use bilinear upsampling in the unet

dlmbl · Aug 17, 2024 · 76eaa2a · 76eaa2a
1 parent f384125
commit 76eaa2a
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diff --git a/exercise.ipynb b/exercise.ipynb
@@ -1368,7 +1368,7 @@
     "history = {\"loss\": []}\n",
     "\n",
     "# Model:\n",
-    "unet_model = UNet(depth=3, in_channels=1, final_activation=nn.Sigmoid())\n",
+    "unet_model = UNet(depth=3, in_channels=1, upsample_mode='bilinear')\n",
     "unet_model = unet_model.to(device)\n",
     "\n",
     "# Loss function:\n",
@@ -1627,7 +1627,7 @@
     "history = {\"loss\": []}\n",
     "\n",
     "# Model:\n",
-    "unet_model = UNet(depth=3, in_channels=1, final_activation=nn.Sigmoid())\n",
+    "unet_model = UNet(depth=3, in_channels=1, upsample_mode='bilinear')\n",
     "unet_model = unet_model.to(device)\n",
     "\n",
     "# Loss function:\n",
@@ -1712,7 +1712,7 @@
     "history = {\"loss\": []}\n",
     "\n",
     "# Model:\n",
-    "unet_model = UNet(depth=3, in_channels=1, final_activation=nn.Sigmoid())\n",
+    "unet_model = UNet(depth=3, in_channels=1, upsample_mode='bilinear')\n",
     "unet_model = unet_model.to(device)\n",
     "\n",
     "# Loss function:\n",

diff --git a/solution.ipynb b/solution.ipynb