diff --git a/Action Detection Refined.ipynb b/Action Detection Refined.ipynb
index 4c196e11c..e40bb921f 100644
--- a/Action Detection Refined.ipynb	
+++ b/Action Detection Refined.ipynb	
@@ -9,16 +9,84 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Defaulting to user installation because normal site-packages is not writeable\n",
+      "Requirement already satisfied: tensorflow==2.12.0 in c:\\users\\ashok\\appdata\\roaming\\python\\python311\\site-packages (2.12.0)\n",
+      "Requirement already satisfied: opencv-python in c:\\programdata\\anaconda3\\lib\\site-packages (4.8.1.78)\n",
+      "Requirement already satisfied: mediapipe in c:\\programdata\\anaconda3\\lib\\site-packages (0.10.7)\n",
+      "Requirement already satisfied: sklearn in c:\\users\\ashok\\appdata\\roaming\\python\\python311\\site-packages (0.0.post11)\n",
+      "Requirement already satisfied: matplotlib in c:\\programdata\\anaconda3\\lib\\site-packages (3.7.2)\n",
+      "Requirement already satisfied: tensorflow-intel==2.12.0 in c:\\users\\ashok\\appdata\\roaming\\python\\python311\\site-packages (from tensorflow==2.12.0) (2.12.0)\n",
+      "Requirement already satisfied: absl-py>=1.0.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.12.0->tensorflow==2.12.0) (2.0.0)\n",
+      "Requirement already satisfied: astunparse>=1.6.0 in c:\\users\\ashok\\appdata\\roaming\\python\\python311\\site-packages (from tensorflow-intel==2.12.0->tensorflow==2.12.0) (1.6.3)\n",
+      "Requirement already satisfied: flatbuffers>=2.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.12.0->tensorflow==2.12.0) (23.5.26)\n",
+      "Requirement already satisfied: gast<=0.4.0,>=0.2.1 in c:\\users\\ashok\\appdata\\roaming\\python\\python311\\site-packages (from tensorflow-intel==2.12.0->tensorflow==2.12.0) (0.4.0)\n",
+      "Requirement already satisfied: google-pasta>=0.1.1 in c:\\users\\ashok\\appdata\\roaming\\python\\python311\\site-packages (from tensorflow-intel==2.12.0->tensorflow==2.12.0) (0.2.0)\n",
+      "Requirement already satisfied: h5py>=2.9.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.12.0->tensorflow==2.12.0) (3.9.0)\n",
+      "Requirement already satisfied: jax>=0.3.15 in c:\\users\\ashok\\appdata\\roaming\\python\\python311\\site-packages (from tensorflow-intel==2.12.0->tensorflow==2.12.0) (0.4.20)\n",
+      "Requirement already satisfied: libclang>=13.0.0 in c:\\users\\ashok\\appdata\\roaming\\python\\python311\\site-packages (from tensorflow-intel==2.12.0->tensorflow==2.12.0) (16.0.6)\n",
+      "Requirement already satisfied: numpy<1.24,>=1.22 in c:\\users\\ashok\\appdata\\roaming\\python\\python311\\site-packages (from tensorflow-intel==2.12.0->tensorflow==2.12.0) (1.23.5)\n",
+      "Requirement already satisfied: opt-einsum>=2.3.2 in c:\\users\\ashok\\appdata\\roaming\\python\\python311\\site-packages (from tensorflow-intel==2.12.0->tensorflow==2.12.0) (3.3.0)\n",
+      "Requirement already satisfied: packaging in c:\\programdata\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.12.0->tensorflow==2.12.0) (23.1)\n",
+      "Requirement already satisfied: protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3 in c:\\programdata\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.12.0->tensorflow==2.12.0) (3.20.3)\n",
+      "Requirement already satisfied: setuptools in c:\\programdata\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.12.0->tensorflow==2.12.0) (68.0.0)\n",
+      "Requirement already satisfied: six>=1.12.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.12.0->tensorflow==2.12.0) (1.16.0)\n",
+      "Requirement already satisfied: termcolor>=1.1.0 in c:\\users\\ashok\\appdata\\roaming\\python\\python311\\site-packages (from tensorflow-intel==2.12.0->tensorflow==2.12.0) (2.3.0)\n",
+      "Requirement already satisfied: typing-extensions>=3.6.6 in c:\\programdata\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.12.0->tensorflow==2.12.0) (4.7.1)\n",
+      "Requirement already satisfied: wrapt<1.15,>=1.11.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.12.0->tensorflow==2.12.0) (1.14.1)\n",
+      "Requirement already satisfied: grpcio<2.0,>=1.24.3 in c:\\users\\ashok\\appdata\\roaming\\python\\python311\\site-packages (from tensorflow-intel==2.12.0->tensorflow==2.12.0) (1.59.2)\n",
+      "Requirement already satisfied: tensorboard<2.13,>=2.12 in c:\\users\\ashok\\appdata\\roaming\\python\\python311\\site-packages (from tensorflow-intel==2.12.0->tensorflow==2.12.0) (2.12.3)\n",
+      "Requirement already satisfied: tensorflow-estimator<2.13,>=2.12.0 in c:\\users\\ashok\\appdata\\roaming\\python\\python311\\site-packages (from tensorflow-intel==2.12.0->tensorflow==2.12.0) (2.12.0)\n",
+      "Requirement already satisfied: keras<2.13,>=2.12.0 in c:\\users\\ashok\\appdata\\roaming\\python\\python311\\site-packages (from tensorflow-intel==2.12.0->tensorflow==2.12.0) (2.12.0)\n",
+      "Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in c:\\users\\ashok\\appdata\\roaming\\python\\python311\\site-packages (from tensorflow-intel==2.12.0->tensorflow==2.12.0) (0.31.0)\n",
+      "Requirement already satisfied: attrs>=19.1.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from mediapipe) (22.1.0)\n",
+      "Requirement already satisfied: opencv-contrib-python in c:\\programdata\\anaconda3\\lib\\site-packages (from mediapipe) (4.8.1.78)\n",
+      "Requirement already satisfied: sounddevice>=0.4.4 in c:\\programdata\\anaconda3\\lib\\site-packages (from mediapipe) (0.4.6)\n",
+      "Requirement already satisfied: contourpy>=1.0.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from matplotlib) (1.0.5)\n",
+      "Requirement already satisfied: cycler>=0.10 in c:\\programdata\\anaconda3\\lib\\site-packages (from matplotlib) (0.11.0)\n",
+      "Requirement already satisfied: fonttools>=4.22.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from matplotlib) (4.25.0)\n",
+      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from matplotlib) (1.4.4)\n",
+      "Requirement already satisfied: pillow>=6.2.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from matplotlib) (9.4.0)\n",
+      "Requirement already satisfied: pyparsing<3.1,>=2.3.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from matplotlib) (3.0.9)\n",
+      "Requirement already satisfied: python-dateutil>=2.7 in c:\\programdata\\anaconda3\\lib\\site-packages (from matplotlib) (2.8.2)\n",
+      "Requirement already satisfied: CFFI>=1.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from sounddevice>=0.4.4->mediapipe) (1.15.1)\n",
+      "Requirement already satisfied: wheel<1.0,>=0.23.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from astunparse>=1.6.0->tensorflow-intel==2.12.0->tensorflow==2.12.0) (0.38.4)\n",
+      "Requirement already satisfied: pycparser in c:\\programdata\\anaconda3\\lib\\site-packages (from CFFI>=1.0->sounddevice>=0.4.4->mediapipe) (2.21)\n",
+      "Requirement already satisfied: ml-dtypes>=0.2.0 in c:\\users\\ashok\\appdata\\roaming\\python\\python311\\site-packages (from jax>=0.3.15->tensorflow-intel==2.12.0->tensorflow==2.12.0) (0.3.1)\n",
+      "Requirement already satisfied: scipy>=1.9 in c:\\programdata\\anaconda3\\lib\\site-packages (from jax>=0.3.15->tensorflow-intel==2.12.0->tensorflow==2.12.0) (1.11.1)\n",
+      "Requirement already satisfied: google-auth<3,>=1.6.3 in c:\\users\\ashok\\appdata\\roaming\\python\\python311\\site-packages (from tensorboard<2.13,>=2.12->tensorflow-intel==2.12.0->tensorflow==2.12.0) (2.23.4)\n",
+      "Requirement already satisfied: google-auth-oauthlib<1.1,>=0.5 in c:\\users\\ashok\\appdata\\roaming\\python\\python311\\site-packages (from tensorboard<2.13,>=2.12->tensorflow-intel==2.12.0->tensorflow==2.12.0) (1.0.0)\n",
+      "Requirement already satisfied: markdown>=2.6.8 in c:\\programdata\\anaconda3\\lib\\site-packages (from tensorboard<2.13,>=2.12->tensorflow-intel==2.12.0->tensorflow==2.12.0) (3.4.1)\n",
+      "Requirement already satisfied: requests<3,>=2.21.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from tensorboard<2.13,>=2.12->tensorflow-intel==2.12.0->tensorflow==2.12.0) (2.31.0)\n",
+      "Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in c:\\users\\ashok\\appdata\\roaming\\python\\python311\\site-packages (from tensorboard<2.13,>=2.12->tensorflow-intel==2.12.0->tensorflow==2.12.0) (0.7.2)\n",
+      "Requirement already satisfied: werkzeug>=1.0.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from tensorboard<2.13,>=2.12->tensorflow-intel==2.12.0->tensorflow==2.12.0) (2.2.3)\n",
+      "Requirement already satisfied: cachetools<6.0,>=2.0.0 in c:\\users\\ashok\\appdata\\roaming\\python\\python311\\site-packages (from google-auth<3,>=1.6.3->tensorboard<2.13,>=2.12->tensorflow-intel==2.12.0->tensorflow==2.12.0) (5.3.2)\n",
+      "Requirement already satisfied: pyasn1-modules>=0.2.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from google-auth<3,>=1.6.3->tensorboard<2.13,>=2.12->tensorflow-intel==2.12.0->tensorflow==2.12.0) (0.2.8)\n",
+      "Requirement already satisfied: rsa<5,>=3.1.4 in c:\\users\\ashok\\appdata\\roaming\\python\\python311\\site-packages (from google-auth<3,>=1.6.3->tensorboard<2.13,>=2.12->tensorflow-intel==2.12.0->tensorflow==2.12.0) (4.9)\n",
+      "Requirement already satisfied: requests-oauthlib>=0.7.0 in c:\\users\\ashok\\appdata\\roaming\\python\\python311\\site-packages (from google-auth-oauthlib<1.1,>=0.5->tensorboard<2.13,>=2.12->tensorflow-intel==2.12.0->tensorflow==2.12.0) (1.3.1)\n",
+      "Requirement already satisfied: charset-normalizer<4,>=2 in c:\\programdata\\anaconda3\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard<2.13,>=2.12->tensorflow-intel==2.12.0->tensorflow==2.12.0) (2.0.4)\n",
+      "Requirement already satisfied: idna<4,>=2.5 in c:\\programdata\\anaconda3\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard<2.13,>=2.12->tensorflow-intel==2.12.0->tensorflow==2.12.0) (3.4)\n",
+      "Requirement already satisfied: urllib3<3,>=1.21.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard<2.13,>=2.12->tensorflow-intel==2.12.0->tensorflow==2.12.0) (1.26.16)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in c:\\programdata\\anaconda3\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard<2.13,>=2.12->tensorflow-intel==2.12.0->tensorflow==2.12.0) (2023.7.22)\n",
+      "Requirement already satisfied: MarkupSafe>=2.1.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from werkzeug>=1.0.1->tensorboard<2.13,>=2.12->tensorflow-intel==2.12.0->tensorflow==2.12.0) (2.1.1)\n",
+      "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in c:\\programdata\\anaconda3\\lib\\site-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.13,>=2.12->tensorflow-intel==2.12.0->tensorflow==2.12.0) (0.4.8)\n",
+      "Requirement already satisfied: oauthlib>=3.0.0 in c:\\users\\ashok\\appdata\\roaming\\python\\python311\\site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<1.1,>=0.5->tensorboard<2.13,>=2.12->tensorflow-intel==2.12.0->tensorflow==2.12.0) (3.2.2)\n"
+     ]
+    }
+   ],
    "source": [
-    "!pip install tensorflow==2.4.1 tensorflow-gpu==2.4.1 opencv-python mediapipe sklearn matplotlib"
+    "!pip install tensorflow==2.12.0 opencv-python mediapipe sklearn matplotlib"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -39,7 +107,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -49,7 +117,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -64,12 +132,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
     "def draw_landmarks(image, results):\n",
-    "    mp_drawing.draw_landmarks(image, results.face_landmarks, mp_holistic.FACE_CONNECTIONS) # Draw face connections\n",
+    "    mp_drawing.draw_landmarks(image, results.face_landmarks, mp_holistic.FACEMESH_CONTOURS) # Draw face connections\n",
     "    mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_holistic.POSE_CONNECTIONS) # Draw pose connections\n",
     "    mp_drawing.draw_landmarks(image, results.left_hand_landmarks, mp_holistic.HAND_CONNECTIONS) # Draw left hand connections\n",
     "    mp_drawing.draw_landmarks(image, results.right_hand_landmarks, mp_holistic.HAND_CONNECTIONS) # Draw right hand connections"
@@ -77,13 +145,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
     "def draw_styled_landmarks(image, results):\n",
     "    # Draw face connections\n",
-    "    mp_drawing.draw_landmarks(image, results.face_landmarks, mp_holistic.FACE_CONNECTIONS, \n",
+    "    mp_drawing.draw_landmarks(image, results.face_landmarks, mp_holistic.FACEMESH_CONTOURS, \n",
     "                             mp_drawing.DrawingSpec(color=(80,110,10), thickness=1, circle_radius=1), \n",
     "                             mp_drawing.DrawingSpec(color=(80,256,121), thickness=1, circle_radius=1)\n",
     "                             ) \n",
@@ -104,78 +172,6 @@
     "                             ) "
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cap = cv2.VideoCapture(0)\n",
-    "# Set mediapipe model \n",
-    "with mp_holistic.Holistic(min_detection_confidence=0.5, min_tracking_confidence=0.5) as holistic:\n",
-    "    while cap.isOpened():\n",
-    "\n",
-    "        # Read feed\n",
-    "        ret, frame = cap.read()\n",
-    "\n",
-    "        # Make detections\n",
-    "        image, results = mediapipe_detection(frame, holistic)\n",
-    "        print(results)\n",
-    "        \n",
-    "        # Draw landmarks\n",
-    "        draw_styled_landmarks(image, results)\n",
-    "\n",
-    "        # Show to screen\n",
-    "        cv2.imshow('OpenCV Feed', image)\n",
-    "\n",
-    "        # Break gracefully\n",
-    "        if cv2.waitKey(10) & 0xFF == ord('q'):\n",
-    "            break\n",
-    "    cap.release()\n",
-    "    cv2.destroyAllWindows()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "draw_landmarks(frame, results)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x1d8945c0cf8>"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.imshow(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -185,76 +181,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 114,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "21"
-      ]
-     },
-     "execution_count": 114,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(results.left_hand_landmarks.landmark)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pose = []\n",
-    "for res in results.pose_landmarks.landmark:\n",
-    "    test = np.array([res.x, res.y, res.z, res.visibility])\n",
-    "    pose.append(test)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 105,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pose = np.array([[res.x, res.y, res.z, res.visibility] for res in results.pose_landmarks.landmark]).flatten() if results.pose_landmarks else np.zeros(132)\n",
-    "face = np.array([[res.x, res.y, res.z] for res in results.face_landmarks.landmark]).flatten() if results.face_landmarks else np.zeros(1404)\n",
-    "lh = np.array([[res.x, res.y, res.z] for res in results.left_hand_landmarks.landmark]).flatten() if results.left_hand_landmarks else np.zeros(21*3)\n",
-    "rh = np.array([[res.x, res.y, res.z] for res in results.right_hand_landmarks.landmark]).flatten() if results.right_hand_landmarks else np.zeros(21*3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 109,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
-       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
-       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
-       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])"
-      ]
-     },
-     "execution_count": 109,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "face = np.array([[res.x, res.y, res.z] for res in results.face_landmarks.landmark]).flatten() \n",
-    "    if results.face_landmarks \n",
-    "    else np.zeros(1404)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -266,66 +193,6 @@
     "    return np.concatenate([pose, face, lh, rh])"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 122,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "result_test = extract_keypoints(results)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 123,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 0.3835876 ,  0.47759178, -0.77978629, ...,  0.        ,\n",
-       "        0.        ,  0.        ])"
-      ]
-     },
-     "execution_count": 123,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "result_test"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 124,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "np.save('0', result_test)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 125,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 0.3835876 ,  0.47759178, -0.77978629, ...,  0.        ,\n",
-       "        0.        ,  0.        ])"
-      ]
-     },
-     "execution_count": 125,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.load('0.npy')"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -335,7 +202,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -343,7 +210,7 @@
     "DATA_PATH = os.path.join('MP_Data') \n",
     "\n",
     "# Actions that we try to detect\n",
-    "actions = np.array(['hello', 'thanks', 'iloveyou'])\n",
+    "actions = np.array(['pose1', 'pose2', 'pose3'])\n",
     "\n",
     "# Thirty videos worth of data\n",
     "no_sequences = 30\n",
@@ -352,20 +219,22 @@
     "sequence_length = 30\n",
     "\n",
     "# Folder start\n",
-    "start_folder = 30"
+    "start_folder = 0"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
     "for action in actions: \n",
-    "    dirmax = np.max(np.array(os.listdir(os.path.join(DATA_PATH, action))).astype(int))\n",
-    "    for sequence in range(1,no_sequences+1):\n",
+    "    #dirmax = np.max(np.array(os.listdir(os.path.join(DATA_PATH, action))).astype(int))\n",
+    "    #for sequence in range(1,no_sequences+1):\n",
+    "     for sequence in range(no_sequences):\n",
     "        try: \n",
-    "            os.makedirs(os.path.join(DATA_PATH, action, str(dirmax+sequence)))\n",
+    "            #os.makedirs(os.path.join(DATA_PATH, action, str(dirmax+sequence)))\n",
+    "            os.makedirs(os.path.join(DATA_PATH, action, str(sequence)))\n",
     "        except:\n",
     "            pass"
    ]
@@ -379,7 +248,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -434,7 +303,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 158,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -451,7 +320,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -461,7 +330,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -470,16 +339,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'hello': 0, 'thanks': 1, 'iloveyou': 2}"
+       "{'pose1': 0, 'pose2': 1, 'pose3': 2}"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -490,7 +359,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -507,16 +376,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(180, 30, 1662)"
+       "(90, 30, 1662)"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -527,16 +396,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(180,)"
+       "(90,)"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -547,7 +416,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -556,16 +425,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(180, 30, 1662)"
+       "(90, 30, 1662)"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -576,7 +445,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -585,7 +454,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -594,16 +463,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(9, 3)"
+       "(5, 3)"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -621,7 +490,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -632,7 +501,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -642,7 +511,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -657,7 +526,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -666,18 +535,4155 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "metadata": {
     "scrolled": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/2000\n",
+      "3/3 [==============================] - 4s 51ms/step - loss: 5.5248 - categorical_accuracy: 0.3647\n",
+      "Epoch 2/2000\n",
+      "3/3 [==============================] - 0s 51ms/step - loss: 22.8344 - categorical_accuracy: 0.3529\n",
+      "Epoch 3/2000\n",
+      "3/3 [==============================] - 0s 50ms/step - loss: 66.4271 - categorical_accuracy: 0.3882\n",
+      "Epoch 4/2000\n",
+      "3/3 [==============================] - 0s 50ms/step - loss: 75.1637 - categorical_accuracy: 0.2824\n",
+      "Epoch 5/2000\n",
+      "3/3 [==============================] - 0s 50ms/step - loss: 156.2271 - categorical_accuracy: 0.3412\n",
+      "Epoch 6/2000\n",
+      "3/3 [==============================] - 0s 50ms/step - loss: 11.3849 - categorical_accuracy: 0.3882\n",
+      "Epoch 7/2000\n",
+      "3/3 [==============================] - 0s 51ms/step - loss: 19.9161 - categorical_accuracy: 0.3412\n",
+      "Epoch 8/2000\n",
+      "3/3 [==============================] - 0s 50ms/step - loss: 3.8393 - categorical_accuracy: 0.3765\n",
+      "Epoch 9/2000\n",
+      "3/3 [==============================] - 0s 51ms/step - loss: 5.8597 - categorical_accuracy: 0.3294\n",
+      "Epoch 10/2000\n",
+      "3/3 [==============================] - 0s 51ms/step - loss: 7.3510 - categorical_accuracy: 0.3176\n",
+      "Epoch 11/2000\n",
+      "3/3 [==============================] - 0s 51ms/step - loss: 7.9936 - categorical_accuracy: 0.4000\n",
+      "Epoch 12/2000\n",
+      "3/3 [==============================] - 0s 51ms/step - loss: 5.8632 - categorical_accuracy: 0.3647\n",
+      "Epoch 13/2000\n",
+      "3/3 [==============================] - 0s 51ms/step - loss: 3.9014 - categorical_accuracy: 0.4000\n",
+      "Epoch 14/2000\n",
+      "3/3 [==============================] - 0s 49ms/step - loss: 3.1816 - categorical_accuracy: 0.3059\n",
+      "Epoch 15/2000\n",
+      "3/3 [==============================] - 0s 52ms/step - loss: 3.3424 - categorical_accuracy: 0.3529\n",
+      "Epoch 16/2000\n",
+      "3/3 [==============================] - 0s 50ms/step - loss: 2.2202 - categorical_accuracy: 0.3529\n",
+      "Epoch 17/2000\n",
+      "3/3 [==============================] - 0s 49ms/step - loss: 1.8599 - categorical_accuracy: 0.4000\n",
+      "Epoch 18/2000\n",
+      "3/3 [==============================] - 0s 49ms/step - loss: 1.2179 - categorical_accuracy: 0.4118\n",
+      "Epoch 19/2000\n",
+      "3/3 [==============================] - 0s 50ms/step - loss: 1.1319 - categorical_accuracy: 0.3765\n",
+      "Epoch 20/2000\n",
+      "3/3 [==============================] - 0s 51ms/step - loss: 1.1457 - categorical_accuracy: 0.4000\n",
+      "Epoch 21/2000\n",
+      "3/3 [==============================] - 0s 51ms/step - loss: 1.0182 - categorical_accuracy: 0.4471\n",
+      "Epoch 22/2000\n",
+      "3/3 [==============================] - 0s 51ms/step - loss: 0.9320 - categorical_accuracy: 0.5529\n",
+      "Epoch 23/2000\n",
+      "3/3 [==============================] - 0s 51ms/step - loss: 0.8045 - categorical_accuracy: 0.6941\n",
+      "Epoch 24/2000\n",
+      "3/3 [==============================] - 0s 53ms/step - loss: 0.8912 - categorical_accuracy: 0.5765\n",
+      "Epoch 25/2000\n",
+      "3/3 [==============================] - 0s 51ms/step - loss: 0.7671 - categorical_accuracy: 0.6471\n",
+      "Epoch 26/2000\n",
+      "3/3 [==============================] - 0s 51ms/step - loss: 0.7400 - categorical_accuracy: 0.6941\n",
+      "Epoch 27/2000\n",
+      "3/3 [==============================] - 0s 50ms/step - loss: 0.6803 - categorical_accuracy: 0.6353\n",
+      "Epoch 28/2000\n",
+      "3/3 [==============================] - 0s 52ms/step - loss: 0.6376 - categorical_accuracy: 0.7294\n",
+      "Epoch 29/2000\n",
+      "3/3 [==============================] - 0s 51ms/step - loss: 0.6672 - categorical_accuracy: 0.6824\n",
+      "Epoch 30/2000\n",
+      "3/3 [==============================] - 0s 50ms/step - loss: 0.8194 - categorical_accuracy: 0.5647\n",
+      "Epoch 31/2000\n",
+      "3/3 [==============================] - 0s 52ms/step - loss: 0.8004 - categorical_accuracy: 0.6824\n",
+      "Epoch 32/2000\n",
+      "3/3 [==============================] - 0s 51ms/step - loss: 0.7687 - categorical_accuracy: 0.6706\n",
+      "Epoch 33/2000\n",
+      "3/3 [==============================] - 0s 52ms/step - loss: 0.6546 - categorical_accuracy: 0.6941\n",
+      "Epoch 34/2000\n",
+      "3/3 [==============================] - 0s 52ms/step - loss: 0.6145 - categorical_accuracy: 0.7294\n",
+      "Epoch 35/2000\n",
+      "3/3 [==============================] - 0s 53ms/step - loss: 0.5914 - categorical_accuracy: 0.7529\n",
+      "Epoch 36/2000\n",
+      "3/3 [==============================] - 0s 53ms/step - loss: 0.5684 - categorical_accuracy: 0.6941\n",
+      "Epoch 37/2000\n",
+      "3/3 [==============================] - 0s 55ms/step - loss: 0.5888 - categorical_accuracy: 0.7059\n",
+      "Epoch 38/2000\n",
+      "3/3 [==============================] - 0s 53ms/step - loss: 0.5593 - categorical_accuracy: 0.7059\n",
+      "Epoch 39/2000\n",
+      "3/3 [==============================] - 0s 52ms/step - loss: 0.6020 - categorical_accuracy: 0.7059\n",
+      "Epoch 40/2000\n",
+      "3/3 [==============================] - 0s 53ms/step - loss: 0.5395 - categorical_accuracy: 0.7647\n",
+      "Epoch 41/2000\n",
+      "3/3 [==============================] - 0s 56ms/step - loss: 0.5202 - categorical_accuracy: 0.7882\n",
+      "Epoch 42/2000\n",
+      "3/3 [==============================] - 0s 54ms/step - loss: 0.5022 - categorical_accuracy: 0.7647\n",
+      "Epoch 43/2000\n",
+      "3/3 [==============================] - 0s 52ms/step - loss: 0.4654 - categorical_accuracy: 0.8118\n",
+      "Epoch 44/2000\n",
+      "3/3 [==============================] - 0s 51ms/step - loss: 0.4691 - categorical_accuracy: 0.7765\n",
+      "Epoch 45/2000\n",
+      "3/3 [==============================] - 0s 53ms/step - loss: 0.4524 - categorical_accuracy: 0.8353\n",
+      "Epoch 46/2000\n",
+      "3/3 [==============================] - 0s 52ms/step - loss: 0.4520 - categorical_accuracy: 0.8000\n",
+      "Epoch 47/2000\n",
+      "3/3 [==============================] - 0s 53ms/step - loss: 0.4796 - categorical_accuracy: 0.7882\n",
+      "Epoch 48/2000\n",
+      "3/3 [==============================] - 0s 55ms/step - loss: 0.4199 - categorical_accuracy: 0.8353\n",
+      "Epoch 49/2000\n",
+      "3/3 [==============================] - 0s 54ms/step - loss: 0.4564 - categorical_accuracy: 0.7765\n",
+      "Epoch 50/2000\n",
+      "3/3 [==============================] - 0s 52ms/step - loss: 0.4789 - categorical_accuracy: 0.7882\n",
+      "Epoch 51/2000\n",
+      "3/3 [==============================] - 0s 53ms/step - loss: 0.4824 - categorical_accuracy: 0.7882\n",
+      "Epoch 52/2000\n",
+      "3/3 [==============================] - 0s 52ms/step - loss: 0.4729 - categorical_accuracy: 0.7412\n",
+      "Epoch 53/2000\n",
+      "3/3 [==============================] - 0s 52ms/step - loss: 0.5107 - categorical_accuracy: 0.7529\n",
+      "Epoch 54/2000\n",
+      "3/3 [==============================] - 0s 54ms/step - loss: 0.4933 - categorical_accuracy: 0.8118\n",
+      "Epoch 55/2000\n",
+      "3/3 [==============================] - 0s 57ms/step - loss: 0.5300 - categorical_accuracy: 0.7412\n",
+      "Epoch 56/2000\n",
+      "3/3 [==============================] - 0s 55ms/step - loss: 0.5891 - categorical_accuracy: 0.6941\n",
+      "Epoch 57/2000\n",
+      "3/3 [==============================] - 0s 52ms/step - loss: 0.6896 - categorical_accuracy: 0.7529\n",
+      "Epoch 58/2000\n",
+      "3/3 [==============================] - 0s 52ms/step - loss: 0.5846 - categorical_accuracy: 0.6824\n",
+      "Epoch 59/2000\n",
+      "3/3 [==============================] - 0s 52ms/step - loss: 0.5140 - categorical_accuracy: 0.7882\n",
+      "Epoch 60/2000\n",
+      "3/3 [==============================] - 0s 53ms/step - loss: 0.5700 - categorical_accuracy: 0.8000\n",
+      "Epoch 61/2000\n",
+      "3/3 [==============================] - 0s 53ms/step - loss: 0.5532 - categorical_accuracy: 0.7882\n",
+      "Epoch 62/2000\n",
+      "3/3 [==============================] - 0s 53ms/step - loss: 0.6016 - categorical_accuracy: 0.7294\n",
+      "Epoch 63/2000\n",
+      "3/3 [==============================] - 0s 54ms/step - loss: 0.5485 - categorical_accuracy: 0.7882\n",
+      "Epoch 64/2000\n",
+      "3/3 [==============================] - 0s 52ms/step - loss: 0.5145 - categorical_accuracy: 0.7647\n",
+      "Epoch 65/2000\n",
+      "3/3 [==============================] - 0s 52ms/step - loss: 0.5374 - categorical_accuracy: 0.7529\n",
+      "Epoch 66/2000\n",
+      "3/3 [==============================] - 0s 52ms/step - loss: 0.5741 - categorical_accuracy: 0.7059\n",
+      "Epoch 67/2000\n",
+      "3/3 [==============================] - 0s 52ms/step - loss: 0.4756 - categorical_accuracy: 0.7647\n",
+      "Epoch 68/2000\n",
+      "3/3 [==============================] - 0s 52ms/step - loss: 0.5376 - categorical_accuracy: 0.7882\n",
+      "Epoch 69/2000\n",
+      "3/3 [==============================] - 0s 54ms/step - loss: 0.5512 - categorical_accuracy: 0.6471\n",
+      "Epoch 70/2000\n",
+      "3/3 [==============================] - 0s 53ms/step - loss: 0.4764 - categorical_accuracy: 0.7765\n",
+      "Epoch 71/2000\n",
+      "3/3 [==============================] - 0s 54ms/step - loss: 0.4907 - categorical_accuracy: 0.7647\n",
+      "Epoch 72/2000\n",
+      "3/3 [==============================] - 0s 53ms/step - loss: 0.4564 - categorical_accuracy: 0.7529\n",
+      "Epoch 73/2000\n",
+      "3/3 [==============================] - 0s 55ms/step - loss: 0.5202 - categorical_accuracy: 0.8000\n",
+      "Epoch 74/2000\n",
+      "3/3 [==============================] - 0s 55ms/step - loss: 0.4829 - categorical_accuracy: 0.7765\n",
+      "Epoch 75/2000\n",
+      "3/3 [==============================] - 0s 53ms/step - loss: 0.6076 - categorical_accuracy: 0.6471\n",
+      "Epoch 76/2000\n",
+      "3/3 [==============================] - 0s 53ms/step - loss: 0.8656 - categorical_accuracy: 0.7529\n",
+      "Epoch 77/2000\n",
+      "3/3 [==============================] - 0s 53ms/step - loss: 0.5959 - categorical_accuracy: 0.7412\n",
+      "Epoch 78/2000\n",
+      "3/3 [==============================] - 0s 54ms/step - loss: 0.5372 - categorical_accuracy: 0.7529\n",
+      "Epoch 79/2000\n",
+      "3/3 [==============================] - 0s 56ms/step - loss: 0.5479 - categorical_accuracy: 0.7647\n",
+      "Epoch 80/2000\n",
+      "3/3 [==============================] - 0s 54ms/step - loss: 0.4909 - categorical_accuracy: 0.7765\n",
+      "Epoch 81/2000\n",
+      "3/3 [==============================] - 0s 55ms/step - loss: 0.5573 - categorical_accuracy: 0.7529\n",
+      "Epoch 82/2000\n",
+      "3/3 [==============================] - 0s 53ms/step - loss: 0.6243 - categorical_accuracy: 0.6118\n",
+      "Epoch 83/2000\n",
+      "3/3 [==============================] - 0s 53ms/step - loss: 0.4606 - categorical_accuracy: 0.8353\n",
+      "Epoch 84/2000\n",
+      "3/3 [==============================] - 0s 54ms/step - loss: 0.5362 - categorical_accuracy: 0.8000\n",
+      "Epoch 85/2000\n",
+      "3/3 [==============================] - 0s 55ms/step - loss: 0.4938 - categorical_accuracy: 0.7765\n",
+      "Epoch 86/2000\n",
+      "3/3 [==============================] - 0s 56ms/step - loss: 0.5146 - categorical_accuracy: 0.7765\n",
+      "Epoch 87/2000\n",
+      "3/3 [==============================] - 0s 53ms/step - loss: 0.5827 - categorical_accuracy: 0.8000\n",
+      "Epoch 88/2000\n",
+      "3/3 [==============================] - 0s 54ms/step - loss: 0.4503 - categorical_accuracy: 0.8235\n",
+      "Epoch 89/2000\n",
+      "3/3 [==============================] - 0s 53ms/step - loss: 0.5121 - categorical_accuracy: 0.7294\n",
+      "Epoch 90/2000\n",
+      "3/3 [==============================] - 0s 53ms/step - loss: 0.5331 - categorical_accuracy: 0.7529\n",
+      "Epoch 91/2000\n",
+      "3/3 [==============================] - 0s 53ms/step - loss: 0.5524 - categorical_accuracy: 0.6471\n",
+      "Epoch 92/2000\n",
+      "3/3 [==============================] - 0s 55ms/step - loss: 0.4773 - categorical_accuracy: 0.7529\n",
+      "Epoch 93/2000\n",
+      "3/3 [==============================] - 0s 55ms/step - loss: 0.4545 - categorical_accuracy: 0.7765\n",
+      "Epoch 94/2000\n",
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+      "Epoch 95/2000\n",
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+      "Epoch 96/2000\n",
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+      "Epoch 97/2000\n",
+      "3/3 [==============================] - 0s 54ms/step - loss: 0.4585 - categorical_accuracy: 0.7882\n",
+      "Epoch 98/2000\n",
+      "3/3 [==============================] - 0s 53ms/step - loss: 0.4681 - categorical_accuracy: 0.7882\n",
+      "Epoch 99/2000\n",
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+      "Epoch 100/2000\n",
+      "3/3 [==============================] - 0s 54ms/step - loss: 0.4561 - categorical_accuracy: 0.8118\n",
+      "Epoch 101/2000\n",
+      "3/3 [==============================] - 0s 54ms/step - loss: 0.4893 - categorical_accuracy: 0.8000\n",
+      "Epoch 102/2000\n",
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+      "Epoch 105/2000\n",
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+      "Epoch 106/2000\n",
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+      "Epoch 107/2000\n",
+      "3/3 [==============================] - 0s 55ms/step - loss: 0.4420 - categorical_accuracy: 0.8235\n",
+      "Epoch 108/2000\n",
+      "3/3 [==============================] - 0s 54ms/step - loss: 0.4885 - categorical_accuracy: 0.8000\n",
+      "Epoch 109/2000\n",
+      "3/3 [==============================] - 0s 55ms/step - loss: 0.3965 - categorical_accuracy: 0.8353\n",
+      "Epoch 110/2000\n",
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+      "Epoch 111/2000\n",
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+      "Epoch 112/2000\n",
+      "3/3 [==============================] - 0s 55ms/step - loss: 0.5483 - categorical_accuracy: 0.7412\n",
+      "Epoch 113/2000\n",
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+      "Epoch 114/2000\n",
+      "3/3 [==============================] - 0s 54ms/step - loss: 0.5368 - categorical_accuracy: 0.6588\n",
+      "Epoch 115/2000\n",
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+      "Epoch 116/2000\n",
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+      "Epoch 117/2000\n",
+      "3/3 [==============================] - 0s 55ms/step - loss: 0.5619 - categorical_accuracy: 0.7176\n",
+      "Epoch 118/2000\n",
+      "3/3 [==============================] - 0s 54ms/step - loss: 0.5180 - categorical_accuracy: 0.8118\n",
+      "Epoch 119/2000\n",
+      "3/3 [==============================] - 0s 55ms/step - loss: 0.4951 - categorical_accuracy: 0.7765\n",
+      "Epoch 120/2000\n",
+      "3/3 [==============================] - 0s 56ms/step - loss: 0.5671 - categorical_accuracy: 0.7647\n",
+      "Epoch 121/2000\n",
+      "3/3 [==============================] - 0s 55ms/step - loss: 0.5256 - categorical_accuracy: 0.7412\n",
+      "Epoch 122/2000\n",
+      "3/3 [==============================] - 0s 55ms/step - loss: 0.5363 - categorical_accuracy: 0.6471\n",
+      "Epoch 123/2000\n",
