Add learner.predict() usage example

nbs/13a_learner.ipynb

@@ -231,8 +231,7 @@
    "source": [
     "`file` can be a `Path` object, a string or an opened file object. If a `device` is passed, the model is loaded on it, otherwise it's loaded on the CPU. \n",
     "\n",
-    "If `strict` is `True`, the file must exactly contain weights for every parameter key in `model`, if `strict` is `False`, only the keys that are in the saved model are loaded in `model`.",
-    "\n",
+    "If `strict` is `True`, the file must exactly contain weights for every parameter key in `model`, if `strict` is `False`, only the keys that are in the saved model are loaded in `model`.\n",
     "You can pass in other kwargs to `torch.load` through `torch_load_kwargs`."
    ]
   },
@@ -3803,10 +3802,10 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "It returns a tuple of three elements with, in reverse order,\n",
-    "- the prediction from the model, potentially passed through the activation of the loss function (if it has one)\n",
-    "- the decoded prediction, using the potential <code>decodes</code> method from it\n",
-    "- the fully decoded prediction, using the transforms used to build the `Datasets`/`DataLoaders`"
+    "It returns a tuple of three elements with, in reverse order:\n",
+    "- probs[] = the model's predictions (output layer), potentially passed through the activation of the loss function (if it has one);        \n",
+    "- ndx = the decoded prediction, using the potential <code>decodes</code> method from it;        \n",
+    "- label = the fully decoded prediction, using the transforms used to build the `Datasets`/`DataLoaders`."
    ]
   },
   {
@@ -3842,7 +3841,11 @@
     "dec = 2*out                        #decodes from loss function\n",
     "full_dec = dec-1                   #decodes from _Add1\n",
     "test_eq(learn.predict(inp), [full_dec,dec,out])\n",
-    "test_eq(learn.predict(inp, with_input=True), [inp,full_dec,dec,out])"
+    "test_eq(learn.predict(inp, with_input=True), [inp,full_dec,dec,out])\n",
+    "\n",
+    "# Example usage:\n",
+    "# label,ndx,probs = learner.predict(inp)\n",
+    "# print( f\"Predicted: {label} with probability {probs[ndx]}\" )"
    ]
   },
   {
@@ -4219,6 +4222,18 @@
    "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
   }
  },
  "nbformat": 4,