diff --git a/notebooks/Introduction_to_Image_Classification/Jupyter Notebook Block 1 - Introduction to Image Classification.ipynb b/notebooks/Introduction_to_Image_Classification/Jupyter Notebook Block 1 - Introduction to Image Classification.ipynb
index d38854357bb253b1854a603fd6dbef87b9e0b57a..837bf06efbfc60f6ce986d666f496629269d32dc 100644
--- a/notebooks/Introduction_to_Image_Classification/Jupyter Notebook Block 1 - Introduction to Image Classification.ipynb	
+++ b/notebooks/Introduction_to_Image_Classification/Jupyter Notebook Block 1 - Introduction to Image Classification.ipynb	
@@ -13,7 +13,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {
     "nbpresent": {
      "id": "409a1ab7-fe1d-4430-b904-7694020a6223"
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {
     "nbpresent": {
      "id": "f77bd9ec-de3b-4c56-b08d-4a65f0780408"
@@ -63,7 +63,7 @@
        "dict"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -85,7 +85,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {
     "nbpresent": {
      "id": "c874a7c9-de0c-4ccd-a0f1-8f8a3265a0b6"
@@ -98,7 +98,7 @@
        "dict_keys([b'batch_label', b'labels', b'data', b'filenames'])"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
diff --git a/notebooks/Introduction_to_Image_Classification/Solutions to Exercises Block 1 - Introduction to Image Classification.ipynb b/notebooks/Introduction_to_Image_Classification/Solutions to Exercises Block 1 - Introduction to Image Classification.ipynb
index 78b4eabc2c9ac1efcbe672fa11798350374cdade..819d5ac6a891275bdb3025c647a3f2f4adaded71 100644
--- a/notebooks/Introduction_to_Image_Classification/Solutions to Exercises Block 1 - Introduction to Image Classification.ipynb	
+++ b/notebooks/Introduction_to_Image_Classification/Solutions to Exercises Block 1 - Introduction to Image Classification.ipynb	
@@ -47,27 +47,40 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Requirement already up-to-date: tensorflow_datasets in /opt/conda/lib/python3.7/site-packages (4.6.0)\n",
-      "Requirement already satisfied, skipping upgrade: absl-py in /opt/conda/lib/python3.7/site-packages (from tensorflow_datasets) (1.2.0)\n",
+      "Collecting tensorflow_datasets\n",
+      "  Downloading tensorflow_datasets-4.6.0-py3-none-any.whl (4.3 MB)\n",
+      "\u001b[K     |████████████████████████████████| 4.3 MB 5.1 MB/s eta 0:00:01\n",
+      "\u001b[?25hRequirement already satisfied, skipping upgrade: termcolor in /opt/conda/lib/python3.7/site-packages (from tensorflow_datasets) (2.0.1)\n",
       "Requirement already satisfied, skipping upgrade: typing-extensions; python_version < \"3.8\" in /opt/conda/lib/python3.7/site-packages (from tensorflow_datasets) (3.7.4.3)\n",
-      "Requirement already satisfied, skipping upgrade: dill in /opt/conda/lib/python3.7/site-packages (from tensorflow_datasets) (0.3.5.1)\n",
-      "Requirement already satisfied, skipping upgrade: termcolor in /opt/conda/lib/python3.7/site-packages (from tensorflow_datasets) (2.0.1)\n",
-      "Requirement already satisfied, skipping upgrade: toml in /opt/conda/lib/python3.7/site-packages (from tensorflow_datasets) (0.10.2)\n",
-      "Requirement already satisfied, skipping upgrade: numpy in /opt/conda/lib/python3.7/site-packages (from tensorflow_datasets) (1.19.1)\n",
-      "Requirement already satisfied, skipping upgrade: protobuf>=3.12.2 in /opt/conda/lib/python3.7/site-packages (from tensorflow_datasets) (3.20.2)\n",
-      "Requirement already satisfied, skipping upgrade: importlib-resources; python_version < \"3.9\" in /opt/conda/lib/python3.7/site-packages (from tensorflow_datasets) (5.9.0)\n",
-      "Requirement already satisfied, skipping upgrade: tensorflow-metadata in /opt/conda/lib/python3.7/site-packages (from tensorflow_datasets) (1.10.0)\n",
-      "Requirement already satisfied, skipping upgrade: etils[epath] in /opt/conda/lib/python3.7/site-packages (from tensorflow_datasets) (0.8.0)\n",
       "Requirement already satisfied, skipping upgrade: tqdm in /opt/conda/lib/python3.7/site-packages (from tensorflow_datasets) (4.45.0)\n",
+      "Requirement already satisfied, skipping upgrade: numpy in /opt/conda/lib/python3.7/site-packages (from tensorflow_datasets) (1.19.1)\n",
       "Requirement already satisfied, skipping upgrade: requests>=2.19.0 in /opt/conda/lib/python3.7/site-packages (from tensorflow_datasets) (2.23.0)\n",
-      "Requirement already satisfied, skipping upgrade: six in /opt/conda/lib/python3.7/site-packages (from tensorflow_datasets) (1.14.0)\n",
+      "Collecting dill\n",
+      "  Downloading dill-0.3.5.1-py2.py3-none-any.whl (95 kB)\n",
+      "\u001b[K     |████████████████████████████████| 95 kB 3.4 MB/s  eta 0:00:01\n",
+      "\u001b[?25hRequirement already satisfied, skipping upgrade: six in /opt/conda/lib/python3.7/site-packages (from tensorflow_datasets) (1.14.0)\n",
+      "Requirement already satisfied, skipping upgrade: protobuf>=3.12.2 in /opt/conda/lib/python3.7/site-packages (from tensorflow_datasets) (3.20.0)\n",
+      "Requirement already satisfied, skipping upgrade: toml in /opt/conda/lib/python3.7/site-packages (from tensorflow_datasets) (0.10.2)\n",
+      "Collecting importlib-resources; python_version < \"3.9\"\n",
+      "  Downloading importlib_resources-5.9.0-py3-none-any.whl (33 kB)\n",
+      "Collecting etils[epath]\n",
+      "  Downloading etils-0.8.0-py3-none-any.whl (127 kB)\n",
+      "\u001b[K     |████████████████████████████████| 127 kB 41.0 MB/s eta 0:00:01\n",
+      "\u001b[?25hRequirement already satisfied, skipping upgrade: absl-py in /opt/conda/lib/python3.7/site-packages (from tensorflow_datasets) (1.2.0)\n",
       "Requirement already satisfied, skipping upgrade: promise in /opt/conda/lib/python3.7/site-packages (from tensorflow_datasets) (2.3)\n",
-      "Requirement already satisfied, skipping upgrade: zipp>=3.1.0; python_version < \"3.10\" in /opt/conda/lib/python3.7/site-packages (from importlib-resources; python_version < \"3.9\"->tensorflow_datasets) (3.1.0)\n",
-      "Requirement already satisfied, skipping upgrade: googleapis-common-protos<2,>=1.52.0 in /opt/conda/lib/python3.7/site-packages (from tensorflow-metadata->tensorflow_datasets) (1.56.4)\n",
+      "Collecting tensorflow-metadata\n",
+      "  Downloading tensorflow_metadata-1.10.0-py3-none-any.whl (50 kB)\n",
+      "\u001b[K     |████████████████████████████████| 50 kB 6.5 MB/s  eta 0:00:01\n",
+      "\u001b[?25hRequirement already satisfied, skipping upgrade: chardet<4,>=3.0.2 in /opt/conda/lib/python3.7/site-packages (from requests>=2.19.0->tensorflow_datasets) (3.0.4)\n",
       "Requirement already satisfied, skipping upgrade: idna<3,>=2.5 in /opt/conda/lib/python3.7/site-packages (from requests>=2.19.0->tensorflow_datasets) (2.9)\n",
-      "Requirement already satisfied, skipping upgrade: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /opt/conda/lib/python3.7/site-packages (from requests>=2.19.0->tensorflow_datasets) (1.25.11)\n",
       "Requirement already satisfied, skipping upgrade: certifi>=2017.4.17 in /opt/conda/lib/python3.7/site-packages (from requests>=2.19.0->tensorflow_datasets) (2020.6.20)\n",
-      "Requirement already satisfied, skipping upgrade: chardet<4,>=3.0.2 in /opt/conda/lib/python3.7/site-packages (from requests>=2.19.0->tensorflow_datasets) (3.0.4)\n",
+      "Requirement already satisfied, skipping upgrade: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /opt/conda/lib/python3.7/site-packages (from requests>=2.19.0->tensorflow_datasets) (1.25.9)\n",
+      "Requirement already satisfied, skipping upgrade: zipp>=3.1.0; python_version < \"3.10\" in /opt/conda/lib/python3.7/site-packages (from importlib-resources; python_version < \"3.9\"->tensorflow_datasets) (3.1.0)\n",
+      "Collecting googleapis-common-protos<2,>=1.52.0\n",
+      "  Downloading googleapis_common_protos-1.56.4-py2.py3-none-any.whl (211 kB)\n",
+      "\u001b[K     |████████████████████████████████| 211 kB 39.6 MB/s eta 0:00:01\n",
+      "\u001b[?25hInstalling collected packages: dill, importlib-resources, etils, googleapis-common-protos, tensorflow-metadata, tensorflow-datasets\n",
+      "Successfully installed dill-0.3.5.1 etils-0.8.0 googleapis-common-protos-1.56.4 importlib-resources-5.9.0 tensorflow-datasets-4.6.0 tensorflow-metadata-1.10.0\n",
       "\u001b[33mWARNING: You are using pip version 20.2.4; however, version 22.2.2 is available.\n",
       "You should consider upgrading via the '/opt/conda/bin/python3 -m pip install --upgrade pip' command.\u001b[0m\n"
      ]
diff --git a/notebooks/Neural_Networks/Jupyter Notebook Block 2 - Neural Networks .ipynb b/notebooks/Neural_Networks/Jupyter Notebook Block 2 - Neural Networks .ipynb
index 5e2c3c70c96df48d6838ee39c8493b94b8d44474..2de0fa80b999cf81564746c52fb04a8b82f6692f 100644
--- a/notebooks/Neural_Networks/Jupyter Notebook Block 2 - Neural Networks .ipynb	
+++ b/notebooks/Neural_Networks/Jupyter Notebook Block 2 - Neural Networks .ipynb	
@@ -39,7 +39,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -125,8 +125,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[ 6.84534358e-03  1.04857769e-02  6.46474626e-05]\n",
-      " [-8.28484830e-03  1.43781703e-02 -9.68779164e-03]]\n",
+      "[[-0.0067635   0.01015244  0.00354555]\n",
+      " [ 0.01211098  0.00444224 -0.01082777]]\n",
       "[[0. 0. 0.]]\n"
      ]
     }
@@ -259,8 +259,8 @@
      "text": [
       "(300, 3)\n",
       "[[0.33333333 0.33333333 0.33333333]\n",
-      " [0.33331233 0.33338795 0.33329972]\n",
-      " [0.33328587 0.33343922 0.33327492]]\n"
+      " [0.33337411 0.33333516 0.33329073]\n",
+      " [0.33338017 0.33336566 0.33325417]]\n"
      ]
     }
    ],
@@ -344,7 +344,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "1.0958145625410027\n"
+      "1.0994815887350224\n"
      ]
     }
    ],
@@ -565,26 +565,26 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "iteration 0: loss 1.099250\n",
-      "iteration 10: loss 0.908294\n",
-      "iteration 20: loss 0.836630\n",
-      "iteration 30: loss 0.803750\n",
-      "iteration 40: loss 0.786502\n",
-      "iteration 50: loss 0.776624\n",
-      "iteration 60: loss 0.770616\n",
-      "iteration 70: loss 0.766799\n",
-      "iteration 80: loss 0.764296\n",
-      "iteration 90: loss 0.762614\n",
-      "iteration 100: loss 0.761461\n",
-      "iteration 110: loss 0.760659\n",
-      "iteration 120: loss 0.760094\n",
-      "iteration 130: loss 0.759692\n",
-      "iteration 140: loss 0.759404\n",
-      "iteration 150: loss 0.759197\n",
-      "iteration 160: loss 0.759046\n",
-      "iteration 170: loss 0.758936\n",
-      "iteration 180: loss 0.758855\n",
-      "iteration 190: loss 0.758796\n"
+      "iteration 0: loss 1.097722\n",
+      "iteration 10: loss 0.903960\n",
+      "iteration 20: loss 0.832360\n",
+      "iteration 30: loss 0.800200\n",
+      "iteration 40: loss 0.783672\n",
+      "iteration 50: loss 0.774387\n",
+      "iteration 60: loss 0.768843\n",
+      "iteration 70: loss 0.765384\n",
+      "iteration 80: loss 0.763155\n",
+      "iteration 90: loss 0.761682\n",
+      "iteration 100: loss 0.760690\n",
+      "iteration 110: loss 0.760011\n",
+      "iteration 120: loss 0.759541\n",
+      "iteration 130: loss 0.759211\n",
+      "iteration 140: loss 0.758979\n",
+      "iteration 150: loss 0.758814\n",
+      "iteration 160: loss 0.758695\n",
+      "iteration 170: loss 0.758610\n",
+      "iteration 180: loss 0.758549\n",
+      "iteration 190: loss 0.758505\n"
      ]
     }
    ],
@@ -651,7 +651,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "training accuracy: 0.54\n"
+      "training accuracy: 0.52\n"
      ]
     }
    ],
@@ -903,16 +903,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "iteration 0: loss 1.098763\n",
-      "iteration 1000: loss 0.322480\n",
-      "iteration 2000: loss 0.256013\n",
-      "iteration 3000: loss 0.251653\n",
-      "iteration 4000: loss 0.250492\n",
-      "iteration 5000: loss 0.249846\n",
-      "iteration 6000: loss 0.249372\n",
-      "iteration 7000: loss 0.248996\n",
-      "iteration 8000: loss 0.248683\n",
-      "iteration 9000: loss 0.248496\n"
+      "iteration 0: loss 1.098635\n",
+      "iteration 1000: loss 0.319796\n",
+      "iteration 2000: loss 0.261349\n",
+      "iteration 3000: loss 0.250143\n",
+      "iteration 4000: loss 0.248536\n",
+      "iteration 5000: loss 0.247981\n",
+      "iteration 6000: loss 0.247715\n",
+      "iteration 7000: loss 0.247485\n",
+      "iteration 8000: loss 0.247280\n",
+      "iteration 9000: loss 0.247058\n"
      ]
     }
    ],
@@ -1062,27 +1062,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
-     "ename": "TypeError",
