log_file = open(args.log_file, 'w')
-print('Logging into ' + args.log_file)
+print(Fore.RED + 'Logging into ' + args.log_file + Style.RESET_ALL)
def log_string(s):
s = Fore.GREEN + time.ctime() + Style.RESET_ALL + ' ' + \
######################################################################
-# 128x128 --conv(9)-> 120x120 --max(4)-> 30x30 --conv(6)-> 25x25 --max(5)-> 5x5
+# 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
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
- self.conv1 = nn.Conv2d(1, 10, kernel_size=9)
- self.conv2 = nn.Conv2d(10, 20, kernel_size=6)
- self.fc1 = nn.Linear(500, 100)
- self.fc2 = nn.Linear(100, 2)
+ self.conv1 = nn.Conv2d(1, 6, kernel_size=21)
+ self.conv2 = nn.Conv2d(6, 16, kernel_size=19)
+ self.conv3 = nn.Conv2d(16, 120, kernel_size=18)
+ self.fc1 = nn.Linear(120, 84)
+ self.fc2 = nn.Linear(84, 2)
def forward(self, x):
- x = fn.relu(fn.max_pool2d(self.conv1(x), kernel_size=4, stride=4))
- x = fn.relu(fn.max_pool2d(self.conv2(x), kernel_size=5, stride=5))
- x = x.view(-1, 500)
+ x = fn.relu(fn.max_pool2d(self.conv1(x), kernel_size=2))
+ x = fn.relu(fn.max_pool2d(self.conv2(x), kernel_size=2))
+ x = fn.relu(self.conv3(x))
+ x = x.view(-1, 120)
x = fn.relu(self.fc1(x))
x = self.fc2(x)
return x
######################################################################
-# for problem_number in range(1, 24):
-
-for problem_number in [ 3 ]:
+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)