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Changed the network structure to Afroze's ShallowNet.
author
Francois Fleuret
<francois@fleuret.org>
Thu, 15 Jun 2017 13:11:59 +0000
(15:11 +0200)
committer
Francois Fleuret
<francois@fleuret.org>
Thu, 15 Jun 2017 13:11:59 +0000
(15:11 +0200)
cnn-svrt.py
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diff --git
a/cnn-svrt.py
b/cnn-svrt.py
index
ad73f0c
..
1d5e887
100755
(executable)
--- a/
cnn-svrt.py
+++ b/
cnn-svrt.py
@@
-87,20
+87,34
@@
def generate_set(p, n):
######################################################################
######################################################################
-# 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__()
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):
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
x = fn.relu(self.fc1(x))
x = self.fc2(x)
return x
@@
-112,7
+126,7
@@
def train_model(train_input, train_target):
model.cuda()
criterion.cuda()
model.cuda()
criterion.cuda()
- optimizer, bs = optim.
Adam(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
for k in range(0, args.nb_epochs):
acc_loss = 0.0