+      "3/3 [==============================] - 0s 54ms/step - loss: 0.4060 - categorical_accuracy: 0.8353\n",
+      "Epoch 124/2000\n",
+      "3/3 [==============================] - 0s 54ms/step - loss: 0.4715 - categorical_accuracy: 0.8000\n",
+      "Epoch 125/2000\n",
+      "3/3 [==============================] - 0s 55ms/step - loss: 0.3984 - categorical_accuracy: 0.8235\n",
+      "Epoch 126/2000\n",
+      "3/3 [==============================] - 0s 54ms/step - loss: 0.4318 - categorical_accuracy: 0.8118\n",
+      "Epoch 127/2000\n",
+      "3/3 [==============================] - 0s 54ms/step - loss: 0.3788 - categorical_accuracy: 0.8941\n",
+      "Epoch 128/2000\n",
+      "3/3 [==============================] - 0s 55ms/step - loss: 0.4653 - categorical_accuracy: 0.7765\n",
+      "Epoch 129/2000\n",
+      "3/3 [==============================] - 0s 55ms/step - loss: 0.4267 - categorical_accuracy: 0.8235\n",
+      "Epoch 130/2000\n",
+      "3/3 [==============================] - 0s 55ms/step - loss: 0.3817 - categorical_accuracy: 0.8588\n",
+      "Epoch 131/2000\n",
+      "3/3 [==============================] - 0s 56ms/step - loss: 0.3604 - categorical_accuracy: 0.8588\n",
+      "Epoch 132/2000\n",
+      "3/3 [==============================] - 0s 56ms/step - loss: 0.3918 - categorical_accuracy: 0.8471\n",
+      "Epoch 133/2000\n",
+      "3/3 [==============================] - 0s 55ms/step - loss: 0.4038 - categorical_accuracy: 0.8235\n",
+      "Epoch 134/2000\n",
+      "3/3 [==============================] - 0s 54ms/step - loss: 0.4131 - categorical_accuracy: 0.8118\n",
+      "Epoch 135/2000\n",
+      "3/3 [==============================] - 0s 56ms/step - loss: 0.3871 - categorical_accuracy: 0.7882\n",
+      "Epoch 136/2000\n",
+      "3/3 [==============================] - 0s 56ms/step - loss: 0.3919 - categorical_accuracy: 0.8353\n",
+      "Epoch 137/2000\n",
+      "3/3 [==============================] - 0s 54ms/step - loss: 0.3505 - categorical_accuracy: 0.8706\n",
+      "Epoch 138/2000\n",
+      "3/3 [==============================] - 0s 55ms/step - loss: 0.4160 - categorical_accuracy: 0.8353\n",
+      "Epoch 139/2000\n",
+      "3/3 [==============================] - 0s 55ms/step - loss: 0.4093 - categorical_accuracy: 0.8235\n",
+      "Epoch 140/2000\n",
+      "3/3 [==============================] - 0s 56ms/step - loss: 0.3861 - categorical_accuracy: 0.8235\n",
+      "Epoch 141/2000\n",
+      "3/3 [==============================] - 0s 57ms/step - loss: 0.3635 - categorical_accuracy: 0.8353\n",
+      "Epoch 142/2000\n",
+      "3/3 [==============================] - 0s 54ms/step - loss: 0.4527 - categorical_accuracy: 0.8353\n",
+      "Epoch 143/2000\n",
+      "3/3 [==============================] - 0s 56ms/step - loss: 0.5410 - categorical_accuracy: 0.7294\n",
+      "Epoch 144/2000\n",
+      "3/3 [==============================] - 0s 55ms/step - loss: 0.4502 - categorical_accuracy: 0.7765\n",
+      "Epoch 145/2000\n",
+      "3/3 [==============================] - 0s 56ms/step - loss: 0.3671 - categorical_accuracy: 0.8471\n",
+      "Epoch 146/2000\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3/3 [==============================] - 0s 55ms/step - loss: 0.4027 - categorical_accuracy: 0.8353\n",
+      "Epoch 147/2000\n",
+      "3/3 [==============================] - 0s 56ms/step - loss: 0.3712 - categorical_accuracy: 0.8471\n",
+      "Epoch 148/2000\n",
+      "3/3 [==============================] - 0s 55ms/step - loss: 0.3848 - categorical_accuracy: 0.8118\n",
+      "Epoch 149/2000\n",
+      "3/3 [==============================] - 0s 53ms/step - loss: 0.4607 - categorical_accuracy: 0.7765\n",
+      "Epoch 150/2000\n",
+      "3/3 [==============================] - 0s 56ms/step - loss: 0.3945 - categorical_accuracy: 0.8000\n",
+      "Epoch 151/2000\n",
+      "3/3 [==============================] - 0s 55ms/step - loss: 0.4339 - categorical_accuracy: 0.7529\n",
+      "Epoch 152/2000\n",
+      "3/3 [==============================] - 0s 54ms/step - loss: 0.3729 - categorical_accuracy: 0.8588\n",
+      "Epoch 153/2000\n",
+      "3/3 [==============================] - 0s 54ms/step - loss: 0.3702 - categorical_accuracy: 0.8471\n",
+      "Epoch 154/2000\n",
+      "3/3 [==============================] - 0s 54ms/step - loss: 0.3813 - categorical_accuracy: 0.8353\n",
+      "Epoch 155/2000\n",
+      "3/3 [==============================] - 0s 52ms/step - loss: 0.3640 - categorical_accuracy: 0.8588\n",
+      "Epoch 156/2000\n",
+      "3/3 [==============================] - 0s 54ms/step - loss: 0.3616 - categorical_accuracy: 0.8353\n",
+      "Epoch 157/2000\n",
+      "3/3 [==============================] - 0s 53ms/step - loss: 0.3689 - categorical_accuracy: 0.8588\n",
+      "Epoch 158/2000\n",
+      "3/3 [==============================] - 0s 54ms/step - loss: 0.3315 - categorical_accuracy: 0.8941\n",
+      "Epoch 159/2000\n",
+      "3/3 [==============================] - 0s 55ms/step - loss: 0.3120 - categorical_accuracy: 0.8824\n",
+      "Epoch 160/2000\n",
+      "3/3 [==============================] - 0s 54ms/step - loss: 0.3103 - categorical_accuracy: 0.8706\n",
+      "Epoch 161/2000\n",
+      "3/3 [==============================] - 0s 56ms/step - loss: 0.3499 - categorical_accuracy: 0.8588\n",
+      "Epoch 162/2000\n",
+      "3/3 [==============================] - 0s 55ms/step - loss: 0.3359 - categorical_accuracy: 0.8941\n",
+      "Epoch 163/2000\n",
+      "3/3 [==============================] - 0s 53ms/step - loss: 0.3409 - categorical_accuracy: 0.8824\n",
+      "Epoch 164/2000\n",
+      "3/3 [==============================] - 0s 53ms/step - loss: 0.3681 - categorical_accuracy: 0.8235\n",
+      "Epoch 165/2000\n",
+      "3/3 [==============================] - 0s 54ms/step - loss: 0.4182 - categorical_accuracy: 0.8353\n",
+      "Epoch 166/2000\n",
+      "3/3 [==============================] - 0s 54ms/step - loss: 0.5544 - categorical_accuracy: 0.6706\n",
+      "Epoch 167/2000\n",
+      "3/3 [==============================] - 0s 53ms/step - loss: 0.3964 - categorical_accuracy: 0.8235\n",
+      "Epoch 168/2000\n",
+      "3/3 [==============================] - 0s 53ms/step - loss: 0.4224 - categorical_accuracy: 0.8588\n",
+      "Epoch 169/2000\n",
+      "3/3 [==============================] - 0s 53ms/step - loss: 0.3806 - categorical_accuracy: 0.8471\n",
+      "Epoch 170/2000\n",
+      "3/3 [==============================] - 0s 54ms/step - loss: 0.3991 - categorical_accuracy: 0.8118\n",
+      "Epoch 171/2000\n",
+      "3/3 [==============================] - 0s 54ms/step - loss: 0.4413 - categorical_accuracy: 0.8471\n",
+      "Epoch 172/2000\n",
+      "3/3 [==============================] - 0s 52ms/step - loss: 0.4364 - categorical_accuracy: 0.8353\n",
+      "Epoch 173/2000\n",
+      "3/3 [==============================] - 0s 54ms/step - loss: 0.3039 - categorical_accuracy: 0.8824\n",
+      "Epoch 174/2000\n",
+      "3/3 [==============================] - 0s 54ms/step - loss: 3.4675 - categorical_accuracy: 0.3059\n",
+      "Epoch 175/2000\n",
+      "3/3 [==============================] - 0s 53ms/step - loss: 1.4676 - categorical_accuracy: 0.4824\n",
+      "Epoch 176/2000\n",
+      "3/3 [==============================] - 0s 54ms/step - loss: 14.5954 - categorical_accuracy: 0.3882\n",
+      "Epoch 177/2000\n",
+      "3/3 [==============================] - 0s 53ms/step - loss: 9.4632 - categorical_accuracy: 0.4235\n",
+      "Epoch 178/2000\n",
+      "3/3 [==============================] - 0s 53ms/step - loss: 82.8598 - categorical_accuracy: 0.3412\n",
+      "Epoch 179/2000\n",
+      "3/3 [==============================] - 0s 54ms/step - loss: 17.1260 - categorical_accuracy: 0.3059\n",
+      "Epoch 180/2000\n",
+      "3/3 [==============================] - 0s 54ms/step - loss: 33.5270 - categorical_accuracy: 0.3176\n",
+      "Epoch 181/2000\n",
+      "3/3 [==============================] - 0s 54ms/step - loss: 43.8087 - categorical_accuracy: 0.3412\n",
+      "Epoch 182/2000\n",
+      "3/3 [==============================] - 0s 52ms/step - loss: 37.4590 - categorical_accuracy: 0.2706\n",
+      "Epoch 183/2000\n",
+      "3/3 [==============================] - 0s 54ms/step - loss: 146.0353 - categorical_accuracy: 0.2941\n",
+      "Epoch 184/2000\n",
+      "3/3 [==============================] - 0s 53ms/step - loss: 332.6913 - categorical_accuracy: 0.3765\n",
+      "Epoch 185/2000\n",
+      "3/3 [==============================] - 0s 53ms/step - loss: 591.3230 - categorical_accuracy: 0.2824\n",
+      "Epoch 186/2000\n",
+      "3/3 [==============================] - 0s 53ms/step - loss: 300.2092 - categorical_accuracy: 0.3294\n",
+      "Epoch 187/2000\n",
+      "3/3 [==============================] - 0s 53ms/step - loss: 179.6980 - categorical_accuracy: 0.2706\n",
+      "Epoch 188/2000\n",
+      "3/3 [==============================] - 0s 52ms/step - loss: 120.1321 - categorical_accuracy: 0.3529\n",
+      "Epoch 189/2000\n",
+      "3/3 [==============================] - 0s 54ms/step - loss: 106.9761 - categorical_accuracy: 0.2118\n",
+      "Epoch 190/2000\n",
+      "3/3 [==============================] - 0s 54ms/step - loss: 46.5140 - categorical_accuracy: 0.3765\n",
+      "Epoch 191/2000\n",
+      "3/3 [==============================] - 0s 52ms/step - loss: 14.7244 - categorical_accuracy: 0.2588\n",
+      "Epoch 192/2000\n",
+      "3/3 [==============================] - 0s 53ms/step - loss: 8.5323 - categorical_accuracy: 0.2118\n",
+      "Epoch 193/2000\n",
+      "3/3 [==============================] - 0s 54ms/step - loss: 20.2484 - categorical_accuracy: 0.3412\n",
+      "Epoch 194/2000\n",
+      "3/3 [==============================] - 0s 53ms/step - loss: 25.9824 - categorical_accuracy: 0.2941\n",
+      "Epoch 195/2000\n",
+      "3/3 [==============================] - 0s 52ms/step - loss: 13.0488 - categorical_accuracy: 0.3412\n",
+      "Epoch 196/2000\n",
+      "3/3 [==============================] - 0s 55ms/step - loss: 9.5996 - categorical_accuracy: 0.3176\n",
+      "Epoch 197/2000\n",
+      "3/3 [==============================] - 0s 54ms/step - loss: 8.1008 - categorical_accuracy: 0.2118\n",
+      "Epoch 198/2000\n",
+      "3/3 [==============================] - 0s 53ms/step - loss: 4.7258 - categorical_accuracy: 0.4000\n",
+      "Epoch 199/2000\n",
+      "3/3 [==============================] - 0s 54ms/step - loss: 4.0796 - categorical_accuracy: 0.3294\n",
+      "Epoch 200/2000\n",
+      "3/3 [==============================] - 0s 54ms/step - loss: 3.3923 - categorical_accuracy: 0.3882\n",
+      "Epoch 201/2000\n",
+      "3/3 [==============================] - 0s 54ms/step - loss: 2.4378 - categorical_accuracy: 0.4471\n",
+      "Epoch 202/2000\n",
+      "3/3 [==============================] - 0s 54ms/step - loss: 1.7919 - categorical_accuracy: 0.3765\n",
+      "Epoch 203/2000\n",
+      "3/3 [==============================] - 0s 53ms/step - loss: 1.3200 - categorical_accuracy: 0.2118\n",
+      "Epoch 204/2000\n",
+      "3/3 [==============================] - 0s 54ms/step - loss: 1.2218 - categorical_accuracy: 0.4235\n",
+      "Epoch 205/2000\n",
+      "3/3 [==============================] - 0s 56ms/step - loss: 1.3680 - categorical_accuracy: 0.6235\n",
+      "Epoch 206/2000\n",
+      "3/3 [==============================] - 0s 54ms/step - loss: 2.2874 - categorical_accuracy: 0.3176\n",
+      "Epoch 207/2000\n",
+      "3/3 [==============================] - 0s 55ms/step - loss: 2.1336 - categorical_accuracy: 0.2588\n",
+      "Epoch 208/2000\n",
+      "3/3 [==============================] - 0s 58ms/step - loss: 1.2563 - categorical_accuracy: 0.3647\n",
+      "Epoch 209/2000\n",
+      "3/3 [==============================] - 0s 56ms/step - loss: 1.4066 - categorical_accuracy: 0.3176\n",
+      "Epoch 210/2000\n",
+      "3/3 [==============================] - 0s 56ms/step - loss: 1.2792 - categorical_accuracy: 0.3647\n",
+      "Epoch 211/2000\n",
+      "3/3 [==============================] - 0s 56ms/step - loss: 0.9902 - categorical_accuracy: 0.4235\n",
+      "Epoch 212/2000\n",
+      "3/3 [==============================] - 0s 55ms/step - loss: 0.9699 - categorical_accuracy: 0.4824\n",
+      "Epoch 213/2000\n",
+      "3/3 [==============================] - 0s 55ms/step - loss: 0.9461 - categorical_accuracy: 0.5412\n",
+      "Epoch 214/2000\n",
+      "3/3 [==============================] - 0s 59ms/step - loss: 0.8921 - categorical_accuracy: 0.6471\n",
+      "Epoch 215/2000\n",
+      "3/3 [==============================] - 0s 55ms/step - loss: 0.8716 - categorical_accuracy: 0.6588\n",
+      "Epoch 216/2000\n",
+      "3/3 [==============================] - 0s 57ms/step - loss: 0.7928 - categorical_accuracy: 0.6471\n",
+      "Epoch 217/2000\n",
+      "3/3 [==============================] - 0s 56ms/step - loss: 0.7858 - categorical_accuracy: 0.6941\n",
+      "Epoch 218/2000\n",
+      "3/3 [==============================] - 0s 55ms/step - loss: 0.7481 - categorical_accuracy: 0.7059\n",
+      "Epoch 219/2000\n",
+      "3/3 [==============================] - 0s 56ms/step - loss: 0.7385 - categorical_accuracy: 0.6353\n",
+      "Epoch 220/2000\n",
+      "3/3 [==============================] - 0s 57ms/step - loss: 0.7252 - categorical_accuracy: 0.6588\n",
+      "Epoch 221/2000\n",
+      "3/3 [==============================] - 0s 56ms/step - loss: 0.7077 - categorical_accuracy: 0.7176\n",
+      "Epoch 222/2000\n",
+      "3/3 [==============================] - 0s 55ms/step - loss: 0.7024 - categorical_accuracy: 0.7059\n",
+      "Epoch 223/2000\n",
+      "3/3 [==============================] - 0s 56ms/step - loss: 0.6825 - categorical_accuracy: 0.6588\n",
+      "Epoch 224/2000\n",
+      "3/3 [==============================] - 0s 56ms/step - loss: 0.6810 - categorical_accuracy: 0.6353\n",
+      "Epoch 225/2000\n",
+      "3/3 [==============================] - 0s 55ms/step - loss: 0.6756 - categorical_accuracy: 0.6471\n",
+      "Epoch 226/2000\n",
+      "3/3 [==============================] - 0s 56ms/step - loss: 0.6714 - categorical_accuracy: 0.6706\n",
+      "Epoch 227/2000\n",
+      "3/3 [==============================] - 0s 56ms/step - loss: 0.6666 - categorical_accuracy: 0.6471\n",
+      "Epoch 228/2000\n",
+      "3/3 [==============================] - 0s 55ms/step - loss: 0.6585 - categorical_accuracy: 0.6235\n",
+      "Epoch 229/2000\n",
+      "3/3 [==============================] - 0s 56ms/step - loss: 0.6528 - categorical_accuracy: 0.6941\n",
+      "Epoch 230/2000\n",
+      "3/3 [==============================] - 0s 55ms/step - loss: 0.6464 - categorical_accuracy: 0.6941\n",
+      "Epoch 231/2000\n",
+      "3/3 [==============================] - 0s 54ms/step - loss: 0.6409 - categorical_accuracy: 0.6941\n",
+      "Epoch 232/2000\n",
+      "3/3 [==============================] - 0s 56ms/step - loss: 0.6388 - categorical_accuracy: 0.6471\n",
+      "Epoch 233/2000\n",
+      "3/3 [==============================] - 0s 59ms/step - loss: 0.6315 - categorical_accuracy: 0.6588\n",
+      "Epoch 234/2000\n",
+      "3/3 [==============================] - 0s 56ms/step - loss: 0.6298 - categorical_accuracy: 0.6588\n",
+      "Epoch 235/2000\n",
+      "3/3 [==============================] - 0s 60ms/step - loss: 0.6268 - categorical_accuracy: 0.7059\n",
+      "Epoch 236/2000\n",
+      "3/3 [==============================] - 0s 67ms/step - loss: 0.6227 - categorical_accuracy: 0.7176\n",
+      "Epoch 237/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.6199 - categorical_accuracy: 0.7176\n",
+      "Epoch 238/2000\n",
+      "3/3 [==============================] - 0s 59ms/step - loss: 0.6167 - categorical_accuracy: 0.7294\n",
+      "Epoch 239/2000\n",
+      "3/3 [==============================] - 0s 57ms/step - loss: 0.6127 - categorical_accuracy: 0.7059\n",
+      "Epoch 240/2000\n",
+      "3/3 [==============================] - 0s 58ms/step - loss: 0.6132 - categorical_accuracy: 0.7059\n",
+      "Epoch 241/2000\n",
+      "3/3 [==============================] - 0s 56ms/step - loss: 0.6100 - categorical_accuracy: 0.6588\n",
+      "Epoch 242/2000\n",
+      "3/3 [==============================] - 0s 58ms/step - loss: 0.6099 - categorical_accuracy: 0.6118\n",
+      "Epoch 243/2000\n",
+      "3/3 [==============================] - 0s 57ms/step - loss: 0.6078 - categorical_accuracy: 0.6000\n",
+      "Epoch 244/2000\n",
+      "3/3 [==============================] - 0s 56ms/step - loss: 0.6021 - categorical_accuracy: 0.7059\n",
+      "Epoch 245/2000\n",
+      "3/3 [==============================] - 0s 55ms/step - loss: 0.6089 - categorical_accuracy: 0.6941\n",
+      "Epoch 246/2000\n",
+      "3/3 [==============================] - 0s 57ms/step - loss: 0.6069 - categorical_accuracy: 0.7176\n",
+      "Epoch 247/2000\n",
+      "3/3 [==============================] - 0s 57ms/step - loss: 0.6045 - categorical_accuracy: 0.7412\n",
+      "Epoch 248/2000\n",
+      "3/3 [==============================] - 0s 58ms/step - loss: 0.5998 - categorical_accuracy: 0.7294\n",
+      "Epoch 249/2000\n",
+      "3/3 [==============================] - 0s 57ms/step - loss: 0.5977 - categorical_accuracy: 0.7765\n",
+      "Epoch 250/2000\n",
+      "3/3 [==============================] - 0s 57ms/step - loss: 0.5977 - categorical_accuracy: 0.6706\n",
+      "Epoch 251/2000\n",
+      "3/3 [==============================] - 0s 58ms/step - loss: 0.5935 - categorical_accuracy: 0.7059\n",
+      "Epoch 252/2000\n",
+      "3/3 [==============================] - 0s 58ms/step - loss: 0.5923 - categorical_accuracy: 0.7059\n",
+      "Epoch 253/2000\n",
+      "3/3 [==============================] - 0s 58ms/step - loss: 0.5916 - categorical_accuracy: 0.7059\n",
+      "Epoch 254/2000\n",
+      "3/3 [==============================] - 0s 58ms/step - loss: 0.5907 - categorical_accuracy: 0.7529\n",
+      "Epoch 255/2000\n",
+      "3/3 [==============================] - 0s 58ms/step - loss: 0.5876 - categorical_accuracy: 0.7529\n",
+      "Epoch 256/2000\n",
+      "3/3 [==============================] - 0s 57ms/step - loss: 0.5865 - categorical_accuracy: 0.7529\n",
+      "Epoch 257/2000\n",
+      "3/3 [==============================] - 0s 57ms/step - loss: 0.5858 - categorical_accuracy: 0.7412\n",
+      "Epoch 258/2000\n",
+      "3/3 [==============================] - 0s 57ms/step - loss: 0.5842 - categorical_accuracy: 0.7294\n",
+      "Epoch 259/2000\n",
+      "3/3 [==============================] - 0s 60ms/step - loss: 0.5847 - categorical_accuracy: 0.7294\n",
+      "Epoch 260/2000\n",
+      "3/3 [==============================] - 0s 59ms/step - loss: 0.5879 - categorical_accuracy: 0.7059\n",
+      "Epoch 261/2000\n",
+      "3/3 [==============================] - 0s 56ms/step - loss: 0.5812 - categorical_accuracy: 0.7059\n",
+      "Epoch 262/2000\n",
+      "3/3 [==============================] - 0s 57ms/step - loss: 0.5858 - categorical_accuracy: 0.7176\n",
+      "Epoch 263/2000\n",
+      "3/3 [==============================] - 0s 57ms/step - loss: 0.5875 - categorical_accuracy: 0.7059\n",
+      "Epoch 264/2000\n",
+      "3/3 [==============================] - 0s 58ms/step - loss: 0.5822 - categorical_accuracy: 0.7412\n",
+      "Epoch 265/2000\n",
+      "3/3 [==============================] - 0s 58ms/step - loss: 0.5829 - categorical_accuracy: 0.6824\n",
+      "Epoch 266/2000\n",
+      "3/3 [==============================] - 0s 60ms/step - loss: 0.5823 - categorical_accuracy: 0.7059\n",
+      "Epoch 267/2000\n",
+      "3/3 [==============================] - 0s 58ms/step - loss: 0.5947 - categorical_accuracy: 0.7412\n",
+      "Epoch 268/2000\n",
+      "3/3 [==============================] - 0s 57ms/step - loss: 0.5764 - categorical_accuracy: 0.7176\n",
+      "Epoch 269/2000\n",
+      "3/3 [==============================] - 0s 57ms/step - loss: 0.5956 - categorical_accuracy: 0.6471\n",
+      "Epoch 270/2000\n",
+      "3/3 [==============================] - 0s 59ms/step - loss: 0.5699 - categorical_accuracy: 0.7059\n",
+      "Epoch 271/2000\n",
+      "3/3 [==============================] - 0s 57ms/step - loss: 0.6036 - categorical_accuracy: 0.7294\n",
+      "Epoch 272/2000\n",
+      "3/3 [==============================] - 0s 59ms/step - loss: 0.5941 - categorical_accuracy: 0.6941\n",
+      "Epoch 273/2000\n",
+      "3/3 [==============================] - 0s 57ms/step - loss: 0.5820 - categorical_accuracy: 0.6235\n",
+      "Epoch 274/2000\n",
+      "3/3 [==============================] - 0s 58ms/step - loss: 0.5786 - categorical_accuracy: 0.6588\n",
+      "Epoch 275/2000\n",
+      "3/3 [==============================] - 0s 58ms/step - loss: 0.5723 - categorical_accuracy: 0.7412\n",
+      "Epoch 276/2000\n",
+      "3/3 [==============================] - 0s 57ms/step - loss: 0.5780 - categorical_accuracy: 0.7765\n",
+      "Epoch 277/2000\n",
+      "3/3 [==============================] - 0s 58ms/step - loss: 0.5750 - categorical_accuracy: 0.7059\n",
+      "Epoch 278/2000\n",
+      "3/3 [==============================] - 0s 60ms/step - loss: 0.5719 - categorical_accuracy: 0.7176\n",
+      "Epoch 279/2000\n",
+      "3/3 [==============================] - 0s 57ms/step - loss: 0.5756 - categorical_accuracy: 0.6824\n",
+      "Epoch 280/2000\n",
+      "3/3 [==============================] - 0s 57ms/step - loss: 0.5785 - categorical_accuracy: 0.7412\n",
+      "Epoch 281/2000\n",
+      "3/3 [==============================] - 0s 57ms/step - loss: 0.5733 - categorical_accuracy: 0.7765\n",
+      "Epoch 282/2000\n",
+      "3/3 [==============================] - 0s 59ms/step - loss: 0.5722 - categorical_accuracy: 0.7294\n",
+      "Epoch 283/2000\n",
+      "3/3 [==============================] - 0s 58ms/step - loss: 0.5674 - categorical_accuracy: 0.7294\n",
+      "Epoch 284/2000\n",
+      "3/3 [==============================] - 0s 58ms/step - loss: 0.5672 - categorical_accuracy: 0.7529\n",
+      "Epoch 285/2000\n",
+      "3/3 [==============================] - 0s 57ms/step - loss: 0.5698 - categorical_accuracy: 0.7529\n",
+      "Epoch 286/2000\n",
+      "3/3 [==============================] - 0s 58ms/step - loss: 0.5654 - categorical_accuracy: 0.7059\n",
+      "Epoch 287/2000\n",
+      "3/3 [==============================] - 0s 58ms/step - loss: 0.5703 - categorical_accuracy: 0.6824\n",
+      "Epoch 288/2000\n",
+      "3/3 [==============================] - 0s 57ms/step - loss: 0.5631 - categorical_accuracy: 0.7176\n",
+      "Epoch 289/2000\n",
+      "3/3 [==============================] - 0s 59ms/step - loss: 0.5633 - categorical_accuracy: 0.7059\n",
+      "Epoch 290/2000\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3/3 [==============================] - 0s 59ms/step - loss: 0.5641 - categorical_accuracy: 0.6941\n",
+      "Epoch 291/2000\n",
+      "3/3 [==============================] - 0s 57ms/step - loss: 0.5642 - categorical_accuracy: 0.7059\n",
+      "Epoch 292/2000\n",
+      "3/3 [==============================] - 0s 58ms/step - loss: 0.5617 - categorical_accuracy: 0.7294\n",
+      "Epoch 293/2000\n",
+      "3/3 [==============================] - 0s 58ms/step - loss: 0.5634 - categorical_accuracy: 0.6706\n",
+      "Epoch 294/2000\n",
+      "3/3 [==============================] - 0s 60ms/step - loss: 0.5620 - categorical_accuracy: 0.6471\n",
+      "Epoch 295/2000\n",
+      "3/3 [==============================] - 0s 60ms/step - loss: 0.5589 - categorical_accuracy: 0.7294\n",
+      "Epoch 296/2000\n",
+      "3/3 [==============================] - 0s 59ms/step - loss: 0.5560 - categorical_accuracy: 0.7059\n",
+      "Epoch 297/2000\n",
+      "3/3 [==============================] - 0s 58ms/step - loss: 0.5580 - categorical_accuracy: 0.6000\n",
+      "Epoch 298/2000\n",
+      "3/3 [==============================] - 0s 58ms/step - loss: 0.5570 - categorical_accuracy: 0.6353\n",
+      "Epoch 299/2000\n",
+      "3/3 [==============================] - 0s 59ms/step - loss: 0.5555 - categorical_accuracy: 0.6941\n",
+      "Epoch 300/2000\n",
+      "3/3 [==============================] - 0s 58ms/step - loss: 0.5554 - categorical_accuracy: 0.7529\n",
+      "Epoch 301/2000\n",
+      "3/3 [==============================] - 0s 57ms/step - loss: 0.5533 - categorical_accuracy: 0.7294\n",
+      "Epoch 302/2000\n",
+      "3/3 [==============================] - 0s 58ms/step - loss: 0.5540 - categorical_accuracy: 0.7647\n",
+      "Epoch 303/2000\n",
+      "3/3 [==============================] - 0s 58ms/step - loss: 0.5510 - categorical_accuracy: 0.7059\n",
+      "Epoch 304/2000\n",
+      "3/3 [==============================] - 0s 58ms/step - loss: 0.5536 - categorical_accuracy: 0.6471\n",
+      "Epoch 305/2000\n",
+      "3/3 [==============================] - 0s 57ms/step - loss: 0.5767 - categorical_accuracy: 0.6471\n",
+      "Epoch 306/2000\n",
+      "3/3 [==============================] - 0s 57ms/step - loss: 0.6207 - categorical_accuracy: 0.6353\n",
+      "Epoch 307/2000\n",
+      "3/3 [==============================] - 0s 58ms/step - loss: 0.6610 - categorical_accuracy: 0.6471\n",
+      "Epoch 308/2000\n",
+      "3/3 [==============================] - 0s 58ms/step - loss: 0.6314 - categorical_accuracy: 0.6353\n",
+      "Epoch 309/2000\n",
+      "3/3 [==============================] - 0s 58ms/step - loss: 0.6048 - categorical_accuracy: 0.6471\n",
+      "Epoch 310/2000\n",
+      "3/3 [==============================] - 0s 58ms/step - loss: 0.5722 - categorical_accuracy: 0.7176\n",
+      "Epoch 311/2000\n",
+      "3/3 [==============================] - 0s 58ms/step - loss: 0.5961 - categorical_accuracy: 0.6588\n",
+      "Epoch 312/2000\n",
+      "3/3 [==============================] - 0s 61ms/step - loss: 0.5773 - categorical_accuracy: 0.6706\n",
+      "Epoch 313/2000\n",
+      "3/3 [==============================] - 0s 58ms/step - loss: 0.5901 - categorical_accuracy: 0.7294\n",
+      "Epoch 314/2000\n",
+      "3/3 [==============================] - 0s 57ms/step - loss: 0.5458 - categorical_accuracy: 0.7412\n",
+      "Epoch 315/2000\n",
+      "3/3 [==============================] - 0s 58ms/step - loss: 0.5944 - categorical_accuracy: 0.6353\n",
+      "Epoch 316/2000\n",
+      "3/3 [==============================] - 0s 56ms/step - loss: 0.5478 - categorical_accuracy: 0.7412\n",
+      "Epoch 317/2000\n",
+      "3/3 [==============================] - 0s 58ms/step - loss: 0.6274 - categorical_accuracy: 0.6824\n",
+      "Epoch 318/2000\n",
+      "3/3 [==============================] - 0s 58ms/step - loss: 0.5651 - categorical_accuracy: 0.7412\n",
+      "Epoch 319/2000\n",
+      "3/3 [==============================] - 0s 57ms/step - loss: 0.5985 - categorical_accuracy: 0.6353\n",
+      "Epoch 320/2000\n",
+      "3/3 [==============================] - 0s 57ms/step - loss: 0.6129 - categorical_accuracy: 0.7176\n",
+      "Epoch 321/2000\n",
+      "3/3 [==============================] - 0s 57ms/step - loss: 0.5622 - categorical_accuracy: 0.6824\n",
+      "Epoch 322/2000\n",
+      "3/3 [==============================] - 0s 56ms/step - loss: 0.5511 - categorical_accuracy: 0.7176\n",
+      "Epoch 323/2000\n",
+      "3/3 [==============================] - 0s 57ms/step - loss: 0.5362 - categorical_accuracy: 0.6824\n",
+      "Epoch 324/2000\n",
+      "3/3 [==============================] - 0s 57ms/step - loss: 0.5284 - categorical_accuracy: 0.7059\n",
+      "Epoch 325/2000\n",
+      "3/3 [==============================] - 0s 57ms/step - loss: 0.5391 - categorical_accuracy: 0.7176\n",
+      "Epoch 326/2000\n",
+      "3/3 [==============================] - 0s 57ms/step - loss: 0.5120 - categorical_accuracy: 0.7176\n",
+      "Epoch 327/2000\n",
+      "3/3 [==============================] - 0s 57ms/step - loss: 0.5105 - categorical_accuracy: 0.6824\n",
+      "Epoch 328/2000\n",
+      "3/3 [==============================] - 0s 56ms/step - loss: 0.5197 - categorical_accuracy: 0.7412\n",
+      "Epoch 329/2000\n",
+      "3/3 [==============================] - 0s 58ms/step - loss: 0.6243 - categorical_accuracy: 0.6353\n",
+      "Epoch 330/2000\n",
+      "3/3 [==============================] - 0s 57ms/step - loss: 0.5938 - categorical_accuracy: 0.6471\n",
+      "Epoch 331/2000\n",
+      "3/3 [==============================] - 0s 58ms/step - loss: 0.5958 - categorical_accuracy: 0.6353\n",
+      "Epoch 332/2000\n",
+      "3/3 [==============================] - 0s 57ms/step - loss: 0.5584 - categorical_accuracy: 0.6471\n",
+      "Epoch 333/2000\n",
+      "3/3 [==============================] - 0s 57ms/step - loss: 0.5763 - categorical_accuracy: 0.6471\n",
+      "Epoch 334/2000\n",
+      "3/3 [==============================] - 0s 58ms/step - loss: 0.5841 - categorical_accuracy: 0.6941\n",
+      "Epoch 335/2000\n",
+      "3/3 [==============================] - 0s 59ms/step - loss: 0.5564 - categorical_accuracy: 0.6588\n",
+      "Epoch 336/2000\n",
+      "3/3 [==============================] - 0s 56ms/step - loss: 0.5614 - categorical_accuracy: 0.6588\n",
+      "Epoch 337/2000\n",
+      "3/3 [==============================] - 0s 58ms/step - loss: 0.5653 - categorical_accuracy: 0.6588\n",
+      "Epoch 338/2000\n",
+      "3/3 [==============================] - 0s 58ms/step - loss: 0.5598 - categorical_accuracy: 0.6235\n",
+      "Epoch 339/2000\n",
+      "3/3 [==============================] - 0s 58ms/step - loss: 0.5485 - categorical_accuracy: 0.7412\n",
+      "Epoch 340/2000\n",
+      "3/3 [==============================] - 0s 57ms/step - loss: 0.5549 - categorical_accuracy: 0.7882\n",
+      "Epoch 341/2000\n",
+      "3/3 [==============================] - 0s 57ms/step - loss: 0.5503 - categorical_accuracy: 0.7765\n",
+      "Epoch 342/2000\n",
+      "3/3 [==============================] - 0s 57ms/step - loss: 0.5456 - categorical_accuracy: 0.7765\n",
+      "Epoch 343/2000\n",
+      "3/3 [==============================] - 0s 58ms/step - loss: 0.5477 - categorical_accuracy: 0.8000\n",
+      "Epoch 344/2000\n",
+      "3/3 [==============================] - 0s 59ms/step - loss: 0.5454 - categorical_accuracy: 0.7765\n",
+      "Epoch 345/2000\n",
+      "3/3 [==============================] - 0s 57ms/step - loss: 0.5430 - categorical_accuracy: 0.7882\n",
+      "Epoch 346/2000\n",
+      "3/3 [==============================] - 0s 57ms/step - loss: 0.5422 - categorical_accuracy: 0.8000\n",
+      "Epoch 347/2000\n",
+      "3/3 [==============================] - 0s 58ms/step - loss: 0.5417 - categorical_accuracy: 0.7882\n",
+      "Epoch 348/2000\n",
+      "3/3 [==============================] - 0s 58ms/step - loss: 0.5383 - categorical_accuracy: 0.7647\n",
+      "Epoch 349/2000\n",
+      "3/3 [==============================] - 0s 60ms/step - loss: 0.5368 - categorical_accuracy: 0.7647\n",
+      "Epoch 350/2000\n",
+      "3/3 [==============================] - 0s 58ms/step - loss: 0.5324 - categorical_accuracy: 0.7765\n",
+      "Epoch 351/2000\n",
+      "3/3 [==============================] - 0s 58ms/step - loss: 0.5320 - categorical_accuracy: 0.7765\n",
+      "Epoch 352/2000\n",
+      "3/3 [==============================] - 0s 58ms/step - loss: 0.5281 - categorical_accuracy: 0.6941\n",
+      "Epoch 353/2000\n",
+      "3/3 [==============================] - 0s 59ms/step - loss: 0.5279 - categorical_accuracy: 0.7176\n",
+      "Epoch 354/2000\n",
+      "3/3 [==============================] - 0s 58ms/step - loss: 0.5256 - categorical_accuracy: 0.7647\n",
+      "Epoch 355/2000\n",
+      "3/3 [==============================] - 0s 58ms/step - loss: 0.5263 - categorical_accuracy: 0.7647\n",
+      "Epoch 356/2000\n",
+      "3/3 [==============================] - 0s 60ms/step - loss: 0.5238 - categorical_accuracy: 0.7412\n",
+      "Epoch 357/2000\n",
+      "3/3 [==============================] - 0s 58ms/step - loss: 0.5286 - categorical_accuracy: 0.6824\n",
+      "Epoch 358/2000\n",
+      "3/3 [==============================] - 0s 60ms/step - loss: 0.5286 - categorical_accuracy: 0.7059\n",
+      "Epoch 359/2000\n",
+      "3/3 [==============================] - 0s 58ms/step - loss: 0.5239 - categorical_accuracy: 0.7529\n",
+      "Epoch 360/2000\n",
+      "3/3 [==============================] - 0s 58ms/step - loss: 0.5247 - categorical_accuracy: 0.7294\n",
+      "Epoch 361/2000\n",
+      "3/3 [==============================] - 0s 61ms/step - loss: 0.5289 - categorical_accuracy: 0.7294\n",
+      "Epoch 362/2000\n",
+      "3/3 [==============================] - 0s 58ms/step - loss: 0.5253 - categorical_accuracy: 0.7529\n",
+      "Epoch 363/2000\n",
+      "3/3 [==============================] - 0s 59ms/step - loss: 0.5217 - categorical_accuracy: 0.6941\n",
+      "Epoch 364/2000\n",
+      "3/3 [==============================] - 0s 59ms/step - loss: 0.5207 - categorical_accuracy: 0.7647\n",
+      "Epoch 365/2000\n",
+      "3/3 [==============================] - 0s 59ms/step - loss: 0.5202 - categorical_accuracy: 0.7882\n",
+      "Epoch 366/2000\n",
+      "3/3 [==============================] - 0s 59ms/step - loss: 0.5214 - categorical_accuracy: 0.7412\n",
+      "Epoch 367/2000\n",
+      "3/3 [==============================] - 0s 58ms/step - loss: 0.5183 - categorical_accuracy: 0.7176\n",
+      "Epoch 368/2000\n",
+      "3/3 [==============================] - 0s 58ms/step - loss: 0.5223 - categorical_accuracy: 0.7765\n",
+      "Epoch 369/2000\n",
+      "3/3 [==============================] - 0s 60ms/step - loss: 0.5231 - categorical_accuracy: 0.7529\n",
+      "Epoch 370/2000\n",
+      "3/3 [==============================] - 0s 59ms/step - loss: 0.5220 - categorical_accuracy: 0.7294\n",
+      "Epoch 371/2000\n",
+      "3/3 [==============================] - 0s 58ms/step - loss: 0.5190 - categorical_accuracy: 0.7294\n",
+      "Epoch 372/2000\n",
+      "3/3 [==============================] - 0s 57ms/step - loss: 0.5155 - categorical_accuracy: 0.8000\n",
+      "Epoch 373/2000\n",
+      "3/3 [==============================] - 0s 56ms/step - loss: 0.5199 - categorical_accuracy: 0.8000\n",
+      "Epoch 374/2000\n",
+      "3/3 [==============================] - 0s 58ms/step - loss: 0.5159 - categorical_accuracy: 0.7529\n",
+      "Epoch 375/2000\n",
+      "3/3 [==============================] - 0s 57ms/step - loss: 0.5179 - categorical_accuracy: 0.7882\n",
+      "Epoch 376/2000\n",
+      "3/3 [==============================] - 0s 57ms/step - loss: 0.5203 - categorical_accuracy: 0.7059\n",
+      "Epoch 377/2000\n",
+      "3/3 [==============================] - 0s 59ms/step - loss: 0.5224 - categorical_accuracy: 0.7765\n",
+      "Epoch 378/2000\n",
+      "3/3 [==============================] - 0s 58ms/step - loss: 0.5135 - categorical_accuracy: 0.7529\n",
+      "Epoch 379/2000\n",
+      "3/3 [==============================] - 0s 58ms/step - loss: 0.5219 - categorical_accuracy: 0.6941\n",
+      "Epoch 380/2000\n",
+      "3/3 [==============================] - 0s 58ms/step - loss: 0.5151 - categorical_accuracy: 0.7765\n",
+      "Epoch 381/2000\n",
+      "3/3 [==============================] - 0s 57ms/step - loss: 0.5137 - categorical_accuracy: 0.7647\n",
+      "Epoch 382/2000\n",
+      "3/3 [==============================] - 0s 58ms/step - loss: 0.5124 - categorical_accuracy: 0.7882\n",
+      "Epoch 383/2000\n",
+      "3/3 [==============================] - 0s 57ms/step - loss: 0.5152 - categorical_accuracy: 0.7647\n",
+      "Epoch 384/2000\n",
+      "3/3 [==============================] - 0s 57ms/step - loss: 0.5135 - categorical_accuracy: 0.8000\n",
+      "Epoch 385/2000\n",
+      "3/3 [==============================] - 0s 59ms/step - loss: 0.5141 - categorical_accuracy: 0.7765\n",
+      "Epoch 386/2000\n",
+      "3/3 [==============================] - 0s 58ms/step - loss: 0.5144 - categorical_accuracy: 0.6941\n",
+      "Epoch 387/2000\n",
+      "3/3 [==============================] - 0s 61ms/step - loss: 0.5121 - categorical_accuracy: 0.8235\n",
+      "Epoch 388/2000\n",
+      "3/3 [==============================] - 0s 61ms/step - loss: 0.5147 - categorical_accuracy: 0.7647\n",
+      "Epoch 389/2000\n",
+      "3/3 [==============================] - 0s 58ms/step - loss: 0.5124 - categorical_accuracy: 0.7647\n",
+      "Epoch 390/2000\n",
+      "3/3 [==============================] - 0s 58ms/step - loss: 0.5133 - categorical_accuracy: 0.8000\n",
+      "Epoch 391/2000\n",
+      "3/3 [==============================] - 0s 56ms/step - loss: 0.5089 - categorical_accuracy: 0.8000\n",
+      "Epoch 392/2000\n",
+      "3/3 [==============================] - 0s 59ms/step - loss: 0.5149 - categorical_accuracy: 0.8000\n",
+      "Epoch 393/2000\n",
+      "3/3 [==============================] - 0s 58ms/step - loss: 0.5323 - categorical_accuracy: 0.6824\n",
+      "Epoch 394/2000\n",
+      "3/3 [==============================] - 0s 58ms/step - loss: 0.5018 - categorical_accuracy: 0.8118\n",
+      "Epoch 395/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.5224 - categorical_accuracy: 0.8000\n",
+      "Epoch 396/2000\n",
+      "3/3 [==============================] - 0s 60ms/step - loss: 0.5107 - categorical_accuracy: 0.7529\n",
+      "Epoch 397/2000\n",
+      "3/3 [==============================] - 0s 59ms/step - loss: 0.5073 - categorical_accuracy: 0.7765\n",
+      "Epoch 398/2000\n",
+      "3/3 [==============================] - 0s 58ms/step - loss: 0.5085 - categorical_accuracy: 0.8000\n",
+      "Epoch 399/2000\n",
+      "3/3 [==============================] - 0s 58ms/step - loss: 0.5118 - categorical_accuracy: 0.7529\n",
+      "Epoch 400/2000\n",
+      "3/3 [==============================] - 0s 59ms/step - loss: 0.5046 - categorical_accuracy: 0.7647\n",
+      "Epoch 401/2000\n",
+      "3/3 [==============================] - 0s 60ms/step - loss: 0.5133 - categorical_accuracy: 0.8118\n",
+      "Epoch 402/2000\n",
+      "3/3 [==============================] - 0s 59ms/step - loss: 0.5034 - categorical_accuracy: 0.8000\n",
+      "Epoch 403/2000\n",
+      "3/3 [==============================] - 0s 61ms/step - loss: 0.5085 - categorical_accuracy: 0.7882\n",
+      "Epoch 404/2000\n",
+      "3/3 [==============================] - 0s 61ms/step - loss: 0.5074 - categorical_accuracy: 0.8000\n",
+      "Epoch 405/2000\n",
+      "3/3 [==============================] - 0s 61ms/step - loss: 0.5030 - categorical_accuracy: 0.7882\n",
+      "Epoch 406/2000\n",
+      "3/3 [==============================] - 0s 61ms/step - loss: 0.5101 - categorical_accuracy: 0.7765\n",
+      "Epoch 407/2000\n",
+      "3/3 [==============================] - 0s 61ms/step - loss: 0.5107 - categorical_accuracy: 0.7412\n",
+      "Epoch 408/2000\n",
+      "3/3 [==============================] - 0s 59ms/step - loss: 0.5389 - categorical_accuracy: 0.7176\n",
+      "Epoch 409/2000\n",
+      "3/3 [==============================] - 0s 61ms/step - loss: 0.5059 - categorical_accuracy: 0.7882\n",
+      "Epoch 410/2000\n",
+      "3/3 [==============================] - 0s 61ms/step - loss: 0.5325 - categorical_accuracy: 0.6471\n",
+      "Epoch 411/2000\n",
+      "3/3 [==============================] - 0s 61ms/step - loss: 0.5182 - categorical_accuracy: 0.8000\n",
+      "Epoch 412/2000\n",
+      "3/3 [==============================] - 0s 60ms/step - loss: 0.5109 - categorical_accuracy: 0.8118\n",
+      "Epoch 413/2000\n",
+      "3/3 [==============================] - 0s 60ms/step - loss: 0.5359 - categorical_accuracy: 0.6471\n",
+      "Epoch 414/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.5298 - categorical_accuracy: 0.6824\n",
+      "Epoch 415/2000\n",
+      "3/3 [==============================] - 0s 60ms/step - loss: 0.5126 - categorical_accuracy: 0.8118\n",
+      "Epoch 416/2000\n",
+      "3/3 [==============================] - 0s 60ms/step - loss: 0.5133 - categorical_accuracy: 0.6824\n",
+      "Epoch 417/2000\n",
+      "3/3 [==============================] - 0s 60ms/step - loss: 0.5041 - categorical_accuracy: 0.7882\n",
+      "Epoch 418/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.5101 - categorical_accuracy: 0.8000\n",
+      "Epoch 419/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.5328 - categorical_accuracy: 0.6118\n",
+      "Epoch 420/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.5216 - categorical_accuracy: 0.7059\n",
+      "Epoch 421/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.5309 - categorical_accuracy: 0.7294\n",
+      "Epoch 422/2000\n",
+      "3/3 [==============================] - 0s 61ms/step - loss: 0.5004 - categorical_accuracy: 0.8118\n",
+      "Epoch 423/2000\n",
+      "3/3 [==============================] - 0s 61ms/step - loss: 0.5204 - categorical_accuracy: 0.6235\n",
+      "Epoch 424/2000\n",
+      "3/3 [==============================] - 0s 60ms/step - loss: 0.5311 - categorical_accuracy: 0.7765\n",
+      "Epoch 425/2000\n",
+      "3/3 [==============================] - 0s 61ms/step - loss: 0.5294 - categorical_accuracy: 0.7529\n",
+      "Epoch 426/2000\n",
+      "3/3 [==============================] - 0s 74ms/step - loss: 0.5063 - categorical_accuracy: 0.7647\n",
+      "Epoch 427/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.4966 - categorical_accuracy: 0.8235\n",
+      "Epoch 428/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4955 - categorical_accuracy: 0.7059\n",
+      "Epoch 429/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4856 - categorical_accuracy: 0.6941\n",
+      "Epoch 430/2000\n",
+      "3/3 [==============================] - 0s 70ms/step - loss: 0.4649 - categorical_accuracy: 0.8118\n",
+      "Epoch 431/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.4361 - categorical_accuracy: 0.7412\n",
+      "Epoch 432/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.4550 - categorical_accuracy: 0.8000\n",
+      "Epoch 433/2000\n",
+      "3/3 [==============================] - 0s 61ms/step - loss: 0.4542 - categorical_accuracy: 0.8118\n",
+      "Epoch 434/2000\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3/3 [==============================] - 0s 61ms/step - loss: 1.0186 - categorical_accuracy: 0.5882\n",
+      "Epoch 435/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.6964 - categorical_accuracy: 0.6353\n",
+      "Epoch 436/2000\n",
+      "3/3 [==============================] - 0s 61ms/step - loss: 0.6201 - categorical_accuracy: 0.6471\n",
+      "Epoch 437/2000\n",
+      "3/3 [==============================] - 0s 61ms/step - loss: 0.6865 - categorical_accuracy: 0.6471\n",
+      "Epoch 438/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.5875 - categorical_accuracy: 0.6000\n",
+      "Epoch 439/2000\n",
+      "3/3 [==============================] - 0s 61ms/step - loss: 0.5588 - categorical_accuracy: 0.7412\n",
+      "Epoch 440/2000\n",
+      "3/3 [==============================] - 0s 60ms/step - loss: 0.5874 - categorical_accuracy: 0.7294\n",
+      "Epoch 441/2000\n",
+      "3/3 [==============================] - 0s 60ms/step - loss: 0.5952 - categorical_accuracy: 0.6471\n",
+      "Epoch 442/2000\n",
+      "3/3 [==============================] - 0s 60ms/step - loss: 0.5611 - categorical_accuracy: 0.6824\n",
+      "Epoch 443/2000\n",
+      "3/3 [==============================] - 0s 60ms/step - loss: 0.5588 - categorical_accuracy: 0.6706\n",
+      "Epoch 444/2000\n",
+      "3/3 [==============================] - 0s 60ms/step - loss: 0.5479 - categorical_accuracy: 0.6588\n",
+      "Epoch 445/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.5552 - categorical_accuracy: 0.6706\n",
+      "Epoch 446/2000\n",
+      "3/3 [==============================] - 0s 61ms/step - loss: 0.5292 - categorical_accuracy: 0.7412\n",
+      "Epoch 447/2000\n",
+      "3/3 [==============================] - 0s 60ms/step - loss: 0.5356 - categorical_accuracy: 0.7412\n",
+      "Epoch 448/2000\n",
+      "3/3 [==============================] - 0s 61ms/step - loss: 0.5369 - categorical_accuracy: 0.7765\n",
+      "Epoch 449/2000\n",
+      "3/3 [==============================] - 0s 61ms/step - loss: 0.5325 - categorical_accuracy: 0.7882\n",
+      "Epoch 450/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.5301 - categorical_accuracy: 0.7529\n",
+      "Epoch 451/2000\n",
+      "3/3 [==============================] - 0s 60ms/step - loss: 0.5243 - categorical_accuracy: 0.7412\n",
+      "Epoch 452/2000\n",
+      "3/3 [==============================] - 0s 60ms/step - loss: 0.5246 - categorical_accuracy: 0.6824\n",
+      "Epoch 453/2000\n",
+      "3/3 [==============================] - 0s 60ms/step - loss: 0.5168 - categorical_accuracy: 0.7059\n",
+      "Epoch 454/2000\n",
+      "3/3 [==============================] - 0s 61ms/step - loss: 0.5234 - categorical_accuracy: 0.7647\n",
+      "Epoch 455/2000\n",
+      "3/3 [==============================] - 0s 61ms/step - loss: 0.5150 - categorical_accuracy: 0.7647\n",
+      "Epoch 456/2000\n",
+      "3/3 [==============================] - 0s 61ms/step - loss: 0.5255 - categorical_accuracy: 0.6706\n",
+      "Epoch 457/2000\n",
+      "3/3 [==============================] - 0s 60ms/step - loss: 0.5048 - categorical_accuracy: 0.6941\n",
+      "Epoch 458/2000\n",
+      "3/3 [==============================] - 0s 60ms/step - loss: 0.5095 - categorical_accuracy: 0.7765\n",
+      "Epoch 459/2000\n",
+      "3/3 [==============================] - 0s 61ms/step - loss: 0.5272 - categorical_accuracy: 0.7176\n",
+      "Epoch 460/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.5063 - categorical_accuracy: 0.8000\n",
+      "Epoch 461/2000\n",
+      "3/3 [==============================] - 0s 60ms/step - loss: 0.4937 - categorical_accuracy: 0.7647\n",
+      "Epoch 462/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.5385 - categorical_accuracy: 0.7059\n",
+      "Epoch 463/2000\n",
+      "3/3 [==============================] - 0s 61ms/step - loss: 0.5478 - categorical_accuracy: 0.7412\n",
+      "Epoch 464/2000\n",
+      "3/3 [==============================] - 0s 61ms/step - loss: 0.4856 - categorical_accuracy: 0.7412\n",
+      "Epoch 465/2000\n",
+      "3/3 [==============================] - 0s 68ms/step - loss: 0.5477 - categorical_accuracy: 0.6706\n",
+      "Epoch 466/2000\n",
+      "3/3 [==============================] - 0s 59ms/step - loss: 0.5174 - categorical_accuracy: 0.7059\n",
+      "Epoch 467/2000\n",
+      "3/3 [==============================] - 0s 61ms/step - loss: 0.5030 - categorical_accuracy: 0.8000\n",
+      "Epoch 468/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.5011 - categorical_accuracy: 0.7647\n",
+      "Epoch 469/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5837 - categorical_accuracy: 0.6471\n",
+      "Epoch 470/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.5107 - categorical_accuracy: 0.6941\n",
+      "Epoch 471/2000\n",
+      "3/3 [==============================] - 0s 67ms/step - loss: 0.5285 - categorical_accuracy: 0.7059\n",
+      "Epoch 472/2000\n",
+      "3/3 [==============================] - 0s 60ms/step - loss: 0.5364 - categorical_accuracy: 0.6824\n",
+      "Epoch 473/2000\n",
+      "3/3 [==============================] - 0s 58ms/step - loss: 0.5266 - categorical_accuracy: 0.6824\n",
+      "Epoch 474/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.5401 - categorical_accuracy: 0.6706\n",
+      "Epoch 475/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.5174 - categorical_accuracy: 0.6588\n",
+      "Epoch 476/2000\n",
+      "3/3 [==============================] - 0s 61ms/step - loss: 0.4990 - categorical_accuracy: 0.6588\n",
+      "Epoch 477/2000\n",
+      "3/3 [==============================] - 0s 61ms/step - loss: 0.5155 - categorical_accuracy: 0.7294\n",
+      "Epoch 478/2000\n",
+      "3/3 [==============================] - 0s 61ms/step - loss: 0.4787 - categorical_accuracy: 0.7647\n",
+      "Epoch 479/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.4977 - categorical_accuracy: 0.7765\n",
+      "Epoch 480/2000\n",
+      "3/3 [==============================] - 0s 58ms/step - loss: 0.4899 - categorical_accuracy: 0.7176\n",
+      "Epoch 481/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.6327 - categorical_accuracy: 0.6353\n",
+      "Epoch 482/2000\n",
+      "3/3 [==============================] - 0s 61ms/step - loss: 0.5768 - categorical_accuracy: 0.6471\n",
+      "Epoch 483/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5702 - categorical_accuracy: 0.6824\n",
+      "Epoch 484/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.5585 - categorical_accuracy: 0.6353\n",
+      "Epoch 485/2000\n",
+      "3/3 [==============================] - 0s 67ms/step - loss: 0.6211 - categorical_accuracy: 0.6353\n",
+      "Epoch 486/2000\n",
+      "3/3 [==============================] - 0s 61ms/step - loss: 0.6109 - categorical_accuracy: 0.6471\n",
+      "Epoch 487/2000\n",
+      "3/3 [==============================] - 0s 58ms/step - loss: 0.5957 - categorical_accuracy: 0.6471\n",
+      "Epoch 488/2000\n",
+      "3/3 [==============================] - 0s 61ms/step - loss: 0.5644 - categorical_accuracy: 0.6471\n",
+      "Epoch 489/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.5333 - categorical_accuracy: 0.7529\n",
+      "Epoch 490/2000\n",
+      "3/3 [==============================] - 0s 60ms/step - loss: 0.5589 - categorical_accuracy: 0.6471\n",
+      "Epoch 491/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5534 - categorical_accuracy: 0.6706\n",
+      "Epoch 492/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.5450 - categorical_accuracy: 0.7647\n",
+      "Epoch 493/2000\n",
+      "3/3 [==============================] - 0s 59ms/step - loss: 0.5345 - categorical_accuracy: 0.6471\n",
+      "Epoch 494/2000\n",
+      "3/3 [==============================] - 0s 60ms/step - loss: 0.5385 - categorical_accuracy: 0.6353\n",
+      "Epoch 495/2000\n",
+      "3/3 [==============================] - 0s 67ms/step - loss: 0.5454 - categorical_accuracy: 0.6706\n",
+      "Epoch 496/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.5307 - categorical_accuracy: 0.6353\n",
+      "Epoch 497/2000\n",
+      "3/3 [==============================] - 0s 59ms/step - loss: 0.5334 - categorical_accuracy: 0.7294\n",
+      "Epoch 498/2000\n",
+      "3/3 [==============================] - 0s 60ms/step - loss: 0.5453 - categorical_accuracy: 0.6706\n",
+      "Epoch 499/2000\n",
+      "3/3 [==============================] - 0s 60ms/step - loss: 0.5428 - categorical_accuracy: 0.6588\n",
+      "Epoch 500/2000\n",
+      "3/3 [==============================] - 0s 59ms/step - loss: 0.5310 - categorical_accuracy: 0.6471\n",
+      "Epoch 501/2000\n",
+      "3/3 [==============================] - 0s 61ms/step - loss: 0.5254 - categorical_accuracy: 0.6471\n",
+      "Epoch 502/2000\n",
+      "3/3 [==============================] - 0s 61ms/step - loss: 0.5349 - categorical_accuracy: 0.7059\n",
+      "Epoch 503/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5238 - categorical_accuracy: 0.6941\n",
+      "Epoch 504/2000\n",
+      "3/3 [==============================] - 0s 61ms/step - loss: 0.5301 - categorical_accuracy: 0.6941\n",
+      "Epoch 505/2000\n",
+      "3/3 [==============================] - 0s 60ms/step - loss: 0.5232 - categorical_accuracy: 0.7647\n",
+      "Epoch 506/2000\n",
+      "3/3 [==============================] - 0s 59ms/step - loss: 0.5297 - categorical_accuracy: 0.8118\n",
+      "Epoch 507/2000\n",
+      "3/3 [==============================] - 0s 58ms/step - loss: 0.5216 - categorical_accuracy: 0.6941\n",
+      "Epoch 508/2000\n",
+      "3/3 [==============================] - 0s 60ms/step - loss: 0.5225 - categorical_accuracy: 0.6588\n",
+      "Epoch 509/2000\n",
+      "3/3 [==============================] - 0s 61ms/step - loss: 0.5216 - categorical_accuracy: 0.7529\n",
+      "Epoch 510/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.5228 - categorical_accuracy: 0.8118\n",
+      "Epoch 511/2000\n",
+      "3/3 [==============================] - 0s 61ms/step - loss: 0.5256 - categorical_accuracy: 0.6471\n",
+      "Epoch 512/2000\n",
+      "3/3 [==============================] - 0s 61ms/step - loss: 0.5239 - categorical_accuracy: 0.6235\n",
+      "Epoch 513/2000\n",
+      "3/3 [==============================] - 0s 61ms/step - loss: 0.5199 - categorical_accuracy: 0.8000\n",
+      "Epoch 514/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.5182 - categorical_accuracy: 0.8000\n",
+      "Epoch 515/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.5221 - categorical_accuracy: 0.8118\n",
+      "Epoch 516/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.5199 - categorical_accuracy: 0.8118\n",
+      "Epoch 517/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.5221 - categorical_accuracy: 0.7882\n",
+      "Epoch 518/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.5355 - categorical_accuracy: 0.6706\n",
+      "Epoch 519/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.5287 - categorical_accuracy: 0.6471\n",
+      "Epoch 520/2000\n",
+      "3/3 [==============================] - 0s 61ms/step - loss: 0.5316 - categorical_accuracy: 0.8000\n",
+      "Epoch 521/2000\n",
+      "3/3 [==============================] - 0s 61ms/step - loss: 0.5202 - categorical_accuracy: 0.8118\n",
+      "Epoch 522/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.5225 - categorical_accuracy: 0.6941\n",
+      "Epoch 523/2000\n",
+      "3/3 [==============================] - 0s 60ms/step - loss: 0.5223 - categorical_accuracy: 0.7294\n",
+      "Epoch 524/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5150 - categorical_accuracy: 0.8118\n",
+      "Epoch 525/2000\n",
+      "3/3 [==============================] - 0s 61ms/step - loss: 0.5230 - categorical_accuracy: 0.7059\n",
+      "Epoch 526/2000\n",
+      "3/3 [==============================] - 0s 61ms/step - loss: 0.5130 - categorical_accuracy: 0.6588\n",
+      "Epoch 527/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.5117 - categorical_accuracy: 0.7882\n",
+      "Epoch 528/2000\n",
+      "3/3 [==============================] - 0s 67ms/step - loss: 0.5143 - categorical_accuracy: 0.8118\n",
+      "Epoch 529/2000\n",
+      "3/3 [==============================] - 0s 60ms/step - loss: 0.5108 - categorical_accuracy: 0.8118\n",
+      "Epoch 530/2000\n",
+      "3/3 [==============================] - 0s 59ms/step - loss: 0.5106 - categorical_accuracy: 0.6353\n",
+      "Epoch 531/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.5103 - categorical_accuracy: 0.8235\n",
+      "Epoch 532/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.5134 - categorical_accuracy: 0.7647\n",
+      "Epoch 533/2000\n",
+      "3/3 [==============================] - 0s 60ms/step - loss: 0.5115 - categorical_accuracy: 0.6941\n",
+      "Epoch 534/2000\n",
+      "3/3 [==============================] - 0s 61ms/step - loss: 0.5101 - categorical_accuracy: 0.7412\n",
+      "Epoch 535/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.5086 - categorical_accuracy: 0.7176\n",
+      "Epoch 536/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.5081 - categorical_accuracy: 0.7059\n",
+      "Epoch 537/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.5094 - categorical_accuracy: 0.7176\n",
+      "Epoch 538/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.5082 - categorical_accuracy: 0.7529\n",
+      "Epoch 539/2000\n",
+      "3/3 [==============================] - 0s 61ms/step - loss: 0.5088 - categorical_accuracy: 0.7294\n",
+      "Epoch 540/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.5042 - categorical_accuracy: 0.7176\n",
+      "Epoch 541/2000\n",
+      "3/3 [==============================] - 0s 61ms/step - loss: 0.5069 - categorical_accuracy: 0.8235\n",
+      "Epoch 542/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.5089 - categorical_accuracy: 0.7529\n",
+      "Epoch 543/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.5061 - categorical_accuracy: 0.7176\n",
+      "Epoch 544/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.5042 - categorical_accuracy: 0.8118\n",
+      "Epoch 545/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.5040 - categorical_accuracy: 0.8353\n",
+      "Epoch 546/2000\n",
+      "3/3 [==============================] - 0s 60ms/step - loss: 0.5084 - categorical_accuracy: 0.7294\n",
+      "Epoch 547/2000\n",
+      "3/3 [==============================] - 0s 60ms/step - loss: 0.5064 - categorical_accuracy: 0.7294\n",
+      "Epoch 548/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.5079 - categorical_accuracy: 0.8118\n",
+      "Epoch 549/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5145 - categorical_accuracy: 0.7765\n",
+      "Epoch 550/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5256 - categorical_accuracy: 0.8000\n",
+      "Epoch 551/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.5171 - categorical_accuracy: 0.6824\n",
+      "Epoch 552/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.5338 - categorical_accuracy: 0.6353\n",
+      "Epoch 553/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.5486 - categorical_accuracy: 0.7176\n",
+      "Epoch 554/2000\n",
+      "3/3 [==============================] - 0s 73ms/step - loss: 0.6278 - categorical_accuracy: 0.6941\n",
+      "Epoch 555/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.5723 - categorical_accuracy: 0.6588\n",
+      "Epoch 556/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.5308 - categorical_accuracy: 0.6588\n",
+      "Epoch 557/2000\n",
+      "3/3 [==============================] - 0s 61ms/step - loss: 0.5508 - categorical_accuracy: 0.6941\n",
+      "Epoch 558/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.5639 - categorical_accuracy: 0.6588\n",
+      "Epoch 559/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.5418 - categorical_accuracy: 0.7882\n",
+      "Epoch 560/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.6652 - categorical_accuracy: 0.6824\n",
+      "Epoch 561/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.6483 - categorical_accuracy: 0.6588\n",
+      "Epoch 562/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.6572 - categorical_accuracy: 0.6353\n",
+      "Epoch 563/2000\n",
+      "3/3 [==============================] - 0s 68ms/step - loss: 0.5631 - categorical_accuracy: 0.6941\n",
+      "Epoch 564/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.6319 - categorical_accuracy: 0.6353\n",
+      "Epoch 565/2000\n",
+      "3/3 [==============================] - 0s 61ms/step - loss: 0.6067 - categorical_accuracy: 0.6353\n",
+      "Epoch 566/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.5418 - categorical_accuracy: 0.6941\n",
+      "Epoch 567/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5822 - categorical_accuracy: 0.7412\n",
+      "Epoch 568/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5648 - categorical_accuracy: 0.6471\n",
+      "Epoch 569/2000\n",
+      "3/3 [==============================] - 0s 74ms/step - loss: 0.5393 - categorical_accuracy: 0.6706\n",
+      "Epoch 570/2000\n",
+      "3/3 [==============================] - 0s 61ms/step - loss: 0.5218 - categorical_accuracy: 0.8000\n",
+      "Epoch 571/2000\n",
+      "3/3 [==============================] - 0s 61ms/step - loss: 0.5215 - categorical_accuracy: 0.7882\n",
+      "Epoch 572/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.5049 - categorical_accuracy: 0.7882\n",
+      "Epoch 573/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.5173 - categorical_accuracy: 0.6588\n",
+      "Epoch 574/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.5229 - categorical_accuracy: 0.6471\n",
+      "Epoch 575/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.5182 - categorical_accuracy: 0.7059\n",
+      "Epoch 576/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.5152 - categorical_accuracy: 0.7882\n",
+      "Epoch 577/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.5089 - categorical_accuracy: 0.8235\n",
+      "Epoch 578/2000\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.5051 - categorical_accuracy: 0.7176\n",
+      "Epoch 579/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.5046 - categorical_accuracy: 0.6941\n",
+      "Epoch 580/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.5093 - categorical_accuracy: 0.7529\n",
+      "Epoch 581/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.4997 - categorical_accuracy: 0.8118\n",
+      "Epoch 582/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5164 - categorical_accuracy: 0.7882\n",
+      "Epoch 583/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.5072 - categorical_accuracy: 0.7647\n",
+      "Epoch 584/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.5094 - categorical_accuracy: 0.6588\n",
+      "Epoch 585/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.5075 - categorical_accuracy: 0.7765\n",
+      "Epoch 586/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.5024 - categorical_accuracy: 0.8000\n",
+      "Epoch 587/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4961 - categorical_accuracy: 0.7765\n",
+      "Epoch 588/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.4953 - categorical_accuracy: 0.8000\n",
+      "Epoch 589/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4943 - categorical_accuracy: 0.8235\n",
+      "Epoch 590/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4907 - categorical_accuracy: 0.8118\n",
+      "Epoch 591/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4937 - categorical_accuracy: 0.7294\n",
+      "Epoch 592/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4937 - categorical_accuracy: 0.7294\n",
+      "Epoch 593/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4877 - categorical_accuracy: 0.8235\n",
+      "Epoch 594/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5150 - categorical_accuracy: 0.7882\n",
+      "Epoch 595/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.5034 - categorical_accuracy: 0.6941\n",
+      "Epoch 596/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.5211 - categorical_accuracy: 0.7412\n",
+      "Epoch 597/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.5020 - categorical_accuracy: 0.7647\n",
+      "Epoch 598/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.5151 - categorical_accuracy: 0.6353\n",
+      "Epoch 599/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.4870 - categorical_accuracy: 0.8118\n",
+      "Epoch 600/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.5031 - categorical_accuracy: 0.8000\n",
+      "Epoch 601/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.4827 - categorical_accuracy: 0.8000\n",
+      "Epoch 602/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.4918 - categorical_accuracy: 0.6706\n",
+      "Epoch 603/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.4852 - categorical_accuracy: 0.8118\n",
+      "Epoch 604/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4950 - categorical_accuracy: 0.7765\n",
+      "Epoch 605/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4893 - categorical_accuracy: 0.7529\n",
+      "Epoch 606/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.4876 - categorical_accuracy: 0.8118\n",
+      "Epoch 607/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4807 - categorical_accuracy: 0.7882\n",
+      "Epoch 608/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.5012 - categorical_accuracy: 0.6588\n",
+      "Epoch 609/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.5259 - categorical_accuracy: 0.7647\n",
+      "Epoch 610/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4868 - categorical_accuracy: 0.7765\n",
+      "Epoch 611/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.5034 - categorical_accuracy: 0.6706\n",
+      "Epoch 612/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5549 - categorical_accuracy: 0.7294\n",
+      "Epoch 613/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.4892 - categorical_accuracy: 0.7882\n",
+      "Epoch 614/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.5375 - categorical_accuracy: 0.6353\n",
+      "Epoch 615/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5415 - categorical_accuracy: 0.7176\n",
+      "Epoch 616/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5171 - categorical_accuracy: 0.7647\n",
+      "Epoch 617/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.4968 - categorical_accuracy: 0.6588\n",
+      "Epoch 618/2000\n",
+      "3/3 [==============================] - 0s 61ms/step - loss: 0.4988 - categorical_accuracy: 0.8000\n",
+      "Epoch 619/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.5022 - categorical_accuracy: 0.7294\n",
+      "Epoch 620/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.5046 - categorical_accuracy: 0.8000\n",
+      "Epoch 621/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.5394 - categorical_accuracy: 0.7647\n",
+      "Epoch 622/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.5260 - categorical_accuracy: 0.6235\n",
+      "Epoch 623/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.5022 - categorical_accuracy: 0.7765\n",
+      "Epoch 624/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.4823 - categorical_accuracy: 0.7765\n",
+      "Epoch 625/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.6127 - categorical_accuracy: 0.7529\n",
+      "Epoch 626/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.4676 - categorical_accuracy: 0.7529\n",
+      "Epoch 627/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5135 - categorical_accuracy: 0.7294\n",
+      "Epoch 628/2000\n",
+      "3/3 [==============================] - 0s 61ms/step - loss: 0.5253 - categorical_accuracy: 0.7412\n",
+      "Epoch 629/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5411 - categorical_accuracy: 0.8118\n",
+      "Epoch 630/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.5575 - categorical_accuracy: 0.8000\n",
+      "Epoch 631/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.5514 - categorical_accuracy: 0.6471\n",
+      "Epoch 632/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.5156 - categorical_accuracy: 0.6824\n",
+      "Epoch 633/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5201 - categorical_accuracy: 0.7882\n",
+      "Epoch 634/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5061 - categorical_accuracy: 0.7647\n",
+      "Epoch 635/2000\n",
+      "3/3 [==============================] - 0s 60ms/step - loss: 0.4824 - categorical_accuracy: 0.8000\n",
+      "Epoch 636/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.5175 - categorical_accuracy: 0.7294\n",
+      "Epoch 637/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.5090 - categorical_accuracy: 0.6706\n",
+      "Epoch 638/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.5029 - categorical_accuracy: 0.7765\n",
+      "Epoch 639/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.5046 - categorical_accuracy: 0.7059\n",
+      "Epoch 640/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.7542 - categorical_accuracy: 0.6471\n",
+      "Epoch 641/2000\n",
+      "3/3 [==============================] - 0s 61ms/step - loss: 0.6598 - categorical_accuracy: 0.6235\n",
+      "Epoch 642/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.6437 - categorical_accuracy: 0.6353\n",
+      "Epoch 643/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.6091 - categorical_accuracy: 0.6941\n",
+      "Epoch 644/2000\n",
+      "3/3 [==============================] - 0s 61ms/step - loss: 0.6523 - categorical_accuracy: 0.6471\n",
+      "Epoch 645/2000\n",
+      "3/3 [==============================] - 0s 60ms/step - loss: 0.5653 - categorical_accuracy: 0.6706\n",
+      "Epoch 646/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.5335 - categorical_accuracy: 0.7059\n",
+      "Epoch 647/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.5775 - categorical_accuracy: 0.7176\n",
+      "Epoch 648/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.5223 - categorical_accuracy: 0.7882\n",
+      "Epoch 649/2000\n",
+      "3/3 [==============================] - 0s 61ms/step - loss: 0.5148 - categorical_accuracy: 0.6706\n",
+      "Epoch 650/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.5262 - categorical_accuracy: 0.6588\n",
+      "Epoch 651/2000\n",
+      "3/3 [==============================] - 0s 60ms/step - loss: 0.5175 - categorical_accuracy: 0.6941\n",
+      "Epoch 652/2000\n",
+      "3/3 [==============================] - 0s 61ms/step - loss: 0.5074 - categorical_accuracy: 0.8000\n",
+      "Epoch 653/2000\n",
+      "3/3 [==============================] - 0s 61ms/step - loss: 0.5027 - categorical_accuracy: 0.8000\n",
+      "Epoch 654/2000\n",
+      "3/3 [==============================] - 0s 60ms/step - loss: 0.5045 - categorical_accuracy: 0.8118\n",
+      "Epoch 655/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.4981 - categorical_accuracy: 0.8118\n",
+      "Epoch 656/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.5044 - categorical_accuracy: 0.7176\n",
+      "Epoch 657/2000\n",
+      "3/3 [==============================] - 0s 61ms/step - loss: 0.4937 - categorical_accuracy: 0.7412\n",
+      "Epoch 658/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.5073 - categorical_accuracy: 0.7882\n",
+      "Epoch 659/2000\n",
+      "3/3 [==============================] - 0s 61ms/step - loss: 0.4965 - categorical_accuracy: 0.7412\n",
+      "Epoch 660/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.4973 - categorical_accuracy: 0.7059\n",
+      "Epoch 661/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.4873 - categorical_accuracy: 0.7529\n",
+      "Epoch 662/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.4788 - categorical_accuracy: 0.7647\n",
+      "Epoch 663/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.4767 - categorical_accuracy: 0.8235\n",
+      "Epoch 664/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.4784 - categorical_accuracy: 0.8118\n",
+      "Epoch 665/2000\n",
+      "3/3 [==============================] - 0s 61ms/step - loss: 0.4801 - categorical_accuracy: 0.8118\n",
+      "Epoch 666/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.4765 - categorical_accuracy: 0.8118\n",
+      "Epoch 667/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.4735 - categorical_accuracy: 0.8118\n",
+      "Epoch 668/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.4885 - categorical_accuracy: 0.7294\n",
+      "Epoch 669/2000\n",
+      "3/3 [==============================] - 0s 61ms/step - loss: 0.4718 - categorical_accuracy: 0.8000\n",
+      "Epoch 670/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.4932 - categorical_accuracy: 0.7647\n",
+      "Epoch 671/2000\n",
+      "3/3 [==============================] - 0s 60ms/step - loss: 0.4697 - categorical_accuracy: 0.7765\n",
+      "Epoch 672/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.4628 - categorical_accuracy: 0.8000\n",
+      "Epoch 673/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.4605 - categorical_accuracy: 0.8235\n",