-     "evalue": "Descriptors cannot not be created directly.\nIf this call came from a _pb2.py file, your generated code is out of date and must be regenerated with protoc >= 3.19.0.\nIf you cannot immediately regenerate your protos, some other possible workarounds are:\n 1. Downgrade the protobuf package to 3.20.x or lower.\n 2. Set PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python (but this will use pure-Python parsing and will be much slower).\n\nMore information: https://developers.google.com/protocol-buffers/docs/news/2022-05-06#python-updates",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-20-071e841fb446>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatasets\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmnist\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmnist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/tensorflow/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     39\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0msys\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0m_sys\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     40\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 41\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtools\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmodule_util\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0m_module_util\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     42\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutil\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlazy_loader\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mLazyLoader\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0m_LazyLoader\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     43\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/tensorflow/python/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     39\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     40\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpython\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpywrap_tensorflow\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0m_pywrap_tensorflow\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 41\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meager\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     42\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     43\u001b[0m \u001b[0;31m# pylint: enable=wildcard-import\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/context.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     31\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0msix\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     32\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 33\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mframework\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mfunction_pb2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     34\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprotobuf\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mconfig_pb2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     35\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprotobuf\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mrewriter_config_pb2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/tensorflow/core/framework/function_pb2.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mframework\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mattr_value_pb2\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mtensorflow_dot_core_dot_framework_dot_attr__value__pb2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     17\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mframework\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnode_def_pb2\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mtensorflow_dot_core_dot_framework_dot_node__def__pb2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mframework\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mop_def_pb2\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mtensorflow_dot_core_dot_framework_dot_op__def__pb2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/tensorflow/core/framework/attr_value_pb2.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mframework\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtensor_pb2\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mtensorflow_dot_core_dot_framework_dot_tensor__pb2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     17\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mframework\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtensor_shape_pb2\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mtensorflow_dot_core_dot_framework_dot_tensor__shape__pb2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mframework\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtypes_pb2\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mtensorflow_dot_core_dot_framework_dot_types__pb2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/tensorflow/core/framework/tensor_pb2.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mframework\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mresource_handle_pb2\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mtensorflow_dot_core_dot_framework_dot_resource__handle__pb2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     17\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mframework\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtensor_shape_pb2\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mtensorflow_dot_core_dot_framework_dot_tensor__shape__pb2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mframework\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtypes_pb2\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mtensorflow_dot_core_dot_framework_dot_types__pb2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/tensorflow/core/framework/resource_handle_pb2.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mframework\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtensor_shape_pb2\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mtensorflow_dot_core_dot_framework_dot_tensor__shape__pb2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     17\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mframework\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtypes_pb2\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mtensorflow_dot_core_dot_framework_dot_types__pb2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/tensorflow/core/framework/tensor_shape_pb2.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     40\u001b[0m       \u001b[0mmessage_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0menum_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontaining_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     41\u001b[0m       \u001b[0mis_extension\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mextension_scope\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 42\u001b[0;31m       serialized_options=None, file=DESCRIPTOR),\n\u001b[0m\u001b[1;32m     43\u001b[0m     _descriptor.FieldDescriptor(\n\u001b[1;32m     44\u001b[0m       \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'name'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfull_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'tensorflow.TensorShapeProto.Dim.name'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/google/protobuf/descriptor.py\u001b[0m in \u001b[0;36m__new__\u001b[0;34m(cls, name, full_name, index, number, type, cpp_type, label, default_value, message_type, enum_type, containing_type, is_extension, extension_scope, options, serialized_options, has_default_value, containing_oneof, json_name, file, create_key)\u001b[0m\n\u001b[1;32m    558\u001b[0m                 \u001b[0mhas_default_value\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontaining_oneof\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mjson_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    559\u001b[0m                 file=None, create_key=None):  # pylint: disable=redefined-builtin\n\u001b[0;32m--> 560\u001b[0;31m       \u001b[0m_message\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMessage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_CheckCalledFromGeneratedFile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    561\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mis_extension\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    562\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0m_message\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdefault_pool\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mFindExtensionByName\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfull_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mTypeError\u001b[0m: Descriptors cannot not be created directly.\nIf this call came from a _pb2.py file, your generated code is out of date and must be regenerated with protoc >= 3.19.0.\nIf you cannot immediately regenerate your protos, some other possible workarounds are:\n 1. Downgrade the protobuf package to 3.20.x or lower.\n 2. Set PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python (but this will use pure-Python parsing and will be much slower).\n\nMore information: https://developers.google.com/protocol-buffers/docs/news/2022-05-06#python-updates"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
+      "11493376/11490434 [==============================] - 0s 0us/step\n",
+      "11501568/11490434 [==============================] - 0s 0us/step\n"
      ]
     }
    ],
@@ -1102,19 +1091,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
-     "ename": "NameError",
-     "evalue": "name 'X_train' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-21-d2ba684acd0f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mX_train\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m: name 'X_train' is not defined"
-     ]
+     "data": {
+      "text/plain": [
+       "(60000, 28, 28)"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -1123,19 +1111,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
-     "ename": "NameError",
-     "evalue": "name 'y_train' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-22-8a7f88268f18>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m: name 'y_train' is not defined"
-     ]
+     "data": {
+      "text/plain": [
+       "60000"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -1144,7 +1131,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -1153,7 +1140,7 @@
        "array([5, 0, 4, ..., 5, 6, 8], dtype=uint8)"
       ]
      },
-     "execution_count": 100,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1171,7 +1158,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 101,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -1196,7 +1183,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -1205,7 +1192,7 @@
        "9"
       ]
      },
-     "execution_count": 102,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1223,7 +1210,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -1232,7 +1219,7 @@
        "(10000, 28, 28)"
       ]
      },
-     "execution_count": 103,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1243,7 +1230,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 104,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -1252,7 +1239,7 @@
        "10000"
       ]
      },
-     "execution_count": 104,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1263,7 +1250,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 105,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -1272,7 +1259,7 @@
        "array([7, 2, 1, ..., 4, 5, 6], dtype=uint8)"
       ]
      },
-     "execution_count": 105,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1291,7 +1278,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 106,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
@@ -1321,7 +1308,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 107,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
@@ -1342,7 +1329,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 108,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
@@ -1385,7 +1372,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 109,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1431,7 +1418,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 110,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1442,22 +1429,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 111,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Model: \"sequential_1\"\n",
+      "Model: \"sequential\"\n",
       "_________________________________________________________________\n",
       " Layer (type)                Output Shape              Param #   \n",
       "=================================================================\n",
-      " dense_3 (Dense)             (None, 500)               392500    \n",
+      " dense (Dense)               (None, 500)               392500    \n",
       "                                                                 \n",
-      " dense_4 (Dense)             (None, 50)                25050     \n",
+      " dense_1 (Dense)             (None, 50)                25050     \n",
       "                                                                 \n",
-      " dense_5 (Dense)             (None, 10)                510       \n",
+      " dense_2 (Dense)             (None, 10)                510       \n",
       "                                                                 \n",
       "=================================================================\n",
       "Total params: 418,060\n",
@@ -1488,7 +1475,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 112,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
@@ -1496,15 +1483,15 @@
      "output_type": "stream",
      "text": [
       "Epoch 1/5\n",
-      "469/469 [==============================] - 7s 15ms/step - loss: 1.0895 - accuracy: 0.7293\n",
+      "469/469 [==============================] - 7s 14ms/step - loss: 1.1334 - accuracy: 0.7209\n",
       "Epoch 2/5\n",
-      "469/469 [==============================] - 7s 14ms/step - loss: 0.4560 - accuracy: 0.8832\n",
+      "469/469 [==============================] - 6s 13ms/step - loss: 0.4582 - accuracy: 0.8802\n",
       "Epoch 3/5\n",
-      "469/469 [==============================] - 7s 14ms/step - loss: 0.3601 - accuracy: 0.9021\n",
+      "469/469 [==============================] - 7s 14ms/step - loss: 0.3607 - accuracy: 0.9013\n",
       "Epoch 4/5\n",
-      "469/469 [==============================] - 7s 14ms/step - loss: 0.3174 - accuracy: 0.9122\n",
+      "469/469 [==============================] - 7s 14ms/step - loss: 0.3182 - accuracy: 0.9116\n",
       "Epoch 5/5\n",
-      "469/469 [==============================] - 7s 15ms/step - loss: 0.2906 - accuracy: 0.9188\n"
+      "469/469 [==============================] - 7s 15ms/step - loss: 0.2917 - accuracy: 0.9179\n"
      ]
     }
    ],
@@ -1532,15 +1519,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 113,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "313/313 [==============================] - 2s 5ms/step - loss: 0.2665 - accuracy: 0.9258\n",
-      "test_acc: 0.9258000254631042\n"
+      "313/313 [==============================] - 2s 4ms/step - loss: 0.2681 - accuracy: 0.9254\n",
+      "test_acc: 0.9254000186920166\n"
      ]
     }
    ],
@@ -1561,7 +1548,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 114,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
@@ -1570,7 +1557,7 @@
        "array([7, 2, 1, 0, 4, 1, 4, 9, 6, 9])"
       ]
      },
-     "execution_count": 114,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1583,7 +1570,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 115,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
@@ -1592,7 +1579,7 @@
        "array([7, 2, 1, 0, 4, 1, 4, 9, 5, 9], dtype=uint8)"
       ]
      },
-     "execution_count": 115,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1612,18 +1599,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 116,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([2.0822265e-05, 2.8483171e-05, 3.3001334e-04, 1.0540996e-03,\n",
-       "       3.7455920e-05, 8.7879715e-05, 4.8982071e-07, 9.9719810e-01,\n",
-       "       2.7312219e-04, 9.6961117e-04], dtype=float32)"
+       "array([2.1942596e-04, 2.1810220e-06, 1.7857095e-04, 1.5695528e-03,\n",
+       "       4.4542562e-06, 1.7820840e-04, 4.8574157e-07, 9.9662966e-01,\n",
+       "       2.9819525e-05, 1.1875785e-03], dtype=float32)"
       ]
      },
