Minor fixes + changed the default number of epochs to 100.

diff --git a/cnn-svrt.py b/cnn-svrt.py
index 3550d85..96fb498 100755 (executable)
--- 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))