From 3f3a2df9cb54730206a94a294d60d48422333a11 Mon Sep 17 00:00:00 2001 From: Francois Fleuret Date: Thu, 15 Jun 2017 15:11:59 +0200 Subject: [PATCH] Changed the network structure to Afroze's ShallowNet. --- cnn-svrt.py | 32 +++++++++++++++++++++++--------- 1 file changed, 23 insertions(+), 9 deletions(-) diff --git a/cnn-svrt.py b/cnn-svrt.py index ad73f0c..1d5e887 100755 --- 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__() - 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 @@ -112,7 +126,7 @@ def train_model(train_input, train_target): 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 -- 2.39.5