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Dummy.py

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+import random
+import numpy as np
+import torch as tr
+
+seed = 4310220                      # <<<<<<<<<<<<<<<< Your UPM ID Goes Here
+random.seed(seed)
+np.random.seed(seed)
+tr.manual_seed(seed)
+
+class DummyNet(tr.nn.Module):
+
+  def __init__(self):
+    super().__init__()
+    self.flatten = tr.nn.Flatten()
+    self.linear1 = tr.nn.Linear(784, 228, bias=True)       # 784 -> 16 - Layer 1 -- Affine Transformation (Linear with Bias)
+    self.linear2 = tr.nn.Linear(228, 228, bias=False)
+    #self.linear3 = tr.nn.Linear(128, 128, bias=False) # 16 -> 16  - Layer 2 -- Linear Transformation (no bias)
+    self.linear4 = tr.nn.Linear(228, 10, bias=True)        # 16 -> 10  - Layer 3 -- Affine Transformation (Linear with Bias)
+    self.init_weights()
+
+  def init_weights(self):
+    tr.nn.init.normal_(self.linear1.weight)
+    tr.nn.init.normal_(self.linear2.weight)
+    #tr.nn.init.normal_(self.linear3.weight)
+    tr.nn.init.normal_(self.linear4.weight)
+    tr.nn.init.normal_(self.linear1.bias)
+    tr.nn.init.normal_(self.linear4.bias)
+  
+  def forward(self, x):
+    x = self.flatten(x)
+    x = self.linear1(x)
+    x = self.linear2(x)
+    #x = self.linear3(x)
+    x = self.linear4(x)
+    return x
+
+                                  # Objective Function [DO NOT CHANGE !]

Vanilla.py

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+import random
+import numpy as np
+import torch as tr
+
+seed = 4310220  # <<<<<<<<<<<<<<<< Your UPM ID Goes Here
+random.seed(seed)
+np.random.seed(seed)
+tr.manual_seed(seed)
+
+
+class VanillaNet(tr.nn.Module):
+
+    def __init__(self):
+        super().__init__()
+        self.flatten = tr.nn.Flatten()
+
+        self.linear1 = tr.nn.Linear(784, 324, bias=True)
+        self.tanh1 = tr.nn.LeakyReLU()
+
+        self.linear2 = tr.nn.Linear(324, 324, bias=True)
+        self.tanh2 = tr.nn.LeakyReLU()
+
+        self.linear3 = tr.nn.Linear(324, 324, bias=True)
+        self.tanh3 = tr.nn.LeakyReLU()
+
+        self.linear4 = tr.nn.Linear(324, 324, bias=True)
+        self.tanh4 = tr.nn.LeakyReLU()
+
+        self.linear5 = tr.nn.Linear(324, 324, bias=True)
+        self.tanh5 = tr.nn.LeakyReLU()
+
+        self.linear6 = tr.nn.Linear(324, 10, bias=True)
+        #self.soft6 = tr.nn.Softmax(dim=1)
+
+        #self.init_weights()
+        self.init_weights()
+
+    def init_weights(self):
+        tr.nn.init.xavier_uniform_(self.linear1.weight)
+        tr.nn.init.xavier_uniform_(self.linear2.weight)
+        tr.nn.init.xavier_uniform_(self.linear3.weight)
+        tr.nn.init.xavier_uniform_(self.linear4.weight)
+        tr.nn.init.xavier_uniform_(self.linear5.weight)
+        tr.nn.init.xavier_uniform_(self.linear6.weight)
+
+        tr.nn.init.zeros_(self.linear1.bias)
+        tr.nn.init.zeros_(self.linear2.bias)
+        tr.nn.init.zeros_(self.linear3.bias)
+        tr.nn.init.zeros_(self.linear4.bias)
+        tr.nn.init.zeros_(self.linear5.bias)
+        tr.nn.init.zeros_(self.linear6.bias)
+
+    def forward(self, x):
+        x = self.flatten(x)
+
+        x = self.linear1(x)
+        x = self.tanh1(x)
+
+        x = self.linear2(x)
+        x = self.tanh2(x)
+
+        x = self.linear3(x)
+        x = self.tanh3(x)
+
+        x = self.linear4(x)
+        x = self.tanh4(x)
+
+        x = self.linear5(x)
+        x = self.tanh5(x)
+
+        x = self.linear6(x)
+        #x = self.soft6(x)
+        return x
+
+
+
+
+

dummy_weights.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f96697cbdd4b977a259dbb57b25771b54a30903c72d5de696c637d880da7537e
+size 935771

hubconf.py

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+import torch
+from Dummy import DummyNet as _DummyNet
+from Vanilla import VanillaNet as _VanillaNet
+
+def DummyNet(pretrained=False):
+    model = _DummyNet()
+    if pretrained:
+        model.load_state_dict(
+            torch.load("dummy_weights.bin", map_location="cpu")
+        )
+    return model
+
+def VanillaNet(pretrained=False):
+    model = _VanillaNet()
+    if pretrained:
+        model.load_state_dict(
+            torch.load("vanilla_weights.bin", map_location="cpu")
+        )
+    return model
+

vanilla_weights.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:222e98611e247ad028e349566a0390203d452e88e991a1d85b07d5b0046a3e88
+size 2719951