Add classification net

1 files changed, 57 insertions, 0 deletions

diff --git a/src/classification.py b/src/classification.py
new file mode 100644
index 0000000..110a8d9
--- /dev/null
+++ b/src/classification.py
@@ -0,0 +1,57 @@
+import torch.nn as nn
+import torch.nn.functional as F
+import torch
+
+def target_transform(labels):
+    return (labels > 0) + 0.
+
+def twoargmax(a):
+    l = list(zip(a, range(len(a))))
+    l.sort()
+    return [x[1] for x in l[-2:]]
+
+def loss_function(output, target):
+    return F.binary_cross_entropy(output, target)
+
+def count_correct(output, target):
+    correct = 0
+    for i in range(len(output)):
+        selected = twoargmax(output[i])
+        both_correct = True
+        for selection in selected:
+            if target[i][selection] != 1:
+                both_correct = False
+        if both_correct:
+            correct += 1
+    return correct
+
+class Net(nn.Module):
+    def __init__(self):
+        super(Net, self).__init__()
+        self.conv1 = nn.Conv2d(1, 6, 3, padding=1)
+        self.conv2 = nn.Conv2d(6, 16, 3, padding=1)
+        self.conv3 = nn.Conv2d(16, 32, 3, padding=1)
+        self.fc1 = nn.Linear(288, 120)
+        self.fc2 = nn.Linear(120, 84)
+        self.fc3 = nn.Linear(84, 6)
+
+    def forward(self, x):
+        x = x.unsqueeze(1)
+        x = self.conv1(x)
+        x = F.relu(x)
+        x = F.max_pool2d(x, (2, 2))
+        x = F.max_pool2d(F.relu(self.conv2(x)), 2)
+        x = F.max_pool2d(F.relu(self.conv3(x)), 2)
+        x = x.view(-1, self.num_flat_features(x))
+        x = F.relu(self.fc1(x))
+        x = F.relu(self.fc2(x))
+        x = self.fc3(x)
+        x = torch.sigmoid(x)
+        return x
+
+    def num_flat_features(self, x):
+        size = x.size()[1:]  # all dimensions except the batch dimension
+        num_features = 1
+        for s in size:
+            num_features *= s
+        return num_features