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MLModel.py

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+# Several basic machine learning models
+import torch
+from torch import nn
+
+
+class LogisticRegression(nn.Module):
+    """A simple implementation of Logistic regression model"""
+    def __init__(self, num_feature, output_size):
+        super(LogisticRegression, self).__init__()
+        self.linear = nn.Linear(num_feature, output_size)
+
+    def forward(self, x):
+        return self.linear(x)
+
+
+
+class NeuralNet(nn.Module):
+    def __init__(self):
+        super(NeuralNet, self).__init__()
+        self.conv1 = nn.Conv2d(1, 6, 5)
+        self.conv2 = nn.Conv2d(6, 16, 5)
+        #self.fc1 = nn.Linear(16*5*5, 120)
+        self.fc1 = nn.Linear(16*4*4, 120)
+        self.fc2 = nn.Linear(120, 84)
+        self.fc3 = nn.Linear(84, 10)
+
+    def forward(self, x):
+        x = F.relu(self.conv1(x))
+        x = F.max_pool2d(x, 2, 2)
+        x = F.relu(self.conv2(x))
+        x = F.max_pool2d(x, 2, 2)     
+        #x = x.view(-1, 4*4*50)
+        x = x.view(-1, 16*4*4)
+        x = F.relu(self.fc1(x))
+        x= F.relu(self.fc2(x))
+        #x = self.fc2(x)
+        x = self.fc3(x)
+        return F.log_softmax(x, dim=1)
+
+