+      "Epoch 674/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4618 - categorical_accuracy: 0.7412\n",
+      "Epoch 675/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4623 - categorical_accuracy: 0.8118\n",
+      "Epoch 676/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.4369 - categorical_accuracy: 0.8000\n",
+      "Epoch 677/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4280 - categorical_accuracy: 0.8000\n",
+      "Epoch 678/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4252 - categorical_accuracy: 0.8235\n",
+      "Epoch 679/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.4989 - categorical_accuracy: 0.7529\n",
+      "Epoch 680/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5107 - categorical_accuracy: 0.7412\n",
+      "Epoch 681/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.5112 - categorical_accuracy: 0.7059\n",
+      "Epoch 682/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.5309 - categorical_accuracy: 0.7529\n",
+      "Epoch 683/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.4866 - categorical_accuracy: 0.7529\n",
+      "Epoch 684/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.4782 - categorical_accuracy: 0.7176\n",
+      "Epoch 685/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.4759 - categorical_accuracy: 0.8000\n",
+      "Epoch 686/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.4470 - categorical_accuracy: 0.7882\n",
+      "Epoch 687/2000\n",
+      "3/3 [==============================] - 0s 60ms/step - loss: 0.4321 - categorical_accuracy: 0.8000\n",
+      "Epoch 688/2000\n",
+      "3/3 [==============================] - 0s 60ms/step - loss: 0.4585 - categorical_accuracy: 0.7529\n",
+      "Epoch 689/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.4680 - categorical_accuracy: 0.8235\n",
+      "Epoch 690/2000\n",
+      "3/3 [==============================] - 0s 61ms/step - loss: 0.5072 - categorical_accuracy: 0.6706\n",
+      "Epoch 691/2000\n",
+      "3/3 [==============================] - 0s 61ms/step - loss: 0.6807 - categorical_accuracy: 0.6471\n",
+      "Epoch 692/2000\n",
+      "3/3 [==============================] - 0s 61ms/step - loss: 0.5672 - categorical_accuracy: 0.7176\n",
+      "Epoch 693/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.5319 - categorical_accuracy: 0.6235\n",
+      "Epoch 694/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.6010 - categorical_accuracy: 0.6588\n",
+      "Epoch 695/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.5195 - categorical_accuracy: 0.6706\n",
+      "Epoch 696/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.4938 - categorical_accuracy: 0.7529\n",
+      "Epoch 697/2000\n",
+      "3/3 [==============================] - 0s 60ms/step - loss: 0.5368 - categorical_accuracy: 0.7059\n",
+      "Epoch 698/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5171 - categorical_accuracy: 0.7765\n",
+      "Epoch 699/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4931 - categorical_accuracy: 0.7647\n",
+      "Epoch 700/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.5348 - categorical_accuracy: 0.6471\n",
+      "Epoch 701/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.5270 - categorical_accuracy: 0.7059\n",
+      "Epoch 702/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.4941 - categorical_accuracy: 0.7765\n",
+      "Epoch 703/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4692 - categorical_accuracy: 0.8000\n",
+      "Epoch 704/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.4720 - categorical_accuracy: 0.8235\n",
+      "Epoch 705/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.4628 - categorical_accuracy: 0.8235\n",
+      "Epoch 706/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.4726 - categorical_accuracy: 0.7647\n",
+      "Epoch 707/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.4576 - categorical_accuracy: 0.7882\n",
+      "Epoch 708/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.4418 - categorical_accuracy: 0.8000\n",
+      "Epoch 709/2000\n",
+      "3/3 [==============================] - 0s 61ms/step - loss: 0.4384 - categorical_accuracy: 0.8118\n",
+      "Epoch 710/2000\n",
+      "3/3 [==============================] - 0s 61ms/step - loss: 0.4136 - categorical_accuracy: 0.8235\n",
+      "Epoch 711/2000\n",
+      "3/3 [==============================] - 0s 61ms/step - loss: 0.4619 - categorical_accuracy: 0.8118\n",
+      "Epoch 712/2000\n",
+      "3/3 [==============================] - 0s 61ms/step - loss: 0.4403 - categorical_accuracy: 0.8118\n",
+      "Epoch 713/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.4134 - categorical_accuracy: 0.8000\n",
+      "Epoch 714/2000\n",
+      "3/3 [==============================] - 0s 60ms/step - loss: 0.4102 - categorical_accuracy: 0.8353\n",
+      "Epoch 715/2000\n",
+      "3/3 [==============================] - 0s 61ms/step - loss: 0.4207 - categorical_accuracy: 0.8118\n",
+      "Epoch 716/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.4102 - categorical_accuracy: 0.8235\n",
+      "Epoch 717/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.4172 - categorical_accuracy: 0.8235\n",
+      "Epoch 718/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.5089 - categorical_accuracy: 0.7529\n",
+      "Epoch 719/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.4875 - categorical_accuracy: 0.7529\n",
+      "Epoch 720/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.5133 - categorical_accuracy: 0.6471\n",
+      "Epoch 721/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.5133 - categorical_accuracy: 0.7882\n",
+      "Epoch 722/2000\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.4873 - categorical_accuracy: 0.8000\n",
+      "Epoch 723/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4964 - categorical_accuracy: 0.6235\n",
+      "Epoch 724/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.4702 - categorical_accuracy: 0.7529\n",
+      "Epoch 725/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4450 - categorical_accuracy: 0.8118\n",
+      "Epoch 726/2000\n",
+      "3/3 [==============================] - 0s 61ms/step - loss: 0.4385 - categorical_accuracy: 0.7765\n",
+      "Epoch 727/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.6662 - categorical_accuracy: 0.6471\n",
+      "Epoch 728/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5405 - categorical_accuracy: 0.6941\n",
+      "Epoch 729/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.5844 - categorical_accuracy: 0.6706\n",
+      "Epoch 730/2000\n",
+      "3/3 [==============================] - 0s 61ms/step - loss: 0.4920 - categorical_accuracy: 0.7059\n",
+      "Epoch 731/2000\n",
+      "3/3 [==============================] - 0s 60ms/step - loss: 0.4771 - categorical_accuracy: 0.7412\n",
+      "Epoch 732/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.4527 - categorical_accuracy: 0.8000\n",
+      "Epoch 733/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.4623 - categorical_accuracy: 0.8118\n",
+      "Epoch 734/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4318 - categorical_accuracy: 0.8118\n",
+      "Epoch 735/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4679 - categorical_accuracy: 0.7882\n",
+      "Epoch 736/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.4389 - categorical_accuracy: 0.8235\n",
+      "Epoch 737/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.4497 - categorical_accuracy: 0.8000\n",
+      "Epoch 738/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.4816 - categorical_accuracy: 0.7647\n",
+      "Epoch 739/2000\n",
+      "3/3 [==============================] - 0s 67ms/step - loss: 0.4873 - categorical_accuracy: 0.7647\n",
+      "Epoch 740/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.5604 - categorical_accuracy: 0.6588\n",
+      "Epoch 741/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5360 - categorical_accuracy: 0.7412\n",
+      "Epoch 742/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5079 - categorical_accuracy: 0.6941\n",
+      "Epoch 743/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5080 - categorical_accuracy: 0.6706\n",
+      "Epoch 744/2000\n",
+      "3/3 [==============================] - 0s 67ms/step - loss: 0.5105 - categorical_accuracy: 0.7294\n",
+      "Epoch 745/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.5113 - categorical_accuracy: 0.7176\n",
+      "Epoch 746/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.4934 - categorical_accuracy: 0.7647\n",
+      "Epoch 747/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4783 - categorical_accuracy: 0.8235\n",
+      "Epoch 748/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.4706 - categorical_accuracy: 0.8118\n",
+      "Epoch 749/2000\n",
+      "3/3 [==============================] - 0s 67ms/step - loss: 0.4707 - categorical_accuracy: 0.8118\n",
+      "Epoch 750/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.4743 - categorical_accuracy: 0.7059\n",
+      "Epoch 751/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.4626 - categorical_accuracy: 0.8000\n",
+      "Epoch 752/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4659 - categorical_accuracy: 0.8000\n",
+      "Epoch 753/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.4595 - categorical_accuracy: 0.7647\n",
+      "Epoch 754/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.4598 - categorical_accuracy: 0.7765\n",
+      "Epoch 755/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4585 - categorical_accuracy: 0.8118\n",
+      "Epoch 756/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.4486 - categorical_accuracy: 0.8353\n",
+      "Epoch 757/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4847 - categorical_accuracy: 0.7765\n",
+      "Epoch 758/2000\n",
+      "3/3 [==============================] - 0s 67ms/step - loss: 0.5083 - categorical_accuracy: 0.8000\n",
+      "Epoch 759/2000\n",
+      "3/3 [==============================] - 0s 68ms/step - loss: 0.5076 - categorical_accuracy: 0.7059\n",
+      "Epoch 760/2000\n",
+      "3/3 [==============================] - 0s 75ms/step - loss: 0.4734 - categorical_accuracy: 0.7529\n",
+      "Epoch 761/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4538 - categorical_accuracy: 0.8118\n",
+      "Epoch 762/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.4681 - categorical_accuracy: 0.8353\n",
+      "Epoch 763/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.4736 - categorical_accuracy: 0.8000\n",
+      "Epoch 764/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.5141 - categorical_accuracy: 0.7647\n",
+      "Epoch 765/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4729 - categorical_accuracy: 0.7412\n",
+      "Epoch 766/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.4988 - categorical_accuracy: 0.8000\n",
+      "Epoch 767/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.4631 - categorical_accuracy: 0.8235\n",
+      "Epoch 768/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.4796 - categorical_accuracy: 0.7765\n",
+      "Epoch 769/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.4925 - categorical_accuracy: 0.7882\n",
+      "Epoch 770/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.5694 - categorical_accuracy: 0.7294\n",
+      "Epoch 771/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.4716 - categorical_accuracy: 0.7765\n",
+      "Epoch 772/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.5154 - categorical_accuracy: 0.7294\n",
+      "Epoch 773/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5071 - categorical_accuracy: 0.7765\n",
+      "Epoch 774/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.4922 - categorical_accuracy: 0.8000\n",
+      "Epoch 775/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.6053 - categorical_accuracy: 0.7412\n",
+      "Epoch 776/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.5383 - categorical_accuracy: 0.6824\n",
+      "Epoch 777/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 1.0498 - categorical_accuracy: 0.6118\n",
+      "Epoch 778/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 1.0602 - categorical_accuracy: 0.5882\n",
+      "Epoch 779/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.9654 - categorical_accuracy: 0.4000\n",
+      "Epoch 780/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.5641 - categorical_accuracy: 0.7176\n",
+      "Epoch 781/2000\n",
+      "3/3 [==============================] - 0s 61ms/step - loss: 0.7042 - categorical_accuracy: 0.6353\n",
+      "Epoch 782/2000\n",
+      "3/3 [==============================] - 0s 61ms/step - loss: 0.5770 - categorical_accuracy: 0.6471\n",
+      "Epoch 783/2000\n",
+      "3/3 [==============================] - 0s 61ms/step - loss: 0.5840 - categorical_accuracy: 0.6588\n",
+      "Epoch 784/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.5687 - categorical_accuracy: 0.6471\n",
+      "Epoch 785/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.6171 - categorical_accuracy: 0.6824\n",
+      "Epoch 786/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.5762 - categorical_accuracy: 0.7059\n",
+      "Epoch 787/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.5558 - categorical_accuracy: 0.6706\n",
+      "Epoch 788/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5411 - categorical_accuracy: 0.6588\n",
+      "Epoch 789/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5246 - categorical_accuracy: 0.6824\n",
+      "Epoch 790/2000\n",
+      "3/3 [==============================] - 0s 67ms/step - loss: 0.5168 - categorical_accuracy: 0.7294\n",
+      "Epoch 791/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5013 - categorical_accuracy: 0.7765\n",
+      "Epoch 792/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.5017 - categorical_accuracy: 0.6824\n",
+      "Epoch 793/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.5384 - categorical_accuracy: 0.6941\n",
+      "Epoch 794/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.4930 - categorical_accuracy: 0.7765\n",
+      "Epoch 795/2000\n",
+      "3/3 [==============================] - 0s 61ms/step - loss: 0.5112 - categorical_accuracy: 0.6706\n",
+      "Epoch 796/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.4835 - categorical_accuracy: 0.8118\n",
+      "Epoch 797/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.4978 - categorical_accuracy: 0.8000\n",
+      "Epoch 798/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.4814 - categorical_accuracy: 0.8471\n",
+      "Epoch 799/2000\n",
+      "3/3 [==============================] - 0s 69ms/step - loss: 0.4979 - categorical_accuracy: 0.6824\n",
+      "Epoch 800/2000\n",
+      "3/3 [==============================] - 0s 70ms/step - loss: 0.4825 - categorical_accuracy: 0.7765\n",
+      "Epoch 801/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4912 - categorical_accuracy: 0.8000\n",
+      "Epoch 802/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.4885 - categorical_accuracy: 0.7294\n",
+      "Epoch 803/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4918 - categorical_accuracy: 0.7059\n",
+      "Epoch 804/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.4836 - categorical_accuracy: 0.7412\n",
+      "Epoch 805/2000\n",
+      "3/3 [==============================] - 0s 71ms/step - loss: 0.4785 - categorical_accuracy: 0.7647\n",
+      "Epoch 806/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4814 - categorical_accuracy: 0.8118\n",
+      "Epoch 807/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4865 - categorical_accuracy: 0.7765\n",
+      "Epoch 808/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4771 - categorical_accuracy: 0.7412\n",
+      "Epoch 809/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4850 - categorical_accuracy: 0.8118\n",
+      "Epoch 810/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.4777 - categorical_accuracy: 0.8118\n",
+      "Epoch 811/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4810 - categorical_accuracy: 0.7059\n",
+      "Epoch 812/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.4788 - categorical_accuracy: 0.6706\n",
+      "Epoch 813/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4870 - categorical_accuracy: 0.8118\n",
+      "Epoch 814/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4813 - categorical_accuracy: 0.8118\n",
+      "Epoch 815/2000\n",
+      "3/3 [==============================] - 0s 71ms/step - loss: 0.4973 - categorical_accuracy: 0.6941\n",
+      "Epoch 816/2000\n",
+      "3/3 [==============================] - 0s 68ms/step - loss: 0.5030 - categorical_accuracy: 0.7765\n",
+      "Epoch 817/2000\n",
+      "3/3 [==============================] - 0s 74ms/step - loss: 0.5072 - categorical_accuracy: 0.7647\n",
+      "Epoch 818/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5404 - categorical_accuracy: 0.6941\n",
+      "Epoch 819/2000\n",
+      "3/3 [==============================] - 0s 74ms/step - loss: 0.5193 - categorical_accuracy: 0.7765\n",
+      "Epoch 820/2000\n",
+      "3/3 [==============================] - 0s 72ms/step - loss: 0.5201 - categorical_accuracy: 0.6353\n",
+      "Epoch 821/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5093 - categorical_accuracy: 0.6706\n",
+      "Epoch 822/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4677 - categorical_accuracy: 0.8000\n",
+      "Epoch 823/2000\n",
+      "3/3 [==============================] - 0s 76ms/step - loss: 0.4757 - categorical_accuracy: 0.8118\n",
+      "Epoch 824/2000\n",
+      "3/3 [==============================] - 0s 72ms/step - loss: 0.4750 - categorical_accuracy: 0.7529\n",
+      "Epoch 825/2000\n",
+      "3/3 [==============================] - 0s 70ms/step - loss: 0.4711 - categorical_accuracy: 0.8000\n",
+      "Epoch 826/2000\n",
+      "3/3 [==============================] - 0s 72ms/step - loss: 0.4663 - categorical_accuracy: 0.8118\n",
+      "Epoch 827/2000\n",
+      "3/3 [==============================] - 0s 73ms/step - loss: 0.4884 - categorical_accuracy: 0.7294\n",
+      "Epoch 828/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4781 - categorical_accuracy: 0.8000\n",
+      "Epoch 829/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4887 - categorical_accuracy: 0.8000\n",
+      "Epoch 830/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.4958 - categorical_accuracy: 0.8000\n",
+      "Epoch 831/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5111 - categorical_accuracy: 0.7529\n",
+      "Epoch 832/2000\n",
+      "3/3 [==============================] - 0s 73ms/step - loss: 0.4979 - categorical_accuracy: 0.6471\n",
+      "Epoch 833/2000\n",
+      "3/3 [==============================] - 0s 74ms/step - loss: 0.5146 - categorical_accuracy: 0.8000\n",
+      "Epoch 834/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.5174 - categorical_accuracy: 0.8118\n",
+      "Epoch 835/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4946 - categorical_accuracy: 0.6588\n",
+      "Epoch 836/2000\n",
+      "3/3 [==============================] - 0s 69ms/step - loss: 0.4912 - categorical_accuracy: 0.7412\n",
+      "Epoch 837/2000\n",
+      "3/3 [==============================] - 0s 72ms/step - loss: 0.4858 - categorical_accuracy: 0.8000\n",
+      "Epoch 838/2000\n",
+      "3/3 [==============================] - 0s 74ms/step - loss: 0.4779 - categorical_accuracy: 0.6706\n",
+      "Epoch 839/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4856 - categorical_accuracy: 0.6941\n",
+      "Epoch 840/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4739 - categorical_accuracy: 0.8118\n",
+      "Epoch 841/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4650 - categorical_accuracy: 0.7176\n",
+      "Epoch 842/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.4651 - categorical_accuracy: 0.8000\n",
+      "Epoch 843/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4620 - categorical_accuracy: 0.7882\n",
+      "Epoch 844/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4679 - categorical_accuracy: 0.8000\n",
+      "Epoch 845/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4555 - categorical_accuracy: 0.8235\n",
+      "Epoch 846/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4836 - categorical_accuracy: 0.6941\n",
+      "Epoch 847/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.4727 - categorical_accuracy: 0.8000\n",
+      "Epoch 848/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.4704 - categorical_accuracy: 0.8235\n",
+      "Epoch 849/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4820 - categorical_accuracy: 0.6706\n",
+      "Epoch 850/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.4647 - categorical_accuracy: 0.7647\n",
+      "Epoch 851/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.4696 - categorical_accuracy: 0.8118\n",
+      "Epoch 852/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4876 - categorical_accuracy: 0.7059\n",
+      "Epoch 853/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.4796 - categorical_accuracy: 0.7647\n",
+      "Epoch 854/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4784 - categorical_accuracy: 0.7882\n",
+      "Epoch 855/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4643 - categorical_accuracy: 0.7529\n",
+      "Epoch 856/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4531 - categorical_accuracy: 0.8353\n",
+      "Epoch 857/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.4505 - categorical_accuracy: 0.8000\n",
+      "Epoch 858/2000\n",
+      "3/3 [==============================] - 0s 72ms/step - loss: 0.4513 - categorical_accuracy: 0.8235\n",
+      "Epoch 859/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4673 - categorical_accuracy: 0.8000\n",
+      "Epoch 860/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4846 - categorical_accuracy: 0.7059\n",
+      "Epoch 861/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.4720 - categorical_accuracy: 0.7765\n",
+      "Epoch 862/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4772 - categorical_accuracy: 0.7882\n",
+      "Epoch 863/2000\n",
+      "3/3 [==============================] - 0s 74ms/step - loss: 0.4781 - categorical_accuracy: 0.6824\n",
+      "Epoch 864/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.4790 - categorical_accuracy: 0.8000\n",
+      "Epoch 865/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.4684 - categorical_accuracy: 0.7647\n",
+      "Epoch 866/2000\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4803 - categorical_accuracy: 0.6941\n",
+      "Epoch 867/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.4877 - categorical_accuracy: 0.7059\n",
+      "Epoch 868/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4467 - categorical_accuracy: 0.8118\n",
+      "Epoch 869/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4561 - categorical_accuracy: 0.8118\n",
+      "Epoch 870/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4678 - categorical_accuracy: 0.7176\n",
+      "Epoch 871/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4508 - categorical_accuracy: 0.8353\n",
+      "Epoch 872/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4846 - categorical_accuracy: 0.7529\n",
+      "Epoch 873/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.4653 - categorical_accuracy: 0.7412\n",
+      "Epoch 874/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4641 - categorical_accuracy: 0.7412\n",
+      "Epoch 875/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4687 - categorical_accuracy: 0.8235\n",
+      "Epoch 876/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4620 - categorical_accuracy: 0.7529\n",
+      "Epoch 877/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4458 - categorical_accuracy: 0.7882\n",
+      "Epoch 878/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4610 - categorical_accuracy: 0.8118\n",
+      "Epoch 879/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4464 - categorical_accuracy: 0.8000\n",
+      "Epoch 880/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4493 - categorical_accuracy: 0.7412\n",
+      "Epoch 881/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4497 - categorical_accuracy: 0.8118\n",
+      "Epoch 882/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.4437 - categorical_accuracy: 0.8000\n",
+      "Epoch 883/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4448 - categorical_accuracy: 0.8235\n",
+      "Epoch 884/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4433 - categorical_accuracy: 0.8235\n",
+      "Epoch 885/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4416 - categorical_accuracy: 0.8235\n",
+      "Epoch 886/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4490 - categorical_accuracy: 0.8118\n",
+      "Epoch 887/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.4599 - categorical_accuracy: 0.7529\n",
+      "Epoch 888/2000\n",
+      "3/3 [==============================] - 0s 70ms/step - loss: 0.4679 - categorical_accuracy: 0.7765\n",
+      "Epoch 889/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4881 - categorical_accuracy: 0.7412\n",
+      "Epoch 890/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.5532 - categorical_accuracy: 0.7294\n",
+      "Epoch 891/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4805 - categorical_accuracy: 0.7647\n",
+      "Epoch 892/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5250 - categorical_accuracy: 0.6706\n",
+      "Epoch 893/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.5122 - categorical_accuracy: 0.7882\n",
+      "Epoch 894/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5241 - categorical_accuracy: 0.7176\n",
+      "Epoch 895/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4815 - categorical_accuracy: 0.7176\n",
+      "Epoch 896/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.4661 - categorical_accuracy: 0.8118\n",
+      "Epoch 897/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4569 - categorical_accuracy: 0.6941\n",
+      "Epoch 898/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.4473 - categorical_accuracy: 0.7765\n",
+      "Epoch 899/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.4533 - categorical_accuracy: 0.8235\n",
+      "Epoch 900/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.4963 - categorical_accuracy: 0.6941\n",
+      "Epoch 901/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.4421 - categorical_accuracy: 0.8118\n",
+      "Epoch 902/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4492 - categorical_accuracy: 0.8235\n",
+      "Epoch 903/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.4406 - categorical_accuracy: 0.8118\n",
+      "Epoch 904/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4355 - categorical_accuracy: 0.8353\n",
+      "Epoch 905/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.4377 - categorical_accuracy: 0.8000\n",
+      "Epoch 906/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.4367 - categorical_accuracy: 0.8118\n",
+      "Epoch 907/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4310 - categorical_accuracy: 0.8118\n",
+      "Epoch 908/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4471 - categorical_accuracy: 0.8000\n",
+      "Epoch 909/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.4337 - categorical_accuracy: 0.8000\n",
+      "Epoch 910/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4331 - categorical_accuracy: 0.8235\n",
+      "Epoch 911/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.4330 - categorical_accuracy: 0.7882\n",
+      "Epoch 912/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.4313 - categorical_accuracy: 0.8118\n",
+      "Epoch 913/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4285 - categorical_accuracy: 0.8118\n",
+      "Epoch 914/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.4215 - categorical_accuracy: 0.8353\n",
+      "Epoch 915/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4290 - categorical_accuracy: 0.8235\n",
+      "Epoch 916/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4238 - categorical_accuracy: 0.8118\n",
+      "Epoch 917/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4269 - categorical_accuracy: 0.8118\n",
+      "Epoch 918/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4076 - categorical_accuracy: 0.8353\n",
+      "Epoch 919/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.4362 - categorical_accuracy: 0.7529\n",
+      "Epoch 920/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4275 - categorical_accuracy: 0.8235\n",
+      "Epoch 921/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.4220 - categorical_accuracy: 0.7882\n",
+      "Epoch 922/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.4257 - categorical_accuracy: 0.8000\n",
+      "Epoch 923/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.4330 - categorical_accuracy: 0.7765\n",
+      "Epoch 924/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.4297 - categorical_accuracy: 0.8235\n",
+      "Epoch 925/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.5339 - categorical_accuracy: 0.7647\n",
+      "Epoch 926/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.8876 - categorical_accuracy: 0.6000\n",
+      "Epoch 927/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.7609 - categorical_accuracy: 0.5412\n",
+      "Epoch 928/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.7670 - categorical_accuracy: 0.6824\n",
+      "Epoch 929/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.7726 - categorical_accuracy: 0.5647\n",
+      "Epoch 930/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 3.2890 - categorical_accuracy: 0.3529\n",
+      "Epoch 931/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 3.4182 - categorical_accuracy: 0.3529\n",
+      "Epoch 932/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 3.1687 - categorical_accuracy: 0.4000\n",
+      "Epoch 933/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 2.4495 - categorical_accuracy: 0.3059\n",
+      "Epoch 934/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 2.0270 - categorical_accuracy: 0.2471\n",
+      "Epoch 935/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 1.3896 - categorical_accuracy: 0.3294\n",
+      "Epoch 936/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 1.2479 - categorical_accuracy: 0.3176\n",
+      "Epoch 937/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 1.1780 - categorical_accuracy: 0.4118\n",
+      "Epoch 938/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 1.2416 - categorical_accuracy: 0.3294\n",
+      "Epoch 939/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 1.1020 - categorical_accuracy: 0.4235\n",
+      "Epoch 940/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 1.1223 - categorical_accuracy: 0.4824\n",
+      "Epoch 941/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 1.0242 - categorical_accuracy: 0.4824\n",
+      "Epoch 942/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.9610 - categorical_accuracy: 0.4353\n",
+      "Epoch 943/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.9619 - categorical_accuracy: 0.4706\n",
+      "Epoch 944/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.9004 - categorical_accuracy: 0.6000\n",
+      "Epoch 945/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.8933 - categorical_accuracy: 0.6353\n",
+      "Epoch 946/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.9082 - categorical_accuracy: 0.6000\n",
+      "Epoch 947/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.9397 - categorical_accuracy: 0.5059\n",
+      "Epoch 948/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.8908 - categorical_accuracy: 0.6471\n",
+      "Epoch 949/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.8866 - categorical_accuracy: 0.5647\n",
+      "Epoch 950/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.9415 - categorical_accuracy: 0.4353\n",
+      "Epoch 951/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.8723 - categorical_accuracy: 0.6353\n",
+      "Epoch 952/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.8861 - categorical_accuracy: 0.6000\n",
+      "Epoch 953/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.8709 - categorical_accuracy: 0.6706\n",
+      "Epoch 954/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.8730 - categorical_accuracy: 0.6353\n",
+      "Epoch 955/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.8242 - categorical_accuracy: 0.7176\n",
+      "Epoch 956/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.8608 - categorical_accuracy: 0.7176\n",
+      "Epoch 957/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.8268 - categorical_accuracy: 0.7529\n",
+      "Epoch 958/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.8173 - categorical_accuracy: 0.6353\n",
+      "Epoch 959/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.8102 - categorical_accuracy: 0.6471\n",
+      "Epoch 960/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.8070 - categorical_accuracy: 0.6471\n",
+      "Epoch 961/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.7999 - categorical_accuracy: 0.7412\n",
+      "Epoch 962/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.8068 - categorical_accuracy: 0.7294\n",
+      "Epoch 963/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.7864 - categorical_accuracy: 0.8000\n",
+      "Epoch 964/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.8064 - categorical_accuracy: 0.6353\n",
+      "Epoch 965/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.8013 - categorical_accuracy: 0.6353\n",
+      "Epoch 966/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.7847 - categorical_accuracy: 0.6824\n",
+      "Epoch 967/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.7834 - categorical_accuracy: 0.7412\n",
+      "Epoch 968/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.7777 - categorical_accuracy: 0.6824\n",
+      "Epoch 969/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.7801 - categorical_accuracy: 0.6471\n",
+      "Epoch 970/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.7741 - categorical_accuracy: 0.7176\n",
+      "Epoch 971/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.8137 - categorical_accuracy: 0.6824\n",
+      "Epoch 972/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.8072 - categorical_accuracy: 0.7059\n",
+      "Epoch 973/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.8112 - categorical_accuracy: 0.6471\n",
+      "Epoch 974/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.8172 - categorical_accuracy: 0.6235\n",
+      "Epoch 975/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.8015 - categorical_accuracy: 0.6941\n",
+      "Epoch 976/2000\n",
+      "3/3 [==============================] - 0s 72ms/step - loss: 0.7970 - categorical_accuracy: 0.7176\n",
+      "Epoch 977/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.7951 - categorical_accuracy: 0.6824\n",
+      "Epoch 978/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.7977 - categorical_accuracy: 0.6353\n",
+      "Epoch 979/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.7868 - categorical_accuracy: 0.6235\n",
+      "Epoch 980/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.8029 - categorical_accuracy: 0.7294\n",
+      "Epoch 981/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.8071 - categorical_accuracy: 0.6353\n",
+      "Epoch 982/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.7751 - categorical_accuracy: 0.7529\n",
