Added activation functions to MLP models.

exercises/01_penguin_classification.ipynb

@@ -143,10 +143,11 @@
    "outputs": [],
    "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",
-    "from torch import tensor, eye, float32 \n",
+    "from torch import tensor, eye, float32\n",
     "\n",
     "# Apply the transforms we need to the PenguinDataset to get out input\n",
     "# targets as Tensors."
@@ -396,7 +397,7 @@
     "\n",
     "# Print the raw output from the net\n",
     "\n",
-    "# Transform the raw output back to human-readable format\n"
+    "# Transform the raw output back to human-readable format"
    ]
   }
  ],

exercises/03_mnist_classification.ipynb

@@ -101,6 +101,7 @@
     "from torchvision.transforms import ToTensor, Compose\n",
     "from torch import Tensor, tensor\n",
     "\n",
+    "\n",
     "def get_img_tfms(training: bool) -> Compose:\n",
     "    \"\"\"Return a composition of image transforms.\n",
     "\n",
@@ -141,7 +142,7 @@
     "        The target as a one-hot-encoded vector.\n",
     "\n",
     "    \"\"\"\n",
-    "    return #???\n",
+    "    return  # ???\n",
     "\n",
     "\n",
     "train_set = MNIST(\n",

src/ml_workshop/_ellipse.py

@@ -1,4 +1,5 @@
 """Ellipse-drawing dataset."""
+
 from typing import Tuple, Optional, Any
 
 from numpy.random import default_rng

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",