From 231c2b2d912d7480af0ce9512b12a909a4fe2a3d Mon Sep 17 00:00:00 2001 From: Francois Fleuret Date: Thu, 15 Jun 2017 15:50:00 +0200 Subject: [PATCH] Minor fixes + changed the default number of epochs to 100. --- cnn-svrt.py | 37 +++++++++++++++++++++---------------- 1 file changed, 21 insertions(+), 16 deletions(-) diff --git a/cnn-svrt.py b/cnn-svrt.py index 3550d85..96fb498 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 @@ -52,7 +53,7 @@ parser.add_argument('--nb_test_samples', help = 'How many samples for test') parser.add_argument('--nb_epochs', - type = int, default = 25, + type = int, default = 100, help = 'How many training epochs') parser.add_argument('--log_file', @@ -68,8 +69,7 @@ log_file = open(args.log_file, 'w') print(Fore.RED + 'Logging into ' + args.log_file + Style.RESET_ALL) def log_string(s): - s = Fore.GREEN + time.ctime() + Style.RESET_ALL + ' ' + \ - str(problem_number) + ' ' + s + s = Fore.GREEN + time.ctime() + Style.RESET_ALL + ' ' + s log_file.write(s + '\n') log_file.flush() print(s) @@ -89,17 +89,17 @@ def generate_set(p, n): # Afroze's ShallowNet -# map size nb. maps -# ---------------------- -# 128x128 1 -# -- conv(21x21) -> 108x108 6 -# -- max(2x2) -> 54x54 6 -# -- conv(19x19) -> 36x36 16 -# -- max(2x2) -> 18x18 16 -# -- conv(18x18) -> 1x1 120 -# -- reshape -> 120 1 -# -- full(120x84) -> 84 1 -# -- full(84x2) -> 2 1 +# map size nb. maps +# ---------------------- +# input 128x128 1 +# -- conv(21x21 x 6) -> 108x108 6 +# -- max(2x2) -> 54x54 6 +# -- conv(19x19 x 16) -> 36x36 16 +# -- max(2x2) -> 18x18 16 +# -- conv(18x18 x 120) -> 1x1 120 +# -- reshape -> 120 1 +# -- full(120x84) -> 84 1 +# -- full(84x2) -> 2 1 class Net(nn.Module): def __init__(self): @@ -163,6 +163,9 @@ def nb_errors(model, data_input, data_target, bs = 100): ###################################################################### +for arg in vars(args): + log_string('ARGUMENT ' + str(arg) + ' ' + str(getattr(args, arg))) + for problem_number in range(1, 24): train_input, train_target = generate_set(problem_number, args.nb_train_samples) test_input, test_target = generate_set(problem_number, args.nb_test_samples) @@ -179,7 +182,8 @@ for problem_number in range(1, 24): nb_train_errors = nb_errors(model, train_input, train_target) - log_string('TRAIN_ERROR {:.02f}% {:d} {:d}'.format( + log_string('TRAIN_ERROR {:d} {:.02f}% {:d} {:d}'.format( + problem_number, 100 * nb_train_errors / train_input.size(0), nb_train_errors, train_input.size(0)) @@ -187,7 +191,8 @@ for problem_number in range(1, 24): nb_test_errors = nb_errors(model, test_input, test_target) - log_string('TEST_ERROR {:.02f}% {:d} {:d}'.format( + log_string('TEST_ERROR {:d} {:.02f}% {:d} {:d}'.format( + problem_number, 100 * nb_test_errors / test_input.size(0), nb_test_errors, test_input.size(0)) -- 2.39.5