-     "execution_count": 116,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1634,7 +1621,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 118,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [
     {
@@ -1643,7 +1630,7 @@
        "7"
       ]
      },
-     "execution_count": 118,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1661,7 +1648,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 119,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
@@ -1670,7 +1657,7 @@
        "7"
       ]
      },
-     "execution_count": 119,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1688,7 +1675,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [
     {
@@ -1732,7 +1719,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1761,7 +1748,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
@@ -1770,7 +1757,7 @@
        "(300, 2)"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1791,20 +1778,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Model: \"sequential_2\"\n",
+      "Model: \"sequential_1\"\n",
       "_________________________________________________________________\n",
       " Layer (type)                Output Shape              Param #   \n",
       "=================================================================\n",
-      " dense_4 (Dense)             (None, 100)               300       \n",
+      " dense_3 (Dense)             (None, 100)               300       \n",
       "                                                                 \n",
-      " dense_5 (Dense)             (None, 3)                 303       \n",
+      " dense_4 (Dense)             (None, 3)                 303       \n",
       "                                                                 \n",
       "=================================================================\n",
       "Total params: 603\n",
@@ -1833,7 +1820,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [
     {
@@ -1870,7 +1857,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [
     {
@@ -1891,2022 +1878,222 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 1/1000\n",
-      "3/3 [==============================] - 1s 3ms/step - loss: 1.0828 - accuracy: 0.3133\n",
-      "Epoch 2/1000\n",
-      "3/3 [==============================] - 0s 4ms/step - loss: 1.0702 - accuracy: 0.3733\n",
-      "Epoch 3/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 1.0585 - accuracy: 0.4300\n",
-      "Epoch 4/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 1.0472 - accuracy: 0.5100\n",
-      "Epoch 5/1000\n",
-      "3/3 [==============================] - 0s 16ms/step - loss: 1.0359 - accuracy: 0.5367\n",
-      "Epoch 6/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 1.0246 - accuracy: 0.5500\n",
-      "Epoch 7/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 1.0135 - accuracy: 0.5533\n",
-      "Epoch 8/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 1.0028 - accuracy: 0.5600\n",
-      "Epoch 9/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.9918 - accuracy: 0.5500\n",
-      "Epoch 10/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.9819 - accuracy: 0.5500\n",
-      "Epoch 11/1000\n",
-      "3/3 [==============================] - 0s 4ms/step - loss: 0.9713 - accuracy: 0.5533\n",
-      "Epoch 12/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.9609 - accuracy: 0.5533\n",
-      "Epoch 13/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.9512 - accuracy: 0.5533\n",
-      "Epoch 14/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.9410 - accuracy: 0.5533\n",
-      "Epoch 15/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.9311 - accuracy: 0.5533\n",
-      "Epoch 16/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.9219 - accuracy: 0.5500\n",
-      "Epoch 17/1000\n",
-      "3/3 [==============================] - 0s 4ms/step - loss: 0.9123 - accuracy: 0.5533\n",
-      "Epoch 18/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.9033 - accuracy: 0.5500\n",
-      "Epoch 19/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.8944 - accuracy: 0.5533\n",
-      "Epoch 20/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.8854 - accuracy: 0.5533\n",
-      "Epoch 21/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.8767 - accuracy: 0.5533\n",
-      "Epoch 22/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.8686 - accuracy: 0.5533\n",
-      "Epoch 23/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.8600 - accuracy: 0.5533\n",
-      "Epoch 24/1000\n",
-      "3/3 [==============================] - 0s 7ms/step - loss: 0.8524 - accuracy: 0.5533\n",
-      "Epoch 25/1000\n",
-      "3/3 [==============================] - 0s 4ms/step - loss: 0.8446 - accuracy: 0.5533\n",
-      "Epoch 26/1000\n",
-      "3/3 [==============================] - 0s 4ms/step - loss: 0.8372 - accuracy: 0.5533\n",
-      "Epoch 27/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.8299 - accuracy: 0.5500\n",
-      "Epoch 28/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.8230 - accuracy: 0.5500\n",
-      "Epoch 29/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.8165 - accuracy: 0.5500\n",
-      "Epoch 30/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.8102 - accuracy: 0.5500\n",
-      "Epoch 31/1000\n",
-      "3/3 [==============================] - 0s 4ms/step - loss: 0.8041 - accuracy: 0.5533\n",
-      "Epoch 32/1000\n",
-      "3/3 [==============================] - 0s 4ms/step - loss: 0.7979 - accuracy: 0.5533\n",
-      "Epoch 33/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.7924 - accuracy: 0.5533\n",
-      "Epoch 34/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.7870 - accuracy: 0.5533\n",
-      "Epoch 35/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.7817 - accuracy: 0.5533\n",
-      "Epoch 36/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.7769 - accuracy: 0.5500\n",
-      "Epoch 37/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.7721 - accuracy: 0.5500\n",
-      "Epoch 38/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.7673 - accuracy: 0.5533\n",
-      "Epoch 39/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.7627 - accuracy: 0.5500\n",
-      "Epoch 40/1000\n",
-      "3/3 [==============================] - 0s 4ms/step - loss: 0.7585 - accuracy: 0.5500\n",
-      "Epoch 41/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.7545 - accuracy: 0.5533\n",
-      "Epoch 42/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.7504 - accuracy: 0.5567\n",
-      "Epoch 43/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.7466 - accuracy: 0.5567\n",
-      "Epoch 44/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.7430 - accuracy: 0.5600\n",
-      "Epoch 45/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.7392 - accuracy: 0.5600\n",
-      "Epoch 46/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.7359 - accuracy: 0.5600\n",
-      "Epoch 47/1000\n",
-      "3/3 [==============================] - 0s 4ms/step - loss: 0.7324 - accuracy: 0.5600\n",
-      "Epoch 48/1000\n",
-      "3/3 [==============================] - 0s 5ms/step - loss: 0.7291 - accuracy: 0.5600\n",
-      "Epoch 49/1000\n",
-      "3/3 [==============================] - 0s 4ms/step - loss: 0.7259 - accuracy: 0.5633\n",
-      "Epoch 50/1000\n",
-      "3/3 [==============================] - 0s 5ms/step - loss: 0.7229 - accuracy: 0.5633\n",
-      "Epoch 51/1000\n",
-      "3/3 [==============================] - 0s 4ms/step - loss: 0.7198 - accuracy: 0.5633\n",
-      "Epoch 52/1000\n",
-      "3/3 [==============================] - 0s 4ms/step - loss: 0.7170 - accuracy: 0.5667\n",
-      "Epoch 53/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.7141 - accuracy: 0.5700\n",
-      "Epoch 54/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.7113 - accuracy: 0.5733\n",
-      "Epoch 55/1000\n",
-      "3/3 [==============================] - 0s 4ms/step - loss: 0.7084 - accuracy: 0.5733\n",
-      "Epoch 56/1000\n",
-      "3/3 [==============================] - 0s 4ms/step - loss: 0.7059 - accuracy: 0.5733\n",
-      "Epoch 57/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.7032 - accuracy: 0.5733\n",
-      "Epoch 58/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.7005 - accuracy: 0.5733\n",
-      "Epoch 59/1000\n",
-      "3/3 [==============================] - 0s 4ms/step - loss: 0.6979 - accuracy: 0.5700\n",
-      "Epoch 60/1000\n",
-      "3/3 [==============================] - 0s 4ms/step - loss: 0.6956 - accuracy: 0.5700\n",
-      "Epoch 61/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.6930 - accuracy: 0.5733\n",
-      "Epoch 62/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.6904 - accuracy: 0.5733\n",
-      "Epoch 63/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.6880 - accuracy: 0.5733\n",
-      "Epoch 64/1000\n",
-      "3/3 [==============================] - 0s 4ms/step - loss: 0.6856 - accuracy: 0.5733\n",
-      "Epoch 65/1000\n",
-      "3/3 [==============================] - 0s 4ms/step - loss: 0.6834 - accuracy: 0.5733\n",
-      "Epoch 66/1000\n",
-      "3/3 [==============================] - 0s 4ms/step - loss: 0.6809 - accuracy: 0.5733\n",
-      "Epoch 67/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.6787 - accuracy: 0.5733\n",
-      "Epoch 68/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.6762 - accuracy: 0.5733\n",
-      "Epoch 69/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.6741 - accuracy: 0.5733\n",
-      "Epoch 70/1000\n",
-      "3/3 [==============================] - 0s 4ms/step - loss: 0.6716 - accuracy: 0.5800\n",
-      "Epoch 71/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.6694 - accuracy: 0.5800\n",
-      "Epoch 72/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.6673 - accuracy: 0.5833\n",
-      "Epoch 73/1000\n",
-      "3/3 [==============================] - 0s 5ms/step - loss: 0.6650 - accuracy: 0.5833\n",
-      "Epoch 74/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.6627 - accuracy: 0.5833\n",
-      "Epoch 75/1000\n",
-      "3/3 [==============================] - 0s 4ms/step - loss: 0.6605 - accuracy: 0.5867\n",
-      "Epoch 76/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.6584 - accuracy: 0.5833\n",
-      "Epoch 77/1000\n",
-      "3/3 [==============================] - 0s 4ms/step - loss: 0.6561 - accuracy: 0.5900\n",
-      "Epoch 78/1000\n",
-      "3/3 [==============================] - 0s 4ms/step - loss: 0.6541 - accuracy: 0.5867\n",
-      "Epoch 79/1000\n",
-      "3/3 [==============================] - 0s 7ms/step - loss: 0.6517 - accuracy: 0.5900\n",
-      "Epoch 80/1000\n",
-      "3/3 [==============================] - 0s 4ms/step - loss: 0.6496 - accuracy: 0.5933\n",
-      "Epoch 81/1000\n",
-      "3/3 [==============================] - 0s 4ms/step - loss: 0.6473 - accuracy: 0.5967\n",
-      "Epoch 82/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.6453 - accuracy: 0.5967\n",
-      "Epoch 83/1000\n",
-      "3/3 [==============================] - 0s 4ms/step - loss: 0.6430 - accuracy: 0.5967\n",
-      "Epoch 84/1000\n",
-      "3/3 [==============================] - 0s 4ms/step - loss: 0.6409 - accuracy: 0.5967\n",
-      "Epoch 85/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.6388 - accuracy: 0.5967\n",
-      "Epoch 86/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.6366 - accuracy: 0.5967\n",
-      "Epoch 87/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.6344 - accuracy: 0.6000\n",
-      "Epoch 88/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.6325 - accuracy: 0.6000\n",
-      "Epoch 89/1000\n",
-      "3/3 [==============================] - 0s 6ms/step - loss: 0.6301 - accuracy: 0.6000\n",
-      "Epoch 90/1000\n",
-      "3/3 [==============================] - 0s 4ms/step - loss: 0.6279 - accuracy: 0.6033\n",
-      "Epoch 91/1000\n",
-      "3/3 [==============================] - 0s 4ms/step - loss: 0.6259 - accuracy: 0.6033\n",
-      "Epoch 92/1000\n",
-      "3/3 [==============================] - 0s 4ms/step - loss: 0.6236 - accuracy: 0.6067\n",
-      "Epoch 93/1000\n",
-      "3/3 [==============================] - 0s 4ms/step - loss: 0.6216 - accuracy: 0.6133\n",
-      "Epoch 94/1000\n",
-      "3/3 [==============================] - 0s 4ms/step - loss: 0.6194 - accuracy: 0.6133\n",
-      "Epoch 95/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.6173 - accuracy: 0.6167\n",
-      "Epoch 96/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.6150 - accuracy: 0.6233\n",
-      "Epoch 97/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.6129 - accuracy: 0.6300\n",
-      "Epoch 98/1000\n",
-      "3/3 [==============================] - 0s 4ms/step - loss: 0.6107 - accuracy: 0.6300\n",
-      "Epoch 99/1000\n",
-      "3/3 [==============================] - 0s 4ms/step - loss: 0.6087 - accuracy: 0.6333\n",
-      "Epoch 100/1000\n",
-      "3/3 [==============================] - 0s 4ms/step - loss: 0.6064 - accuracy: 0.6367\n",
-      "Epoch 101/1000\n",
-      "3/3 [==============================] - 0s 4ms/step - loss: 0.6044 - accuracy: 0.6367\n",
-      "Epoch 102/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.6022 - accuracy: 0.6400\n",
-      "Epoch 103/1000\n",
-      "3/3 [==============================] - 0s 4ms/step - loss: 0.6000 - accuracy: 0.6433\n",
-      "Epoch 104/1000\n",
-      "3/3 [==============================] - 0s 4ms/step - loss: 0.5979 - accuracy: 0.6433\n",
-      "Epoch 105/1000\n",
-      "3/3 [==============================] - 0s 5ms/step - loss: 0.5957 - accuracy: 0.6433\n",
-      "Epoch 106/1000\n",
-      "3/3 [==============================] - 0s 4ms/step - loss: 0.5937 - accuracy: 0.6500\n",
-      "Epoch 107/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.5915 - accuracy: 0.6533\n",
-      "Epoch 108/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.5895 - accuracy: 0.6600\n",
-      "Epoch 109/1000\n",
-      "3/3 [==============================] - 0s 4ms/step - loss: 0.5871 - accuracy: 0.6633\n",
-      "Epoch 110/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.5849 - accuracy: 0.6633\n",
-      "Epoch 111/1000\n",
-      "3/3 [==============================] - 0s 4ms/step - loss: 0.5829 - accuracy: 0.6700\n",
-      "Epoch 112/1000\n",
-      "3/3 [==============================] - 0s 6ms/step - loss: 0.5806 - accuracy: 0.6733\n",
-      "Epoch 113/1000\n",
-      "3/3 [==============================] - 0s 4ms/step - loss: 0.5786 - accuracy: 0.6767\n",
-      "Epoch 114/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.5767 - accuracy: 0.6800\n",
-      "Epoch 115/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.5744 - accuracy: 0.6800\n",
-      "Epoch 116/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.5722 - accuracy: 0.6867\n",
-      "Epoch 117/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.5701 - accuracy: 0.6867\n",
-      "Epoch 118/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.5678 - accuracy: 0.6867\n",
-      "Epoch 119/1000\n",
-      "3/3 [==============================] - 0s 4ms/step - loss: 0.5657 - accuracy: 0.6867\n",
-      "Epoch 120/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.5635 - accuracy: 0.6900\n",
-      "Epoch 121/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.5615 - accuracy: 0.6900\n",
-      "Epoch 122/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.5593 - accuracy: 0.6900\n",
-      "Epoch 123/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.5572 - accuracy: 0.6867\n",
-      "Epoch 124/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.5551 - accuracy: 0.6867\n",
-      "Epoch 125/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.5528 - accuracy: 0.6900\n",
-      "Epoch 126/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.5506 - accuracy: 0.6900\n",
-      "Epoch 127/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.5485 - accuracy: 0.6900\n",
-      "Epoch 128/1000\n",
-      "3/3 [==============================] - 0s 4ms/step - loss: 0.5465 - accuracy: 0.6967\n",
-      "Epoch 129/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.5442 - accuracy: 0.7000\n",
-      "Epoch 130/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.5421 - accuracy: 0.7000\n",
-      "Epoch 131/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.5399 - accuracy: 0.7033\n",
-      "Epoch 132/1000\n",
-      "3/3 [==============================] - 0s 4ms/step - loss: 0.5378 - accuracy: 0.7067\n",
-      "Epoch 133/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.5357 - accuracy: 0.7067\n",
-      "Epoch 134/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.5336 - accuracy: 0.7067\n",
-      "Epoch 135/1000\n",
-      "3/3 [==============================] - 0s 4ms/step - loss: 0.5314 - accuracy: 0.7067\n",
-      "Epoch 136/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.5295 - accuracy: 0.7067\n",
-      "Epoch 137/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.5271 - accuracy: 0.7100\n",
-      "Epoch 138/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.5249 - accuracy: 0.7100\n",
-      "Epoch 139/1000\n",
-      "3/3 [==============================] - 0s 4ms/step - loss: 0.5231 - accuracy: 0.7100\n",
-      "Epoch 140/1000\n",
-      "3/3 [==============================] - 0s 4ms/step - loss: 0.5208 - accuracy: 0.7133\n",
-      "Epoch 141/1000\n",
-      "3/3 [==============================] - ETA: 0s - loss: 0.5236 - accuracy: 0.72 - 0s 3ms/step - loss: 0.5185 - accuracy: 0.7133\n",
-      "Epoch 142/1000\n",
-      "3/3 [==============================] - 0s 4ms/step - loss: 0.5164 - accuracy: 0.7133\n",
-      "Epoch 143/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.5143 - accuracy: 0.7133\n",
-      "Epoch 144/1000\n",
-      "3/3 [==============================] - 0s 4ms/step - loss: 0.5120 - accuracy: 0.7133\n",
-      "Epoch 145/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.5102 - accuracy: 0.7167\n",
-      "Epoch 146/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.5079 - accuracy: 0.7167\n",
-      "Epoch 147/1000\n",
-      "3/3 [==============================] - 0s 4ms/step - loss: 0.5058 - accuracy: 0.7233\n",
-      "Epoch 148/1000\n",
-      "3/3 [==============================] - 0s 4ms/step - loss: 0.5037 - accuracy: 0.7233\n",
-      "Epoch 149/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.5017 - accuracy: 0.7233\n",
-      "Epoch 150/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.4995 - accuracy: 0.7200\n",
-      "Epoch 151/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.4974 - accuracy: 0.7233\n",
-      "Epoch 152/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.4953 - accuracy: 0.7233\n",
-      "Epoch 153/1000\n",
-      "3/3 [==============================] - 0s 4ms/step - loss: 0.4933 - accuracy: 0.7233\n",
-      "Epoch 154/1000\n",
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-      "Epoch 800/1000\n",
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-      "Epoch 811/1000\n",
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-      "Epoch 888/1000\n",
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-      "Epoch 889/1000\n",
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-      "Epoch 891/1000\n",
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-      "Epoch 898/1000\n",
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-      "Epoch 899/1000\n",
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-      "Epoch 900/1000\n",
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-      "Epoch 907/1000\n",
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-      "Epoch 909/1000\n",
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-      "Epoch 911/1000\n",
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-      "Epoch 912/1000\n",
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-      "Epoch 928/1000\n",
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-      "Epoch 938/1000\n",
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-      "Epoch 939/1000\n",
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-      "Epoch 940/1000\n",
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-      "Epoch 948/1000\n",
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-      "Epoch 949/1000\n",
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-      "Epoch 950/1000\n",
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-      "Epoch 951/1000\n",
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-      "Epoch 954/1000\n",
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-      "Epoch 959/1000\n",
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-      "Epoch 960/1000\n",
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-      "Epoch 962/1000\n",
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-      "Epoch 963/1000\n",
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-      "Epoch 964/1000\n",
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-      "Epoch 965/1000\n",
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-      "Epoch 966/1000\n",
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-      "Epoch 967/1000\n",
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-      "Epoch 968/1000\n",
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-      "Epoch 969/1000\n",
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-      "Epoch 970/1000\n",
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-      "Epoch 971/1000\n",
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-      "Epoch 973/1000\n",
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-      "Epoch 974/1000\n",
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-      "Epoch 975/1000\n",
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-      "Epoch 976/1000\n",
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-      "Epoch 977/1000\n",
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-      "Epoch 978/1000\n",
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-      "Epoch 979/1000\n",
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-      "Epoch 980/1000\n",
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-      "Epoch 981/1000\n",
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-      "Epoch 982/1000\n",
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-      "Epoch 983/1000\n",
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-      "Epoch 984/1000\n",
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-      "Epoch 985/1000\n",
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-      "Epoch 986/1000\n",
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-      "Epoch 987/1000\n",
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-      "Epoch 988/1000\n",
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-      "Epoch 989/1000\n",
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-      "Epoch 990/1000\n",
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-      "Epoch 991/1000\n",
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-      "Epoch 992/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.0651 - accuracy: 0.9900\n",
-      "Epoch 993/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.0650 - accuracy: 0.9900\n",
-      "Epoch 994/1000\n",
-      "3/3 [==============================] - 0s 4ms/step - loss: 0.0649 - accuracy: 0.9900\n",
-      "Epoch 995/1000\n",
-      "3/3 [==============================] - 0s 5ms/step - loss: 0.0649 - accuracy: 0.9900\n",
-      "Epoch 996/1000\n",
-      "3/3 [==============================] - 0s 4ms/step - loss: 0.0647 - accuracy: 0.9900\n",
-      "Epoch 997/1000\n",
-      "3/3 [==============================] - 0s 4ms/step - loss: 0.0646 - accuracy: 0.9900\n",
-      "Epoch 998/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.0645 - accuracy: 0.9900\n",
-      "Epoch 999/1000\n",
+      "Epoch 1/100\n",
+      "3/3 [==============================] - 0s 6ms/step - loss: 0.0648 - accuracy: 0.9867\n",
+      "Epoch 2/100\n",
+      "3/3 [==============================] - 0s 4ms/step - loss: 0.0648 - accuracy: 0.9867\n",
+      "Epoch 3/100\n",
+      "3/3 [==============================] - 0s 4ms/step - loss: 0.0646 - accuracy: 0.9867\n",
+      "Epoch 4/100\n",
       "3/3 [==============================] - 0s 3ms/step - loss: 0.0646 - accuracy: 0.9867\n",
-      "Epoch 1000/1000\n",
-      "3/3 [==============================] - 0s 3ms/step - loss: 0.0644 - accuracy: 0.9900\n"
+      "Epoch 5/100\n",
+      "3/3 [==============================] - 0s 4ms/step - loss: 0.0645 - accuracy: 0.9833\n",
+      "Epoch 6/100\n",
+      "3/3 [==============================] - 0s 6ms/step - loss: 0.0644 - accuracy: 0.9867\n",
+      "Epoch 7/100\n",
+      "3/3 [==============================] - 0s 4ms/step - loss: 0.0643 - accuracy: 0.9867\n",
+      "Epoch 8/100\n",
+      "3/3 [==============================] - 0s 5ms/step - loss: 0.0643 - accuracy: 0.9867\n",
+      "Epoch 9/100\n",
+      "3/3 [==============================] - 0s 8ms/step - loss: 0.0642 - accuracy: 0.9867\n",
+      "Epoch 10/100\n",
+      "3/3 [==============================] - 0s 6ms/step - loss: 0.0641 - accuracy: 0.9867\n",
+      "Epoch 11/100\n",
+      "3/3 [==============================] - 0s 7ms/step - loss: 0.0640 - accuracy: 0.9867\n",
+      "Epoch 12/100\n",
+      "3/3 [==============================] - 0s 4ms/step - loss: 0.0639 - accuracy: 0.9867\n",
+      "Epoch 13/100\n",
+      "3/3 [==============================] - 0s 3ms/step - loss: 0.0639 - accuracy: 0.9833\n",
+      "Epoch 14/100\n",
+      "3/3 [==============================] - 0s 5ms/step - loss: 0.0638 - accuracy: 0.9867\n",
+      "Epoch 15/100\n",
+      "3/3 [==============================] - 0s 5ms/step - loss: 0.0637 - accuracy: 0.9867\n",
+      "Epoch 16/100\n",
+      "3/3 [==============================] - 0s 7ms/step - loss: 0.0636 - accuracy: 0.9867\n",
+      "Epoch 17/100\n",
+      "3/3 [==============================] - 0s 7ms/step - loss: 0.0636 - accuracy: 0.9833\n",
+      "Epoch 18/100\n",
+      "3/3 [==============================] - 0s 5ms/step - loss: 0.0635 - accuracy: 0.9867\n",
+      "Epoch 19/100\n",
+      "3/3 [==============================] - 0s 4ms/step - loss: 0.0634 - accuracy: 0.9900\n",
+      "Epoch 20/100\n",
+      "3/3 [==============================] - 0s 3ms/step - loss: 0.0633 - accuracy: 0.9867\n",
+      "Epoch 21/100\n",
+      "3/3 [==============================] - 0s 7ms/step - loss: 0.0632 - accuracy: 0.9867\n",
+      "Epoch 22/100\n",
+      "3/3 [==============================] - 0s 6ms/step - loss: 0.0633 - accuracy: 0.9833\n",
+      "Epoch 23/100\n",
+      "3/3 [==============================] - 0s 7ms/step - loss: 0.0630 - accuracy: 0.9867\n",
+      "Epoch 24/100\n",
+      "3/3 [==============================] - 0s 6ms/step - loss: 0.0631 - accuracy: 0.9867\n",
+      "Epoch 25/100\n",
+      "3/3 [==============================] - 0s 4ms/step - loss: 0.0629 - accuracy: 0.9867\n",
+      "Epoch 26/100\n",
+      "3/3 [==============================] - 0s 4ms/step - loss: 0.0628 - accuracy: 0.9867\n",
+      "Epoch 27/100\n",
+      "3/3 [==============================] - 0s 5ms/step - loss: 0.0628 - accuracy: 0.9867\n",
+      "Epoch 28/100\n",
+      "3/3 [==============================] - 0s 6ms/step - loss: 0.0627 - accuracy: 0.9867\n",
+      "Epoch 29/100\n",
+      "3/3 [==============================] - 0s 6ms/step - loss: 0.0626 - accuracy: 0.9867\n",
+      "Epoch 30/100\n",
+      "3/3 [==============================] - 0s 6ms/step - loss: 0.0626 - accuracy: 0.9833\n",
+      "Epoch 31/100\n",
+      "3/3 [==============================] - 0s 8ms/step - loss: 0.0624 - accuracy: 0.9867\n",
+      "Epoch 32/100\n",
+      "3/3 [==============================] - 0s 5ms/step - loss: 0.0624 - accuracy: 0.9867\n",
+      "Epoch 33/100\n",
+      "3/3 [==============================] - 0s 6ms/step - loss: 0.0623 - accuracy: 0.9867\n",
+      "Epoch 34/100\n",
+      "3/3 [==============================] - 0s 6ms/step - loss: 0.0623 - accuracy: 0.9867\n",
+      "Epoch 35/100\n",
+      "3/3 [==============================] - 0s 7ms/step - loss: 0.0621 - accuracy: 0.9867\n",
+      "Epoch 36/100\n",
+      "3/3 [==============================] - 0s 8ms/step - loss: 0.0621 - accuracy: 0.9867\n",
+      "Epoch 37/100\n",
+      "3/3 [==============================] - 0s 5ms/step - loss: 0.0621 - accuracy: 0.9867\n",
+      "Epoch 38/100\n",
+      "3/3 [==============================] - 0s 7ms/step - loss: 0.0619 - accuracy: 0.9867\n",
+      "Epoch 39/100\n",
+      "3/3 [==============================] - 0s 7ms/step - loss: 0.0619 - accuracy: 0.9867\n",
+      "Epoch 40/100\n",
+      "3/3 [==============================] - 0s 5ms/step - loss: 0.0618 - accuracy: 0.9900\n",
+      "Epoch 41/100\n",
+      "3/3 [==============================] - 0s 5ms/step - loss: 0.0617 - accuracy: 0.9867\n",
+      "Epoch 42/100\n",
+      "3/3 [==============================] - 0s 5ms/step - loss: 0.0616 - accuracy: 0.9833\n",
+      "Epoch 43/100\n",
+      "3/3 [==============================] - 0s 4ms/step - loss: 0.0615 - accuracy: 0.9867\n",
+      "Epoch 44/100\n",
+      "3/3 [==============================] - 0s 7ms/step - loss: 0.0616 - accuracy: 0.9833\n",
+      "Epoch 45/100\n",
+      "3/3 [==============================] - 0s 5ms/step - loss: 0.0615 - accuracy: 0.9867\n",
+      "Epoch 46/100\n",
+      "3/3 [==============================] - 0s 6ms/step - loss: 0.0613 - accuracy: 0.9833\n",
+      "Epoch 47/100\n",
+      "3/3 [==============================] - 0s 4ms/step - loss: 0.0613 - accuracy: 0.9833\n",
+      "Epoch 48/100\n",
+      "3/3 [==============================] - 0s 9ms/step - loss: 0.0613 - accuracy: 0.9867\n",
+      "Epoch 49/100\n",
+      "3/3 [==============================] - 0s 11ms/step - loss: 0.0611 - accuracy: 0.9867\n",
+      "Epoch 50/100\n",
+      "3/3 [==============================] - 0s 4ms/step - loss: 0.0611 - accuracy: 0.9867\n",
+      "Epoch 51/100\n",
+      "3/3 [==============================] - 0s 5ms/step - loss: 0.0609 - accuracy: 0.9867\n",
+      "Epoch 52/100\n",
+      "3/3 [==============================] - 0s 4ms/step - loss: 0.0609 - accuracy: 0.9867\n",
+      "Epoch 53/100\n",
+      "3/3 [==============================] - 0s 3ms/step - loss: 0.0608 - accuracy: 0.9867\n",
+      "Epoch 54/100\n",
+      "3/3 [==============================] - 0s 16ms/step - loss: 0.0607 - accuracy: 0.9833\n",
+      "Epoch 55/100\n",
+      "3/3 [==============================] - 0s 3ms/step - loss: 0.0608 - accuracy: 0.9867\n",
+      "Epoch 56/100\n",
+      "3/3 [==============================] - 0s 4ms/step - loss: 0.0606 - accuracy: 0.9900\n",
+      "Epoch 57/100\n",
+      "3/3 [==============================] - 0s 5ms/step - loss: 0.0606 - accuracy: 0.9900\n",
+      "Epoch 58/100\n",
+      "3/3 [==============================] - 0s 20ms/step - loss: 0.0605 - accuracy: 0.9900\n",
+      "Epoch 59/100\n",
+      "3/3 [==============================] - 0s 7ms/step - loss: 0.0604 - accuracy: 0.9900\n",
+      "Epoch 60/100\n",
+      "3/3 [==============================] - 0s 6ms/step - loss: 0.0603 - accuracy: 0.9900\n",
+      "Epoch 61/100\n",
+      "3/3 [==============================] - 0s 21ms/step - loss: 0.0603 - accuracy: 0.9900\n",
+      "Epoch 62/100\n",
+      "3/3 [==============================] - 0s 9ms/step - loss: 0.0602 - accuracy: 0.9900\n",
+      "Epoch 63/100\n",
+      "3/3 [==============================] - 0s 5ms/step - loss: 0.0601 - accuracy: 0.9833\n",
+      "Epoch 64/100\n",
+      "3/3 [==============================] - 0s 6ms/step - loss: 0.0601 - accuracy: 0.9867\n",
+      "Epoch 65/100\n",
+      "3/3 [==============================] - 0s 4ms/step - loss: 0.0600 - accuracy: 0.9867\n",
+      "Epoch 66/100\n",
+      "3/3 [==============================] - 0s 5ms/step - loss: 0.0599 - accuracy: 0.9867\n",
+      "Epoch 67/100\n",
+      "3/3 [==============================] - 0s 5ms/step - loss: 0.0598 - accuracy: 0.9867\n",
+      "Epoch 68/100\n",
+      "3/3 [==============================] - 0s 15ms/step - loss: 0.0598 - accuracy: 0.9867\n",
+      "Epoch 69/100\n",
+      "3/3 [==============================] - 0s 5ms/step - loss: 0.0597 - accuracy: 0.9867\n",
+      "Epoch 70/100\n",
+      "3/3 [==============================] - 0s 6ms/step - loss: 0.0598 - accuracy: 0.9900\n",
+      "Epoch 71/100\n",
+      "3/3 [==============================] - 0s 4ms/step - loss: 0.0596 - accuracy: 0.9900\n",
+      "Epoch 72/100\n",
+      "3/3 [==============================] - 0s 4ms/step - loss: 0.0595 - accuracy: 0.9867\n",
+      "Epoch 73/100\n",
+      "3/3 [==============================] - 0s 4ms/step - loss: 0.0594 - accuracy: 0.9867\n",
+      "Epoch 74/100\n",
+      "3/3 [==============================] - 0s 4ms/step - loss: 0.0594 - accuracy: 0.9900\n",
+      "Epoch 75/100\n",
+      "3/3 [==============================] - 0s 4ms/step - loss: 0.0593 - accuracy: 0.9933\n",
+      "Epoch 76/100\n",
+      "3/3 [==============================] - 0s 4ms/step - loss: 0.0593 - accuracy: 0.9867\n",
+      "Epoch 77/100\n",
+      "3/3 [==============================] - 0s 4ms/step - loss: 0.0591 - accuracy: 0.9867\n",
+      "Epoch 78/100\n",
+      "3/3 [==============================] - 0s 4ms/step - loss: 0.0591 - accuracy: 0.9867\n",
+      "Epoch 79/100\n",
+      "3/3 [==============================] - 0s 9ms/step - loss: 0.0591 - accuracy: 0.9867\n",
+      "Epoch 80/100\n",
+      "3/3 [==============================] - 0s 5ms/step - loss: 0.0589 - accuracy: 0.9867\n",
+      "Epoch 81/100\n",
+      "3/3 [==============================] - 0s 4ms/step - loss: 0.0589 - accuracy: 0.9867\n",
+      "Epoch 82/100\n",
+      "3/3 [==============================] - 0s 5ms/step - loss: 0.0588 - accuracy: 0.9867\n",
+      "Epoch 83/100\n",
+      "3/3 [==============================] - 0s 4ms/step - loss: 0.0588 - accuracy: 0.9833\n",
+      "Epoch 84/100\n",
+      "3/3 [==============================] - 0s 3ms/step - loss: 0.0587 - accuracy: 0.9900\n",
+      "Epoch 85/100\n",
+      "3/3 [==============================] - 0s 4ms/step - loss: 0.0586 - accuracy: 0.9900\n",
+      "Epoch 86/100\n",
+      "3/3 [==============================] - 0s 6ms/step - loss: 0.0585 - accuracy: 0.9900\n",
+      "Epoch 87/100\n",
+      "3/3 [==============================] - 0s 6ms/step - loss: 0.0585 - accuracy: 0.9900\n",
+      "Epoch 88/100\n",
+      "3/3 [==============================] - 0s 6ms/step - loss: 0.0584 - accuracy: 0.9900\n",
+      "Epoch 89/100\n",
+      "3/3 [==============================] - 0s 5ms/step - loss: 0.0583 - accuracy: 0.9900\n",
+      "Epoch 90/100\n",
+      "3/3 [==============================] - 0s 5ms/step - loss: 0.0583 - accuracy: 0.9900\n",
+      "Epoch 91/100\n",
+      "3/3 [==============================] - 0s 5ms/step - loss: 0.0582 - accuracy: 0.9900\n",
+      "Epoch 92/100\n",
+      "3/3 [==============================] - 0s 4ms/step - loss: 0.0581 - accuracy: 0.9900\n",
+      "Epoch 93/100\n",
+      "3/3 [==============================] - 0s 3ms/step - loss: 0.0582 - accuracy: 0.9867\n",
+      "Epoch 94/100\n",
+      "3/3 [==============================] - 0s 5ms/step - loss: 0.0580 - accuracy: 0.9900\n",
+      "Epoch 95/100\n",
+      "3/3 [==============================] - 0s 7ms/step - loss: 0.0579 - accuracy: 0.9900\n",
+      "Epoch 96/100\n",
+      "3/3 [==============================] - 0s 8ms/step - loss: 0.0579 - accuracy: 0.9900\n",
+      "Epoch 97/100\n",
+      "3/3 [==============================] - 0s 7ms/step - loss: 0.0578 - accuracy: 0.9900\n",
+      "Epoch 98/100\n",
+      "3/3 [==============================] - 0s 4ms/step - loss: 0.0577 - accuracy: 0.9900\n",
+      "Epoch 99/100\n",
+      "3/3 [==============================] - 0s 3ms/step - loss: 0.0577 - accuracy: 0.9867\n",
+      "Epoch 100/100\n",
+      "3/3 [==============================] - 0s 6ms/step - loss: 0.0576 - accuracy: 0.9900\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<keras.callbacks.History at 0x7f472477ad50>"
+       "<keras.callbacks.History at 0x7fe47c15ae90>"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 49,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3915,7 +2102,7 @@
     "BATCH_SIZE=128\n",
     "num_train_examples = X.shape[0]\n",
     "num_train_examples\n",
-    "model.fit(X, y_cat, epochs=1000, steps_per_epoch=math.ceil(num_train_examples/BATCH_SIZE))"
+    "model.fit(X, y_cat, epochs=100, steps_per_epoch=math.ceil(num_train_examples/BATCH_SIZE))"
    ]
   },
   {
@@ -3929,14 +2116,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "10/10 [==============================] - 0s 2ms/step - loss: 0.0643 - accuracy: 0.9900\n",
+      "10/10 [==============================] - 0s 3ms/step - loss: 0.0575 - accuracy: 0.9900\n",
       "Accuracy on test dataset: 0.9900000095367432\n"
      ]
     }
diff --git a/notebooks/Neural_Networks/Solutions to Exercises - Block 2.ipynb b/notebooks/Neural_Networks/Solutions to Exercises - Block 2.ipynb
index cac74e59aa3ec5d767da031d7792cb8c30e5725b..60627b2f0efcd1419d5bda7f8e7ef12cef76958d 100644
--- a/notebooks/Neural_Networks/Solutions to Exercises - Block 2.ipynb	
+++ b/notebooks/Neural_Networks/Solutions to Exercises - Block 2.ipynb	
@@ -45,7 +45,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 1,
    "metadata": {
     "colab": {},
     "colab_type": "code",
@@ -69,7 +69,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 2,
    "metadata": {
     "colab": {},
     "colab_type": "code",
@@ -96,7 +96,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
+   "execution_count": 3,
    "metadata": {
     "colab": {},
     "colab_type": "code",
@@ -164,7 +164,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 104,
+   "execution_count": 4,
    "metadata": {
     "colab": {},
     "colab_type": "code",
@@ -191,7 +191,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 105,
+   "execution_count": 5,
    "metadata": {
     "colab": {},
     "colab_type": "code",
@@ -206,18 +206,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 106,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Model: \"sequential_11\"\n",
+      "Model: \"sequential\"\n",
       "_________________________________________________________________\n",
       " Layer (type)                Output Shape              Param #   \n",
       "=================================================================\n",
-      " dense_15 (Dense)            (None, 1)                 2         \n",
+      " dense_1 (Dense)             (None, 1)                 2         \n",
       "                                                                 \n",
       "=================================================================\n",
       "Total params: 2\n",
@@ -249,7 +249,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 7,
    "metadata": {
     "colab": {},
     "colab_type": "code",
@@ -297,7 +297,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 8,
    "metadata": {
     "colab": {},
     "colab_type": "code",
@@ -334,7 +334,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 9,
    "metadata": {
     "colab": {},
     "colab_type": "code",
@@ -344,16 +344,16 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7ff9ec12a190>]"
+       "[<matplotlib.lines.Line2D at 0x7f69083a1b10>]"
       ]
      },
-     "execution_count": 83,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -387,7 +387,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 10,
    "metadata": {
     "colab": {},
     "colab_type": "code",
@@ -398,7 +398,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[211.31021]]\n"
+      "[[211.33777]]\n"
      ]
     }
    ],
@@ -440,7 +440,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 11,
    "metadata": {
     "colab": {},
     "colab_type": "code",
@@ -451,7 +451,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "These are the layer variables: [array([[1.8242955]], dtype=float32), array([28.880667], dtype=float32)]\n"
+      "These are the layer variables: [array([[1.8201027]], dtype=float32), array([29.327496], dtype=float32)]\n"
      ]
     }
    ],
@@ -481,7 +481,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 12,
    "metadata": {
     "colab": {},
     "colab_type": "code",
@@ -493,18 +493,19 @@
      "output_type": "stream",
      "text": [
       "Finished training the model\n",
-      "[[211.74742]]\n",
-      "Model predicts that 100 degrees Celsius is: [[211.74742]] degrees Fahrenheit\n",
-      "These are the l0 variables: [array([[0.5968949 , 0.02139384, 0.01172368, 0.35185102]], dtype=float32), array([ 3.484818 , -3.0073252, -2.7715163,  3.010582 ], dtype=float32)]\n",
-      "These are the l1 variables: [array([[ 0.20319672, -0.41102245,  0.7771168 , -0.7402758 ],\n",
-      "       [-0.7281887 ,  0.9473572 , -0.15004049,  0.12950596],\n",
-      "       [-0.96478873,  0.05949384, -0.2531712 ,  0.72464406],\n",
-      "       [-0.10805991, -0.88057184,  1.0134443 , -0.15393376]],\n",
-      "      dtype=float32), array([ 1.8714209, -3.126011 ,  3.3828623, -2.6109185], dtype=float32)]\n",
-      "These are the l2 variables: [array([[ 0.23399544],\n",
-      "       [-0.9353689 ],\n",
-      "       [ 1.1917399 ],\n",
-      "       [-0.6491724 ]], dtype=float32), array([3.2284596], dtype=float32)]\n"
+      "[[211.74744]]\n",
+      "Model predicts that 100 degrees Celsius is: [[211.74744]] degrees Fahrenheit\n",
+      "These are the l0 variables: [array([[-0.3999552 , -0.19984037, -0.3993555 ,  0.23797645]],\n",
+      "      dtype=float32), array([-3.64464   , -0.05620334, -3.7051175 ,  3.6476896 ], dtype=float32)]\n",
+      "These are the l1 variables: [array([[-0.6539953 ,  1.2016883 , -0.15631415, -0.09634368],\n",
+      "       [-0.044912  ,  0.33702853,  0.4186295 ,  0.56916535],\n",
+      "       [-0.13941915,  0.3288872 , -0.6001862 , -1.3318719 ],\n",
+      "       [ 0.4855473 , -0.6946453 ,  0.04858445,  0.9635439 ]],\n",
+      "      dtype=float32), array([ 0.8685159, -3.7419426, -1.925964 ,  3.4745376], dtype=float32)]\n",
+      "These are the l2 variables: [array([[ 0.16444923],\n",
+      "       [-1.1736581 ],\n",
+      "       [-0.13025026],\n",
+      "       [ 1.1136886 ]], dtype=float32), array([3.5807242], dtype=float32)]\n"
      ]
     }
    ],
@@ -569,7 +570,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -578,7 +579,7 @@
        "Text(0, 0.5, 'Broken O-rings')"
       ]
      },
-     "execution_count": 87,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -613,7 +614,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -654,7 +655,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -703,7 +704,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -764,7 +765,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -792,9 +793,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<keras.callbacks.History at 0x7f68e9ef45d0>"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "l0 = tf.keras.layers.Dense(units=1, activation = tf.nn.sigmoid, input_shape=[1])\n",
     "model = tf.keras.Sequential([l0])\n",
@@ -804,22 +816,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7ff9ec0405d0>]"
+       "[<matplotlib.lines.Line2D at 0x7f68e9e7ad50>]"
       ]
      },
-     "execution_count": 93,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -841,14 +853,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 94,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[array([[-0.23217289]], dtype=float32), array([15.043545], dtype=float32)]\n"
+      "[array([[-0.23211505]], dtype=float32), array([15.04408], dtype=float32)]\n"
      ]
     }
    ],
@@ -858,20 +870,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Cross-entropy:  0.4416346\n",
+      "Cross-entropy:  0.44163597\n",
       "Accuracy:  0.8695652173913043\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -905,23 +917,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 96,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "1/1 [==============================] - 0s 118ms/step - loss: 0.4416 - accuracy: 0.8696\n"
+      "1/1 [==============================] - 0s 176ms/step - loss: 0.4416 - accuracy: 0.8696\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "[0.4416346251964569, 0.8695651888847351]"
+       "[0.44163596630096436, 0.8695651888847351]"
       ]
      },
-     "execution_count": 96,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -962,36 +974,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
-      "11493376/11490434 [==============================] - 0s 0us/step\n",
-      "11501568/11490434 [==============================] - 0s 0us/step\n",
       "Epoch 1/10\n",
-      "1875/1875 [==============================] - 9s 5ms/step - loss: 320.4593 - accuracy: 0.8417 - val_loss: 242.9400 - val_accuracy: 0.8801\n",
+      "1875/1875 [==============================] - 8s 4ms/step - loss: 313.8780 - accuracy: 0.8421 - val_loss: 233.5310 - val_accuracy: 0.8710\n",
       "Epoch 2/10\n",
-      "1875/1875 [==============================] - 8s 4ms/step - loss: 257.2356 - accuracy: 0.8691 - val_loss: 381.1338 - val_accuracy: 0.8126\n",
+      "1875/1875 [==============================] - 8s 4ms/step - loss: 259.4649 - accuracy: 0.8686 - val_loss: 243.6489 - val_accuracy: 0.8813\n",
       "Epoch 3/10\n",
-      "1875/1875 [==============================] - 8s 5ms/step - loss: 246.9380 - accuracy: 0.8741 - val_loss: 225.4031 - val_accuracy: 0.8802\n",
+      "1875/1875 [==============================] - 8s 4ms/step - loss: 253.8582 - accuracy: 0.8727 - val_loss: 216.4600 - val_accuracy: 0.8947\n",
       "Epoch 4/10\n",
-      "1875/1875 [==============================] - 8s 4ms/step - loss: 238.4442 - accuracy: 0.8784 - val_loss: 312.3720 - val_accuracy: 0.8513\n",
+      "1875/1875 [==============================] - 8s 4ms/step - loss: 246.0525 - accuracy: 0.8751 - val_loss: 250.1058 - val_accuracy: 0.8761\n",
       "Epoch 5/10\n",
-      "1875/1875 [==============================] - 9s 5ms/step - loss: 238.5885 - accuracy: 0.8783 - val_loss: 195.5100 - val_accuracy: 0.9018\n",
+      "1875/1875 [==============================] - 8s 4ms/step - loss: 239.2593 - accuracy: 0.8785 - val_loss: 305.9203 - val_accuracy: 0.8644\n",
       "Epoch 6/10\n",
-      "1875/1875 [==============================] - 9s 5ms/step - loss: 238.2655 - accuracy: 0.8773 - val_loss: 253.4705 - val_accuracy: 0.8795\n",
+      "1875/1875 [==============================] - 8s 4ms/step - loss: 243.5337 - accuracy: 0.8783 - val_loss: 521.3034 - val_accuracy: 0.7749\n",
       "Epoch 7/10\n",
-      "1875/1875 [==============================] - 9s 5ms/step - loss: 237.9472 - accuracy: 0.8798 - val_loss: 250.7999 - val_accuracy: 0.8824\n",
+      "1875/1875 [==============================] - 7s 4ms/step - loss: 236.1099 - accuracy: 0.8800 - val_loss: 243.0667 - val_accuracy: 0.8847\n",
       "Epoch 8/10\n",
-      "1875/1875 [==============================] - 8s 5ms/step - loss: 237.2793 - accuracy: 0.8802 - val_loss: 285.5836 - val_accuracy: 0.8707\n",
+      "1875/1875 [==============================] - 7s 4ms/step - loss: 236.1764 - accuracy: 0.8816 - val_loss: 219.8499 - val_accuracy: 0.8889\n",
       "Epoch 9/10\n",
-      "1875/1875 [==============================] - 8s 4ms/step - loss: 235.1660 - accuracy: 0.8808 - val_loss: 277.7219 - val_accuracy: 0.8809\n",
+      "1875/1875 [==============================] - 8s 4ms/step - loss: 235.2368 - accuracy: 0.8809 - val_loss: 253.1557 - val_accuracy: 0.8788\n",
       "Epoch 10/10\n",
-      "1875/1875 [==============================] - 9s 5ms/step - loss: 233.3158 - accuracy: 0.8822 - val_loss: 263.0470 - val_accuracy: 0.8765\n"
+      "1875/1875 [==============================] - 8s 4ms/step - loss: 227.3272 - accuracy: 0.8842 - val_loss: 257.5094 - val_accuracy: 0.8736\n"
      ]
     }
    ],
@@ -1031,15 +1040,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "313/313 [==============================] - 1s 4ms/step - loss: 263.0470 - accuracy: 0.8765\n",
-      "Accuracy on test dataset: 0.8765000104904175\n"
+      "313/313 [==============================] - 1s 3ms/step - loss: 257.5094 - accuracy: 0.8736\n",
+      "Accuracy on test dataset: 0.8736000061035156\n"
      ]
     }
    ],
@@ -1057,7 +1066,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -1065,25 +1074,25 @@
      "output_type": "stream",
      "text": [
       "Epoch 1/10\n",
-      "1875/1875 [==============================] - 9s 5ms/step - loss: 338.3148 - accuracy: 0.8376 - val_loss: 217.6844 - val_accuracy: 0.8881\n",
+      "1875/1875 [==============================] - 8s 4ms/step - loss: 332.5468 - accuracy: 0.8372 - val_loss: 227.9497 - val_accuracy: 0.8880\n",
       "Epoch 2/10\n",
-      "1875/1875 [==============================] - 9s 5ms/step - loss: 287.3254 - accuracy: 0.8573 - val_loss: 281.7627 - val_accuracy: 0.8726\n",
+      "1875/1875 [==============================] - 8s 4ms/step - loss: 285.6625 - accuracy: 0.8580 - val_loss: 238.9889 - val_accuracy: 0.8792\n",
       "Epoch 3/10\n",
-      "1875/1875 [==============================] - 9s 5ms/step - loss: 288.3959 - accuracy: 0.8602 - val_loss: 320.5165 - val_accuracy: 0.8475\n",
+      "1875/1875 [==============================] - 8s 4ms/step - loss: 283.5294 - accuracy: 0.8605 - val_loss: 248.4659 - val_accuracy: 0.8846\n",
       "Epoch 4/10\n",
-      "1875/1875 [==============================] - 9s 5ms/step - loss: 296.8785 - accuracy: 0.8590 - val_loss: 222.1539 - val_accuracy: 0.8899\n",
+      "1875/1875 [==============================] - 7s 4ms/step - loss: 279.4513 - accuracy: 0.8627 - val_loss: 304.6351 - val_accuracy: 0.8469\n",
       "Epoch 5/10\n",
-      "1875/1875 [==============================] - 9s 5ms/step - loss: 283.6608 - accuracy: 0.8618 - val_loss: 460.9875 - val_accuracy: 0.8016\n",
+      "1875/1875 [==============================] - 8s 4ms/step - loss: 286.8078 - accuracy: 0.8604 - val_loss: 233.9412 - val_accuracy: 0.8851\n",
       "Epoch 6/10\n",
-      "1875/1875 [==============================] - 9s 5ms/step - loss: 277.9357 - accuracy: 0.8639 - val_loss: 245.5682 - val_accuracy: 0.8875\n",
+      "1875/1875 [==============================] - 8s 4ms/step - loss: 280.9360 - accuracy: 0.8623 - val_loss: 240.2189 - val_accuracy: 0.8822\n",
       "Epoch 7/10\n",
-      "1875/1875 [==============================] - 9s 5ms/step - loss: 287.6865 - accuracy: 0.8610 - val_loss: 252.3817 - val_accuracy: 0.8678\n",
+      "1875/1875 [==============================] - 9s 5ms/step - loss: 286.4092 - accuracy: 0.8612 - val_loss: 238.1035 - val_accuracy: 0.8875\n",
       "Epoch 8/10\n",
-      "1875/1875 [==============================] - 9s 5ms/step - loss: 284.3436 - accuracy: 0.8620 - val_loss: 228.4689 - val_accuracy: 0.8884\n",
+      "1875/1875 [==============================] - 8s 4ms/step - loss: 286.7316 - accuracy: 0.8616 - val_loss: 293.3754 - val_accuracy: 0.8583\n",
       "Epoch 9/10\n",
-      "1875/1875 [==============================] - 9s 5ms/step - loss: 282.4845 - accuracy: 0.8623 - val_loss: 258.1628 - val_accuracy: 0.8879\n",
+      "1875/1875 [==============================] - 8s 4ms/step - loss: 283.3217 - accuracy: 0.8633 - val_loss: 348.7958 - val_accuracy: 0.8326\n",
       "Epoch 10/10\n",
-      "1875/1875 [==============================] - 9s 5ms/step - loss: 285.5985 - accuracy: 0.8620 - val_loss: 484.2548 - val_accuracy: 0.7784\n"
+      "1875/1875 [==============================] - 8s 4ms/step - loss: 285.2917 - accuracy: 0.8599 - val_loss: 370.8463 - val_accuracy: 0.8216\n"
      ]
     }
    ],
@@ -1125,15 +1134,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "313/313 [==============================] - 1s 3ms/step - loss: 484.2548 - accuracy: 0.7784\n",
-      "Accuracy on test dataset: 0.7784000039100647\n"
+      "313/313 [==============================] - 1s 3ms/step - loss: 370.8463 - accuracy: 0.8216\n",
+      "Accuracy on test dataset: 0.8216000199317932\n"
      ]
     }
    ],
@@ -1172,7 +1181,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -1199,7 +1208,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
@@ -1228,7 +1237,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
@@ -1273,7 +1282,7 @@
        "       11.8, 24.4, 13.8, 19.4, 25.2, 19.4, 19.4, 29.1])"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1308,7 +1317,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1347,7 +1356,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1400,7 +1409,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
@@ -1447,19 +1456,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[1.9184445142745972,\n",
-       " 2.4037296772003174,\n",
-       " 2.4944815635681152,\n",
-       " 2.4431681632995605]"
+       "[1.9098734855651855, 2.591566324234009, 2.4361863136291504, 2.5663185119628906]"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1470,16 +1476,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "2.3149559795856476"
+       "2.375986158847809"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1509,7 +1515,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
@@ -1559,7 +1565,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1576,12 +1582,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1618,12 +1624,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1664,14 +1670,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "4/4 [==============================] - 0s 3ms/step - loss: 17.8865 - mae: 2.7644\n"
+      "4/4 [==============================] - 0s 2ms/step - loss: 17.6020 - mae: 2.5814\n"
      ]
     }
    ],
@@ -1693,16 +1699,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "2.7643771171569824"
+       "2.5813896656036377"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1728,16 +1734,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([8.708372], dtype=float32)"
+       "array([9.2581005], dtype=float32)"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     }
diff --git a/notebooks/Neural_Networks/Supplementary Jupyter Notebook Block 2 - Linear Classifier.ipynb b/notebooks/Neural_Networks/Supplementary Jupyter Notebook Block 2 - Linear Classifier.ipynb
index 5411bc3076c17a1d5a86ff9d8b9beefaf017fc81..a8800877ed51ac53c646dba16ff35a9f42c7535d 100644
--- a/notebooks/Neural_Networks/Supplementary Jupyter Notebook Block 2 - Linear Classifier.ipynb	
+++ b/notebooks/Neural_Networks/Supplementary Jupyter Notebook Block 2 - Linear Classifier.ipynb	
@@ -21,7 +21,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 70 Axes>"
       ]
@@ -53,12 +53,12 @@
     "    with open(file, 'rb') as fo:\n",
     "        dict = pickle.load(fo, encoding='bytes')\n",
     "    return dict\n",
-    "data_batch_1 = unpickle(\"/work/hslu-deep-learning/notebooks/Block 1/data/data_batch_1\")\n",
-    "data_batch_2 = unpickle(\"/work/hslu-deep-learning/notebooks/Block 1/data/data_batch_2\")\n",
-    "data_batch_3 = unpickle(\"/work/hslu-deep-learning/notebooks/Block 1/data/data_batch_3\")\n",
-    "data_batch_4 = unpickle(\"/work/hslu-deep-learning/notebooks/Block 1/data/data_batch_4\")\n",
-    "data_batch_5 = unpickle(\"/work/hslu-deep-learning/notebooks/Block 1/data/data_batch_5\")\n",
-    "test_batch = unpickle(\"/work/hslu-deep-learning/notebooks/Block 1/data/test_batch\")\n",
+    "data_batch_1 = unpickle(\"../Introduction_to_Image_Classification/data/data_batch_1\")\n",
+    "data_batch_2 = unpickle(\"../Introduction_to_Image_Classification/data/data_batch_2\")\n",
+    "data_batch_3 = unpickle(\"../Introduction_to_Image_Classification/data/data_batch_3\")\n",
+    "data_batch_4 = unpickle(\"../Introduction_to_Image_Classification/data/data_batch_4\")\n",
+    "data_batch_5 = unpickle(\"../Introduction_to_Image_Classification/data/data_batch_5\")\n",
+    "test_batch = unpickle(\"../Introduction_to_Image_Classification/data/test_batch\")\n",
     "\n",
     "# Let us concatenate the batch training data \n",
     "X_train=np.concatenate([data_batch_1[b'data'], \n",
@@ -158,7 +158,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -179,13 +179,6 @@
       "needs_background": "light"
      },
      "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(49000, 3073) (1000, 3073) (1000, 3073) (500, 3073)\n"
-     ]
     }
    ],
    "source": [
@@ -223,17 +216,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "loss: 10.720145\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from random import shuffle\n",
     "\n",
@@ -324,36 +309,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "numerical: 71.516000 analytic: 71.516000, relative error: 6.564244e-12\n",
-      "numerical: -126.906269 analytic: -126.848000, relative error: 2.296261e-04\n",
-      "numerical: -50.112000 analytic: -50.112000, relative error: 4.409556e-12\n",
-      "numerical: -35.675488 analytic: -35.766000, relative error: 1.266941e-03\n",
-      "numerical: 60.005488 analytic: 60.052000, relative error: 3.874156e-04\n",
-      "numerical: 44.968488 analytic: 45.118000, relative error: 1.659651e-03\n",
-      "numerical: 59.621593 analytic: 59.534000, relative error: 7.351184e-04\n",
-      "numerical: -4.310000 analytic: -4.310000, relative error: 7.127158e-11\n",
-      "numerical: 68.692032 analytic: 68.714000, relative error: 1.598752e-04\n",
-      "numerical: 72.078000 analytic: 72.078000, relative error: 1.215882e-12\n",
-      "numerical: -36.601235 analytic: -36.616157, relative error: 2.038029e-04\n",
-      "numerical: 53.960978 analytic: 54.154745, relative error: 1.792221e-03\n",
-      "numerical: 54.514393 analytic: 54.545953, relative error: 2.893760e-04\n",
-      "numerical: 54.401085 analytic: 54.393542, relative error: 6.932729e-05\n",
-      "numerical: 58.464939 analytic: 58.512725, relative error: 4.085121e-04\n",
-      "numerical: 48.058741 analytic: 48.144127, relative error: 8.875581e-04\n",
-      "numerical: -44.734106 analytic: -44.783852, relative error: 5.557024e-04\n",
-      "numerical: 59.547595 analytic: 59.541798, relative error: 4.868257e-05\n",
-      "numerical: -6.863554 analytic: -6.726368, relative error: 1.009464e-02\n",
-      "numerical: -8.582602 analytic: -8.597100, relative error: 8.438569e-04\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "def grad_check_sparse(f, x, analytic_grad, num_checks=10, h=1e-5):\n",
     "  \"\"\"\n",
@@ -416,7 +374,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -480,23 +438,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Naive loss: 1.072014e+01 computed in 0.063963s\n",
-      "Vectorized loss: 1.072014e+01 computed in 0.004526s\n",
-      "difference: 0.000000\n",
