diff --git a/PythonAI/JupyterLab/PythonAI_5.ipynb b/PythonAI/JupyterLab/PythonAI_5.ipynb
index b33f330..6d9076c 100644
--- a/PythonAI/JupyterLab/PythonAI_5.ipynb
+++ b/PythonAI/JupyterLab/PythonAI_5.ipynb
@@ -70,9 +70,7 @@
   {
    "cell_type": "markdown",
    "id": "9d98ba73-f954-4a6a-ac09-3a6b1afa58ce",
-   "metadata": {
-    "jp-MarkdownHeadingCollapsed": true
-   },
+   "metadata": {},
    "source": [
     "# Kod"
    ]
@@ -80,16 +78,14 @@
   {
    "cell_type": "markdown",
    "id": "11ee196e-06a8-47a4-a411-aa7dd06faaf8",
-   "metadata": {
-    "jp-MarkdownHeadingCollapsed": true
-   },
+   "metadata": {},
    "source": [
     "## Setup i wczytanie danych"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 134,
+   "execution_count": 33,
    "id": "ed806007-30fb-4048-8fa5-f31c7eed433a",
    "metadata": {},
    "outputs": [],
@@ -101,7 +97,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 135,
+   "execution_count": 34,
    "id": "4b4881e2-fdaf-4ccc-b68e-15ec7391146e",
    "metadata": {},
    "outputs": [],
@@ -113,7 +109,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 136,
+   "execution_count": 35,
    "id": "455c540d-bad5-4a66-9b13-35113ae41f3d",
    "metadata": {},
    "outputs": [],
@@ -129,7 +125,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 137,
+   "execution_count": 36,
    "id": "beee8e5a-348e-45d1-9a40-79976aed712f",
    "metadata": {},
    "outputs": [
@@ -169,9 +165,7 @@
   {
    "cell_type": "markdown",
    "id": "37735925-efe6-4540-97a3-c0c130274fae",
-   "metadata": {
-    "jp-MarkdownHeadingCollapsed": true
-   },
+   "metadata": {},
    "source": [
     "## Model"
    ]
@@ -186,18 +180,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 37,
    "id": "4ec7f823-09ee-4b22-a2f6-f3f74853211e",
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/sasza/.local/share/virtualenvs/JupyterLab-9JRWupKp/lib/python3.12/site-packages/keras/src/layers/core/dense.py:93: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
+      "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
+     ]
+    },
     {
      "data": {
       "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_15\"</span>\n",
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_4\"</span>\n",
        "</pre>\n"
       ],
       "text/plain": [
-       "\u001b[1mModel: \"sequential_15\"\u001b[0m\n"
+       "\u001b[1mModel: \"sequential_4\"\u001b[0m\n"
       ]
      },
      "metadata": {},
@@ -318,7 +320,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": 38,
    "id": "6d0dbbe5-7eac-4917-aff1-6f127ab46dfb",
    "metadata": {},
    "outputs": [],
@@ -339,7 +341,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 94,
+   "execution_count": 39,
    "id": "30ce3fe1-2cc6-4dad-8b71-c04763d525fa",
    "metadata": {},
    "outputs": [
@@ -347,65 +349,78 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 1/25\n",
-      "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 8ms/step - accuracy: 0.7797 - loss: 0.6840 - val_accuracy: 0.9529 - val_loss: 0.1505\n",
+      "Epoch 1/25\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2025-05-20 13:23:23.834163: W external/local_xla/xla/tsl/framework/cpu_allocator_impl.cc:83] Allocation of 301056000 exceeds 10% of free system memory.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 9ms/step - accuracy: 0.7850 - loss: 0.6727 - val_accuracy: 0.9557 - val_loss: 0.1503\n",
       "Epoch 2/25\n",
-      "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.9466 - loss: 0.1820 - val_accuracy: 0.9659 - val_loss: 0.1156\n",
+      "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 9ms/step - accuracy: 0.9457 - loss: 0.1848 - val_accuracy: 0.9625 - val_loss: 0.1182\n",
       "Epoch 3/25\n",
-      "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.9579 - loss: 0.1361 - val_accuracy: 0.9683 - val_loss: 0.1057\n",
+      "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 9ms/step - accuracy: 0.9584 - loss: 0.1359 - val_accuracy: 0.9688 - val_loss: 0.1069\n",
       "Epoch 4/25\n",
-      "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.9674 - loss: 0.1131 - val_accuracy: 0.9681 - val_loss: 0.1111\n",
