[DLFL25] Fix res.plot_lib dependency and remove test() side effects in 06-convnet.ipynb

01-tensor_tutorial.ipynb

@@ -306,6 +306,14 @@
     "v"
    ]
   },
+  {
+    "cell_type": "markdown",
+    "metadata": {},
+    "source": [
+      "> **Note**: `torch.Tensor(2, 3, 4)` creates an **uninitialized** tensor.\n",
+      "> For predictable values, prefer `torch.zeros`, `torch.ones`, or `torch.rand`.\n"
+    ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,

02-space_stretching.ipynb

@@ -113,7 +113,8 @@
     "    # transform points\n",
     "    Y = X @ W.t()\n",
     "    # compute singular values\n",
-    "    U, S, V = torch.svd(W)\n",
+    "    U, S, Vh = torch.linalg.svd(W)\n",
+    "    V = Vh.T\n",
     "    # plot transformed points\n",
     "    show_scatterplot(Y, colors, title='y = Wx, singular values : [{:.3f}, {:.3f}]'.format(S[0], S[1]))\n",
     "    # transform the basis\n",

05-regression.ipynb

@@ -173,8 +173,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "plt.scatter(X.data.cpu().numpy(), y.data.cpu().numpy())\n",
-    "plt.plot(X.data.cpu().numpy(), y_pred.data.cpu().numpy(), 'r-', lw=5)\n",
+    "plt.scatter(X.detach().cpu().numpy(), y.detach().cpu().numpy())
+    "plt.plot(X.detach().cpu().numpy(), y_pred.detach().cpu().numpy(), 'r-', lw=5)
     "plt.axis('equal');"
    ]
   },

06-convnet.ipynb

@@ -33,7 +33,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from res.plot_lib import plot_data, plot_model, set_default"
+    "try:\n",
+    "    from res.plot_lib import plot_data, plot_model, set_default\n",
+    "    set_default()\n",
+    "except ImportError:\n",
+    "    print(\"Warning: res.plot_lib not found, using default matplotlib settings.\")\n"
    ]
   },
   {
@@ -45,6 +49,11 @@
     "set_default()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -222,25 +231,31 @@
     "    model.eval()\n",
     "    test_loss = 0\n",
     "    correct = 0\n",
+    "\n",
     "    for data, target in test_loader:\n",
     "        # send to device\n",
     "        data, target = data.to(device), target.to(device)\n",
-    "        \n",
+    "\n",
     "        # permute pixels\n",
-    "        data = data.view(-1, 28*28)\n",
+    "        data = data.view(-1, 28 * 28)\n",
     "        data = data[:, perm]\n",
     "        data = data.view(-1, 1, 28, 28)\n",
+    "\n",
     "        output = model(data)\n",
-    "        test_loss += F.nll_loss(output, target, reduction='sum').item() # sum up batch loss                                                               \n",
-    "        pred = output.data.max(1, keepdim=True)[1] # get the index of the max log-probability                                                                 \n",
+    "        test_loss += F.nll_loss(output, target, reduction='sum').item()\n",
+    "        pred = output.data.max(1, keepdim=True)[1]\n",
     "        correct += pred.eq(target.data.view_as(pred)).cpu().sum().item()\n",
     "\n",
     "    test_loss /= len(test_loader.dataset)\n",
     "    accuracy = 100. * correct / len(test_loader.dataset)\n",
-    "    accuracy_list.append(accuracy)\n",
-    "    print('\\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\\n'.format(\n",
-    "        test_loss, correct, len(test_loader.dataset),\n",
-    "        accuracy))"
+    "    \n",
+    "    print(\n",
+    "        '\\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\\n'.format(\n",
+    "            test_loss, correct, len(test_loader.dataset), accuracy\n",
+    "        )\n",
+    "    )\n",
+    "\n",
+    "    return accuracy\n"
    ]
   },
   {
@@ -265,7 +280,7 @@
     "\n",
     "for epoch in range(0, 1):\n",
     "    train(epoch, model_fnn)\n",
-    "    test(model_fnn)"
+    "    acc = test(model_fnn)\n"
    ]
   },
   {