update directory structure

models/alphago.py

@@ -0,0 +1,52 @@
+import torch
+import torch.nn as nn
+
+class AlphaGoModel(nn.Module):
+    def __init__(self, input_shape, is_policy_net=False,
+                #  num_filters=192, first_kernel_size=5, other_kernel_size=3):
+                num_filters=192, first_kernel_size=5, other_kernel_size=3, num_classes=None):
+        super(AlphaGoModel, self).__init__()
+
+        layers = [
+            nn.Conv2d(input_shape[0], num_filters, first_kernel_size, padding=first_kernel_size//2),
+            nn.ReLU()
+        ]
+
+        for i in range(2, 12):
+            layers.extend([
+                nn.Conv2d(num_filters, num_filters, other_kernel_size, padding=other_kernel_size//2),
+                nn.ReLU()
+            ])
+
+        if is_policy_net:
+            assert num_classes is not None, "num_classes must be provided for policy network"
+            layers.extend([
+                nn.Conv2d(num_filters, 1, kernel_size=1, padding=0),
+                # nn.Softmax(dim=1),
+                nn.Flatten()
+            ])
+        else:
+            layers.extend([
+                nn.Conv2d(num_filters, num_filters, other_kernel_size, padding=other_kernel_size//2),
+                nn.ReLU(),
+                nn.Conv2d(num_filters, 1, kernel_size=1, padding=0),
+                nn.ReLU(),
+                nn.Flatten(),
+                nn.Linear(input_shape[1] * input_shape[2], 256),
+                nn.ReLU(),
+                nn.Linear(256, 1),
+                nn.Tanh()
+            ])
+
+        self.model = nn.Sequential(*layers)
+
+    def forward(self, x):
+        return self.model(x)
+
+# Instantiate policy network
+# alphago_policy_model = AlphaGoModel(input_shape=(3, 19, 19), is_policy_net=True)
+alphago_policy_model = AlphaGoModel(input_shape=(3, 19, 19), is_policy_net=True, num_classes=361)
+
+# Instantiate value network
+alphago_value_model = AlphaGoModel(input_shape=(3, 19, 19), is_policy_net=False)
+