Test training

.gitignore

@@ -2,4 +2,5 @@
 .idea
 data/
 __pycache__
-image.jpeg
+image.jpeg
+.ipynb_checkpoints

generate_prediction.py

@@ -5,13 +5,14 @@ from model import build_hybrid_model
 from data_loaders import load_single_image, load_dataset, get_class_names
 from helpers import imshow
 
+
 def softmax(x):
-    """Compute softmax values for each set of scores in x."""
+    # Compute softmax values for each set of scores in x.
     e_x = np.exp(x - np.max(x))  # Subtracting max for numerical stability
     return e_x / e_x.sum(axis=0)
 
-def generate_prediction(img_path="./image.jpeg"):
 
+def generate_prediction(img_path="./image.jpeg"):
     N_QUBITS = 4
     PARAMETER_FILEPATH = "state.pt"
 
@@ -36,23 +37,23 @@ def generate_prediction(img_path="./image.jpeg"):
     # Load [image] from Disk
     X = load_single_image(img_path)
     imshow(X)
-    input = torch.stack([X,X,X,X])
+    input = torch.stack([X, X, X, X])
 
     # Generate Model Prediction
     model.eval()
 
     with torch.no_grad():
-            input = input.to(device)
-            output = model(input)
-            _, preds = torch.max(output, 1)
+        input = input.to(device)
+        output = model(input)
+        _, preds = torch.max(output, 1)
 
     # Load Dataset for class_names
     class_names = get_class_names(load_dataset())
-    print("OUTPUT VEC:",output[0])
-    print("HOT INDEX:",preds[0])
+    print("OUTPUT VEC:", output[0])
+    print("HOT INDEX:", preds[0])
 
     # Return Model Prediction
     probabilities = softmax(output[0].cpu().numpy())
     prediction = {class_name.replace("_", " "): prob for class_name, prob in zip(class_names, probabilities)}
 
-    return prediction
+    return prediction

notebook.ipynb

@@ -73,11 +73,55 @@
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "start_time": "2024-09-10T22:21:48.168855Z"
+     "end_time": "2024-09-11T22:38:45.705122Z",
+     "start_time": "2024-09-11T22:16:02.896239Z"
     }
    },
-   "outputs": [],
-   "execution_count": null
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\MaxHu\\anaconda3\\envs\\quantum-env\\lib\\site-packages\\torchvision\\models\\_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
+      "  warnings.warn(\n",
+      "C:\\Users\\MaxHu\\anaconda3\\envs\\quantum-env\\lib\\site-packages\\torchvision\\models\\_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.\n",
+      "  warnings.warn(msg)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading Model Weights from Disk...\n",
+      "Training started:\n",
+      "Data Loading Completed in 0m 0s\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\MaxHu\\AppData\\Local\\Temp\\ipykernel_12896\\449253314.py:35: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
+      "  model.load_state_dict(torch.load(PARAMETER_FILEPATH))\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Phase: train Epoch: 1/3 Loss: 0.8986 Acc: 0.5877        33\n",
+      "Phase: validation   Epoch: 1/3 Loss: 1.9974 Acc: 0.3929        \n",
+      "Phase: train Epoch: 2/3 Loss: 0.8994 Acc: 0.5849        76\n",
+      "Phase: validation   Epoch: 2/3 Loss: 1.9718 Acc: 0.3929        \n",
+      "Phase: train Epoch: 3/3 Loss: 0.8977 Acc: 0.5860        76\n",
+      "Phase: validation   Epoch: 3/3 Loss: 2.0105 Acc: 0.3750        \n",
+      "Training Completed in 22m 34s\n",
+      "Best test loss: 1.9718 | Best test accuracy: 0.3929\n",
+      "Saving Model Weights to Disk...\n"
+     ]
+    }
+   ],
+   "execution_count": 1
   },
   {
    "cell_type": "markdown",

state.pt

@@ -1,3 +1,3 @@
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-size 44785225
+oid sha256:3e65747cc44ba516c9bc0c73af4887345fc0b032de1f0ea7c32ddc308ffa9cab
+size 44785654