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Cambridge-ICCS · Apr 6, 2024 · b37663f · b37663f
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diff --git a/worked-solutions/01_penguin_classification_solutions.ipynb b/worked-solutions/01_penguin_classification_solutions.ipynb
@@ -243,6 +243,7 @@
    ],
    "source": [
     "from torchvision.transforms import Compose\n",
+    "\n",
     "# import some useful functions here, see https://pytorch.org/docs/stable/torch.html\n",
     "# where `tensor` and `eye` are used for constructing tensors,\n",
     "# and using a lower-precision float32 is advised for performance\n",
@@ -736,24 +737,22 @@
     "train_metrics, valid_metrics = [], []\n",
     "\n",
     "\n",
-    "\n",
     "for epoch in range(epochs):\n",
     "\n",
     "    if epoch % print_interval == 0:\n",
     "        if epoch != 0:\n",
-    "            print(f\"Epoch {max(epoch - print_interval, 0)}-{epoch} time: {perf_counter() - tic:.6f} seconds\")\n",
+    "            print(\n",
+    "                f\"Epoch {max(epoch - print_interval, 0)}-{epoch} time: {perf_counter() - tic:.6f} seconds\"\n",
+    "            )\n",
     "        tic = perf_counter()\n",
     "\n",
-    "\n",
-    "\n",
     "    train_metrics.append(train_one_epoch(model, train_loader, optimiser, loss_func))\n",
     "\n",
     "    valid_metrics.append(validate_one_epoch(model, valid_loader, loss_func))\n",
     "\n",
     "    stop_time = perf_counter()\n",
     "\n",
     "\n",
-    "\n",
     "print(\"\\n\")\n",
     "\n",
     "train_metrics = DataFrame(train_metrics)\n",

diff --git a/worked-solutions/02_penguin_regression_solutions.ipynb b/worked-solutions/02_penguin_regression_solutions.ipynb
@@ -657,10 +657,11 @@
     "\n",
     "    if epoch % print_interval == 0:\n",
     "        if epoch != 0:\n",
-    "            print(f\"Epoch {max(epoch - print_interval, 0)}-{epoch} time: {perf_counter() - tic:.6f} seconds\")\n",
+    "            print(\n",
+    "                f\"Epoch {max(epoch - print_interval, 0)}-{epoch} time: {perf_counter() - tic:.6f} seconds\"\n",
+    "            )\n",
     "        tic = perf_counter()\n",
     "\n",
-    "\n",
     "    train_metrics.append(train_one_epoch(model, train_loader, optimiser, loss_func))\n",
     "\n",
     "    valid_metrics.append(validate_one_epoch(model, valid_loader, loss_func))\n",
@@ -767,7 +768,11 @@
     "\n",
     "fig, axis = plt.subplots(1, 1, figsize=(10, 5))\n",
     "\n",
-    "axis.hist(ratios, bins=10, label=fr\"Mean {ratios.mean():.3f}; Std dev. {predictions.std():.3f}\",)\n",
+    "axis.hist(\n",
+    "    ratios,\n",
+    "    bins=10,\n",
+    "    label=rf\"Mean {ratios.mean():.3f}; Std dev. {predictions.std():.3f}\",\n",
+    ")\n",
     "axis.set_xlabel(\"Prediction:target ratio\", fontsize=12)\n",
     "axis.set_ylabel(\"Frequency\", fontsize=12)\n",
     "\n",

diff --git a/worked-solutions/03_mnist_classification_solutions.ipynb b/worked-solutions/03_mnist_classification_solutions.ipynb
@@ -829,7 +829,7 @@
     "    Notes\n",
     "    -----\n",
     "    - This function assumes the ``preds`` have had the softmax applied to them\n",
-    "      along dimension 1, and that the predicted class is therefore \n",
+    "      along dimension 1, and that the predicted class is therefore\n",
     "      ``preds.argmax(dim=1)``.\n",
     "\n",
     "    \"\"\"\n",