added train files and code 2

.files/MNIST/raw/t10k-images-idx3-ubyte

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+size 7840016

.files/MNIST/raw/t10k-images-idx3-ubyte.gz

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+size 1648877

.files/MNIST/raw/t10k-labels-idx1-ubyte

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+size 10008

.files/MNIST/raw/t10k-labels-idx1-ubyte.gz

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+oid sha256:f7ae60f92e00ec6debd23a6088c31dbd2371eca3ffa0defaefb259924204aec6
+size 4542

.files/MNIST/raw/train-images-idx3-ubyte

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.files/MNIST/raw/train-images-idx3-ubyte.gz

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+size 9912422

.files/MNIST/raw/train-labels-idx1-ubyte

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+size 60008

.files/MNIST/raw/train-labels-idx1-ubyte.gz

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+oid sha256:3552534a0a558bbed6aed32b30c495cca23d567ec52cac8be1a0730e8010255c
+size 28881

.gitattributes

@@ -25,3 +25,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zstandard filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*ubyte filter=lfs diff=lfs merge=lfs -text

app.py

@@ -8,7 +8,7 @@ import torch.optim as optim
 
 # This is just to show an interface where one draws a number and gets prediction. 
 
-n_epochs = 10
+n_epochs = 100
 batch_size_train = 128
 batch_size_test = 1000
 learning_rate = 0.01
@@ -16,7 +16,8 @@ momentum = 0.5
 log_interval = 10
 random_seed = 1
 TRAIN_CUTOFF = 10
-MODEL_PATH = 'model' 
+MODEL_PATH = 'weights' 
+os.makedirs(MODEL_PATH,exist_ok=True)
 METRIC_PATH = os.path.join(MODEL_PATH,'metrics.json')
 MODEL_WEIGHTS_PATH = os.path.join(MODEL_PATH,'mnist_model.pth')
 OPTIMIZER_PATH = os.path.join(MODEL_PATH,'optimizer.pth')
@@ -65,6 +66,62 @@ class MNIST_Model(nn.Module):
         x = self.fc2(x)
         return F.log_softmax(x)
 
+train_loader = torch.utils.data.DataLoader(
+  torchvision.datasets.MNIST('.files/', train=True, download=True,
+                             transform=torchvision.transforms.Compose([
+                               torchvision.transforms.ToTensor(),
+                               torchvision.transforms.Normalize(
+                                 (0.1307,), (0.3081,))
+                             ])),
+  batch_size=batch_size_train, shuffle=True)
+
+test_loader = torch.utils.data.DataLoader(
+  torchvision.datasets.MNIST('.files/', train=False, download=True,
+                             transform=torchvision.transforms.Compose([
+                               torchvision.transforms.ToTensor(),
+                               torchvision.transforms.Normalize(
+                                 (0.1307,), (0.3081,))
+                             ])),
+  batch_size=batch_size_test, shuffle=True)
+
+def train(epochs,network,optimizer,train_loader):
+    
+    train_losses=[]
+    network.train()
+    for epoch in range(epochs):
+        for batch_idx, (data, target) in enumerate(train_loader):
+            optimizer.zero_grad()
+            output = network(data)
+            loss = F.nll_loss(output, target)
+            loss.backward()
+            optimizer.step()
+            if batch_idx % log_interval == 0:
+                print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
+                epoch, batch_idx * len(data), len(train_loader.dataset),
+                100. * batch_idx / len(train_loader), loss.item()))
+                train_losses.append(loss.item())
+        
+                torch.save(network.state_dict(), MODEL_WEIGHTS_PATH)
+                torch.save(optimizer.state_dict(), OPTIMIZER_PATH)
+
+def test():
+    test_losses=[]
+    network.eval()
+    test_loss = 0
+    correct = 0
+    with torch.no_grad():
+        for data, target in test_loader:
+            output = network(data)
+            test_loss += F.nll_loss(output, target, size_average=False).item()
+            pred = output.data.max(1, keepdim=True)[1]
+            correct += pred.eq(target.data.view_as(pred)).sum()
+            test_loss /= len(test_loader.dataset)
+        test_losses.append(test_loss)
+        acc = 100. * correct / len(test_loader.dataset)
+        acc = acc.item()
+        test_metric = '〽Current test metric -> Avg. loss: `{:.4f}`, Accuracy: `{:.0f}%`\n'.format(
+        test_loss,acc)
+        return test_metric,acc
 
 
 
@@ -77,8 +134,17 @@ optimizer = optim.SGD(network.parameters(), lr=learning_rate,
                       momentum=momentum)        
 
 
+model_state_dict = MODEL_WEIGHTS_PATH
+optimizer_state_dict = OPTIMIZER_PATH
+if os.path.exists(model_state_dict) and os.path.exists(optimizer_state_dict):
+    network_state_dict = torch.load(model_state_dict)
+    network.load_state_dict(network_state_dict)
+
+    optimizer_state_dict = torch.load(optimizer_state_dict)
+    optimizer.load_state_dict(optimizer_state_dict)   
 # Train
-#train(n_epochs,network,optimizer)
+train(n_epochs,network,optimizer,train_loader)
+test()
 
 
 def image_classifier(inp):