+      "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.9668 - loss: 0.1052 - val_accuracy: 0.9682 - val_loss: 0.1080\n",
       "Epoch 5/25\n",
-      "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 7ms/step - accuracy: 0.9706 - loss: 0.0968 - val_accuracy: 0.9723 - val_loss: 0.0927\n",
+      "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.9735 - loss: 0.0867 - val_accuracy: 0.9723 - val_loss: 0.0939\n",
       "Epoch 6/25\n",
-      "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 7ms/step - accuracy: 0.9751 - loss: 0.0820 - val_accuracy: 0.9718 - val_loss: 0.0968\n",
+      "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.9725 - loss: 0.0900 - val_accuracy: 0.9736 - val_loss: 0.0926\n",
       "Epoch 7/25\n",
-      "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 7ms/step - accuracy: 0.9772 - loss: 0.0747 - val_accuracy: 0.9715 - val_loss: 0.1028\n",
+      "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 10ms/step - accuracy: 0.9771 - loss: 0.0740 - val_accuracy: 0.9722 - val_loss: 0.1025\n",
       "Epoch 8/25\n",
-      "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.9773 - loss: 0.0707 - val_accuracy: 0.9731 - val_loss: 0.0950\n",
+      "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 9ms/step - accuracy: 0.9781 - loss: 0.0684 - val_accuracy: 0.9713 - val_loss: 0.0975\n",
       "Epoch 9/25\n",
-      "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.9801 - loss: 0.0605 - val_accuracy: 0.9747 - val_loss: 0.0953\n",
+      "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.9814 - loss: 0.0592 - val_accuracy: 0.9726 - val_loss: 0.0988\n",
       "Epoch 10/25\n",
-      "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.9813 - loss: 0.0599 - val_accuracy: 0.9731 - val_loss: 0.0968\n",
+      "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.9830 - loss: 0.0560 - val_accuracy: 0.9713 - val_loss: 0.1129\n",
       "Epoch 11/25\n",
-      "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.9836 - loss: 0.0521 - val_accuracy: 0.9738 - val_loss: 0.1034\n",
+      "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.9831 - loss: 0.0554 - val_accuracy: 0.9734 - val_loss: 0.1071\n",
       "Epoch 12/25\n",
-      "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 7ms/step - accuracy: 0.9853 - loss: 0.0463 - val_accuracy: 0.9758 - val_loss: 0.0912\n",
+      "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.9844 - loss: 0.0486 - val_accuracy: 0.9753 - val_loss: 0.0967\n",
       "Epoch 13/25\n",
-      "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 7ms/step - accuracy: 0.9847 - loss: 0.0494 - val_accuracy: 0.9744 - val_loss: 0.1016\n",
+      "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 10ms/step - accuracy: 0.9857 - loss: 0.0467 - val_accuracy: 0.9758 - val_loss: 0.0978\n",
       "Epoch 14/25\n",
-      "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.9859 - loss: 0.0459 - val_accuracy: 0.9751 - val_loss: 0.1031\n",
+      "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 9ms/step - accuracy: 0.9857 - loss: 0.0456 - val_accuracy: 0.9728 - val_loss: 0.1028\n",
       "Epoch 15/25\n",
-      "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 8ms/step - accuracy: 0.9854 - loss: 0.0456 - val_accuracy: 0.9742 - val_loss: 0.1089\n",
+      "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 10ms/step - accuracy: 0.9866 - loss: 0.0438 - val_accuracy: 0.9742 - val_loss: 0.1067\n",
       "Epoch 16/25\n",
-      "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 7ms/step - accuracy: 0.9874 - loss: 0.0435 - val_accuracy: 0.9772 - val_loss: 0.0990\n",
+      "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 10ms/step - accuracy: 0.9861 - loss: 0.0439 - val_accuracy: 0.9746 - val_loss: 0.0986\n",
       "Epoch 17/25\n",
-      "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 7ms/step - accuracy: 0.9877 - loss: 0.0401 - val_accuracy: 0.9752 - val_loss: 0.1109\n",
+      "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 11ms/step - accuracy: 0.9865 - loss: 0.0433 - val_accuracy: 0.9766 - val_loss: 0.1007\n",
       "Epoch 18/25\n",
-      "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.9891 - loss: 0.0346 - val_accuracy: 0.9747 - val_loss: 0.1082\n",
+      "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.9891 - loss: 0.0366 - val_accuracy: 0.9764 - val_loss: 0.1119\n",
       "Epoch 19/25\n",
-      "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.9882 - loss: 0.0383 - val_accuracy: 0.9775 - val_loss: 0.1048\n",
+      "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 9ms/step - accuracy: 0.9884 - loss: 0.0371 - val_accuracy: 0.9751 - val_loss: 0.1098\n",
       "Epoch 20/25\n",
-      "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.9897 - loss: 0.0325 - val_accuracy: 0.9778 - val_loss: 0.1025\n",
+      "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.9894 - loss: 0.0352 - val_accuracy: 0.9758 - val_loss: 0.1167\n",
       "Epoch 21/25\n",
-      "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.9895 - loss: 0.0346 - val_accuracy: 0.9768 - val_loss: 0.1113\n",
+      "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.9894 - loss: 0.0347 - val_accuracy: 0.9756 - val_loss: 0.1151\n",
       "Epoch 22/25\n",
-      "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 7ms/step - accuracy: 0.9899 - loss: 0.0338 - val_accuracy: 0.9743 - val_loss: 0.1128\n",
+      "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.9891 - loss: 0.0357 - val_accuracy: 0.9738 - val_loss: 0.1123\n",
       "Epoch 23/25\n",
-      "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 7ms/step - accuracy: 0.9907 - loss: 0.0305 - val_accuracy: 0.9769 - val_loss: 0.1209\n",
+      "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.9904 - loss: 0.0300 - val_accuracy: 0.9753 - val_loss: 0.1220\n",
       "Epoch 24/25\n",
-      "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 7ms/step - accuracy: 0.9893 - loss: 0.0349 - val_accuracy: 0.9753 - val_loss: 0.1185\n",
+      "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.9894 - loss: 0.0355 - val_accuracy: 0.9759 - val_loss: 0.1151\n",
       "Epoch 25/25\n",
-      "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 7ms/step - accuracy: 0.9909 - loss: 0.0311 - val_accuracy: 0.9760 - val_loss: 0.1198\n"
+      "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 9ms/step - accuracy: 0.9913 - loss: 0.0292 - val_accuracy: 0.9764 - val_loss: 0.1148\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<keras.src.callbacks.history.History at 0x70c8450faf60>"
+       "<keras.src.callbacks.history.History at 0x7f4486a77620>"
       ]
      },
-     "execution_count": 94,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -421,7 +436,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
+   "execution_count": 40,
    "id": "da23d88f-8187-41eb-919b-c925b3330924",
    "metadata": {},
    "outputs": [
@@ -429,8 +444,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9767 - loss: 0.1173\n",
-      "Test accuracy: 0.9783999919891357\n"
+      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9766 - loss: 0.1092\n",
+      "Test accuracy: 0.9790999889373779\n"
      ]
     }
    ],
@@ -1907,9 +1922,7 @@
   {
    "cell_type": "markdown",
    "id": "cd20cae1-be7f-49ee-b112-15f4cd9cfc6f",
-   "metadata": {
-    "jp-MarkdownHeadingCollapsed": true
-   },
+   "metadata": {},
    "source": [
     "# CNN - Convolutional Neural Network\n",
     "## zalety \n",
@@ -1920,7 +1933,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 31,
    "id": "0c993d2e-5730-4084-a3c1-1bc001041ba1",
    "metadata": {},
    "outputs": [],
@@ -1938,7 +1951,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 41,
    "id": "2ee7ebc4-4be9-4720-b605-abe3153477dd",
    "metadata": {},
    "outputs": [],
@@ -1950,7 +1963,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 42,
    "id": "8f6fd327-3219-4ad5-a5f6-c043ccd108ea",
    "metadata": {},
    "outputs": [],
@@ -1971,7 +1984,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 43,
    "id": "1184eeaf-1cac-4a3f-b37d-b8edc3af40bd",
    "metadata": {},
    "outputs": [],
@@ -1990,19 +2003,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 44,