+      "Epoch 983/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.8050 - categorical_accuracy: 0.6588\n",
+      "Epoch 984/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.7644 - categorical_accuracy: 0.6353\n",
+      "Epoch 985/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.7841 - categorical_accuracy: 0.6706\n",
+      "Epoch 986/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.7528 - categorical_accuracy: 0.7529\n",
+      "Epoch 987/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.7621 - categorical_accuracy: 0.8118\n",
+      "Epoch 988/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.7453 - categorical_accuracy: 0.7412\n",
+      "Epoch 989/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.7509 - categorical_accuracy: 0.7059\n",
+      "Epoch 990/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.7564 - categorical_accuracy: 0.6824\n",
+      "Epoch 991/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.7420 - categorical_accuracy: 0.6941\n",
+      "Epoch 992/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.7256 - categorical_accuracy: 0.8353\n",
+      "Epoch 993/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.7355 - categorical_accuracy: 0.7059\n",
+      "Epoch 994/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.7267 - categorical_accuracy: 0.6471\n",
+      "Epoch 995/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.7199 - categorical_accuracy: 0.6941\n",
+      "Epoch 996/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.7267 - categorical_accuracy: 0.6588\n",
+      "Epoch 997/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.6987 - categorical_accuracy: 0.7882\n",
+      "Epoch 998/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.7114 - categorical_accuracy: 0.6471\n",
+      "Epoch 999/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.7095 - categorical_accuracy: 0.6471\n",
+      "Epoch 1000/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.6915 - categorical_accuracy: 0.7412\n",
+      "Epoch 1001/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.6848 - categorical_accuracy: 0.7529\n",
+      "Epoch 1002/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.6836 - categorical_accuracy: 0.6471\n",
+      "Epoch 1003/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.6892 - categorical_accuracy: 0.6471\n",
+      "Epoch 1004/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.6770 - categorical_accuracy: 0.7882\n",
+      "Epoch 1005/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.6791 - categorical_accuracy: 0.7294\n",
+      "Epoch 1006/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.6620 - categorical_accuracy: 0.8353\n",
+      "Epoch 1007/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.6609 - categorical_accuracy: 0.7529\n",
+      "Epoch 1008/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.6602 - categorical_accuracy: 0.6706\n",
+      "Epoch 1009/2000\n",
+      "3/3 [==============================] - 0s 69ms/step - loss: 0.6646 - categorical_accuracy: 0.6706\n",
+      "Epoch 1010/2000\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.6565 - categorical_accuracy: 0.6235\n",
+      "Epoch 1011/2000\n",
+      "3/3 [==============================] - 0s 74ms/step - loss: 0.6419 - categorical_accuracy: 0.8235\n",
+      "Epoch 1012/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.6647 - categorical_accuracy: 0.6588\n",
+      "Epoch 1013/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.6598 - categorical_accuracy: 0.6824\n",
+      "Epoch 1014/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.6902 - categorical_accuracy: 0.5882\n",
+      "Epoch 1015/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.6805 - categorical_accuracy: 0.7412\n",
+      "Epoch 1016/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.6596 - categorical_accuracy: 0.6941\n",
+      "Epoch 1017/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.6460 - categorical_accuracy: 0.7059\n",
+      "Epoch 1018/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.6270 - categorical_accuracy: 0.6588\n",
+      "Epoch 1019/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.6218 - categorical_accuracy: 0.7529\n",
+      "Epoch 1020/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.6189 - categorical_accuracy: 0.6824\n",
+      "Epoch 1021/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.6154 - categorical_accuracy: 0.7294\n",
+      "Epoch 1022/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.6123 - categorical_accuracy: 0.6824\n",
+      "Epoch 1023/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.6024 - categorical_accuracy: 0.7765\n",
+      "Epoch 1024/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.5982 - categorical_accuracy: 0.7176\n",
+      "Epoch 1025/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.5928 - categorical_accuracy: 0.7059\n",
+      "Epoch 1026/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.6039 - categorical_accuracy: 0.7882\n",
+      "Epoch 1027/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.6114 - categorical_accuracy: 0.7059\n",
+      "Epoch 1028/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.6141 - categorical_accuracy: 0.6941\n",
+      "Epoch 1029/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.6013 - categorical_accuracy: 0.6706\n",
+      "Epoch 1030/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.6003 - categorical_accuracy: 0.7059\n",
+      "Epoch 1031/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.6268 - categorical_accuracy: 0.6471\n",
+      "Epoch 1032/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5655 - categorical_accuracy: 0.7647\n",
+      "Epoch 1033/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.6065 - categorical_accuracy: 0.6706\n",
+      "Epoch 1034/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.6013 - categorical_accuracy: 0.7176\n",
+      "Epoch 1035/2000\n",
+      "3/3 [==============================] - 0s 78ms/step - loss: 0.5607 - categorical_accuracy: 0.7412\n",
+      "Epoch 1036/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.6105 - categorical_accuracy: 0.6588\n",
+      "Epoch 1037/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5720 - categorical_accuracy: 0.7529\n",
+      "Epoch 1038/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5675 - categorical_accuracy: 0.6941\n",
+      "Epoch 1039/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.5824 - categorical_accuracy: 0.6941\n",
+      "Epoch 1040/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5693 - categorical_accuracy: 0.6824\n",
+      "Epoch 1041/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.5645 - categorical_accuracy: 0.7529\n",
+      "Epoch 1042/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5650 - categorical_accuracy: 0.7529\n",
+      "Epoch 1043/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.5699 - categorical_accuracy: 0.7059\n",
+      "Epoch 1044/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.5694 - categorical_accuracy: 0.7412\n",
+      "Epoch 1045/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.5617 - categorical_accuracy: 0.6941\n",
+      "Epoch 1046/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.5665 - categorical_accuracy: 0.6588\n",
+      "Epoch 1047/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5575 - categorical_accuracy: 0.7412\n",
+      "Epoch 1048/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5537 - categorical_accuracy: 0.7529\n",
+      "Epoch 1049/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.5617 - categorical_accuracy: 0.6706\n",
+      "Epoch 1050/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.5538 - categorical_accuracy: 0.8118\n",
+      "Epoch 1051/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.5547 - categorical_accuracy: 0.7765\n",
+      "Epoch 1052/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5500 - categorical_accuracy: 0.7059\n",
+      "Epoch 1053/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.5562 - categorical_accuracy: 0.8000\n",
+      "Epoch 1054/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.5545 - categorical_accuracy: 0.6824\n",
+      "Epoch 1055/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.5479 - categorical_accuracy: 0.7412\n",
+      "Epoch 1056/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5599 - categorical_accuracy: 0.7529\n",
+      "Epoch 1057/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.5779 - categorical_accuracy: 0.6941\n",
+      "Epoch 1058/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.5703 - categorical_accuracy: 0.6588\n",
+      "Epoch 1059/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.5667 - categorical_accuracy: 0.6235\n",
+      "Epoch 1060/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.5540 - categorical_accuracy: 0.6941\n",
+      "Epoch 1061/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.5602 - categorical_accuracy: 0.6706\n",
+      "Epoch 1062/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5792 - categorical_accuracy: 0.5882\n",
+      "Epoch 1063/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.5504 - categorical_accuracy: 0.7059\n",
+      "Epoch 1064/2000\n",
+      "3/3 [==============================] - 0s 67ms/step - loss: 0.5568 - categorical_accuracy: 0.6941\n",
+      "Epoch 1065/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5483 - categorical_accuracy: 0.7765\n",
+      "Epoch 1066/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.5409 - categorical_accuracy: 0.6706\n",
+      "Epoch 1067/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5405 - categorical_accuracy: 0.7294\n",
+      "Epoch 1068/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5418 - categorical_accuracy: 0.7412\n",
+      "Epoch 1069/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.5471 - categorical_accuracy: 0.7412\n",
+      "Epoch 1070/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5371 - categorical_accuracy: 0.7529\n",
+      "Epoch 1071/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5357 - categorical_accuracy: 0.7294\n",
+      "Epoch 1072/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5340 - categorical_accuracy: 0.6941\n",
+      "Epoch 1073/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.5339 - categorical_accuracy: 0.7765\n",
+      "Epoch 1074/2000\n",
+      "3/3 [==============================] - 0s 68ms/step - loss: 0.5325 - categorical_accuracy: 0.7529\n",
+      "Epoch 1075/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5363 - categorical_accuracy: 0.7882\n",
+      "Epoch 1076/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.5329 - categorical_accuracy: 0.7529\n",
+      "Epoch 1077/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.5415 - categorical_accuracy: 0.7294\n",
+      "Epoch 1078/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.5259 - categorical_accuracy: 0.8000\n",
+      "Epoch 1079/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.5462 - categorical_accuracy: 0.6941\n",
+      "Epoch 1080/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5315 - categorical_accuracy: 0.6941\n",
+      "Epoch 1081/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.5397 - categorical_accuracy: 0.6824\n",
+      "Epoch 1082/2000\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.5274 - categorical_accuracy: 0.7647\n",
+      "Epoch 1083/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5428 - categorical_accuracy: 0.6706\n",
+      "Epoch 1084/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.5469 - categorical_accuracy: 0.6941\n",
+      "Epoch 1085/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.5567 - categorical_accuracy: 0.6235\n",
+      "Epoch 1086/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.5357 - categorical_accuracy: 0.7059\n",
+      "Epoch 1087/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5321 - categorical_accuracy: 0.7176\n",
+      "Epoch 1088/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5385 - categorical_accuracy: 0.7412\n",
+      "Epoch 1089/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.5242 - categorical_accuracy: 0.7647\n",
+      "Epoch 1090/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5342 - categorical_accuracy: 0.7529\n",
+      "Epoch 1091/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.5236 - categorical_accuracy: 0.6941\n",
+      "Epoch 1092/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5379 - categorical_accuracy: 0.7059\n",
+      "Epoch 1093/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.5305 - categorical_accuracy: 0.6824\n",
+      "Epoch 1094/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.5389 - categorical_accuracy: 0.6941\n",
+      "Epoch 1095/2000\n",
+      "3/3 [==============================] - 0s 67ms/step - loss: 0.5436 - categorical_accuracy: 0.6471\n",
+      "Epoch 1096/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.5212 - categorical_accuracy: 0.7882\n",
+      "Epoch 1097/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5297 - categorical_accuracy: 0.6706\n",
+      "Epoch 1098/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.5383 - categorical_accuracy: 0.7647\n",
+      "Epoch 1099/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.5270 - categorical_accuracy: 0.7529\n",
+      "Epoch 1100/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5337 - categorical_accuracy: 0.6471\n",
+      "Epoch 1101/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5317 - categorical_accuracy: 0.7765\n",
+      "Epoch 1102/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5315 - categorical_accuracy: 0.7529\n",
+      "Epoch 1103/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5262 - categorical_accuracy: 0.7647\n",
+      "Epoch 1104/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5349 - categorical_accuracy: 0.6588\n",
+      "Epoch 1105/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.5815 - categorical_accuracy: 0.5765\n",
+      "Epoch 1106/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5264 - categorical_accuracy: 0.6824\n",
+      "Epoch 1107/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.5494 - categorical_accuracy: 0.6588\n",
+      "Epoch 1108/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5431 - categorical_accuracy: 0.7059\n",
+      "Epoch 1109/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5310 - categorical_accuracy: 0.6941\n",
+      "Epoch 1110/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.5589 - categorical_accuracy: 0.6588\n",
+      "Epoch 1111/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.5339 - categorical_accuracy: 0.6941\n",
+      "Epoch 1112/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.5459 - categorical_accuracy: 0.6706\n",
+      "Epoch 1113/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5464 - categorical_accuracy: 0.6706\n",
+      "Epoch 1114/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5173 - categorical_accuracy: 0.6941\n",
+      "Epoch 1115/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5459 - categorical_accuracy: 0.6824\n",
+      "Epoch 1116/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.5370 - categorical_accuracy: 0.7647\n",
+      "Epoch 1117/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5216 - categorical_accuracy: 0.7412\n",
+      "Epoch 1118/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.5331 - categorical_accuracy: 0.7529\n",
+      "Epoch 1119/2000\n",
+      "3/3 [==============================] - 0s 67ms/step - loss: 0.5431 - categorical_accuracy: 0.7294\n",
+      "Epoch 1120/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.5274 - categorical_accuracy: 0.7176\n",
+      "Epoch 1121/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.5325 - categorical_accuracy: 0.6941\n",
+      "Epoch 1122/2000\n",
+      "3/3 [==============================] - 0s 61ms/step - loss: 0.5143 - categorical_accuracy: 0.6706\n",
+      "Epoch 1123/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.5182 - categorical_accuracy: 0.6824\n",
+      "Epoch 1124/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.5153 - categorical_accuracy: 0.7882\n",
+      "Epoch 1125/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.5182 - categorical_accuracy: 0.7529\n",
+      "Epoch 1126/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.5397 - categorical_accuracy: 0.6941\n",
+      "Epoch 1127/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.5155 - categorical_accuracy: 0.7294\n",
+      "Epoch 1128/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.5254 - categorical_accuracy: 0.7176\n",
+      "Epoch 1129/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5198 - categorical_accuracy: 0.7176\n",
+      "Epoch 1130/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.5194 - categorical_accuracy: 0.7059\n",
+      "Epoch 1131/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.5247 - categorical_accuracy: 0.7529\n",
+      "Epoch 1132/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.5136 - categorical_accuracy: 0.7882\n",
+      "Epoch 1133/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.5048 - categorical_accuracy: 0.7882\n",
+      "Epoch 1134/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.5178 - categorical_accuracy: 0.7176\n",
+      "Epoch 1135/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.5056 - categorical_accuracy: 0.8235\n",
+      "Epoch 1136/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.5145 - categorical_accuracy: 0.6941\n",
+      "Epoch 1137/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.5077 - categorical_accuracy: 0.7765\n",
+      "Epoch 1138/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5124 - categorical_accuracy: 0.7529\n",
+      "Epoch 1139/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.5158 - categorical_accuracy: 0.7882\n",
+      "Epoch 1140/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.5158 - categorical_accuracy: 0.8000\n",
+      "Epoch 1141/2000\n",
+      "3/3 [==============================] - 0s 61ms/step - loss: 0.5114 - categorical_accuracy: 0.7765\n",
+      "Epoch 1142/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.5158 - categorical_accuracy: 0.6941\n",
+      "Epoch 1143/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.5189 - categorical_accuracy: 0.7647\n",
+      "Epoch 1144/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.5005 - categorical_accuracy: 0.7529\n",
+      "Epoch 1145/2000\n",
+      "3/3 [==============================] - 0s 60ms/step - loss: 0.5401 - categorical_accuracy: 0.6941\n",
+      "Epoch 1146/2000\n",
+      "3/3 [==============================] - 0s 60ms/step - loss: 0.5118 - categorical_accuracy: 0.7765\n",
+      "Epoch 1147/2000\n",
+      "3/3 [==============================] - 0s 60ms/step - loss: 0.5185 - categorical_accuracy: 0.7765\n",
+      "Epoch 1148/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.5124 - categorical_accuracy: 0.6706\n",
+      "Epoch 1149/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.5046 - categorical_accuracy: 0.7176\n",
+      "Epoch 1150/2000\n",
+      "3/3 [==============================] - 0s 61ms/step - loss: 0.5159 - categorical_accuracy: 0.7412\n",
+      "Epoch 1151/2000\n",
+      "3/3 [==============================] - 0s 61ms/step - loss: 0.5087 - categorical_accuracy: 0.7647\n",
+      "Epoch 1152/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.5074 - categorical_accuracy: 0.7647\n",
+      "Epoch 1153/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.5173 - categorical_accuracy: 0.7529\n",
+      "Epoch 1154/2000\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.5037 - categorical_accuracy: 0.7176\n",
+      "Epoch 1155/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.4992 - categorical_accuracy: 0.7765\n",
+      "Epoch 1156/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.5153 - categorical_accuracy: 0.7529\n",
+      "Epoch 1157/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.5142 - categorical_accuracy: 0.7059\n",
+      "Epoch 1158/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.5218 - categorical_accuracy: 0.6824\n",
+      "Epoch 1159/2000\n",
+      "3/3 [==============================] - 0s 61ms/step - loss: 0.5163 - categorical_accuracy: 0.7294\n",
+      "Epoch 1160/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5042 - categorical_accuracy: 0.8000\n",
+      "Epoch 1161/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.5309 - categorical_accuracy: 0.6471\n",
+      "Epoch 1162/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5302 - categorical_accuracy: 0.7647\n",
+      "Epoch 1163/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.5122 - categorical_accuracy: 0.7882\n",
+      "Epoch 1164/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.5197 - categorical_accuracy: 0.6706\n",
+      "Epoch 1165/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5354 - categorical_accuracy: 0.7294\n",
+      "Epoch 1166/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5047 - categorical_accuracy: 0.8353\n",
+      "Epoch 1167/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5215 - categorical_accuracy: 0.6588\n",
+      "Epoch 1168/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.4939 - categorical_accuracy: 0.7765\n",
+      "Epoch 1169/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.5061 - categorical_accuracy: 0.8118\n",
+      "Epoch 1170/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4931 - categorical_accuracy: 0.7529\n",
+      "Epoch 1171/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5291 - categorical_accuracy: 0.6706\n",
+      "Epoch 1172/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.5126 - categorical_accuracy: 0.7294\n",
+      "Epoch 1173/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.5145 - categorical_accuracy: 0.7294\n",
+      "Epoch 1174/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.5113 - categorical_accuracy: 0.7412\n",
+      "Epoch 1175/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5032 - categorical_accuracy: 0.7647\n",
+      "Epoch 1176/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5024 - categorical_accuracy: 0.7882\n",
+      "Epoch 1177/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.5054 - categorical_accuracy: 0.7059\n",
+      "Epoch 1178/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.4917 - categorical_accuracy: 0.8000\n",
+      "Epoch 1179/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.4941 - categorical_accuracy: 0.8235\n",
+      "Epoch 1180/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.4964 - categorical_accuracy: 0.7647\n",
+      "Epoch 1181/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4910 - categorical_accuracy: 0.8118\n",
+      "Epoch 1182/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4925 - categorical_accuracy: 0.8118\n",
+      "Epoch 1183/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.4891 - categorical_accuracy: 0.8118\n",
+      "Epoch 1184/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.5006 - categorical_accuracy: 0.7529\n",
+      "Epoch 1185/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4855 - categorical_accuracy: 0.8353\n",
+      "Epoch 1186/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.5055 - categorical_accuracy: 0.7294\n",
+      "Epoch 1187/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4994 - categorical_accuracy: 0.7529\n",
+      "Epoch 1188/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5203 - categorical_accuracy: 0.7529\n",
+      "Epoch 1189/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4888 - categorical_accuracy: 0.7882\n",
+      "Epoch 1190/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.5008 - categorical_accuracy: 0.6824\n",
+      "Epoch 1191/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.4837 - categorical_accuracy: 0.8118\n",
+      "Epoch 1192/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4950 - categorical_accuracy: 0.7765\n",
+      "Epoch 1193/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5371 - categorical_accuracy: 0.6941\n",
+      "Epoch 1194/2000\n",
+      "3/3 [==============================] - 0s 67ms/step - loss: 0.4839 - categorical_accuracy: 0.7765\n",
+      "Epoch 1195/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5419 - categorical_accuracy: 0.7529\n",
+      "Epoch 1196/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4829 - categorical_accuracy: 0.7882\n",
+      "Epoch 1197/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.5143 - categorical_accuracy: 0.7647\n",
+      "Epoch 1198/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.4850 - categorical_accuracy: 0.7412\n",
+      "Epoch 1199/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4960 - categorical_accuracy: 0.7059\n",
+      "Epoch 1200/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.5021 - categorical_accuracy: 0.7647\n",
+      "Epoch 1201/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.5001 - categorical_accuracy: 0.7412\n",
+      "Epoch 1202/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4727 - categorical_accuracy: 0.8353\n",
+      "Epoch 1203/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.5061 - categorical_accuracy: 0.7529\n",
+      "Epoch 1204/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.4793 - categorical_accuracy: 0.7765\n",
+      "Epoch 1205/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4889 - categorical_accuracy: 0.7529\n",
+      "Epoch 1206/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.5097 - categorical_accuracy: 0.7412\n",
+      "Epoch 1207/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4733 - categorical_accuracy: 0.7529\n",
+      "Epoch 1208/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.5016 - categorical_accuracy: 0.7059\n",
+      "Epoch 1209/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4916 - categorical_accuracy: 0.7647\n",
+      "Epoch 1210/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.4839 - categorical_accuracy: 0.7765\n",
+      "Epoch 1211/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4806 - categorical_accuracy: 0.8000\n",
+      "Epoch 1212/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4744 - categorical_accuracy: 0.8118\n",
+      "Epoch 1213/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4816 - categorical_accuracy: 0.7765\n",
+      "Epoch 1214/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4677 - categorical_accuracy: 0.8000\n",
+      "Epoch 1215/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4741 - categorical_accuracy: 0.8118\n",
+      "Epoch 1216/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4745 - categorical_accuracy: 0.8000\n",
+      "Epoch 1217/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4858 - categorical_accuracy: 0.7647\n",
+      "Epoch 1218/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4764 - categorical_accuracy: 0.7765\n",
+      "Epoch 1219/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.5184 - categorical_accuracy: 0.7294\n",
+      "Epoch 1220/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5495 - categorical_accuracy: 0.7059\n",
+      "Epoch 1221/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.5122 - categorical_accuracy: 0.7059\n",
+      "Epoch 1222/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.5103 - categorical_accuracy: 0.7647\n",
+      "Epoch 1223/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.5365 - categorical_accuracy: 0.7529\n",
+      "Epoch 1224/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.5050 - categorical_accuracy: 0.6941\n",
+      "Epoch 1225/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4685 - categorical_accuracy: 0.8235\n",
+      "Epoch 1226/2000\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.5291 - categorical_accuracy: 0.7059\n",
+      "Epoch 1227/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.4750 - categorical_accuracy: 0.7765\n",
+      "Epoch 1228/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.4960 - categorical_accuracy: 0.7059\n",
+      "Epoch 1229/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5207 - categorical_accuracy: 0.7294\n",
+      "Epoch 1230/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4674 - categorical_accuracy: 0.7882\n",
+      "Epoch 1231/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4939 - categorical_accuracy: 0.7529\n",
+      "Epoch 1232/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4662 - categorical_accuracy: 0.8118\n",
+      "Epoch 1233/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4794 - categorical_accuracy: 0.7647\n",
+      "Epoch 1234/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4611 - categorical_accuracy: 0.8000\n",
+      "Epoch 1235/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4671 - categorical_accuracy: 0.7882\n",
+      "Epoch 1236/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4655 - categorical_accuracy: 0.7882\n",
+      "Epoch 1237/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4566 - categorical_accuracy: 0.8000\n",
+      "Epoch 1238/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4880 - categorical_accuracy: 0.7647\n",
+      "Epoch 1239/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.5077 - categorical_accuracy: 0.6941\n",
+      "Epoch 1240/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4826 - categorical_accuracy: 0.8235\n",
+      "Epoch 1241/2000\n",
+      "3/3 [==============================] - 0s 74ms/step - loss: 0.4724 - categorical_accuracy: 0.7882\n",
+      "Epoch 1242/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4825 - categorical_accuracy: 0.7529\n",
+      "Epoch 1243/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4771 - categorical_accuracy: 0.7765\n",
+      "Epoch 1244/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4601 - categorical_accuracy: 0.8235\n",
+      "Epoch 1245/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.4550 - categorical_accuracy: 0.8235\n",
+      "Epoch 1246/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4616 - categorical_accuracy: 0.8000\n",
+      "Epoch 1247/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4709 - categorical_accuracy: 0.8235\n",
+      "Epoch 1248/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4704 - categorical_accuracy: 0.7882\n",
+      "Epoch 1249/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4478 - categorical_accuracy: 0.8353\n",
+      "Epoch 1250/2000\n",
+      "3/3 [==============================] - 0s 70ms/step - loss: 0.4569 - categorical_accuracy: 0.8235\n",
+      "Epoch 1251/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4828 - categorical_accuracy: 0.7765\n",
+      "Epoch 1252/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4710 - categorical_accuracy: 0.7647\n",
+      "Epoch 1253/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4664 - categorical_accuracy: 0.8000\n",
+      "Epoch 1254/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4862 - categorical_accuracy: 0.7765\n",
+      "Epoch 1255/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5185 - categorical_accuracy: 0.6824\n",
+      "Epoch 1256/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4505 - categorical_accuracy: 0.8471\n",
+      "Epoch 1257/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4548 - categorical_accuracy: 0.8118\n",
+      "Epoch 1258/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.4679 - categorical_accuracy: 0.7765\n",
+      "Epoch 1259/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4454 - categorical_accuracy: 0.8471\n",
+      "Epoch 1260/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4513 - categorical_accuracy: 0.8235\n",
+      "Epoch 1261/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4850 - categorical_accuracy: 0.7647\n",
+      "Epoch 1262/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4549 - categorical_accuracy: 0.7647\n",
+      "Epoch 1263/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.4413 - categorical_accuracy: 0.8000\n",
+      "Epoch 1264/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4603 - categorical_accuracy: 0.7765\n",
+      "Epoch 1265/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4856 - categorical_accuracy: 0.7882\n",
+      "Epoch 1266/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4831 - categorical_accuracy: 0.7765\n",
+      "Epoch 1267/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4324 - categorical_accuracy: 0.8588\n",
+      "Epoch 1268/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.4503 - categorical_accuracy: 0.7882\n",
+      "Epoch 1269/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4828 - categorical_accuracy: 0.7765\n",
+      "Epoch 1270/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4603 - categorical_accuracy: 0.7765\n",
+      "Epoch 1271/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.4629 - categorical_accuracy: 0.8118\n",
+      "Epoch 1272/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4688 - categorical_accuracy: 0.7882\n",
+      "Epoch 1273/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.4813 - categorical_accuracy: 0.8000\n",
+      "Epoch 1274/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.4829 - categorical_accuracy: 0.7294\n",
+      "Epoch 1275/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.4356 - categorical_accuracy: 0.8118\n",
+      "Epoch 1276/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.4495 - categorical_accuracy: 0.8118\n",
+      "Epoch 1277/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.4393 - categorical_accuracy: 0.7765\n",
+      "Epoch 1278/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4528 - categorical_accuracy: 0.8000\n",
+      "Epoch 1279/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4705 - categorical_accuracy: 0.7412\n",
+      "Epoch 1280/2000\n",
+      "3/3 [==============================] - 0s 72ms/step - loss: 0.4521 - categorical_accuracy: 0.8000\n",
+      "Epoch 1281/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.4766 - categorical_accuracy: 0.7294\n",
+      "Epoch 1282/2000\n",
+      "3/3 [==============================] - 0s 67ms/step - loss: 0.5109 - categorical_accuracy: 0.7294\n",
+      "Epoch 1283/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4507 - categorical_accuracy: 0.7882\n",
+      "Epoch 1284/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4337 - categorical_accuracy: 0.8235\n",
+      "Epoch 1285/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4378 - categorical_accuracy: 0.8000\n",
+      "Epoch 1286/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4609 - categorical_accuracy: 0.8000\n",
+      "Epoch 1287/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4231 - categorical_accuracy: 0.8588\n",
+      "Epoch 1288/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4363 - categorical_accuracy: 0.8353\n",
+      "Epoch 1289/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4412 - categorical_accuracy: 0.7882\n",
+      "Epoch 1290/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4354 - categorical_accuracy: 0.8235\n",
+      "Epoch 1291/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4234 - categorical_accuracy: 0.8353\n",
+      "Epoch 1292/2000\n",
+      "3/3 [==============================] - 0s 67ms/step - loss: 0.4273 - categorical_accuracy: 0.8000\n",
+      "Epoch 1293/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.5019 - categorical_accuracy: 0.7647\n",
+      "Epoch 1294/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.6015 - categorical_accuracy: 0.7059\n",
+      "Epoch 1295/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.5337 - categorical_accuracy: 0.7882\n",
+      "Epoch 1296/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4590 - categorical_accuracy: 0.8118\n",
