diff --git a/exercises/TL.ipynb b/exercises/TL.ipynb
index 62ea63f..40f80d4 100644
--- a/exercises/TL.ipynb
+++ b/exercises/TL.ipynb
@@ -22,8 +22,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-05-19T21:15:54.174121Z",
-     "start_time": "2024-05-19T21:15:53.827081Z"
+     "end_time": "2024-05-20T08:29:46.404464Z",
+     "start_time": "2024-05-20T08:29:46.382722Z"
     }
    },
    "cell_type": "code",
@@ -36,8 +36,9 @@
     "import matplotlib.pylab as plt\n",
     "\n",
     "import tensorflow as tf\n",
+    "import tf_keras as keras\n",
     "import tensorflow_hub as hub\n",
-    "from tensorflow.keras import datasets\n",
+    "from keras import datasets\n",
     "\n",
     "import datetime\n",
     "import os\n",
@@ -60,7 +61,7 @@
      ]
     }
    ],
-   "execution_count": 12
+   "execution_count": 22
   },
   {
    "metadata": {},
@@ -74,8 +75,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-05-19T20:45:11.469411Z",
-     "start_time": "2024-05-19T20:45:11.464721Z"
+     "end_time": "2024-05-20T08:25:22.129303Z",
+     "start_time": "2024-05-20T08:25:22.117417Z"
     }
    },
    "cell_type": "code",
@@ -88,13 +89,13 @@
    ],
    "id": "e75a0e562e19e450",
    "outputs": [],
-   "execution_count": 2
+   "execution_count": 3
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-05-19T21:21:25.048337Z",
-     "start_time": "2024-05-19T21:21:18.264524Z"
+     "end_time": "2024-05-20T08:25:58.341810Z",
+     "start_time": "2024-05-20T08:25:55.329612Z"
     }
    },
    "cell_type": "code",
@@ -102,13 +103,13 @@
     "IMAGE_SIZE = 96\n",
     "IMAGE_SHAPE = (IMAGE_SIZE, IMAGE_SIZE)\n",
     "\n",
-    "classifier = tf.keras.Sequential([\n",
+    "classifier = keras.Sequential([\n",
     "    hub.KerasLayer(classifier_model, input_shape=IMAGE_SHAPE+(3,))\n",
     "])"
    ],
    "id": "a7136361859e97fa",
    "outputs": [],
-   "execution_count": 18
+   "execution_count": 5
   },
   {
    "metadata": {},
@@ -119,13 +120,13 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-05-19T22:00:26.131418Z",
-     "start_time": "2024-05-19T22:00:25.307459Z"
+     "end_time": "2024-05-20T08:28:26.216314Z",
+     "start_time": "2024-05-20T08:28:25.142673Z"
     }
    },
    "cell_type": "code",
    "source": [
-    "path = tf.keras.utils.get_file('image.jpg','https://static.reserved.com/media/catalog/product/5/6/5698Y-99X-011-1-808148.jpg')\n",
+    "path = keras.utils.get_file('image.jpg','https://static.reserved.com/media/catalog/product/5/6/5698Y-99X-011-1-808148.jpg')\n",
     "\n",
     "img = Image.open(path).resize((IMAGE_SIZE, IMAGE_SIZE))\n",
     "os.remove(path)\n",
@@ -141,7 +142,7 @@
      "output_type": "stream",
      "text": [
       "Downloading data from https://static.reserved.com/media/catalog/product/5/6/5698Y-99X-011-1-808148.jpg\n",
-      "211086/211086 [==============================] - 0s 2us/step\n"
+      "211086/211086 [==============================] - 0s 0us/step\n"
      ]
     },
     {
@@ -155,13 +156,13 @@
      "output_type": "display_data"
     }
    ],
-   "execution_count": 41
+   "execution_count": 19
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-05-19T22:01:25.535482Z",
-     "start_time": "2024-05-19T22:01:25.090431Z"
+     "end_time": "2024-05-20T08:26:04.472091Z",
+     "start_time": "2024-05-20T08:26:03.985009Z"
     }
    },
    "cell_type": "code",
@@ -181,18 +182,18 @@
        "(96, 96, 3)"
       ]
      },
-     "execution_count": 44,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 44
+   "execution_count": 7
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-05-19T22:01:28.188002Z",
-     "start_time": "2024-05-19T22:01:28.008090Z"