-      "Naive loss and gradient: computed in 0.053587s\n",
-      "Vectorized loss and gradient: computed in 0.010512s\n",
-      "2.56 ms ± 380 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
-      "difference: 0.000000\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import time\n",
     "\n",
@@ -546,52 +490,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "iteration 0 / 1500: loss 826.155049\n",
-      "iteration 100 / 1500: loss 294.536101\n",
-      "iteration 200 / 1500: loss 112.701283\n",
-      "iteration 300 / 1500: loss 47.893062\n",
-      "iteration 400 / 1500: loss 24.353534\n",
-      "iteration 500 / 1500: loss 13.717950\n",
-      "iteration 600 / 1500: loss 13.514528\n",
-      "iteration 700 / 1500: loss 11.366763\n",
-      "iteration 800 / 1500: loss 11.376218\n",
-      "iteration 900 / 1500: loss 12.441962\n",
-      "iteration 1000 / 1500: loss 10.662319\n",
-      "iteration 1100 / 1500: loss 11.286613\n",
-      "iteration 1200 / 1500: loss 12.290911\n",
-      "iteration 1300 / 1500: loss 13.109479\n",
-      "iteration 1400 / 1500: loss 10.732881\n",
-      "That took 3.730253s\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "training accuracy: 0.168143\n",
-      "validation accuracy: 0.183000\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "\n",
     "class LinearClassifier():\n",
@@ -751,7 +652,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -772,7 +673,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -788,7 +689,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -804,7 +705,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -820,7 +721,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -847,22 +748,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:33: RuntimeWarning: overflow encountered in double_scalars\n",
-      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:33: RuntimeWarning: overflow encountered in multiply\n",
-      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:28: RuntimeWarning: overflow encountered in subtract\n",
-      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:28: RuntimeWarning: invalid value encountered in subtract\n",
-      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:46: RuntimeWarning: overflow encountered in multiply\n",
-      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:59: RuntimeWarning: invalid value encountered in add\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "for l in learning_rates:\n",
     "    for r in regularization_strengths:\n",
@@ -882,42 +770,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "lr 1.000000e-05 reg 1.000000e-05 train accuracy: 0.210082 val accuracy: 0.219000\n",
-      "lr 1.000000e-05 reg 5.623413e-04 train accuracy: 0.178735 val accuracy: 0.188000\n",
-      "lr 1.000000e-05 reg 3.162278e-02 train accuracy: 0.214122 val accuracy: 0.189000\n",
-      "lr 1.000000e-05 reg 1.778279e+00 train accuracy: 0.157980 val accuracy: 0.165000\n",
-      "lr 1.000000e-05 reg 1.000000e+02 train accuracy: 0.128265 val accuracy: 0.138000\n",
-      "lr 1.778279e-04 reg 1.000000e-05 train accuracy: 0.193367 val accuracy: 0.172000\n",
-      "lr 1.778279e-04 reg 5.623413e-04 train accuracy: 0.153327 val accuracy: 0.140000\n",
-      "lr 1.778279e-04 reg 3.162278e-02 train accuracy: 0.202735 val accuracy: 0.180000\n",
-      "lr 1.778279e-04 reg 1.778279e+00 train accuracy: 0.167755 val accuracy: 0.132000\n",
-      "lr 1.778279e-04 reg 1.000000e+02 train accuracy: 0.153327 val accuracy: 0.135000\n",
-      "lr 3.162278e-03 reg 1.000000e-05 train accuracy: 0.171571 val accuracy: 0.188000\n",
-      "lr 3.162278e-03 reg 5.623413e-04 train accuracy: 0.223633 val accuracy: 0.224000\n",
-      "lr 3.162278e-03 reg 3.162278e-02 train accuracy: 0.234041 val accuracy: 0.226000\n",
-      "lr 3.162278e-03 reg 1.778279e+00 train accuracy: 0.135449 val accuracy: 0.135000\n",
-      "lr 3.162278e-03 reg 1.000000e+02 train accuracy: 0.100265 val accuracy: 0.087000\n",
-      "lr 5.623413e-02 reg 1.000000e-05 train accuracy: 0.199898 val accuracy: 0.211000\n",
-      "lr 5.623413e-02 reg 5.623413e-04 train accuracy: 0.242510 val accuracy: 0.244000\n",
-      "lr 5.623413e-02 reg 3.162278e-02 train accuracy: 0.171184 val accuracy: 0.165000\n",
-      "lr 5.623413e-02 reg 1.778279e+00 train accuracy: 0.101878 val accuracy: 0.117000\n",
-      "lr 5.623413e-02 reg 1.000000e+02 train accuracy: 0.100265 val accuracy: 0.087000\n",
-      "lr 1.000000e+00 reg 1.000000e-05 train accuracy: 0.205816 val accuracy: 0.187000\n",
-      "lr 1.000000e+00 reg 5.623413e-04 train accuracy: 0.185612 val accuracy: 0.188000\n",
-      "lr 1.000000e+00 reg 3.162278e-02 train accuracy: 0.113327 val accuracy: 0.086000\n",
-      "lr 1.000000e+00 reg 1.778279e+00 train accuracy: 0.100449 val accuracy: 0.078000\n",
-      "lr 1.000000e+00 reg 1.000000e+02 train accuracy: 0.100265 val accuracy: 0.087000\n",
-      "best validation accuracy achieved during cross-validation: 0.244000\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Print out results.\n",
     "for lr, reg in sorted(results):\n",
@@ -943,32 +798,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.5, 1.0, 'CIFAR-10 training accuracy')"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "import math\n",
     "x_scatter = [math.log10(x[0]) for x in results]\n",
@@ -993,22 +825,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "colors = [results[x][1] for x in results] \n",
     "plt.subplot(2, 1, 2)\n",
@@ -1029,17 +848,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "linear SVM on raw pixels final test set accuracy: 0.229000\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "y_test_pred = best_svm.predict(X_test)\n",
     "test_accuracy = np.mean(y_test == y_test_pred)\n",
@@ -1055,7 +866,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1078,17 +889,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "linear SVM on raw pixels final test set accuracy: 0.229000\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "y_test_pred = best_svm.predict(X_test)\n",
     "test_accuracy = np.mean(y_test == y_test_pred)\n",
@@ -1106,22 +909,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 10 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "w = best_svm.W[:-1,:] # strip out the bias\n",
     "w = w.T.reshape(10,3,32,32).transpose(2,3,1,0)\n",
@@ -1146,38 +936,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "loss: 2.815840\n",
-      "sanity check: 2.302585\n",
-      "numerical: -11.326073 analytic: -11.326074, relative error: 5.570015e-08\n",
-      "numerical: 42.757579 analytic: 42.757575, relative error: 4.960940e-08\n",
-      "numerical: -8.512970 analytic: -8.512972, relative error: 1.628273e-07\n",
-      "numerical: -2.310167 analytic: -2.310172, relative error: 1.127461e-06\n",
-      "numerical: 3.331756 analytic: 3.331751, relative error: 8.078272e-07\n",
-      "numerical: 41.554669 analytic: 41.554665, relative error: 4.672347e-08\n",
-      "numerical: 0.672296 analytic: 0.672290, relative error: 4.873474e-06\n",
-      "numerical: 0.702232 analytic: 0.702226, relative error: 4.903399e-06\n",
-      "numerical: 0.930738 analytic: 0.930732, relative error: 3.328067e-06\n",
-      "numerical: -9.117642 analytic: -9.117645, relative error: 1.572709e-07\n",
-      "numerical: -4.133261 analytic: -4.133266, relative error: 5.926428e-07\n",
-      "numerical: -7.194303 analytic: -7.194305, relative error: 1.378896e-07\n",
-      "numerical: -1.945254 analytic: -1.945259, relative error: 1.482778e-06\n",
-      "numerical: -9.633691 analytic: -9.633692, relative error: 8.475122e-08\n",
-      "numerical: -5.922326 analytic: -5.922331, relative error: 4.720344e-07\n",
-      "numerical: 3.141638 analytic: 3.141632, relative error: 8.952355e-07\n",
-      "numerical: -1.948249 analytic: -1.948253, relative error: 1.166767e-06\n",
-      "numerical: -2.445040 analytic: -2.445046, relative error: 1.232413e-06\n",
-      "numerical: -6.880944 analytic: -6.880947, relative error: 2.244305e-07\n",
-      "numerical: -0.251031 analytic: -0.251038, relative error: 1.239322e-05\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "def softmax_loss_naive(W, X, y, reg):\n",
     "  \"\"\"\n",
@@ -1286,7 +1047,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1335,20 +1096,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "naive loss: 2.815840e+00 computed in 0.143722s\n",
-      "vectorized loss: 2.815840e+00 computed in 0.004067s\n",
-      "Loss difference: 0.000000\n",
-      "Gradient difference: 0.000000\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "class Softmax(LinearClassifier):\n",
     "  \"\"\" Softmax is a\n",
@@ -1406,37 +1156,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:22: RuntimeWarning: divide by zero encountered in log\n",
-      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:29: RuntimeWarning: overflow encountered in double_scalars\n",
-      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:29: RuntimeWarning: overflow encountered in multiply\n",
-      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:31: RuntimeWarning: overflow encountered in multiply\n",
-      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:18: RuntimeWarning: overflow encountered in subtract\n",
-      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:18: RuntimeWarning: invalid value encountered in subtract\n",
-      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:59: RuntimeWarning: overflow encountered in multiply\n"
-     ]
-    },
-    {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-24-a93ef57888e0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     11\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mrs\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mregularization_strengths\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m         \u001b[0msoftmax\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSoftmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m         \u001b[0msoftmax\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlearning_rate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreg\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_iters\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0miters\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     14\u001b[0m         \u001b[0my_train_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msoftmax\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m         \u001b[0macc_train\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_train\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0my_train_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m<ipython-input-7-d3cfde09d80d>\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, X, y, learning_rate, reg, num_iters, batch_size, verbose)\u001b[0m\n\u001b[1;32m     50\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     51\u001b[0m       \u001b[0;31m# evaluate loss and gradient\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 52\u001b[0;31m       \u001b[0mloss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrad\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_batch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_batch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     53\u001b[0m       \u001b[0mloss_history\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m<ipython-input-23-129e491731bb>\u001b[0m in \u001b[0;36mloss\u001b[0;34m(self, X_batch, y_batch, reg)\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_batch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_batch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0msoftmax_loss_vectorized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mW\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_batch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_batch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m<ipython-input-22-3e4ec51743e2>\u001b[0m in \u001b[0;36msoftmax_loss_vectorized\u001b[0;34m(W, X, y, reg)\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m   \u001b[0mnum_train\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m   \u001b[0mf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mW\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m   \u001b[0mf\u001b[0m \u001b[0;34m-=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeepdims\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# max of every sample\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m   \u001b[0msum_f\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeepdims\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "results = {}\n",
     "best_val = -1\n",
diff --git a/notebooks/Preliminaries_Numpy_Pandas/Jupyter Notebook - Introduction Numpy and Pandas.ipynb b/notebooks/Preliminaries_Numpy_Pandas/Jupyter Notebook - Introduction Numpy and Pandas.ipynb
index 6001ae22d0348884b6d15fb61405d54d1b96da6c..b98682b34110ea3bf81e893f321a6a43f3b6fd33 100644
--- a/notebooks/Preliminaries_Numpy_Pandas/Jupyter Notebook - Introduction Numpy and Pandas.ipynb	
+++ b/notebooks/Preliminaries_Numpy_Pandas/Jupyter Notebook - Introduction Numpy and Pandas.ipynb	
@@ -11,7 +11,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -45,7 +45,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -87,7 +87,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -145,6 +145,23 @@
     "print(\"while broken\")"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0 1 2 3 4 5 6 7 8 9]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(np.arange(10))"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -171,7 +188,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 186,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -221,7 +238,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 187,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -230,7 +247,7 @@
        "array(5)"
       ]
      },
-     "execution_count": 187,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -252,7 +269,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 188,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -261,7 +278,7 @@
        "0"
       ]
      },
-     "execution_count": 188,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -280,7 +297,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 189,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -289,7 +306,7 @@
        "()"
       ]
      },
-     "execution_count": 189,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -311,7 +328,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 190,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -320,7 +337,7 @@
        "numpy.int64"
       ]
      },