+      "Epoch 1297/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4123 - categorical_accuracy: 0.8235\n",
+      "Epoch 1298/2000\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4768 - categorical_accuracy: 0.7882\n",
+      "Epoch 1299/2000\n",
+      "3/3 [==============================] - 0s 73ms/step - loss: 0.4507 - categorical_accuracy: 0.7765\n",
+      "Epoch 1300/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4278 - categorical_accuracy: 0.8118\n",
+      "Epoch 1301/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.4148 - categorical_accuracy: 0.8471\n",
+      "Epoch 1302/2000\n",
+      "3/3 [==============================] - 0s 67ms/step - loss: 0.4332 - categorical_accuracy: 0.8000\n",
+      "Epoch 1303/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.5092 - categorical_accuracy: 0.7412\n",
+      "Epoch 1304/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.4483 - categorical_accuracy: 0.8118\n",
+      "Epoch 1305/2000\n",
+      "3/3 [==============================] - 0s 74ms/step - loss: 0.4487 - categorical_accuracy: 0.8000\n",
+      "Epoch 1306/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.4263 - categorical_accuracy: 0.8353\n",
+      "Epoch 1307/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4740 - categorical_accuracy: 0.7294\n",
+      "Epoch 1308/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.5042 - categorical_accuracy: 0.7412\n",
+      "Epoch 1309/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4148 - categorical_accuracy: 0.8235\n",
+      "Epoch 1310/2000\n",
+      "3/3 [==============================] - 0s 71ms/step - loss: 0.4192 - categorical_accuracy: 0.8000\n",
+      "Epoch 1311/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4960 - categorical_accuracy: 0.7765\n",
+      "Epoch 1312/2000\n",
+      "3/3 [==============================] - 0s 74ms/step - loss: 0.4327 - categorical_accuracy: 0.8000\n",
+      "Epoch 1313/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4858 - categorical_accuracy: 0.7882\n",
+      "Epoch 1314/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4244 - categorical_accuracy: 0.8235\n",
+      "Epoch 1315/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4314 - categorical_accuracy: 0.8000\n",
+      "Epoch 1316/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4725 - categorical_accuracy: 0.7765\n",
+      "Epoch 1317/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4551 - categorical_accuracy: 0.7765\n",
+      "Epoch 1318/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4379 - categorical_accuracy: 0.8118\n",
+      "Epoch 1319/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4120 - categorical_accuracy: 0.8118\n",
+      "Epoch 1320/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4146 - categorical_accuracy: 0.8588\n",
+      "Epoch 1321/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3958 - categorical_accuracy: 0.8588\n",
+      "Epoch 1322/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.4325 - categorical_accuracy: 0.8118\n",
+      "Epoch 1323/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4873 - categorical_accuracy: 0.7765\n",
+      "Epoch 1324/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4129 - categorical_accuracy: 0.8235\n",
+      "Epoch 1325/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4134 - categorical_accuracy: 0.8471\n",
+      "Epoch 1326/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4133 - categorical_accuracy: 0.8471\n",
+      "Epoch 1327/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4002 - categorical_accuracy: 0.8353\n",
+      "Epoch 1328/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4082 - categorical_accuracy: 0.8353\n",
+      "Epoch 1329/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4344 - categorical_accuracy: 0.8588\n",
+      "Epoch 1330/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.3933 - categorical_accuracy: 0.8471\n",
+      "Epoch 1331/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4144 - categorical_accuracy: 0.8118\n",
+      "Epoch 1332/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4117 - categorical_accuracy: 0.8471\n",
+      "Epoch 1333/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3998 - categorical_accuracy: 0.8235\n",
+      "Epoch 1334/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4048 - categorical_accuracy: 0.8353\n",
+      "Epoch 1335/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4246 - categorical_accuracy: 0.8118\n",
+      "Epoch 1336/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4204 - categorical_accuracy: 0.8235\n",
+      "Epoch 1337/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4063 - categorical_accuracy: 0.8353\n",
+      "Epoch 1338/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3839 - categorical_accuracy: 0.8235\n",
+      "Epoch 1339/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4641 - categorical_accuracy: 0.8000\n",
+      "Epoch 1340/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4449 - categorical_accuracy: 0.7765\n",
+      "Epoch 1341/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3950 - categorical_accuracy: 0.8588\n",
+      "Epoch 1342/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4662 - categorical_accuracy: 0.7647\n",
+      "Epoch 1343/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5564 - categorical_accuracy: 0.7765\n",
+      "Epoch 1344/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4260 - categorical_accuracy: 0.8118\n",
+      "Epoch 1345/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3874 - categorical_accuracy: 0.8588\n",
+      "Epoch 1346/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3853 - categorical_accuracy: 0.8471\n",
+      "Epoch 1347/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4236 - categorical_accuracy: 0.7882\n",
+      "Epoch 1348/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.4413 - categorical_accuracy: 0.8471\n",
+      "Epoch 1349/2000\n",
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+      "Epoch 1350/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4312 - categorical_accuracy: 0.8118\n",
+      "Epoch 1351/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4057 - categorical_accuracy: 0.8118\n",
+      "Epoch 1352/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4104 - categorical_accuracy: 0.7882\n",
+      "Epoch 1353/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4013 - categorical_accuracy: 0.8353\n",
+      "Epoch 1354/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3830 - categorical_accuracy: 0.8353\n",
+      "Epoch 1355/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4201 - categorical_accuracy: 0.8471\n",
+      "Epoch 1356/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3906 - categorical_accuracy: 0.8353\n",
+      "Epoch 1357/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.4014 - categorical_accuracy: 0.8471\n",
+      "Epoch 1358/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4096 - categorical_accuracy: 0.8235\n",
+      "Epoch 1359/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4162 - categorical_accuracy: 0.8118\n",
+      "Epoch 1360/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4823 - categorical_accuracy: 0.7882\n",
+      "Epoch 1361/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4178 - categorical_accuracy: 0.8000\n",
+      "Epoch 1362/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4926 - categorical_accuracy: 0.7882\n",
+      "Epoch 1363/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4115 - categorical_accuracy: 0.8235\n",
+      "Epoch 1364/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4895 - categorical_accuracy: 0.7882\n",
+      "Epoch 1365/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3494 - categorical_accuracy: 0.8588\n",
+      "Epoch 1366/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4466 - categorical_accuracy: 0.7882\n",
+      "Epoch 1367/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4417 - categorical_accuracy: 0.8353\n",
+      "Epoch 1368/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4438 - categorical_accuracy: 0.7882\n",
+      "Epoch 1369/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4157 - categorical_accuracy: 0.8471\n",
+      "Epoch 1370/2000\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3755 - categorical_accuracy: 0.8588\n",
+      "Epoch 1371/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4617 - categorical_accuracy: 0.7647\n",
+      "Epoch 1372/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3847 - categorical_accuracy: 0.8471\n",
+      "Epoch 1373/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4056 - categorical_accuracy: 0.8588\n",
+      "Epoch 1374/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.3973 - categorical_accuracy: 0.8118\n",
+      "Epoch 1375/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3642 - categorical_accuracy: 0.8588\n",
+      "Epoch 1376/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.3970 - categorical_accuracy: 0.8471\n",
+      "Epoch 1377/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3698 - categorical_accuracy: 0.8471\n",
+      "Epoch 1378/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3880 - categorical_accuracy: 0.8588\n",
+      "Epoch 1379/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3791 - categorical_accuracy: 0.8588\n",
+      "Epoch 1380/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3689 - categorical_accuracy: 0.8588\n",
+      "Epoch 1381/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3693 - categorical_accuracy: 0.8471\n",
+      "Epoch 1382/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.3678 - categorical_accuracy: 0.8471\n",
+      "Epoch 1383/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3776 - categorical_accuracy: 0.8471\n",
+      "Epoch 1384/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3694 - categorical_accuracy: 0.8588\n",
+      "Epoch 1385/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4068 - categorical_accuracy: 0.8588\n",
+      "Epoch 1386/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4255 - categorical_accuracy: 0.8235\n",
+      "Epoch 1387/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4316 - categorical_accuracy: 0.8000\n",
+      "Epoch 1388/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.3800 - categorical_accuracy: 0.8588\n",
+      "Epoch 1389/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3728 - categorical_accuracy: 0.8588\n",
+      "Epoch 1390/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3696 - categorical_accuracy: 0.8588\n",
+      "Epoch 1391/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.3718 - categorical_accuracy: 0.8353\n",
+      "Epoch 1392/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3924 - categorical_accuracy: 0.8235\n",
+      "Epoch 1393/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3764 - categorical_accuracy: 0.8471\n",
+      "Epoch 1394/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3773 - categorical_accuracy: 0.8588\n",
+      "Epoch 1395/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3788 - categorical_accuracy: 0.8353\n",
+      "Epoch 1396/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3977 - categorical_accuracy: 0.8471\n",
+      "Epoch 1397/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3702 - categorical_accuracy: 0.8588\n",
+      "Epoch 1398/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.3715 - categorical_accuracy: 0.8471\n",
+      "Epoch 1399/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3732 - categorical_accuracy: 0.8471\n",
+      "Epoch 1400/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.3572 - categorical_accuracy: 0.8588\n",
+      "Epoch 1401/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3602 - categorical_accuracy: 0.8706\n",
+      "Epoch 1402/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3637 - categorical_accuracy: 0.8588\n",
+      "Epoch 1403/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3809 - categorical_accuracy: 0.8588\n",
+      "Epoch 1404/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.3629 - categorical_accuracy: 0.8471\n",
+      "Epoch 1405/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4199 - categorical_accuracy: 0.7882\n",
+      "Epoch 1406/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4900 - categorical_accuracy: 0.7882\n",
+      "Epoch 1407/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4564 - categorical_accuracy: 0.8118\n",
+      "Epoch 1408/2000\n",
+      "3/3 [==============================] - 0s 73ms/step - loss: 0.4614 - categorical_accuracy: 0.8118\n",
+      "Epoch 1409/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.4170 - categorical_accuracy: 0.8000\n",
+      "Epoch 1410/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4847 - categorical_accuracy: 0.8000\n",
+      "Epoch 1411/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.4037 - categorical_accuracy: 0.7882\n",
+      "Epoch 1412/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3914 - categorical_accuracy: 0.8588\n",
+      "Epoch 1413/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3752 - categorical_accuracy: 0.8353\n",
+      "Epoch 1414/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3549 - categorical_accuracy: 0.8706\n",
+      "Epoch 1415/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3984 - categorical_accuracy: 0.8588\n",
+      "Epoch 1416/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3710 - categorical_accuracy: 0.8588\n",
+      "Epoch 1417/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3996 - categorical_accuracy: 0.8235\n",
+      "Epoch 1418/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.4412 - categorical_accuracy: 0.8118\n",
+      "Epoch 1419/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.4358 - categorical_accuracy: 0.7882\n",
+      "Epoch 1420/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.4564 - categorical_accuracy: 0.7882\n",
+      "Epoch 1421/2000\n",
+      "3/3 [==============================] - 0s 73ms/step - loss: 0.4031 - categorical_accuracy: 0.8118\n",
+      "Epoch 1422/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3719 - categorical_accuracy: 0.8824\n",
+      "Epoch 1423/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4079 - categorical_accuracy: 0.8353\n",
+      "Epoch 1424/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3672 - categorical_accuracy: 0.8588\n",
+      "Epoch 1425/2000\n",
+      "3/3 [==============================] - 0s 67ms/step - loss: 0.3969 - categorical_accuracy: 0.8118\n",
+      "Epoch 1426/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3884 - categorical_accuracy: 0.8824\n",
+      "Epoch 1427/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3537 - categorical_accuracy: 0.8471\n",
+      "Epoch 1428/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3664 - categorical_accuracy: 0.8588\n",
+      "Epoch 1429/2000\n",
+      "3/3 [==============================] - 0s 72ms/step - loss: 0.4187 - categorical_accuracy: 0.8588\n",
+      "Epoch 1430/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.3525 - categorical_accuracy: 0.8471\n",
+      "Epoch 1431/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.4181 - categorical_accuracy: 0.7882\n",
+      "Epoch 1432/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4214 - categorical_accuracy: 0.8471\n",
+      "Epoch 1433/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3711 - categorical_accuracy: 0.8000\n",
+      "Epoch 1434/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.4031 - categorical_accuracy: 0.8353\n",
+      "Epoch 1435/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.4289 - categorical_accuracy: 0.7882\n",
+      "Epoch 1436/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3525 - categorical_accuracy: 0.8588\n",
+      "Epoch 1437/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3583 - categorical_accuracy: 0.8471\n",
+      "Epoch 1438/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3725 - categorical_accuracy: 0.8588\n",
+      "Epoch 1439/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3919 - categorical_accuracy: 0.8471\n",
+      "Epoch 1440/2000\n",
+      "3/3 [==============================] - 0s 74ms/step - loss: 0.4358 - categorical_accuracy: 0.8235\n",
+      "Epoch 1441/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4060 - categorical_accuracy: 0.8000\n",
+      "Epoch 1442/2000\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3870 - categorical_accuracy: 0.8706\n",
+      "Epoch 1443/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4357 - categorical_accuracy: 0.8118\n",
+      "Epoch 1444/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3696 - categorical_accuracy: 0.8588\n",
+      "Epoch 1445/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.4049 - categorical_accuracy: 0.8471\n",
+      "Epoch 1446/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.4296 - categorical_accuracy: 0.8941\n",
+      "Epoch 1447/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.4000 - categorical_accuracy: 0.8118\n",
+      "Epoch 1448/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3870 - categorical_accuracy: 0.8706\n",
+      "Epoch 1449/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3333 - categorical_accuracy: 0.8588\n",
+      "Epoch 1450/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3724 - categorical_accuracy: 0.8353\n",
+      "Epoch 1451/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3802 - categorical_accuracy: 0.8471\n",
+      "Epoch 1452/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3411 - categorical_accuracy: 0.8706\n",
+      "Epoch 1453/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3668 - categorical_accuracy: 0.8471\n",
+      "Epoch 1454/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3921 - categorical_accuracy: 0.8588\n",
+      "Epoch 1455/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4443 - categorical_accuracy: 0.8353\n",
+      "Epoch 1456/2000\n",
+      "3/3 [==============================] - 0s 72ms/step - loss: 0.3848 - categorical_accuracy: 0.8235\n",
+      "Epoch 1457/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3629 - categorical_accuracy: 0.8471\n",
+      "Epoch 1458/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4090 - categorical_accuracy: 0.8824\n",
+      "Epoch 1459/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.4084 - categorical_accuracy: 0.8000\n",
+      "Epoch 1460/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4250 - categorical_accuracy: 0.8471\n",
+      "Epoch 1461/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3426 - categorical_accuracy: 0.8471\n",
+      "Epoch 1462/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.3861 - categorical_accuracy: 0.8471\n",
+      "Epoch 1463/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.3722 - categorical_accuracy: 0.8588\n",
+      "Epoch 1464/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4398 - categorical_accuracy: 0.8353\n",
+      "Epoch 1465/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4171 - categorical_accuracy: 0.7765\n",
+      "Epoch 1466/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3442 - categorical_accuracy: 0.8588\n",
+      "Epoch 1467/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3611 - categorical_accuracy: 0.8588\n",
+      "Epoch 1468/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.4168 - categorical_accuracy: 0.8235\n",
+      "Epoch 1469/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.3872 - categorical_accuracy: 0.8471\n",
+      "Epoch 1470/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3835 - categorical_accuracy: 0.8118\n",
+      "Epoch 1471/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3475 - categorical_accuracy: 0.8353\n",
+      "Epoch 1472/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3513 - categorical_accuracy: 0.8588\n",
+      "Epoch 1473/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3456 - categorical_accuracy: 0.8588\n",
+      "Epoch 1474/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3502 - categorical_accuracy: 0.8471\n",
+      "Epoch 1475/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3878 - categorical_accuracy: 0.8235\n",
+      "Epoch 1476/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.3621 - categorical_accuracy: 0.8471\n",
+      "Epoch 1477/2000\n",
+      "3/3 [==============================] - 0s 74ms/step - loss: 0.3912 - categorical_accuracy: 0.8235\n",
+      "Epoch 1478/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.3835 - categorical_accuracy: 0.8353\n",
+      "Epoch 1479/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3921 - categorical_accuracy: 0.8118\n",
+      "Epoch 1480/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5495 - categorical_accuracy: 0.8118\n",
+      "Epoch 1481/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.6445 - categorical_accuracy: 0.7176\n",
+      "Epoch 1482/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.6207 - categorical_accuracy: 0.7529\n",
+      "Epoch 1483/2000\n",
+      "3/3 [==============================] - 0s 67ms/step - loss: 0.5583 - categorical_accuracy: 0.7059\n",
+      "Epoch 1484/2000\n",
+      "3/3 [==============================] - 0s 68ms/step - loss: 0.4758 - categorical_accuracy: 0.7882\n",
+      "Epoch 1485/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4507 - categorical_accuracy: 0.8353\n",
+      "Epoch 1486/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5781 - categorical_accuracy: 0.7059\n",
+      "Epoch 1487/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4296 - categorical_accuracy: 0.8588\n",
+      "Epoch 1488/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3878 - categorical_accuracy: 0.8353\n",
+      "Epoch 1489/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3511 - categorical_accuracy: 0.8588\n",
+      "Epoch 1490/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.3632 - categorical_accuracy: 0.8706\n",
+      "Epoch 1491/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.3467 - categorical_accuracy: 0.8941\n",
+      "Epoch 1492/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3500 - categorical_accuracy: 0.8353\n",
+      "Epoch 1493/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3432 - categorical_accuracy: 0.8588\n",
+      "Epoch 1494/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3388 - categorical_accuracy: 0.8588\n",
+      "Epoch 1495/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3374 - categorical_accuracy: 0.8588\n",
+      "Epoch 1496/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3343 - categorical_accuracy: 0.8706\n",
+      "Epoch 1497/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3380 - categorical_accuracy: 0.8588\n",
+      "Epoch 1498/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3571 - categorical_accuracy: 0.8471\n",
+      "Epoch 1499/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3355 - categorical_accuracy: 0.8471\n",
+      "Epoch 1500/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3332 - categorical_accuracy: 0.8706\n",
+      "Epoch 1501/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3385 - categorical_accuracy: 0.8588\n",
+      "Epoch 1502/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3472 - categorical_accuracy: 0.8706\n",
+      "Epoch 1503/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3501 - categorical_accuracy: 0.8471\n",
+      "Epoch 1504/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3261 - categorical_accuracy: 0.8706\n",
+      "Epoch 1505/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.3528 - categorical_accuracy: 0.8588\n",
+      "Epoch 1506/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3430 - categorical_accuracy: 0.8353\n",
+      "Epoch 1507/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3852 - categorical_accuracy: 0.8588\n",
+      "Epoch 1508/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3303 - categorical_accuracy: 0.8588\n",
+      "Epoch 1509/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.3748 - categorical_accuracy: 0.8588\n",
+      "Epoch 1510/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3592 - categorical_accuracy: 0.8353\n",
+      "Epoch 1511/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3336 - categorical_accuracy: 0.8588\n",
+      "Epoch 1512/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.3523 - categorical_accuracy: 0.8941\n",
+      "Epoch 1513/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3368 - categorical_accuracy: 0.8353\n",
+      "Epoch 1514/2000\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3510 - categorical_accuracy: 0.8588\n",
+      "Epoch 1515/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3386 - categorical_accuracy: 0.8588\n",
+      "Epoch 1516/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3213 - categorical_accuracy: 0.8824\n",
+      "Epoch 1517/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3497 - categorical_accuracy: 0.8471\n",
+      "Epoch 1518/2000\n",
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+      "Epoch 1519/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.3500 - categorical_accuracy: 0.8471\n",
+      "Epoch 1520/2000\n",
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+      "Epoch 1521/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3199 - categorical_accuracy: 0.8706\n",
+      "Epoch 1522/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3204 - categorical_accuracy: 0.8706\n",
+      "Epoch 1523/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3199 - categorical_accuracy: 0.8824\n",
+      "Epoch 1524/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.3430 - categorical_accuracy: 0.8706\n",
+      "Epoch 1525/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3119 - categorical_accuracy: 0.8588\n",
+      "Epoch 1526/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3374 - categorical_accuracy: 0.8588\n",
+      "Epoch 1527/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.3587 - categorical_accuracy: 0.8588\n",
+      "Epoch 1528/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.3614 - categorical_accuracy: 0.8471\n",
+      "Epoch 1529/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3374 - categorical_accuracy: 0.8471\n",
+      "Epoch 1530/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.3184 - categorical_accuracy: 0.8706\n",
+      "Epoch 1531/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.3295 - categorical_accuracy: 0.8588\n",
+      "Epoch 1532/2000\n",
+      "3/3 [==============================] - 0s 73ms/step - loss: 0.3398 - categorical_accuracy: 0.8588\n",
+      "Epoch 1533/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3288 - categorical_accuracy: 0.8471\n",
+      "Epoch 1534/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3088 - categorical_accuracy: 0.8824\n",
+      "Epoch 1535/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3100 - categorical_accuracy: 0.8588\n",
+      "Epoch 1536/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3224 - categorical_accuracy: 0.8471\n",
+      "Epoch 1537/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.3097 - categorical_accuracy: 0.8588\n",
+      "Epoch 1538/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3312 - categorical_accuracy: 0.8706\n",
+      "Epoch 1539/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3148 - categorical_accuracy: 0.8471\n",
+      "Epoch 1540/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.3227 - categorical_accuracy: 0.8706\n",
+      "Epoch 1541/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3132 - categorical_accuracy: 0.8824\n",
+      "Epoch 1542/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3250 - categorical_accuracy: 0.8588\n",
+      "Epoch 1543/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3578 - categorical_accuracy: 0.8235\n",
+      "Epoch 1544/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.4531 - categorical_accuracy: 0.8471\n",
+      "Epoch 1545/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.3283 - categorical_accuracy: 0.8588\n",
+      "Epoch 1546/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.3098 - categorical_accuracy: 0.8824\n",
+      "Epoch 1547/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.3257 - categorical_accuracy: 0.8588\n",
+      "Epoch 1548/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3186 - categorical_accuracy: 0.8588\n",
+      "Epoch 1549/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3143 - categorical_accuracy: 0.8706\n",
+      "Epoch 1550/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.3043 - categorical_accuracy: 0.8824\n",
+      "Epoch 1551/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.3112 - categorical_accuracy: 0.8706\n",
+      "Epoch 1552/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.3296 - categorical_accuracy: 0.8588\n",
+      "Epoch 1553/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.3719 - categorical_accuracy: 0.8588\n",
+      "Epoch 1554/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.3256 - categorical_accuracy: 0.8588\n",
+      "Epoch 1555/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3243 - categorical_accuracy: 0.8706\n",
+      "Epoch 1556/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.3635 - categorical_accuracy: 0.8118\n",
+      "Epoch 1557/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.3090 - categorical_accuracy: 0.8706\n",
+      "Epoch 1558/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3033 - categorical_accuracy: 0.8824\n",
+      "Epoch 1559/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.3056 - categorical_accuracy: 0.8588\n",
+      "Epoch 1560/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3116 - categorical_accuracy: 0.8588\n",
+      "Epoch 1561/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.3107 - categorical_accuracy: 0.8706\n",
+      "Epoch 1562/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.3160 - categorical_accuracy: 0.8706\n",
+      "Epoch 1563/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.3662 - categorical_accuracy: 0.8471\n",
+      "Epoch 1564/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3229 - categorical_accuracy: 0.8588\n",
+      "Epoch 1565/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.3001 - categorical_accuracy: 0.8706\n",
+      "Epoch 1566/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.2991 - categorical_accuracy: 0.8588\n",
+      "Epoch 1567/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.3138 - categorical_accuracy: 0.8706\n",
+      "Epoch 1568/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.3198 - categorical_accuracy: 0.8824\n",
+      "Epoch 1569/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3359 - categorical_accuracy: 0.8706\n",
+      "Epoch 1570/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.3232 - categorical_accuracy: 0.8706\n",
+      "Epoch 1571/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.3493 - categorical_accuracy: 0.8588\n",
+      "Epoch 1572/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.3705 - categorical_accuracy: 0.8588\n",
+      "Epoch 1573/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.4364 - categorical_accuracy: 0.8353\n",
+      "Epoch 1574/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.3762 - categorical_accuracy: 0.8118\n",
+      "Epoch 1575/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.3162 - categorical_accuracy: 0.8706\n",
+      "Epoch 1576/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.3044 - categorical_accuracy: 0.8706\n",
+      "Epoch 1577/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.3049 - categorical_accuracy: 0.8824\n",
+      "Epoch 1578/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.3093 - categorical_accuracy: 0.8588\n",
+      "Epoch 1579/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.3022 - categorical_accuracy: 0.8706\n",
+      "Epoch 1580/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.2927 - categorical_accuracy: 0.8588\n",
+      "Epoch 1581/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.3006 - categorical_accuracy: 0.8706\n",
+      "Epoch 1582/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.3070 - categorical_accuracy: 0.8941\n",
+      "Epoch 1583/2000\n",
+      "3/3 [==============================] - 0s 70ms/step - loss: 0.2960 - categorical_accuracy: 0.8706\n",
+      "Epoch 1584/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.2990 - categorical_accuracy: 0.8706\n",
+      "Epoch 1585/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.2940 - categorical_accuracy: 0.8588\n",
+      "Epoch 1586/2000\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.2809 - categorical_accuracy: 0.8706\n",
+      "Epoch 1587/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.2821 - categorical_accuracy: 0.8706\n",
+      "Epoch 1588/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.2945 - categorical_accuracy: 0.8824\n",
+      "Epoch 1589/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3106 - categorical_accuracy: 0.8588\n",
+      "Epoch 1590/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3124 - categorical_accuracy: 0.8588\n",
+      "Epoch 1591/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3540 - categorical_accuracy: 0.8471\n",
+      "Epoch 1592/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3107 - categorical_accuracy: 0.8471\n",
+      "Epoch 1593/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.2777 - categorical_accuracy: 0.8824\n",
+      "Epoch 1594/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.2967 - categorical_accuracy: 0.8941\n",
+      "Epoch 1595/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.3316 - categorical_accuracy: 0.8706\n",
+      "Epoch 1596/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4576 - categorical_accuracy: 0.7765\n",
+      "Epoch 1597/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.4580 - categorical_accuracy: 0.8235\n",
+      "Epoch 1598/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4750 - categorical_accuracy: 0.7647\n",
+      "Epoch 1599/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4520 - categorical_accuracy: 0.8235\n",
+      "Epoch 1600/2000\n",
+      "3/3 [==============================] - 0s 71ms/step - loss: 0.4360 - categorical_accuracy: 0.7765\n",
+      "Epoch 1601/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.5113 - categorical_accuracy: 0.8471\n",
+      "Epoch 1602/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4444 - categorical_accuracy: 0.7882\n",
+      "Epoch 1603/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.3499 - categorical_accuracy: 0.8824\n",
+      "Epoch 1604/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4423 - categorical_accuracy: 0.7412\n",
+      "Epoch 1605/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4203 - categorical_accuracy: 0.8471\n",
+      "Epoch 1606/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4248 - categorical_accuracy: 0.8118\n",
+      "Epoch 1607/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3515 - categorical_accuracy: 0.8706\n",
+      "Epoch 1608/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3740 - categorical_accuracy: 0.8471\n",
+      "Epoch 1609/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.3518 - categorical_accuracy: 0.8235\n",
+      "Epoch 1610/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.4132 - categorical_accuracy: 0.8471\n",
+      "Epoch 1611/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.4457 - categorical_accuracy: 0.7412\n",
