ploting notebooks

notebooks/caml.ipynb

@@ -282,7 +282,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.4"
+   "version": "3.13.1"
   }
  },
  "nbformat": 4,

notebooks/pmf.ipynb

@@ -887,7 +887,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.4"
+   "version": "3.13.1"
   }
  },
  "nbformat": 4,

notebooks/resnet50.ipynb

@@ -2,14 +2,18 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "imports imported\n"
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'torchvision'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[1], line 8\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n\u001b[1;32m      7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m optim, nn\n\u001b[0;32m----> 8\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtorchvision\u001b[39;00m\n\u001b[1;32m      9\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtorchvision\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m datasets, models, transforms\n\u001b[1;32m     10\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01malbumentations\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mA\u001b[39;00m\n",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'torchvision'"
      ]
     }
    ],
@@ -238,9 +242,9 @@
     "\n",
     "print(resnetshotnr0)\n",
     "# Step 2: Modify the model to output features from the layer before the fully connected layer\n",
-    "class ResNetshotnr0Embeddings(nn.Module):\n",
+    "class ResNet50Embeddings(nn.Module):\n",
     "    def __init__(self, original_model, layernr):\n",
-    "        super(ResNetshotnr0Embeddings, self).__init__()\n",
+    "        super(ResNet50Embeddings, self).__init__()\n",
     "        #print(list(original_model.children())[4 + layernr])\n",
     "        #print(nn.Sequential(*list(original_model.children())[:4 + shotnr]))\n",
     "        self.features = nn.Sequential(*list(original_model.children())[:4+layernr])\n",
@@ -252,7 +256,7 @@
     "        return x\n",
     "\n",
     "# Instantiate the modified model\n",
-    "model = ResNetshotnr0Embeddings(resnetshotnr0, shotnr) # 3 = layer before fully connected one\n",
+    "model = ResNet50Embeddings(resnetshotnr0, shotnr) # 3 = layer before fully connected one\n",
     "model.eval()  # Set the model to evaluation mode\n",
     "print()\n"
    ]
@@ -487,9 +491,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.14"
+   "version": "3.13.1"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }