train

swarms_torch/cellular_transformer.py

@@ -89,3 +89,4 @@ def forward(self, x):
 
                 x = cell(x, neighbors)
         return x
+

swarms_torch/graph_cellular_automa.py

@@ -70,15 +70,71 @@ def forward(self, node_embeddings, adjacency_matrix):
 
         return updated_embeddings, replication_decisions, edge_weights
 
-# Usage examples
+# # Usage examples
+# embedding_dim = 16
+# hidden_dim = 32
+# node_embeddings = torch.rand((10, embedding_dim))  # For 10 nodes
+# adjacency_matrix = torch.rand((10, 10))  # Dummy adjacency matrix for 10 nodes
+
+# model = NDP(embedding_dim, hidden_dim)
+# updated_embeddings, replication_decisions, edge_weights = model(node_embeddings, adjacency_matrix)
+
+# print(updated_embeddings.shape)
+# print(replication_decisions.shape)
+# print(edge_weights.shape)
+
+
+
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torchvision import datasets, transforms
+
+
+
+# Define the training function
+def train(model, train_loader, optimizer, criterion):
+    model.train()
+
+    for batch_idx, (data, target) in enumerate(train_loader):
+        optimizer.zero_grad()
+        output = model(data)
+        loss = criterion(output, target)
+        loss.backward()
+        optimizer.step()
+
+# Set hyperparameters
 embedding_dim = 16
 hidden_dim = 32
+learning_rate = 0.001
+batch_size = 64
+epochs = 10
+
+# Load MNIST dataset
+transform = transforms.Compose([
+    transforms.ToTensor(),
+    transforms.Normalize((0.1307,), (0.3081,))
+])
+
+train_dataset = datasets.MNIST(root='./data', train=True, download=True, transform=transform)
+train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
+
+# Initialize the model, optimizer, and loss function
+model = NDP(embedding_dim, hidden_dim)
+optimizer = optim.Adam(model.parameters(), lr=learning_rate)
+criterion = nn.CrossEntropyLoss()
+
+# Training loop
+for epoch in range(epochs):
+    train(model, train_loader, optimizer, criterion)
+    print(f"Epoch {epoch+1}/{epochs} completed")
+
+# Usage examples
 node_embeddings = torch.rand((10, embedding_dim))  # For 10 nodes
 adjacency_matrix = torch.rand((10, 10))  # Dummy adjacency matrix for 10 nodes
 
-model = NDP(embedding_dim, hidden_dim)
 updated_embeddings, replication_decisions, edge_weights = model(node_embeddings, adjacency_matrix)
 
 print(updated_embeddings.shape)
 print(replication_decisions.shape)
-print(edge_weights.shape)
+print(edge_weights.shape)