+      "Epoch 1612/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.4110 - categorical_accuracy: 0.8235\n",
+      "Epoch 1613/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4300 - categorical_accuracy: 0.8118\n",
+      "Epoch 1614/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.4714 - categorical_accuracy: 0.8235\n",
+      "Epoch 1615/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.4499 - categorical_accuracy: 0.7647\n",
+      "Epoch 1616/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4158 - categorical_accuracy: 0.8353\n",
+      "Epoch 1617/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.3533 - categorical_accuracy: 0.8706\n",
+      "Epoch 1618/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4002 - categorical_accuracy: 0.7882\n",
+      "Epoch 1619/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3523 - categorical_accuracy: 0.8824\n",
+      "Epoch 1620/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3366 - categorical_accuracy: 0.8471\n",
+      "Epoch 1621/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3080 - categorical_accuracy: 0.8706\n",
+      "Epoch 1622/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3530 - categorical_accuracy: 0.8471\n",
+      "Epoch 1623/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3573 - categorical_accuracy: 0.8824\n",
+      "Epoch 1624/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.3845 - categorical_accuracy: 0.8235\n",
+      "Epoch 1625/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.3934 - categorical_accuracy: 0.8353\n",
+      "Epoch 1626/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3514 - categorical_accuracy: 0.8941\n",
+      "Epoch 1627/2000\n",
+      "3/3 [==============================] - 0s 61ms/step - loss: 0.4743 - categorical_accuracy: 0.7882\n",
+      "Epoch 1628/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3912 - categorical_accuracy: 0.8471\n",
+      "Epoch 1629/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3142 - categorical_accuracy: 0.9412\n",
+      "Epoch 1630/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3993 - categorical_accuracy: 0.8706\n",
+      "Epoch 1631/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3949 - categorical_accuracy: 0.8235\n",
+      "Epoch 1632/2000\n",
+      "3/3 [==============================] - 0s 72ms/step - loss: 0.3622 - categorical_accuracy: 0.8706\n",
+      "Epoch 1633/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.2731 - categorical_accuracy: 0.8941\n",
+      "Epoch 1634/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3379 - categorical_accuracy: 0.8588\n",
+      "Epoch 1635/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3594 - categorical_accuracy: 0.8824\n",
+      "Epoch 1636/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3533 - categorical_accuracy: 0.8588\n",
+      "Epoch 1637/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3787 - categorical_accuracy: 0.8941\n",
+      "Epoch 1638/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3347 - categorical_accuracy: 0.8588\n",
+      "Epoch 1639/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.2744 - categorical_accuracy: 0.8941\n",
+      "Epoch 1640/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3265 - categorical_accuracy: 0.8706\n",
+      "Epoch 1641/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3585 - categorical_accuracy: 0.8353\n",
+      "Epoch 1642/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4527 - categorical_accuracy: 0.8471\n",
+      "Epoch 1643/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3230 - categorical_accuracy: 0.8706\n",
+      "Epoch 1644/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.2754 - categorical_accuracy: 0.8706\n",
+      "Epoch 1645/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.3100 - categorical_accuracy: 0.8706\n",
+      "Epoch 1646/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.2779 - categorical_accuracy: 0.8824\n",
+      "Epoch 1647/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3021 - categorical_accuracy: 0.8706\n",
+      "Epoch 1648/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3561 - categorical_accuracy: 0.8353\n",
+      "Epoch 1649/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.3907 - categorical_accuracy: 0.8824\n",
+      "Epoch 1650/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3127 - categorical_accuracy: 0.8941\n",
+      "Epoch 1651/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.2579 - categorical_accuracy: 0.8824\n",
+      "Epoch 1652/2000\n",
+      "3/3 [==============================] - 0s 72ms/step - loss: 0.3184 - categorical_accuracy: 0.8706\n",
+      "Epoch 1653/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3011 - categorical_accuracy: 0.8824\n",
+      "Epoch 1654/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3273 - categorical_accuracy: 0.8471\n",
+      "Epoch 1655/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.2783 - categorical_accuracy: 0.8706\n",
+      "Epoch 1656/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.2757 - categorical_accuracy: 0.9059\n",
+      "Epoch 1657/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3123 - categorical_accuracy: 0.8824\n",
+      "Epoch 1658/2000\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.2848 - categorical_accuracy: 0.8941\n",
+      "Epoch 1659/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.2632 - categorical_accuracy: 0.8824\n",
+      "Epoch 1660/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.2862 - categorical_accuracy: 0.8824\n",
+      "Epoch 1661/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3236 - categorical_accuracy: 0.8471\n",
+      "Epoch 1662/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.2707 - categorical_accuracy: 0.8706\n",
+      "Epoch 1663/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3423 - categorical_accuracy: 0.8706\n",
+      "Epoch 1664/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.3885 - categorical_accuracy: 0.8824\n",
+      "Epoch 1665/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.2875 - categorical_accuracy: 0.8941\n",
+      "Epoch 1666/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.2852 - categorical_accuracy: 0.8941\n",
+      "Epoch 1667/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.2899 - categorical_accuracy: 0.8706\n",
+      "Epoch 1668/2000\n",
+      "3/3 [==============================] - 0s 73ms/step - loss: 0.2820 - categorical_accuracy: 0.8824\n",
+      "Epoch 1669/2000\n",
+      "3/3 [==============================] - 0s 67ms/step - loss: 0.2827 - categorical_accuracy: 0.8706\n",
+      "Epoch 1670/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.2646 - categorical_accuracy: 0.8941\n",
+      "Epoch 1671/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.2819 - categorical_accuracy: 0.8588\n",
+      "Epoch 1672/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.2647 - categorical_accuracy: 0.8824\n",
+      "Epoch 1673/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.2656 - categorical_accuracy: 0.8941\n",
+      "Epoch 1674/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.2660 - categorical_accuracy: 0.8706\n",
+      "Epoch 1675/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.2730 - categorical_accuracy: 0.8941\n",
+      "Epoch 1676/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.2787 - categorical_accuracy: 0.8824\n",
+      "Epoch 1677/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.2797 - categorical_accuracy: 0.9176\n",
+      "Epoch 1678/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.2686 - categorical_accuracy: 0.8824\n",
+      "Epoch 1679/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.2639 - categorical_accuracy: 0.9059\n",
+      "Epoch 1680/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.2599 - categorical_accuracy: 0.8941\n",
+      "Epoch 1681/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.2673 - categorical_accuracy: 0.8824\n",
+      "Epoch 1682/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.2712 - categorical_accuracy: 0.8706\n",
+      "Epoch 1683/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.2800 - categorical_accuracy: 0.8941\n",
+      "Epoch 1684/2000\n",
+      "3/3 [==============================] - 0s 74ms/step - loss: 0.2816 - categorical_accuracy: 0.8941\n",
+      "Epoch 1685/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3159 - categorical_accuracy: 0.8824\n",
+      "Epoch 1686/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4451 - categorical_accuracy: 0.8000\n",
+      "Epoch 1687/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4552 - categorical_accuracy: 0.8353\n",
+      "Epoch 1688/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4453 - categorical_accuracy: 0.8000\n",
+      "Epoch 1689/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4014 - categorical_accuracy: 0.8588\n",
+      "Epoch 1690/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.2781 - categorical_accuracy: 0.9059\n",
+      "Epoch 1691/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3169 - categorical_accuracy: 0.8824\n",
+      "Epoch 1692/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.2878 - categorical_accuracy: 0.8824\n",
+      "Epoch 1693/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.2912 - categorical_accuracy: 0.8824\n",
+      "Epoch 1694/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.2717 - categorical_accuracy: 0.8706\n",
+      "Epoch 1695/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.2723 - categorical_accuracy: 0.8824\n",
+      "Epoch 1696/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.2853 - categorical_accuracy: 0.8941\n",
+      "Epoch 1697/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.2638 - categorical_accuracy: 0.9294\n",
+      "Epoch 1698/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.2572 - categorical_accuracy: 0.8941\n",
+      "Epoch 1699/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.2755 - categorical_accuracy: 0.8824\n",
+      "Epoch 1700/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.2740 - categorical_accuracy: 0.9059\n",
+      "Epoch 1701/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.2723 - categorical_accuracy: 0.8706\n",
+      "Epoch 1702/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.2792 - categorical_accuracy: 0.8706\n",
+      "Epoch 1703/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.2700 - categorical_accuracy: 0.8706\n",
+      "Epoch 1704/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.2664 - categorical_accuracy: 0.8941\n",
+      "Epoch 1705/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.2591 - categorical_accuracy: 0.8824\n",
+      "Epoch 1706/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.2544 - categorical_accuracy: 0.8824\n",
+      "Epoch 1707/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.2528 - categorical_accuracy: 0.8941\n",
+      "Epoch 1708/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.2588 - categorical_accuracy: 0.9176\n",
+      "Epoch 1709/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.2776 - categorical_accuracy: 0.8824\n",
+      "Epoch 1710/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.2967 - categorical_accuracy: 0.8706\n",
+      "Epoch 1711/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3312 - categorical_accuracy: 0.8353\n",
+      "Epoch 1712/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.3694 - categorical_accuracy: 0.8706\n",
+      "Epoch 1713/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.2619 - categorical_accuracy: 0.9059\n",
+      "Epoch 1714/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.2601 - categorical_accuracy: 0.8706\n",
+      "Epoch 1715/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.2526 - categorical_accuracy: 0.9059\n",
+      "Epoch 1716/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.2971 - categorical_accuracy: 0.8706\n",
+      "Epoch 1717/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.2676 - categorical_accuracy: 0.9176\n",
+      "Epoch 1718/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.2547 - categorical_accuracy: 0.8706\n",
+      "Epoch 1719/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.2619 - categorical_accuracy: 0.8941\n",
+      "Epoch 1720/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.2486 - categorical_accuracy: 0.8941\n",
+      "Epoch 1721/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.2511 - categorical_accuracy: 0.8824\n",
+      "Epoch 1722/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.2456 - categorical_accuracy: 0.8941\n",
+      "Epoch 1723/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.2734 - categorical_accuracy: 0.8824\n",
+      "Epoch 1724/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.4973 - categorical_accuracy: 0.8000\n",
+      "Epoch 1725/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4805 - categorical_accuracy: 0.8588\n",
+      "Epoch 1726/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3668 - categorical_accuracy: 0.8118\n",
+      "Epoch 1727/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3502 - categorical_accuracy: 0.8706\n",
+      "Epoch 1728/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3560 - categorical_accuracy: 0.8353\n",
+      "Epoch 1729/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3433 - categorical_accuracy: 0.8824\n",
+      "Epoch 1730/2000\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3237 - categorical_accuracy: 0.8353\n",
+      "Epoch 1731/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.2912 - categorical_accuracy: 0.8824\n",
+      "Epoch 1732/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.2787 - categorical_accuracy: 0.8824\n",
+      "Epoch 1733/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.2820 - categorical_accuracy: 0.8941\n",
+      "Epoch 1734/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4128 - categorical_accuracy: 0.8588\n",
+      "Epoch 1735/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.2979 - categorical_accuracy: 0.8941\n",
+      "Epoch 1736/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.2865 - categorical_accuracy: 0.8824\n",
+      "Epoch 1737/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3079 - categorical_accuracy: 0.8235\n",
+      "Epoch 1738/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.3787 - categorical_accuracy: 0.8706\n",
+      "Epoch 1739/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4162 - categorical_accuracy: 0.7765\n",
+      "Epoch 1740/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4026 - categorical_accuracy: 0.8588\n",
+      "Epoch 1741/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.3382 - categorical_accuracy: 0.8471\n",
+      "Epoch 1742/2000\n",
+      "3/3 [==============================] - 0s 62ms/step - loss: 0.3506 - categorical_accuracy: 0.8706\n",
+      "Epoch 1743/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3792 - categorical_accuracy: 0.8353\n",
+      "Epoch 1744/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3449 - categorical_accuracy: 0.8941\n",
+      "Epoch 1745/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3692 - categorical_accuracy: 0.8471\n",
+      "Epoch 1746/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.2850 - categorical_accuracy: 0.8941\n",
+      "Epoch 1747/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.2652 - categorical_accuracy: 0.9176\n",
+      "Epoch 1748/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.2978 - categorical_accuracy: 0.8706\n",
+      "Epoch 1749/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3720 - categorical_accuracy: 0.8824\n",
+      "Epoch 1750/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3229 - categorical_accuracy: 0.8471\n",
+      "Epoch 1751/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.3149 - categorical_accuracy: 0.8941\n",
+      "Epoch 1752/2000\n",
+      "3/3 [==============================] - 0s 72ms/step - loss: 0.3261 - categorical_accuracy: 0.8353\n",
+      "Epoch 1753/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.2797 - categorical_accuracy: 0.9059\n",
+      "Epoch 1754/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3097 - categorical_accuracy: 0.8941\n",
+      "Epoch 1755/2000\n",
+      "3/3 [==============================] - 0s 67ms/step - loss: 0.3121 - categorical_accuracy: 0.8941\n",
+      "Epoch 1756/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.3386 - categorical_accuracy: 0.8471\n",
+      "Epoch 1757/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.2937 - categorical_accuracy: 0.8941\n",
+      "Epoch 1758/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3140 - categorical_accuracy: 0.8353\n",
+      "Epoch 1759/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3248 - categorical_accuracy: 0.8706\n",
+      "Epoch 1760/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.2877 - categorical_accuracy: 0.8588\n",
+      "Epoch 1761/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.2586 - categorical_accuracy: 0.9059\n",
+      "Epoch 1762/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.2685 - categorical_accuracy: 0.8941\n",
+      "Epoch 1763/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.2924 - categorical_accuracy: 0.8588\n",
+      "Epoch 1764/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3237 - categorical_accuracy: 0.8941\n",
+      "Epoch 1765/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.2623 - categorical_accuracy: 0.9176\n",
+      "Epoch 1766/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.2682 - categorical_accuracy: 0.9059\n",
+      "Epoch 1767/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3539 - categorical_accuracy: 0.8000\n",
+      "Epoch 1768/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.5095 - categorical_accuracy: 0.8353\n",
+      "Epoch 1769/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.4374 - categorical_accuracy: 0.7882\n",
+      "Epoch 1770/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.4276 - categorical_accuracy: 0.8471\n",
+      "Epoch 1771/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3271 - categorical_accuracy: 0.8353\n",
+      "Epoch 1772/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3498 - categorical_accuracy: 0.8353\n",
+      "Epoch 1773/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3687 - categorical_accuracy: 0.8588\n",
+      "Epoch 1774/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3553 - categorical_accuracy: 0.8353\n",
+      "Epoch 1775/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4497 - categorical_accuracy: 0.8235\n",
+      "Epoch 1776/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.3002 - categorical_accuracy: 0.8941\n",
+      "Epoch 1777/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3098 - categorical_accuracy: 0.8941\n",
+      "Epoch 1778/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.2826 - categorical_accuracy: 0.9059\n",
+      "Epoch 1779/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.2795 - categorical_accuracy: 0.9059\n",
+      "Epoch 1780/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.2827 - categorical_accuracy: 0.8824\n",
+      "Epoch 1781/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.2950 - categorical_accuracy: 0.8941\n",
+      "Epoch 1782/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.2677 - categorical_accuracy: 0.8824\n",
+      "Epoch 1783/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.2693 - categorical_accuracy: 0.8941\n",
+      "Epoch 1784/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.2694 - categorical_accuracy: 0.8706\n",
+      "Epoch 1785/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.2633 - categorical_accuracy: 0.9059\n",
+      "Epoch 1786/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3335 - categorical_accuracy: 0.8941\n",
+      "Epoch 1787/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3533 - categorical_accuracy: 0.8235\n",
+      "Epoch 1788/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4439 - categorical_accuracy: 0.8353\n",
+      "Epoch 1789/2000\n",
+      "3/3 [==============================] - 0s 63ms/step - loss: 0.3731 - categorical_accuracy: 0.8353\n",
+      "Epoch 1790/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3581 - categorical_accuracy: 0.8706\n",
+      "Epoch 1791/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3662 - categorical_accuracy: 0.8471\n",
+      "Epoch 1792/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3081 - categorical_accuracy: 0.8824\n",
+      "Epoch 1793/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.4120 - categorical_accuracy: 0.8471\n",
+      "Epoch 1794/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.2976 - categorical_accuracy: 0.8824\n",
+      "Epoch 1795/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.2579 - categorical_accuracy: 0.8941\n",
+      "Epoch 1796/2000\n",
+      "3/3 [==============================] - 0s 68ms/step - loss: 0.2653 - categorical_accuracy: 0.8824\n",
+      "Epoch 1797/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.2903 - categorical_accuracy: 0.8706\n",
+      "Epoch 1798/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.2575 - categorical_accuracy: 0.8941\n",
+      "Epoch 1799/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.3726 - categorical_accuracy: 0.7882\n",
+      "Epoch 1800/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.3017 - categorical_accuracy: 0.8941\n",
+      "Epoch 1801/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.2423 - categorical_accuracy: 0.9294\n",
+      "Epoch 1802/2000\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.2537 - categorical_accuracy: 0.8824\n",
+      "Epoch 1803/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.2564 - categorical_accuracy: 0.9059\n",
+      "Epoch 1804/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.2279 - categorical_accuracy: 0.9059\n",
+      "Epoch 1805/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.2375 - categorical_accuracy: 0.9059\n",
+      "Epoch 1806/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.2990 - categorical_accuracy: 0.8588\n",
+      "Epoch 1807/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3815 - categorical_accuracy: 0.8471\n",
+      "Epoch 1808/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3053 - categorical_accuracy: 0.8706\n",
+      "Epoch 1809/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.2514 - categorical_accuracy: 0.9059\n",
+      "Epoch 1810/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.2653 - categorical_accuracy: 0.9059\n",
+      "Epoch 1811/2000\n",
+      "3/3 [==============================] - 0s 73ms/step - loss: 0.2706 - categorical_accuracy: 0.8941\n",
+      "Epoch 1812/2000\n",
+      "3/3 [==============================] - 0s 67ms/step - loss: 0.2856 - categorical_accuracy: 0.8941\n",
+      "Epoch 1813/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3543 - categorical_accuracy: 0.8118\n",
+      "Epoch 1814/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3042 - categorical_accuracy: 0.8941\n",
+      "Epoch 1815/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.2999 - categorical_accuracy: 0.8471\n",
+      "Epoch 1816/2000\n",
+      "3/3 [==============================] - 0s 67ms/step - loss: 0.3746 - categorical_accuracy: 0.8588\n",
+      "Epoch 1817/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.4116 - categorical_accuracy: 0.8235\n",
+      "Epoch 1818/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.4021 - categorical_accuracy: 0.8588\n",
+      "Epoch 1819/2000\n",
+      "3/3 [==============================] - 0s 67ms/step - loss: 0.3363 - categorical_accuracy: 0.8471\n",
+      "Epoch 1820/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.2937 - categorical_accuracy: 0.8706\n",
+      "Epoch 1821/2000\n",
+      "3/3 [==============================] - 0s 73ms/step - loss: 0.3348 - categorical_accuracy: 0.8471\n",
+      "Epoch 1822/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.2531 - categorical_accuracy: 0.9059\n",
+      "Epoch 1823/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.2554 - categorical_accuracy: 0.8941\n",
+      "Epoch 1824/2000\n",
+      "3/3 [==============================] - 0s 67ms/step - loss: 0.2843 - categorical_accuracy: 0.8824\n",
+      "Epoch 1825/2000\n",
+      "3/3 [==============================] - 0s 67ms/step - loss: 0.3019 - categorical_accuracy: 0.8824\n",
+      "Epoch 1826/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.2968 - categorical_accuracy: 0.8941\n",
+      "Epoch 1827/2000\n",
+      "3/3 [==============================] - 0s 75ms/step - loss: 0.3012 - categorical_accuracy: 0.8706\n",
+      "Epoch 1828/2000\n",
+      "3/3 [==============================] - 0s 74ms/step - loss: 0.2995 - categorical_accuracy: 0.8706\n",
+      "Epoch 1829/2000\n",
+      "3/3 [==============================] - 0s 67ms/step - loss: 0.2425 - categorical_accuracy: 0.9176\n",
+      "Epoch 1830/2000\n",
+      "3/3 [==============================] - 0s 74ms/step - loss: 0.2549 - categorical_accuracy: 0.8824\n",
+      "Epoch 1831/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.2770 - categorical_accuracy: 0.8471\n",
+      "Epoch 1832/2000\n",
+      "3/3 [==============================] - 0s 74ms/step - loss: 0.2711 - categorical_accuracy: 0.8941\n",
+      "Epoch 1833/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.2623 - categorical_accuracy: 0.8824\n",
+      "Epoch 1834/2000\n",
+      "3/3 [==============================] - 0s 75ms/step - loss: 0.2902 - categorical_accuracy: 0.8824\n",
+      "Epoch 1835/2000\n",
+      "3/3 [==============================] - 0s 74ms/step - loss: 0.3603 - categorical_accuracy: 0.8000\n",
+      "Epoch 1836/2000\n",
+      "3/3 [==============================] - 0s 76ms/step - loss: 0.2546 - categorical_accuracy: 0.8941\n",
+      "Epoch 1837/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.2514 - categorical_accuracy: 0.8941\n",
+      "Epoch 1838/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.3393 - categorical_accuracy: 0.8824\n",
+      "Epoch 1839/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.2678 - categorical_accuracy: 0.8588\n",
+      "Epoch 1840/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.2823 - categorical_accuracy: 0.8824\n",
+      "Epoch 1841/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.2326 - categorical_accuracy: 0.9176\n",
+      "Epoch 1842/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.2471 - categorical_accuracy: 0.9176\n",
+      "Epoch 1843/2000\n",
+      "3/3 [==============================] - 0s 67ms/step - loss: 0.3877 - categorical_accuracy: 0.8471\n",
+      "Epoch 1844/2000\n",
+      "3/3 [==============================] - 0s 67ms/step - loss: 0.3970 - categorical_accuracy: 0.8824\n",
+      "Epoch 1845/2000\n",
+      "3/3 [==============================] - 0s 75ms/step - loss: 0.2885 - categorical_accuracy: 0.8941\n",
+      "Epoch 1846/2000\n",
+      "3/3 [==============================] - 0s 67ms/step - loss: 0.2421 - categorical_accuracy: 0.8941\n",
+      "Epoch 1847/2000\n",
+      "3/3 [==============================] - 0s 67ms/step - loss: 0.2462 - categorical_accuracy: 0.9176\n",
+      "Epoch 1848/2000\n",
+      "3/3 [==============================] - 0s 68ms/step - loss: 0.2259 - categorical_accuracy: 0.9059\n",
+      "Epoch 1849/2000\n",
+      "3/3 [==============================] - 0s 73ms/step - loss: 0.2416 - categorical_accuracy: 0.9059\n",
+      "Epoch 1850/2000\n",
+      "3/3 [==============================] - 0s 74ms/step - loss: 0.2606 - categorical_accuracy: 0.8941\n",
+      "Epoch 1851/2000\n",
+      "3/3 [==============================] - 0s 74ms/step - loss: 0.2180 - categorical_accuracy: 0.9059\n",
+      "Epoch 1852/2000\n",
+      "3/3 [==============================] - 0s 67ms/step - loss: 0.2426 - categorical_accuracy: 0.8824\n",
+      "Epoch 1853/2000\n",
+      "3/3 [==============================] - 0s 67ms/step - loss: 0.2432 - categorical_accuracy: 0.8824\n",
+      "Epoch 1854/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.2552 - categorical_accuracy: 0.8588\n",
+      "Epoch 1855/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.2845 - categorical_accuracy: 0.8941\n",
+      "Epoch 1856/2000\n",
+      "3/3 [==============================] - 0s 75ms/step - loss: 0.3315 - categorical_accuracy: 0.8353\n",
+      "Epoch 1857/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.2274 - categorical_accuracy: 0.9059\n",
+      "Epoch 1858/2000\n",
+      "3/3 [==============================] - 0s 67ms/step - loss: 0.2299 - categorical_accuracy: 0.9294\n",
+      "Epoch 1859/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.2269 - categorical_accuracy: 0.8824\n",
+      "Epoch 1860/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.2529 - categorical_accuracy: 0.9059\n",
+      "Epoch 1861/2000\n",
+      "3/3 [==============================] - 0s 67ms/step - loss: 0.2842 - categorical_accuracy: 0.9059\n",
+      "Epoch 1862/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.2218 - categorical_accuracy: 0.9412\n",
+      "Epoch 1863/2000\n",
+      "3/3 [==============================] - 0s 67ms/step - loss: 0.2363 - categorical_accuracy: 0.8941\n",
+      "Epoch 1864/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.2254 - categorical_accuracy: 0.9176\n",
+      "Epoch 1865/2000\n",
+      "3/3 [==============================] - 0s 73ms/step - loss: 0.2457 - categorical_accuracy: 0.8824\n",
+      "Epoch 1866/2000\n",
+      "3/3 [==============================] - 0s 75ms/step - loss: 0.2289 - categorical_accuracy: 0.8824\n",
+      "Epoch 1867/2000\n",
+      "3/3 [==============================] - 0s 74ms/step - loss: 0.2659 - categorical_accuracy: 0.9059\n",
+      "Epoch 1868/2000\n",
+      "3/3 [==============================] - 0s 67ms/step - loss: 0.3679 - categorical_accuracy: 0.8353\n",
+      "Epoch 1869/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.4605 - categorical_accuracy: 0.8353\n",
+      "Epoch 1870/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.3492 - categorical_accuracy: 0.8353\n",
+      "Epoch 1871/2000\n",
+      "3/3 [==============================] - 0s 67ms/step - loss: 0.3915 - categorical_accuracy: 0.8588\n",
+      "Epoch 1872/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3143 - categorical_accuracy: 0.8824\n",
+      "Epoch 1873/2000\n",
+      "3/3 [==============================] - 0s 67ms/step - loss: 0.2508 - categorical_accuracy: 0.9059\n",
+      "Epoch 1874/2000\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.3327 - categorical_accuracy: 0.8824\n",
+      "Epoch 1875/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.2584 - categorical_accuracy: 0.8941\n",
+      "Epoch 1876/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.2648 - categorical_accuracy: 0.8941\n",
+      "Epoch 1877/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.2472 - categorical_accuracy: 0.9176\n",
+      "Epoch 1878/2000\n",
+      "3/3 [==============================] - 0s 74ms/step - loss: 0.2452 - categorical_accuracy: 0.9294\n",
+      "Epoch 1879/2000\n",
+      "3/3 [==============================] - 0s 67ms/step - loss: 0.2073 - categorical_accuracy: 0.9176\n",
+      "Epoch 1880/2000\n",
+      "3/3 [==============================] - 0s 67ms/step - loss: 0.2480 - categorical_accuracy: 0.8824\n",
+      "Epoch 1881/2000\n",
+      "3/3 [==============================] - 0s 67ms/step - loss: 0.2419 - categorical_accuracy: 0.8941\n",
+      "Epoch 1882/2000\n",
+      "3/3 [==============================] - 0s 67ms/step - loss: 0.2426 - categorical_accuracy: 0.9176\n",
+      "Epoch 1883/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.2111 - categorical_accuracy: 0.9176\n",
+      "Epoch 1884/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.2066 - categorical_accuracy: 0.9412\n",
+      "Epoch 1885/2000\n",
+      "3/3 [==============================] - 0s 68ms/step - loss: 0.2385 - categorical_accuracy: 0.9059\n",
+      "Epoch 1886/2000\n",
+      "3/3 [==============================] - 0s 72ms/step - loss: 0.2567 - categorical_accuracy: 0.8706\n",
+      "Epoch 1887/2000\n",
+      "3/3 [==============================] - 0s 72ms/step - loss: 0.2140 - categorical_accuracy: 0.9412\n",
+      "Epoch 1888/2000\n",
+      "3/3 [==============================] - 0s 67ms/step - loss: 0.2894 - categorical_accuracy: 0.9059\n",
+      "Epoch 1889/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.2782 - categorical_accuracy: 0.8588\n",
+      "Epoch 1890/2000\n",
+      "3/3 [==============================] - 0s 68ms/step - loss: 0.2151 - categorical_accuracy: 0.9059\n",
+      "Epoch 1891/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.1971 - categorical_accuracy: 0.9412\n",
+      "Epoch 1892/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.2182 - categorical_accuracy: 0.9412\n",
+      "Epoch 1893/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.2776 - categorical_accuracy: 0.8588\n",
+      "Epoch 1894/2000\n",
+      "3/3 [==============================] - 0s 67ms/step - loss: 0.2224 - categorical_accuracy: 0.9176\n",
+      "Epoch 1895/2000\n",
+      "3/3 [==============================] - 0s 67ms/step - loss: 0.2169 - categorical_accuracy: 0.9176\n",
+      "Epoch 1896/2000\n",
+      "3/3 [==============================] - 0s 73ms/step - loss: 0.2890 - categorical_accuracy: 0.8941\n",
+      "Epoch 1897/2000\n",
+      "3/3 [==============================] - 0s 67ms/step - loss: 0.2516 - categorical_accuracy: 0.8941\n",
+      "Epoch 1898/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.2651 - categorical_accuracy: 0.8824\n",
+      "Epoch 1899/2000\n",
+      "3/3 [==============================] - 0s 67ms/step - loss: 0.2781 - categorical_accuracy: 0.8706\n",
+      "Epoch 1900/2000\n",
+      "3/3 [==============================] - 0s 67ms/step - loss: 0.3401 - categorical_accuracy: 0.8706\n",
+      "Epoch 1901/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.2431 - categorical_accuracy: 0.9059\n",
+      "Epoch 1902/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.2282 - categorical_accuracy: 0.9059\n",
+      "Epoch 1903/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.2820 - categorical_accuracy: 0.8941\n",
+      "Epoch 1904/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3048 - categorical_accuracy: 0.8706\n",
+      "Epoch 1905/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.3809 - categorical_accuracy: 0.8353\n",
+      "Epoch 1906/2000\n",
+      "3/3 [==============================] - 0s 67ms/step - loss: 0.4692 - categorical_accuracy: 0.8235\n",
+      "Epoch 1907/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3306 - categorical_accuracy: 0.8588\n",
+      "Epoch 1908/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.2659 - categorical_accuracy: 0.8824\n",
+      "Epoch 1909/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.2756 - categorical_accuracy: 0.8824\n",
+      "Epoch 1910/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.2258 - categorical_accuracy: 0.9294\n",
+      "Epoch 1911/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.2783 - categorical_accuracy: 0.8588\n",
+      "Epoch 1912/2000\n",
+      "3/3 [==============================] - 0s 67ms/step - loss: 0.2104 - categorical_accuracy: 0.9412\n",
+      "Epoch 1913/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.2945 - categorical_accuracy: 0.9059\n",
+      "Epoch 1914/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.2407 - categorical_accuracy: 0.9059\n",
