Updated calculation of train_loss and valid_loss

Using len(train_loader.dataset) and len(valid_loader.dataset) will result in wrong values as they return size of 'train_data' (i.e 60000 images). This is the reason for such difference in train_loss and valid_loss.
'train_loader' actually has size of 0.8*train_data (i.e 48000 images) and valid_loader actually has size of 0.2*train_data (i.e 12000 images). 
Hence calculate avg batch loss in each batch and divide by total batches. This works because all the batches in both the dataloaders are of 20(reason: 48000%20=0 and 12000%20=0)

Author: abhinavnayak11

convolutional-neural-networks/mnist-mlp/mnist_mlp_solution_with_validation.ipynb

@@ -418,7 +418,7 @@
     "        # perform a single optimization step (parameter update)\n",
     "        optimizer.step()\n",
     "        # update running training loss\n",
-    "        train_loss += loss.item()*data.size(0)\n",
+    "        train_loss += loss.item()\n",
     "        \n",
     "    ######################    \n",
     "    # validate the model #\n",
@@ -430,12 +430,12 @@
     "        # calculate the loss\n",
     "        loss = criterion(output, target)\n",
     "        # update running validation loss \n",
-    "        valid_loss += loss.item()*data.size(0)\n",
+    "        valid_loss += loss.item()\n",
     "        \n",
     "    # print training/validation statistics \n",
     "    # calculate average loss over an epoch\n",
-    "    train_loss = train_loss/len(train_loader.dataset)\n",
-    "    valid_loss = valid_loss/len(valid_loader.dataset)\n",
+    "    train_loss = train_loss/len(train_loader)\n",
+    "    valid_loss = valid_loss/len(valid_loader)\n",
     "    \n",
     "    print('Epoch: {} \\tTraining Loss: {:.6f} \\tValidation Loss: {:.6f}'.format(\n",
     "        epoch+1, \n",