adds network class back

networktorch.py

@@ -0,0 +1,50 @@
+from torch import nn
+
+
+class NeuralNetworkTorch(nn.Module):
+    def __init__(self):
+        super().__init__()
+
+        self.stack = nn.Sequential(
+            nn.Linear(784, 64),
+            nn.Sigmoid(),
+
+            nn.Linear(64, 10),
+            nn.Sigmoid()
+        )
+
+    def forward(self, x):
+        return self.stack(x)
+
+
+class ConvNeuralNetworkTorch(nn.Module):
+    def __init__(self):
+        super().__init__()
+
+        self.conv = nn.Sequential(
+            nn.Conv2d(1, 16, kernel_size=3, stride=1, padding=1),
+            nn.ReLU(),
+
+            nn.MaxPool2d(kernel_size=2, stride=2),
+
+            nn.Conv2d(16, 16, kernel_size=3, stride=1, padding=1),
+            nn.ReLU(),
+
+            # nn.MaxPool2d(kernel_size=2, stride=2),
+        )
+
+        self.fc = nn.Sequential(
+            nn.Linear(16 * 14 * 14, 10),
+            nn.Softmax(dim=1),
+        )
+
+    def forward(self, x):
+        # we do some reshaping here simply to avoid making changes to the caller
+        # so it continues to work with the fully conected network above
+        x = x.reshape(-1, 1, 28, 28) / 255
+
+        conv_output = self.conv(x)
+        flat = conv_output.reshape(len(x), -1)
+        final_output = self.fc(flat)
+
+        return final_output