    "id": "56509616-c5fa-44ba-875c-5739e7411c60",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/sasza/.local/share/virtualenvs/JupyterLab-9JRWupKp/lib/python3.12/site-packages/keras/src/layers/convolutional/base_conv.py:107: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
-      "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "model = Sequential(layers=[\n",
     "    # blok 1\n",
@@ -2043,7 +2047,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 45,
    "id": "4d837958-3e5e-45a5-8d6c-33b2d7e1528b",
    "metadata": {},
    "outputs": [],
@@ -2057,18 +2061,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 46,
    "id": "04b45a8e-58b2-43d8-8cf9-2fd86e961eb5",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_3\"</span>\n",
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_5\"</span>\n",
        "</pre>\n"
       ],
       "text/plain": [
-       "\u001b[1mModel: \"sequential_3\"\u001b[0m\n"
+       "\u001b[1mModel: \"sequential_5\"\u001b[0m\n"
       ]
      },
      "metadata": {},
@@ -2134,9 +2138,9 @@
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
        "│ dropout_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ flatten (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2048</span>)           │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "│ flatten_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2048</span>)           │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)             │        <span style=\"color: #00af00; text-decoration-color: #00af00\">20,490</span> │\n",
+       "│ dense_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)             │        <span style=\"color: #00af00; text-decoration-color: #00af00\">20,490</span> │\n",
        "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
        "</pre>\n"
       ],
@@ -2198,9 +2202,9 @@
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
        "│ dropout_8 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m128\u001b[0m)      │             \u001b[38;5;34m0\u001b[0m │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ flatten (\u001b[38;5;33mFlatten\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2048\u001b[0m)           │             \u001b[38;5;34m0\u001b[0m │\n",
+       "│ flatten_2 (\u001b[38;5;33mFlatten\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2048\u001b[0m)           │             \u001b[38;5;34m0\u001b[0m │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)             │        \u001b[38;5;34m20,490\u001b[0m │\n",
+       "│ dense_4 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)             │        \u001b[38;5;34m20,490\u001b[0m │\n",
        "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
       ]
      },
@@ -2261,7 +2265,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 47,
    "id": "227d98ed-a60d-4fcf-aa0a-2f9fc1b4050e",
    "metadata": {},
    "outputs": [
@@ -2270,32 +2274,32 @@
      "output_type": "stream",
      "text": [
       "Epoch 1/5\n",
-      "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m49s\u001b[0m 222ms/step - accuracy: 0.1819 - loss: 2.8103 - val_accuracy: 0.1018 - val_loss: 97.6675\n",
+      "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m44s\u001b[0m 193ms/step - accuracy: 0.1846 - loss: 2.9937 - val_accuracy: 0.1139 - val_loss: 119.8130\n",
       "Epoch 2/5\n",
-      "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m43s\u001b[0m 222ms/step - accuracy: 0.2692 - loss: 2.1237 - val_accuracy: 0.1161 - val_loss: 82.1247\n",
+      "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m38s\u001b[0m 194ms/step - accuracy: 0.2884 - loss: 2.0893 - val_accuracy: 0.0997 - val_loss: 174.2058\n",
       "Epoch 3/5\n",
-      "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m48s\u001b[0m 248ms/step - accuracy: 0.3134 - loss: 1.9531 - val_accuracy: 0.1122 - val_loss: 135.0129\n",
+      "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m34s\u001b[0m 174ms/step - accuracy: 0.3147 - loss: 1.9001 - val_accuracy: 0.1000 - val_loss: 198.8870\n",
       "Epoch 4/5\n",
-      "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m44s\u001b[0m 226ms/step - accuracy: 0.3663 - loss: 1.7459 - val_accuracy: 0.1282 - val_loss: 175.1613\n",
