diff --git a/Final_Project_Loedige.ipynb b/Final_Project_Loedige.ipynb
index 86c3d5c..d3c8392 100644
--- a/Final_Project_Loedige.ipynb
+++ b/Final_Project_Loedige.ipynb
@@ -72,9 +72,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2023-02-01 23:37:09.558933: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
+      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
+      "2023-02-01 23:37:09.710778: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n",
+      "2023-02-01 23:37:09.710807: I tensorflow/compiler/xla/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n",
+      "2023-02-01 23:37:10.735053: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory\n",
+      "2023-02-01 23:37:10.735109: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory\n",
+      "2023-02-01 23:37:10.735116: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n",
+      "/home/paul/.local/lib/python3.10/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    }
+   ],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "import tensorflow as tf\n",
@@ -94,9 +110,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2023-02-01 23:37:12.516064: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory\n",
+      "2023-02-01 23:37:12.516115: W tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:265] failed call to cuInit: UNKNOWN ERROR (303)\n",
+      "2023-02-01 23:37:12.516142: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (paul-laptop): /proc/driver/nvidia/version does not exist\n",
+      "2023-02-01 23:37:12.516432: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
+      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
+     ]
+    }
+   ],
    "source": [
     "# split into training and test data\n",
     "ds_train, ds_test = tfds.load(\n",
@@ -117,7 +145,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -147,7 +175,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -165,7 +193,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -185,7 +213,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1524,6 +1552,57 @@
     "for config_name, config in configs.items():\n",
     "    config['model'].save(f'hdf5_models/{config_name}.h5')\n"
    ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Test Save File"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: \"sequential_11\"\n",
+      "_________________________________________________________________\n",
+      " Layer (type)                Output Shape              Param #   \n",
+      "=================================================================\n",
+      " vgg16 (Functional)          (None, 1, 1, 512)         14714688  \n",
+      "                                                                 \n",
+      " flatten_11 (Flatten)        (None, 512)               0         \n",
+      "                                                                 \n",
+      " dense_47 (Dense)            (None, 64)                32832     \n",
+      "                                                                 \n",
+      " dense_48 (Dense)            (None, 32)                2080      \n",
+      "                                                                 \n",
+      " dense_49 (Dense)            (None, 10)                330       \n",
+      "                                                                 \n",
+      "=================================================================\n",
+      "Total params: 14,749,930\n",
+      "Trainable params: 14,749,930\n",
+      "Non-trainable params: 0\n",
+      "_________________________________________________________________\n",
+      "None\n",
+      "204/204 [==============================] - 64s 313ms/step - loss: 0.2786 - accuracy: 0.9249\n",
+      "Restored model, accuracy: 92.49%\n"
+     ]
+    }
+   ],
+   "source": [
+    "new_model = tf.keras.models.load_model('hdf5_models/fully trainable VGG16 base model.h5')\n",
+    "print(new_model.summary())\n",
+    "\n",
+    "# Evaluate the restored model\n",
+    "loss, acc = new_model.evaluate(ds_test)\n",
+    "print('Restored model, accuracy: {:5.2f}%'.format(100 * acc))"
+   ]
   }
  ],
  "metadata": {
diff --git a/test.py b/test.py
deleted file mode 100644
index c079df0..0000000
--- a/test.py
+++ /dev/null
@@ -1,14 +0,0 @@
-import tensorflow as tf
-import tensorflow_datasets as tfds
-
-new_model = tf.keras.models.load_model('hdf5_models/fully trainable VGG16 base model.h5')
-new_model.summary()
-ds_train, ds_test = tfds.load(
-    'svhn_cropped', 
-    split=['train', 'test'],
-    shuffle_files=True, 
-    as_supervised=True
-    )
-
-loss, acc = new_model.evaluate(ds_test, verbose=2)
-print('Restored model, accuracy: {:5.2f}%'.format(100 * acc))