettes(p, target) + t = time.time() - t + log_string('DATA_SET_GENERATION {:.02f} sample/s'.format(n / t)) input = input.view(input.size(0), 1, input.size(1), input.size(2)).float() return Variable(input), Variable(target) @@ -101,7 +112,7 @@ def train_model(train_input, train_target): model.cuda() criterion.cuda() - optimizer, bs = optim.SGD(model.parameters(), lr = 1e-1), 100 + optimizer, bs = optim.SGD(model.parameters(), lr = 1e-2), 100 for k in range(0, args.nb_epochs): acc_loss = 0.0 @@ -133,7 +144,9 @@ def nb_errors(model, data_input, data_target, bs = 100): ###################################################################### -for problem_number in range(1, 24): +# for problem_number in range(1, 24): + +for problem_number in [ 3 ]: train_input, train_target = generate_set(problem_number, args.nb_train_samples) test_input, test_target = generate_set(problem_number, args.nb_test_samples) @@ -147,9 +160,17 @@ for problem_number in range(1, 24): model = train_model(train_input, train_target) + nb_train_errors = nb_errors(model, train_input, train_target) + + log_string('TRAIN_ERROR {:.02f}% {:d} {:d}'.format( + 100 * nb_train_errors / train_input.size(0), + nb_train_errors, + train_input.size(0)) + ) + nb_test_errors = nb_errors(model, test_input, test_target) - log_string('TEST_ERROR {:.02f}% ({:d}/{:d})'.format( + log_string('TEST_ERROR {:.02f}% {:d} {:d}'.format( 100 * nb_test_errors / test_input.size(0), nb_test_errors, test_input.size(0)) -- 2.20.1