Changes since Teledyne

higher/smart_aug/nets/LeNet.py

@@ -0,0 +1,56 @@
+import math
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+## Basic CNN ##
+class LeNet(nn.Module):
+    """Basic CNN.
+
+    """
+    def __init__(self, num_inp, num_out):
+        """Init LeNet.
+
+        """
+        super(LeNet, self).__init__()
+        self.conv1 = nn.Conv2d(num_inp, 20, 5)
+        self.pool = nn.MaxPool2d(2, 2)
+        self.conv2 = nn.Conv2d(20, 50, 5)
+        self.pool2 = nn.MaxPool2d(2, 2)
+        #self.fc1 = nn.Linear(4*4*50, 500)
+        self.fc1 = nn.Linear(5*5*50, 500)
+        self.fc2 = nn.Linear(500, num_out)
+
+    def forward(self, x):
+        """Main method of LeNet
+
+        """
+        x = self.pool(F.relu(self.conv1(x)))
+        x = self.pool2(F.relu(self.conv2(x)))
+        x = x.view(x.size(0), -1)
+        x = F.relu(self.fc1(x))
+        x = self.fc2(x)
+        return x
+
+    def __str__(self):
+        """ Get name of model
+
+        """
+        return "LeNet"
+
+#MNIST
+class MLPNet(nn.Module):
+    def __init__(self):
+        super(MLPNet, self).__init__()
+        self.fc1 = nn.Linear(28*28, 500)
+        self.fc2 = nn.Linear(500, 256)
+        self.fc3 = nn.Linear(256, 10)
+    def forward(self, x):
+        x = x.view(-1, 28*28)
+        x = F.relu(self.fc1(x))
+        x = F.relu(self.fc2(x))
+        x = self.fc3(x)
+        return x
+    
+    def name(self):
+        return "MLP"