train function

GraphTransformer.py

         mlp_output = self.MLP(gat_output).reshape(1,-1)
 
         return mlp_output
+
+''''
+hala train
+''''
+def build_model(gcn_input, model_dim, head):
+    model = Hydra(gcn_input, model_dim, head).cuda()
+    return model
+
+
+def fn(batch):
+    return batch[0]
+
+
+def train_model(model, trainloader, epoch, print_every=100):
+    optim = torch.optim.Adam(model.parameters(), lr=0.001, betas=(0.9, 0.98), eps=1e-9)
+
+    model.train()
+    start = time.time()
+    temp = start
+    total_loss = 0
+
+    for i in range(epoch):
+      for batch, data in enumerate(trainloader, 0):
+        adj, features, true_links = data
+        adj, features, true_links  = torch.tensor(adj).cuda(), torch.tensor(features).cuda(), torch.tensor(true_links).cuda()
+        # print(adj.shape)
+        # print(features.shape)
+        # print(true_links.shape)
+        preds = model(features, adj)
+        optim.zero_grad()
+        loss = F.binary_cross_entropy(preds.double(), true_links.double())
+        loss.backward()
+        optim.step()
+        total_loss += loss.item()
+      if (i + 1) % print_every == 0:
+        loss_avg = total_loss / print_every
+        print("time = %dm, epoch %d, iter = %d, loss = %.3f,\
+        %ds per %d iters" % ((time.time() - start) // 60,\
+        epoch + 1, i + 1, loss_avg, time.time() - temp,\
+        print_every))
+        total_loss = 0
+        temp = time.time()
+
+
+# prepare data
+# coop = sum([list(filter(lambda x: x is not None, [prepare_graph_data(g) for g in graphs])) for i in range(10)], [])
+coop = list(filter(lambda x: x is not None, [prepare_graph_data(g) for g in graphs]))
+trainloader = torch.utils.data.DataLoader(coop, collate_fn=fn, batch_size=1)
+model  = build_model(3, 243, 9)
+train_model(model, trainloader, 100, 10)