-     "execution_count": 190,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -355,7 +372,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 191,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -364,7 +381,7 @@
        "array([1, 2, 3])"
       ]
      },
-     "execution_count": 191,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -384,7 +401,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 192,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
@@ -393,7 +410,7 @@
        "(3,)"
       ]
      },
-     "execution_count": 192,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -411,7 +428,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 193,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
@@ -420,7 +437,7 @@
        "1"
       ]
      },
-     "execution_count": 193,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -442,7 +459,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 194,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
@@ -451,7 +468,7 @@
        "2"
       ]
      },
-     "execution_count": 194,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -470,7 +487,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 195,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
@@ -479,7 +496,7 @@
        "array([2, 3])"
       ]
      },
-     "execution_count": 195,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -511,7 +528,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 196,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
@@ -522,7 +539,7 @@
        "       [7, 8, 9]])"
       ]
      },
-     "execution_count": 196,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -542,7 +559,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 197,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
@@ -551,7 +568,7 @@
        "2"
       ]
      },
-     "execution_count": 197,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -569,7 +586,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 198,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
@@ -578,7 +595,7 @@
        "(3, 3)"
       ]
      },
-     "execution_count": 198,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -627,7 +644,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 200,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
@@ -663,7 +680,7 @@
        "         [17]]]])"
       ]
      },
-     "execution_count": 200,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -703,7 +720,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 202,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
@@ -712,7 +729,7 @@
        "4"
       ]
      },
-     "execution_count": 202,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -763,7 +780,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 204,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
@@ -772,7 +789,7 @@
        "(4,)"
       ]
      },
-     "execution_count": 204,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -792,7 +809,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 205,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
@@ -801,7 +818,7 @@
        "array([[1, 2, 3, 4]])"
       ]
      },
-     "execution_count": 205,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -821,7 +838,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 206,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [
     {
@@ -833,7 +850,7 @@
        "       [4]])"
       ]
      },
-     "execution_count": 206,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -963,14 +980,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 211,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.0002567768096923828\n",
+      "0.00026798248291015625\n",
       "[6, 7, 8, 9, 10]\n"
      ]
     }
@@ -1073,7 +1090,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 214,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [
     {
@@ -1082,7 +1099,7 @@
        "array([ 5, 10, 15, 20])"
       ]
      },
-     "execution_count": 214,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1094,7 +1111,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 215,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
@@ -1103,7 +1120,7 @@
        "array([ 5, 10, 15, 20])"
       ]
      },
-     "execution_count": 215,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1127,7 +1144,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 216,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
@@ -1138,7 +1155,7 @@
        "       [0, 0, 0]])"
       ]
      },
-     "execution_count": 216,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1169,7 +1186,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 217,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [
     {
@@ -1179,7 +1196,7 @@
        "       [5, 7]])"
       ]
      },
-     "execution_count": 217,
+     "execution_count": 44,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1191,7 +1208,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 218,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [
     {
@@ -1201,7 +1218,7 @@
        "       [6, 8]])"
       ]
      },
-     "execution_count": 218,
+     "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1213,7 +1230,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 219,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [
     {
@@ -1223,7 +1240,7 @@
        "       [11, 15]])"
       ]
      },
-     "execution_count": 219,
+     "execution_count": 46,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1241,7 +1258,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 220,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [
     {
@@ -1251,7 +1268,7 @@
        "       [5, 7]])"
       ]
      },
-     "execution_count": 220,
+     "execution_count": 47,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1263,7 +1280,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 221,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [
     {
@@ -1274,7 +1291,7 @@
        "       [1, 8, 7]])"
       ]
      },
-     "execution_count": 221,
+     "execution_count": 48,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1306,7 +1323,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 223,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [
     {
@@ -1315,7 +1332,7 @@
        "(3, 3)"
       ]
      },
-     "execution_count": 223,
+     "execution_count": 49,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1326,7 +1343,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 224,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [
     {
@@ -1336,7 +1353,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-224-e81e582b6fa9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ma\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-50-e81e582b6fa9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ma\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m: operands could not be broadcast together with shapes (2,2) (3,3) "
      ]
     }
@@ -1347,7 +1364,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 225,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [
     {
@@ -1357,7 +1374,7 @@
        "       [4, 5, 6]])"
       ]
      },
-     "execution_count": 225,
+     "execution_count": 51,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1369,7 +1386,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 226,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [
     {
@@ -1379,7 +1396,7 @@
        "       [1.  , 1.25, 1.5 ]])"
       ]
      },
-     "execution_count": 226,
+     "execution_count": 52,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1391,7 +1408,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 227,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [
     {
@@ -1401,7 +1418,7 @@
        "       [4.  , 6.25, 9.  ]])"
       ]
      },
-     "execution_count": 227,
+     "execution_count": 53,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1412,7 +1429,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 228,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [
     {
@@ -1422,7 +1439,7 @@
        "       [4.  , 6.25, 9.  ]])"
       ]
      },
-     "execution_count": 228,
+     "execution_count": 54,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1442,7 +1459,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 229,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [
     {
@@ -1452,7 +1469,7 @@
        "       [5, 6, 7, 8]])"
       ]
      },
-     "execution_count": 229,
+     "execution_count": 55,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1464,7 +1481,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 230,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [
     {
@@ -1473,7 +1490,7 @@
        "(2, 4)"
       ]
      },
-     "execution_count": 230,
+     "execution_count": 56,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1484,7 +1501,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 231,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [
     {
@@ -1496,7 +1513,7 @@
        "       [10, 11, 12]])"
       ]
      },
-     "execution_count": 231,
+     "execution_count": 57,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1508,7 +1525,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 232,
+   "execution_count": 58,
    "metadata": {},
    "outputs": [
     {
@@ -1517,7 +1534,7 @@
        "(4, 3)"
       ]
      },
-     "execution_count": 232,
+     "execution_count": 58,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1528,7 +1545,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 233,
+   "execution_count": 59,
    "metadata": {},
    "outputs": [
     {
@@ -1538,7 +1555,7 @@
        "       [158, 184, 210]])"
       ]
      },
-     "execution_count": 233,
+     "execution_count": 59,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1550,7 +1567,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 234,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [
     {
@@ -1559,7 +1576,7 @@
        "(2, 3)"
       ]
      },
-     "execution_count": 234,
+     "execution_count": 60,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1577,7 +1594,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 235,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [
     {
@@ -1587,7 +1604,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-235-af3b88aa2232>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmatmul\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-61-af3b88aa2232>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmatmul\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m: matmul: Input operand 1 has a mismatch in its core dimension 0, with gufunc signature (n?,k),(k,m?)->(n?,m?) (size 2 is different from 3)"
      ]
     }
@@ -1610,7 +1627,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 236,
+   "execution_count": 62,
    "metadata": {},
    "outputs": [
     {
@@ -1620,7 +1637,7 @@
        "       [3, 4]])"
       ]
      },
-     "execution_count": 236,
+     "execution_count": 62,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1632,7 +1649,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 237,
+   "execution_count": 63,
    "metadata": {},
    "outputs": [
     {
@@ -1642,7 +1659,7 @@
        "       [15, 22]])"
       ]
      },
-     "execution_count": 237,
+     "execution_count": 63,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1653,7 +1670,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 238,
+   "execution_count": 64,
    "metadata": {},
    "outputs": [
     {
@@ -1663,7 +1680,7 @@
        "       [15, 22]])"
       ]
      },
-     "execution_count": 238,
+     "execution_count": 64,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1674,7 +1691,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 239,
+   "execution_count": 65,
    "metadata": {},
    "outputs": [
     {
@@ -1684,7 +1701,7 @@
        "       [15, 22]])"
       ]
      },
-     "execution_count": 239,
+     "execution_count": 65,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1715,7 +1732,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 240,
+   "execution_count": 66,
    "metadata": {},
    "outputs": [
     {
@@ -1726,7 +1743,7 @@
        "       [ 9, 10, 11, 12]])"
       ]
      },
-     "execution_count": 240,
+     "execution_count": 66,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1738,7 +1755,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 241,
+   "execution_count": 67,
    "metadata": {},
    "outputs": [
     {
@@ -1750,7 +1767,7 @@
        "       [ 4,  8, 12]])"
       ]
      },
-     "execution_count": 241,
+     "execution_count": 67,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1775,7 +1792,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 242,
+   "execution_count": 68,
    "metadata": {},
    "outputs": [
     {
@@ -1787,7 +1804,7 @@
        "       [  4, 200,  12]])"
       ]
      },
-     "execution_count": 242,
+     "execution_count": 68,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1800,7 +1817,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 243,
+   "execution_count": 69,
    "metadata": {},
    "outputs": [
     {
@@ -1811,7 +1828,7 @@
        "       [  9,  10,  11,  12]])"
       ]
      },
-     "execution_count": 243,
+     "execution_count": 69,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1871,7 +1888,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 70,
    "metadata": {},
    "outputs": [
     {
@@ -1907,7 +1924,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 71,
    "metadata": {},
    "outputs": [
     {
@@ -2073,7 +2090,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 72,
    "metadata": {},
    "outputs": [
     {
@@ -2298,7 +2315,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 108,
+   "execution_count": 73,
    "metadata": {},
    "outputs": [
     {
@@ -2335,7 +2352,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 109,
+   "execution_count": 74,
    "metadata": {},
    "outputs": [
     {
@@ -2364,7 +2381,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 110,
+   "execution_count": 75,
    "metadata": {},
    "outputs": [
     {
@@ -2433,7 +2450,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 112,
+   "execution_count": 76,
    "metadata": {},
    "outputs": [
     {
@@ -2477,7 +2494,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 113,
+   "execution_count": 77,
    "metadata": {},
    "outputs": [
     {
@@ -2513,7 +2530,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 114,
+   "execution_count": 78,
    "metadata": {},
    "outputs": [
     {
@@ -2554,7 +2571,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 115,
+   "execution_count": 79,
    "metadata": {},
    "outputs": [
     {