adjusted ZILNLoss to return mean

examples/14_Losses-temp.ipynb

@@ -18,7 +18,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 71,
    "id": "d40952c1",
    "metadata": {},
    "outputs": [],
@@ -41,12 +41,13 @@
     "# Pytorch implementation\n",
     "* reduction=\"none\" in binary_cross_entropy_with_logits gives same result for given testing usecase as authors implementation https://pytorch.org/docs/stable/generated/torch.nn.functional.binary_cross_entropy_with_logits.html\n",
     "  * hovewer keras BCE uses auto reduction by default https://www.tensorflow.org/api_docs/python/tf/keras/losses/BinaryCrossentropy which changes according to usecase : so wtf? https://www.tensorflow.org/api_docs/python/tf/keras/losses/Reduction\n",
-    "* keras also return flattened BCE result"
+    "* keras also return flattened BCE result\n",
+    "* keras needs loss per sample and it averages it in the backend: https://keras.io/api/losses/"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 83,
    "id": "9864678c",
    "metadata": {},
    "outputs": [],
@@ -56,6 +57,7 @@
     "import torch.nn.functional as F\n",
     "from pytorch_widedeep.wdtypes import *\n",
     "\n",
+    "\n",
     "def _predict_ziln(preds: Tensor) -> Tensor:\n",
     "    \"\"\"Calculates predicted mean of zero inflated lognormal logits.\n",
     "    Adjusted implementaion of `code\n",
@@ -123,54 +125,33 @@
     "            positive * torch.distributions.log_normal.LogNormal(loc=loc, scale=scale).log_prob(safe_labels),\n",
     "            dim=-1)\n",
     "\n",
-    "        return classification_loss + regression_loss"
+    "        return torch.mean(classification_loss + regression_loss)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 84,
    "id": "d1073cc6",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "tensor([0.7444, 1.8784])"
+       "tensor(1.3114)"
       ]
      },
-     "execution_count": 65,
+     "execution_count": 84,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
+    "use_cuda = torch.cuda.is_available()\n",
     "target = torch.tensor([[0., 1.5]]).view(-1, 1)\n",
     "input = torch.tensor([[.1, .2, .3], [.4, .5, .6]])\n",
     "ZILNLoss()(input, target)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "id": "632af7b0",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[0.9236],\n",
-       "        [1.6908]])"
-      ]
-     },
-     "execution_count": 66,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "_predict_ziln(input)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "6f91526c",

pytorch_widedeep/losses.py

@@ -112,7 +112,7 @@ class ZILNLoss(nn.Module):
             positive * torch.distributions.log_normal.LogNormal(loc=loc, scale=scale).log_prob(safe_labels),
             dim=-1)
 
-        return classification_loss + regression_loss
+        return torch.mean(classification_loss + regression_loss)
 
 
 class FocalLoss(nn.Module):