+     "end_time": "2024-05-20T08:26:32.405912Z",
+     "start_time": "2024-05-20T08:26:05.082971Z"
     }
    },
    "cell_type": "code",
@@ -202,11 +203,19 @@
    ],
    "id": "a5b9865672a6e8e7",
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-20 10:26:26.069855: I external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:454] Loaded cuDNN version 8904\n",
+      "2024-05-20 10:26:27.430147: I external/local_tsl/tsl/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "1/1 [==============================] - 0s 64ms/step\n"
+      "1/1 [==============================] - 26s 26s/step\n"
      ]
     },
     {
@@ -215,18 +224,18 @@
        "(1, 1001)"
       ]
      },
-     "execution_count": 45,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 45
+   "execution_count": 8
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-05-19T22:01:29.354375Z",
-     "start_time": "2024-05-19T22:01:29.337046Z"
+     "end_time": "2024-05-20T08:27:55.569800Z",
+     "start_time": "2024-05-20T08:27:55.553420Z"
     }
    },
    "cell_type": "code",
@@ -242,12 +251,12 @@
        "<tf.Tensor: shape=(), dtype=int64, numpy=917>"
       ]
      },
-     "execution_count": 46,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 46
+   "execution_count": 15
   },
   {
    "metadata": {},
@@ -258,24 +267,24 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-05-19T22:01:31.994749Z",
-     "start_time": "2024-05-19T22:01:31.990679Z"
+     "end_time": "2024-05-20T08:28:36.490311Z",
+     "start_time": "2024-05-20T08:28:36.482464Z"
     }
    },
    "cell_type": "code",
    "source": [
-    "labels_path = tf.keras.utils.get_file('ImageNetLabels.txt','https://storage.googleapis.com/download.tensorflow.org/data/ImageNetLabels.txt')\n",
+    "labels_path = keras.utils.get_file('ImageNetLabels.txt','https://storage.googleapis.com/download.tensorflow.org/data/ImageNetLabels.txt')\n",
     "imagenet_labels = np.array(open(labels_path).read().splitlines())"
    ],
    "id": "ad6b945217e8f7b",
    "outputs": [],
-   "execution_count": 47
+   "execution_count": 20
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-05-19T22:01:33.067776Z",
-     "start_time": "2024-05-19T22:01:32.896686Z"
+     "end_time": "2024-05-20T08:28:41.199358Z",
+     "start_time": "2024-05-20T08:28:41.024161Z"
     }
    },
    "cell_type": "code",
@@ -292,13 +301,13 @@
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 48
+   "execution_count": 21
   },
   {
    "metadata": {},
@@ -312,8 +321,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-05-19T22:01:53.589717Z",
-     "start_time": "2024-05-19T22:01:52.499464Z"
+     "end_time": "2024-05-20T08:30:01.302156Z",
+     "start_time": "2024-05-20T08:30:00.369432Z"
     }
    },
    "cell_type": "code",
@@ -327,13 +336,13 @@
    ],
    "id": "2d3a9d77a2537187",
    "outputs": [],
-   "execution_count": 49
+   "execution_count": 23
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-05-19T22:01:54.066064Z",
-     "start_time": "2024-05-19T22:01:53.591231Z"
+     "end_time": "2024-05-20T08:30:02.932149Z",
+     "start_time": "2024-05-20T08:30:02.607099Z"
     }
    },
    "cell_type": "code",
@@ -369,13 +378,13 @@
      ]
     }
    ],
-   "execution_count": 50
+   "execution_count": 24
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-05-19T22:01:55.569267Z",
-     "start_time": "2024-05-19T22:01:54.343317Z"
+     "end_time": "2024-05-20T08:30:07.152180Z",
+     "start_time": "2024-05-20T08:30:05.301074Z"
     }
    },
    "cell_type": "code",
@@ -403,7 +412,7 @@
      "output_type": "display_data"
     }
    ],
-   "execution_count": 51
+   "execution_count": 25
   },
   {
    "metadata": {},