feat(train code):

for #2

train.py

@@ -0,0 +1,47 @@
+import torch
+def train(dl,model,lossf,opt,device='cuda'):
+    model.train()
+    for x,y in dl:
+        x,y = x.to(device),y.to(device)
+        pre = model(x)
+        loss = lossf(pre,y)
+
+        opt.zero_grad()
+        loss.backward()
+        opt.step()
+
+def test(dl,model,lossf,epoch=None,exist_acc=True,device='cuda'):
+    model.eval()
+    size, acc , losses = len(dl.dataset) ,0,0
+    with torch.no_grad():
+        for x,y in dl:
+            x,y = x.to(device),y.to(device)
+            pre = model(x)
+            loss = lossf(pre,y)
+
+            if exist_acc: 
+                acc += (pre.argmax(1)==y).type(torch.float).sum().item()
+            losses += loss.item()
+    if exist_acc:
+        accuracy = round(acc/size,4)
+    else:
+        accuracy = None
+    val_loss = round(losses/size,6)
+    print(f'[{epoch}] acc/loss: {accuracy}/{val_loss}')
+    return accuracy,val_loss
+
+import copy
+def run(trdl, valdl, model, loss, opt, epoch=100,patience = 5, exist_acc=True, device='cuda'):
+    val_losses = {0:1}
+    model = model.to(device)
+    for i in range(epoch):
+        train(trdl,model,loss,opt,device=device)
+        acc,val_loss = test(valdl,model,loss,epoch=i,exist_acc=exist_acc,device=device)
+
+
+        if min(val_losses.values() ) > val_loss:
+            val_losses[i] = val_loss
+            best_model = copy.deepcopy(model)
+        if i == min(val_losses,key=val_losses.get)+patience:
+            break
+    return best_model, val_losses