+      "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m35s\u001b[0m 181ms/step - accuracy: 0.3143 - loss: 1.8317 - val_accuracy: 0.1010 - val_loss: 159.6765\n",
       "Epoch 5/5\n",
-      "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 204ms/step - accuracy: 0.3564 - loss: 1.7099 - val_accuracy: 0.1055 - val_loss: 170.8446\n"
+      "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 206ms/step - accuracy: 0.3589 - loss: 1.7084 - val_accuracy: 0.1327 - val_loss: 79.0597\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<keras.src.callbacks.history.History at 0x7732e8e355e0>"
+       "<keras.src.callbacks.history.History at 0x7f44840df1a0>"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 47,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "model.fit(\n",
-    "    x=data_generator.flow(X_train, Y_train, batch_size=16),\n",
-    "    epochs=5,\n",
+    "    x=data_generator.flow(X_train, Y_train, batch_size=256),\n",
+    "    epochs=100,\n",
     "    verbose=1,\n",
     "    validation_data=(X_validation, Y_validation),\n",
     "    steps_per_epoch=len(X_train) // 256\n",
@@ -2737,7 +2741,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 19,
    "id": "9bde4f30-f26c-4b37-bfa6-8b64460fe13d",
    "metadata": {},
    "outputs": [],
@@ -2749,7 +2753,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 20,
    "id": "d2e9da87-1912-466f-b902-e45130d8fd32",
    "metadata": {},
    "outputs": [],
@@ -2761,19 +2765,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 21,
    "id": "88156e8c-3c2a-467e-a018-a09c0f292de0",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/sasza/.local/share/virtualenvs/JupyterLab-9JRWupKp/lib/python3.12/site-packages/keras/src/layers/core/embedding.py:97: UserWarning: Argument `input_length` is deprecated. Just remove it.\n",
-      "  warnings.warn(\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "model = Sequential()\n",
     "model.add(Embedding(vocab_size, embedding_dim, input_length=sequence_length))\n",
@@ -2783,9 +2778,129 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "id": "f976e119-8888-4316-a1b8-2352f74c42e8",
    "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_2\"</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1mModel: \"sequential_2\"\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ embedding_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Embedding</span>)         │ ?                      │   <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (unbuilt) │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ simple_rnn_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SimpleRNN</span>)        │ ?                      │   <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (unbuilt) │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ ?                      │   <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (unbuilt) │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ embedding_1 (\u001b[38;5;33mEmbedding\u001b[0m)         │ ?                      │   \u001b[38;5;34m0\u001b[0m (unbuilt) │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ simple_rnn_1 (\u001b[38;5;33mSimpleRNN\u001b[0m)        │ ?                      │   \u001b[38;5;34m0\u001b[0m (unbuilt) │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_2 (\u001b[38;5;33mDense\u001b[0m)                 │ ?                      │   \u001b[38;5;34m0\u001b[0m (unbuilt) │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6b024476-a946-4672-b1e9-8bb0de26c8e8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import tensorflow as tf\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from tensorflow.keras.preprocessing.text import Tokenizer\n",
+    "from tensorflow.keras.utils import to_categorical\n",
+    "from tensorflow.keras.preprocessing.sequence import pad_sequences\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "862e9746-1f60-4036-b007-13240b285e2f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "text = \"\"\"\n",
+    "Ala ma kota. Kot ma na imię Mruczek. Mruczek lubi mleko. Ala daje mu mleko każdego dnia.\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "76e6afde-0194-4714-8b77-8a1876c5d128",
+   "metadata": {},
    "outputs": [],
    "source": []
   }