+      "Epoch 1915/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.3390 - categorical_accuracy: 0.8235\n",
+      "Epoch 1916/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.2879 - categorical_accuracy: 0.8824\n",
+      "Epoch 1917/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.2332 - categorical_accuracy: 0.9059\n",
+      "Epoch 1918/2000\n",
+      "3/3 [==============================] - 0s 67ms/step - loss: 0.2301 - categorical_accuracy: 0.9294\n",
+      "Epoch 1919/2000\n",
+      "3/3 [==============================] - 0s 72ms/step - loss: 0.2535 - categorical_accuracy: 0.8941\n",
+      "Epoch 1920/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.2146 - categorical_accuracy: 0.9176\n",
+      "Epoch 1921/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.2340 - categorical_accuracy: 0.8706\n",
+      "Epoch 1922/2000\n",
+      "3/3 [==============================] - 0s 73ms/step - loss: 0.2307 - categorical_accuracy: 0.8941\n",
+      "Epoch 1923/2000\n",
+      "3/3 [==============================] - 0s 74ms/step - loss: 0.2235 - categorical_accuracy: 0.9294\n",
+      "Epoch 1924/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.2334 - categorical_accuracy: 0.9176\n",
+      "Epoch 1925/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.2588 - categorical_accuracy: 0.8706\n",
+      "Epoch 1926/2000\n",
+      "3/3 [==============================] - 0s 67ms/step - loss: 0.2379 - categorical_accuracy: 0.9176\n",
+      "Epoch 1927/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.2388 - categorical_accuracy: 0.8824\n",
+      "Epoch 1928/2000\n",
+      "3/3 [==============================] - 0s 67ms/step - loss: 0.2943 - categorical_accuracy: 0.8941\n",
+      "Epoch 1929/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3643 - categorical_accuracy: 0.8353\n",
+      "Epoch 1930/2000\n",
+      "3/3 [==============================] - 0s 67ms/step - loss: 0.3765 - categorical_accuracy: 0.8941\n",
+      "Epoch 1931/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.2912 - categorical_accuracy: 0.8471\n",
+      "Epoch 1932/2000\n",
+      "3/3 [==============================] - 0s 70ms/step - loss: 0.3170 - categorical_accuracy: 0.8941\n",
+      "Epoch 1933/2000\n",
+      "3/3 [==============================] - 0s 71ms/step - loss: 0.2172 - categorical_accuracy: 0.9176\n",
+      "Epoch 1934/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.1926 - categorical_accuracy: 0.9529\n",
+      "Epoch 1935/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.2803 - categorical_accuracy: 0.8941\n",
+      "Epoch 1936/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.2233 - categorical_accuracy: 0.9294\n",
+      "Epoch 1937/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.2599 - categorical_accuracy: 0.8824\n",
+      "Epoch 1938/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.2455 - categorical_accuracy: 0.8706\n",
+      "Epoch 1939/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.2131 - categorical_accuracy: 0.9059\n",
+      "Epoch 1940/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.1919 - categorical_accuracy: 0.9294\n",
+      "Epoch 1941/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.2133 - categorical_accuracy: 0.9294\n",
+      "Epoch 1942/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3041 - categorical_accuracy: 0.8824\n",
+      "Epoch 1943/2000\n",
+      "3/3 [==============================] - 0s 67ms/step - loss: 0.2466 - categorical_accuracy: 0.8706\n",
+      "Epoch 1944/2000\n",
+      "3/3 [==============================] - 0s 71ms/step - loss: 0.2219 - categorical_accuracy: 0.9059\n",
+      "Epoch 1945/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.2777 - categorical_accuracy: 0.8706\n",
+      "Epoch 1946/2000\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.2134 - categorical_accuracy: 0.9412\n",
+      "Epoch 1947/2000\n",
+      "3/3 [==============================] - 0s 72ms/step - loss: 0.3046 - categorical_accuracy: 0.8706\n",
+      "Epoch 1948/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.2435 - categorical_accuracy: 0.9059\n",
+      "Epoch 1949/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.2063 - categorical_accuracy: 0.9412\n",
+      "Epoch 1950/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.2528 - categorical_accuracy: 0.9294\n",
+      "Epoch 1951/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.2097 - categorical_accuracy: 0.9294\n",
+      "Epoch 1952/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.1942 - categorical_accuracy: 0.9294\n",
+      "Epoch 1953/2000\n",
+      "3/3 [==============================] - 0s 72ms/step - loss: 0.1879 - categorical_accuracy: 0.9294\n",
+      "Epoch 1954/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.1941 - categorical_accuracy: 0.9294\n",
+      "Epoch 1955/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.2376 - categorical_accuracy: 0.9294\n",
+      "Epoch 1956/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.2728 - categorical_accuracy: 0.8941\n",
+      "Epoch 1957/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.2242 - categorical_accuracy: 0.9059\n",
+      "Epoch 1958/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.2063 - categorical_accuracy: 0.9412\n",
+      "Epoch 1959/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.2285 - categorical_accuracy: 0.9059\n",
+      "Epoch 1960/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.3246 - categorical_accuracy: 0.8824\n",
+      "Epoch 1961/2000\n",
+      "3/3 [==============================] - 0s 72ms/step - loss: 0.2989 - categorical_accuracy: 0.8588\n",
+      "Epoch 1962/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.2710 - categorical_accuracy: 0.9176\n",
+      "Epoch 1963/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.2153 - categorical_accuracy: 0.9294\n",
+      "Epoch 1964/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.2257 - categorical_accuracy: 0.9176\n",
+      "Epoch 1965/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.1934 - categorical_accuracy: 0.9529\n",
+      "Epoch 1966/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.1927 - categorical_accuracy: 0.9412\n",
+      "Epoch 1967/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.1821 - categorical_accuracy: 0.9647\n",
+      "Epoch 1968/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.2381 - categorical_accuracy: 0.9294\n",
+      "Epoch 1969/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.2352 - categorical_accuracy: 0.9059\n",
+      "Epoch 1970/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.2296 - categorical_accuracy: 0.9059\n",
+      "Epoch 1971/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.2097 - categorical_accuracy: 0.9176\n",
+      "Epoch 1972/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.2325 - categorical_accuracy: 0.9059\n",
+      "Epoch 1973/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3907 - categorical_accuracy: 0.8235\n",
+      "Epoch 1974/2000\n",
+      "3/3 [==============================] - 0s 71ms/step - loss: 0.5332 - categorical_accuracy: 0.8118\n",
+      "Epoch 1975/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.6647 - categorical_accuracy: 0.8118\n",
+      "Epoch 1976/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.4874 - categorical_accuracy: 0.8000\n",
+      "Epoch 1977/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.6369 - categorical_accuracy: 0.7412\n",
+      "Epoch 1978/2000\n",
+      "3/3 [==============================] - 0s 72ms/step - loss: 0.8348 - categorical_accuracy: 0.7294\n",
+      "Epoch 1979/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.6178 - categorical_accuracy: 0.7412\n",
+      "Epoch 1980/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3577 - categorical_accuracy: 0.8000\n",
+      "Epoch 1981/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.4164 - categorical_accuracy: 0.8235\n",
+      "Epoch 1982/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.3818 - categorical_accuracy: 0.8588\n",
+      "Epoch 1983/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3647 - categorical_accuracy: 0.8235\n",
+      "Epoch 1984/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3106 - categorical_accuracy: 0.9059\n",
+      "Epoch 1985/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.2858 - categorical_accuracy: 0.8941\n",
+      "Epoch 1986/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.2547 - categorical_accuracy: 0.9294\n",
+      "Epoch 1987/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3105 - categorical_accuracy: 0.8588\n",
+      "Epoch 1988/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.3103 - categorical_accuracy: 0.8941\n",
+      "Epoch 1989/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3141 - categorical_accuracy: 0.8353\n",
+      "Epoch 1990/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.2601 - categorical_accuracy: 0.8941\n",
+      "Epoch 1991/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.2704 - categorical_accuracy: 0.8824\n",
+      "Epoch 1992/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.2532 - categorical_accuracy: 0.8941\n",
+      "Epoch 1993/2000\n",
+      "3/3 [==============================] - 0s 68ms/step - loss: 0.2405 - categorical_accuracy: 0.8941\n",
+      "Epoch 1994/2000\n",
+      "3/3 [==============================] - 0s 72ms/step - loss: 0.2184 - categorical_accuracy: 0.9176\n",
+      "Epoch 1995/2000\n",
+      "3/3 [==============================] - 0s 66ms/step - loss: 0.1986 - categorical_accuracy: 0.9059\n",
+      "Epoch 1996/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.2705 - categorical_accuracy: 0.9059\n",
+      "Epoch 1997/2000\n",
+      "3/3 [==============================] - 0s 72ms/step - loss: 0.2526 - categorical_accuracy: 0.8941\n",
+      "Epoch 1998/2000\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 0.3230 - categorical_accuracy: 0.8824\n",
+      "Epoch 1999/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3652 - categorical_accuracy: 0.8235\n",
+      "Epoch 2000/2000\n",
+      "3/3 [==============================] - 0s 64ms/step - loss: 0.3646 - categorical_accuracy: 0.8471\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.callbacks.History at 0x1f6b635f050>"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "model.fit(X_train, y_train, epochs=2000, callbacks=[tb_callback])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
@@ -686,19 +4692,20 @@
      "text": [
       "Model: \"sequential\"\n",
       "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
+      " Layer (type)                Output Shape              Param #   \n",
       "=================================================================\n",
-      "lstm (LSTM)                  (None, 30, 64)            442112    \n",
-      "_________________________________________________________________\n",
-      "lstm_1 (LSTM)                (None, 30, 128)           98816     \n",
-      "_________________________________________________________________\n",
-      "lstm_2 (LSTM)                (None, 64)                49408     \n",
-      "_________________________________________________________________\n",
-      "dense (Dense)                (None, 64)                4160      \n",
-      "_________________________________________________________________\n",
-      "dense_1 (Dense)              (None, 32)                2080      \n",
-      "_________________________________________________________________\n",
-      "dense_2 (Dense)              (None, 3)                 99        \n",
+      " lstm (LSTM)                 (None, 30, 64)            442112    \n",
+      "                                                                 \n",
+      " lstm_1 (LSTM)               (None, 30, 128)           98816     \n",
+      "                                                                 \n",
+      " lstm_2 (LSTM)               (None, 64)                49408     \n",
+      "                                                                 \n",
+      " dense (Dense)               (None, 64)                4160      \n",
+      "                                                                 \n",
+      " dense_1 (Dense)             (None, 32)                2080      \n",
+      "                                                                 \n",
+      " dense_2 (Dense)             (None, 3)                 99        \n",
+      "                                                                 \n",
       "=================================================================\n",
       "Total params: 596,675\n",
       "Trainable params: 596,675\n",
@@ -720,25 +4727,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 30,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1/1 [==============================] - 0s 345ms/step\n"
+     ]
+    }
+   ],
    "source": [
     "res = model.predict(X_test)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'hello'"
+       "'pose1'"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -749,16 +4764,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'hello'"
+       "'pose1'"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -776,7 +4791,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -785,16 +4800,66 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 217,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'model' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[8], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m model\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'model' is not defined"
+     ]
+    }
+   ],
    "source": [
     "del model"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'Sequential' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[9], line 3\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# Once training is done, u do not have to train it again (no need to call the fit() finction). Just need to setup the model\u001b[39;00m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;66;03m# and compile it. then load the saved weights.\u001b[39;00m\n\u001b[1;32m----> 3\u001b[0m model \u001b[38;5;241m=\u001b[39m Sequential()\n\u001b[0;32m      4\u001b[0m model\u001b[38;5;241m.\u001b[39madd(LSTM(\u001b[38;5;241m64\u001b[39m, return_sequences\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, activation\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrelu\u001b[39m\u001b[38;5;124m'\u001b[39m, input_shape\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m30\u001b[39m,\u001b[38;5;241m1662\u001b[39m)))\n\u001b[0;32m      5\u001b[0m model\u001b[38;5;241m.\u001b[39madd(LSTM(\u001b[38;5;241m128\u001b[39m, return_sequences\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, activation\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrelu\u001b[39m\u001b[38;5;124m'\u001b[39m))\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'Sequential' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "# Once training is done, u do not have to train it again (no need to call the fit() finction). Just need to setup the model\n",
+    "# and compile it. then load the saved weights.\n",
+    "model = Sequential()\n",
+    "model.add(LSTM(64, return_sequences=True, activation='relu', input_shape=(30,1662)))\n",
+    "model.add(LSTM(128, return_sequences=True, activation='relu'))\n",
+    "model.add(LSTM(64, return_sequences=False, activation='relu'))\n",
+    "model.add(Dense(64, activation='relu'))\n",
+    "model.add(Dense(32, activation='relu'))\n",
+    "model.add(Dense(actions.shape[0], activation='softmax'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.compile(optimizer='Adam', loss='categorical_crossentropy', metrics=['categorical_accuracy'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -810,7 +4875,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -819,16 +4884,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 40,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1/1 [==============================] - 0s 327ms/step\n"
+     ]
+    }
+   ],
    "source": [
     "yhat = model.predict(X_test)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -838,23 +4911,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[[5, 0],\n",
-       "        [0, 4]],\n",
+       "array([[[3, 0],\n",
+       "        [0, 2]],\n",
        "\n",
-       "       [[5, 0],\n",
-       "        [0, 4]],\n",
+       "       [[4, 0],\n",
+       "        [1, 0]],\n",
        "\n",
-       "       [[8, 0],\n",
-       "        [0, 1]]], dtype=int64)"
+       "       [[2, 1],\n",
+       "        [0, 2]]], dtype=int64)"
       ]
      },
-     "execution_count": 43,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -865,16 +4938,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "1.0"
+       "0.8"
       ]
      },
-     "execution_count": 44,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -892,7 +4965,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -901,7 +4974,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -917,31 +4990,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 262,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 46,
+   "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x1daff6aa278>"
-      ]
-     },
-     "execution_count": 262,
-     "metadata": {},
-     "output_type": "execute_result"
+     "ename": "TypeError",
+     "evalue": "only size-1 arrays can be converted to Python scalars",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[46], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m18\u001b[39m,\u001b[38;5;241m18\u001b[39m))\n\u001b[1;32m----> 2\u001b[0m plt\u001b[38;5;241m.\u001b[39mimshow(prob_viz(res, actions, image, colors))\n",
+      "Cell \u001b[1;32mIn[45], line 5\u001b[0m, in \u001b[0;36mprob_viz\u001b[1;34m(res, actions, input_frame, colors)\u001b[0m\n\u001b[0;32m      3\u001b[0m output_frame \u001b[38;5;241m=\u001b[39m input_frame\u001b[38;5;241m.\u001b[39mcopy()\n\u001b[0;32m      4\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m num, prob \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(res):\n\u001b[1;32m----> 5\u001b[0m     cv2\u001b[38;5;241m.\u001b[39mrectangle(output_frame, (\u001b[38;5;241m0\u001b[39m,\u001b[38;5;241m60\u001b[39m\u001b[38;5;241m+\u001b[39mnum\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m40\u001b[39m), (\u001b[38;5;28mint\u001b[39m(prob\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m100\u001b[39m), \u001b[38;5;241m90\u001b[39m\u001b[38;5;241m+\u001b[39mnum\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m40\u001b[39m), colors[num], \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m      6\u001b[0m     cv2\u001b[38;5;241m.\u001b[39mputText(output_frame, actions[num], (\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m85\u001b[39m\u001b[38;5;241m+\u001b[39mnum\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m40\u001b[39m), cv2\u001b[38;5;241m.\u001b[39mFONT_HERSHEY_SIMPLEX, \u001b[38;5;241m1\u001b[39m, (\u001b[38;5;241m255\u001b[39m,\u001b[38;5;241m255\u001b[39m,\u001b[38;5;241m255\u001b[39m), \u001b[38;5;241m2\u001b[39m, cv2\u001b[38;5;241m.\u001b[39mLINE_AA)\n\u001b[0;32m      8\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m output_frame\n",
+      "\u001b[1;31mTypeError\u001b[0m: only size-1 arrays can be converted to Python scalars"
+     ]
     },
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
-       "<Figure size 1296x1296 with 1 Axes>"
+       "<Figure size 1800x1800 with 0 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -952,9 +5022,1275 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 47,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 15ms/step\n",
+      "pose2\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 15ms/step\n",
+      "pose2\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 16ms/step\n",
+      "pose2\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 15ms/step\n",
+      "pose2\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 14ms/step\n",
+      "pose2\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 14ms/step\n",
+      "pose2\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 15ms/step\n",
+      "pose2\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 15ms/step\n",
+      "pose2\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 14ms/step\n",
+      "pose2\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 15ms/step\n",
+      "pose2\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 14ms/step\n",
+      "pose2\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 15ms/step\n",
+      "pose2\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 14ms/step\n",
+      "pose2\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 14ms/step\n",
+      "pose2\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 14ms/step\n",
+      "pose2\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 15ms/step\n",
+      "pose2\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 16ms/step\n",
+      "pose2\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 16ms/step\n",
+      "pose2\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 17ms/step\n",
+      "pose2\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 14ms/step\n",
+      "pose2\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 14ms/step\n",
+      "pose2\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 14ms/step\n",
+      "pose2\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 14ms/step\n",
+      "pose2\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 14ms/step\n",
+      "pose2\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 15ms/step\n",
+      "pose2\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 15ms/step\n",
+      "pose2\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 14ms/step\n",
+      "pose2\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 14ms/step\n",
+      "pose2\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 15ms/step\n",
+      "pose2\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 15ms/step\n",
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+      "pose2\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 16ms/step\n",
+      "pose2\n",
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+      "1/1 [==============================] - 0s 14ms/step\n",
+      "pose2\n",
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+      "1/1 [==============================] - 0s 15ms/step\n",
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+      "1/1 [==============================] - 0s 16ms/step\n",
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+      "1/1 [==============================] - 0s 15ms/step\n",
+      "pose1\n",
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+      "1/1 [==============================] - 0s 14ms/step\n",
+      "pose1\n",
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+      "1/1 [==============================] - 0s 15ms/step\n",
+      "pose1\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 14ms/step\n",
+      "pose1\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 14ms/step\n",
+      "pose1\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 15ms/step\n",
+      "pose1\n",
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+      "1/1 [==============================] - 0s 15ms/step\n",
+      "pose1\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 15ms/step\n",
+      "pose2\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 15ms/step\n",
+      "pose2\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 14ms/step\n",
+      "pose2\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 14ms/step\n",
+      "pose2\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 16ms/step\n",
+      "pose2\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 16ms/step\n",
+      "pose2\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 15ms/step\n",
+      "pose2\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 15ms/step\n",
+      "pose2\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 15ms/step\n",
+      "pose2\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 14ms/step\n",
+      "pose2\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 15ms/step\n",
+      "pose2\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 15ms/step\n",
+      "pose2\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 15ms/step\n",
+      "pose2\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 15ms/step\n",
+      "pose3\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 14ms/step\n",
+      "pose3\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 15ms/step\n",
+      "pose3\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 16ms/step\n",
+      "pose3\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 14ms/step\n",
+      "pose3\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 14ms/step\n",
+      "pose3\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 15ms/step\n",
+      "pose3\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 14ms/step\n",
+      "pose3\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 15ms/step\n",
+      "pose2\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 15ms/step\n",
+      "pose2\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 14ms/step\n",
+      "pose2\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 14ms/step\n",
+      "pose2\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 14ms/step\n",
+      "pose2\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 14ms/step\n",
+      "pose2\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 15ms/step\n",
+      "pose2\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 15ms/step\n",
+      "pose2\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 15ms/step\n",
+      "pose2\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 14ms/step\n",
+      "pose2\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 15ms/step\n",
+      "pose2\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 15ms/step\n",
+      "pose2\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 15ms/step\n",
+      "pose2\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 16ms/step\n",
+      "pose2\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 15ms/step\n",
+      "pose2\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 15ms/step\n",
+      "pose2\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 15ms/step\n",
+      "pose2\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 15ms/step\n",
+      "pose2\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 15ms/step\n",
+      "pose2\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 14ms/step\n",
+      "pose2\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 15ms/step\n",
+      "pose2\n",
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+      "1/1 [==============================] - 0s 14ms/step\n",
+      "pose2\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 14ms/step\n",
+      "pose2\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 14ms/step\n",
+      "pose2\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 15ms/step\n",
+      "pose2\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 15ms/step\n",
+      "pose2\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 15ms/step\n",
+      "pose2\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 15ms/step\n",
+      "pose3\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 15ms/step\n",
+      "pose2\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 15ms/step\n",
+      "pose2\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 15ms/step\n",
+      "pose2\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 15ms/step\n",
+      "pose2\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 15ms/step\n",
+      "pose2\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 15ms/step\n",
+      "pose2\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 15ms/step\n",
+      "pose2\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 14ms/step\n",
+      "pose1\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 15ms/step\n",
+      "pose1\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 14ms/step\n",
+      "pose1\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 15ms/step\n",
+      "pose1\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 15ms/step\n",
+      "pose1\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 16ms/step\n",
+      "pose1\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 15ms/step\n",
+      "pose1\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 16ms/step\n",
+      "pose1\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 18ms/step\n",
+      "pose1\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 15ms/step\n",
+      "pose1\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 15ms/step\n",
+      "pose2\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 14ms/step\n",
+      "pose1\n",
+      "<class 'mediapipe.python.solution_base.SolutionOutputs'>\n",
+      "1/1 [==============================] - 0s 16ms/step\n",
+      "pose1\n"
+     ]
+    }
+   ],
    "source": [
     "# 1. New detection variables\n",
     "sequence = []\n",
@@ -1017,13 +6353,20 @@
     "    cap.release()\n",
     "    cv2.destroyAllWindows()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "action",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "action"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -1035,7 +6378,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.11.5"
   }
  },
  "nbformat": 4,
diff --git a/Action Detection Tutorial.ipynb b/Action Detection Tutorial.ipynb
index f1604195f..5e318c22d 100644
--- a/Action Detection Tutorial.ipynb	
+++ b/Action Detection Tutorial.ipynb	
@@ -9,16 +9,84 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Defaulting to user installation because normal site-packages is not writeable\n",
+      "Requirement already satisfied: tensorflow==2.12.0 in c:\\users\\ashok\\appdata\\roaming\\python\\python311\\site-packages (2.12.0)\n",
+      "Requirement already satisfied: opencv-python in c:\\programdata\\anaconda3\\lib\\site-packages (4.8.1.78)\n",
+      "Requirement already satisfied: mediapipe in c:\\programdata\\anaconda3\\lib\\site-packages (0.10.7)\n",
+      "Requirement already satisfied: sklearn in c:\\users\\ashok\\appdata\\roaming\\python\\python311\\site-packages (0.0.post11)\n",
+      "Requirement already satisfied: matplotlib in c:\\programdata\\anaconda3\\lib\\site-packages (3.7.2)\n",
+      "Requirement already satisfied: tensorflow-intel==2.12.0 in c:\\users\\ashok\\appdata\\roaming\\python\\python311\\site-packages (from tensorflow==2.12.0) (2.12.0)\n",
+      "Requirement already satisfied: absl-py>=1.0.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.12.0->tensorflow==2.12.0) (2.0.0)\n",
+      "Requirement already satisfied: astunparse>=1.6.0 in c:\\users\\ashok\\appdata\\roaming\\python\\python311\\site-packages (from tensorflow-intel==2.12.0->tensorflow==2.12.0) (1.6.3)\n",
+      "Requirement already satisfied: flatbuffers>=2.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.12.0->tensorflow==2.12.0) (23.5.26)\n",
+      "Requirement already satisfied: gast<=0.4.0,>=0.2.1 in c:\\users\\ashok\\appdata\\roaming\\python\\python311\\site-packages (from tensorflow-intel==2.12.0->tensorflow==2.12.0) (0.4.0)\n",
+      "Requirement already satisfied: google-pasta>=0.1.1 in c:\\users\\ashok\\appdata\\roaming\\python\\python311\\site-packages (from tensorflow-intel==2.12.0->tensorflow==2.12.0) (0.2.0)\n",
+      "Requirement already satisfied: h5py>=2.9.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.12.0->tensorflow==2.12.0) (3.9.0)\n",
+      "Requirement already satisfied: jax>=0.3.15 in c:\\users\\ashok\\appdata\\roaming\\python\\python311\\site-packages (from tensorflow-intel==2.12.0->tensorflow==2.12.0) (0.4.20)\n",
+      "Requirement already satisfied: libclang>=13.0.0 in c:\\users\\ashok\\appdata\\roaming\\python\\python311\\site-packages (from tensorflow-intel==2.12.0->tensorflow==2.12.0) (16.0.6)\n",
+      "Requirement already satisfied: numpy<1.24,>=1.22 in c:\\users\\ashok\\appdata\\roaming\\python\\python311\\site-packages (from tensorflow-intel==2.12.0->tensorflow==2.12.0) (1.23.5)\n",
+      "Requirement already satisfied: opt-einsum>=2.3.2 in c:\\users\\ashok\\appdata\\roaming\\python\\python311\\site-packages (from tensorflow-intel==2.12.0->tensorflow==2.12.0) (3.3.0)\n",
+      "Requirement already satisfied: packaging in c:\\programdata\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.12.0->tensorflow==2.12.0) (23.1)\n",
+      "Requirement already satisfied: protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3 in c:\\programdata\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.12.0->tensorflow==2.12.0) (3.20.3)\n",
+      "Requirement already satisfied: setuptools in c:\\programdata\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.12.0->tensorflow==2.12.0) (68.0.0)\n",
+      "Requirement already satisfied: six>=1.12.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.12.0->tensorflow==2.12.0) (1.16.0)\n",
+      "Requirement already satisfied: termcolor>=1.1.0 in c:\\users\\ashok\\appdata\\roaming\\python\\python311\\site-packages (from tensorflow-intel==2.12.0->tensorflow==2.12.0) (2.3.0)\n",
+      "Requirement already satisfied: typing-extensions>=3.6.6 in c:\\programdata\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.12.0->tensorflow==2.12.0) (4.7.1)\n",
+      "Requirement already satisfied: wrapt<1.15,>=1.11.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.12.0->tensorflow==2.12.0) (1.14.1)\n",
+      "Requirement already satisfied: grpcio<2.0,>=1.24.3 in c:\\users\\ashok\\appdata\\roaming\\python\\python311\\site-packages (from tensorflow-intel==2.12.0->tensorflow==2.12.0) (1.59.2)\n",
+      "Requirement already satisfied: tensorboard<2.13,>=2.12 in c:\\users\\ashok\\appdata\\roaming\\python\\python311\\site-packages (from tensorflow-intel==2.12.0->tensorflow==2.12.0) (2.12.3)\n",
+      "Requirement already satisfied: tensorflow-estimator<2.13,>=2.12.0 in c:\\users\\ashok\\appdata\\roaming\\python\\python311\\site-packages (from tensorflow-intel==2.12.0->tensorflow==2.12.0) (2.12.0)\n",
+      "Requirement already satisfied: keras<2.13,>=2.12.0 in c:\\users\\ashok\\appdata\\roaming\\python\\python311\\site-packages (from tensorflow-intel==2.12.0->tensorflow==2.12.0) (2.12.0)\n",
+      "Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in c:\\users\\ashok\\appdata\\roaming\\python\\python311\\site-packages (from tensorflow-intel==2.12.0->tensorflow==2.12.0) (0.31.0)\n",
+      "Requirement already satisfied: attrs>=19.1.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from mediapipe) (22.1.0)\n",
+      "Requirement already satisfied: opencv-contrib-python in c:\\programdata\\anaconda3\\lib\\site-packages (from mediapipe) (4.8.1.78)\n",
+      "Requirement already satisfied: sounddevice>=0.4.4 in c:\\programdata\\anaconda3\\lib\\site-packages (from mediapipe) (0.4.6)\n",
+      "Requirement already satisfied: contourpy>=1.0.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from matplotlib) (1.0.5)\n",
+      "Requirement already satisfied: cycler>=0.10 in c:\\programdata\\anaconda3\\lib\\site-packages (from matplotlib) (0.11.0)\n",
+      "Requirement already satisfied: fonttools>=4.22.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from matplotlib) (4.25.0)\n",
+      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from matplotlib) (1.4.4)\n",
+      "Requirement already satisfied: pillow>=6.2.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from matplotlib) (9.4.0)\n",
+      "Requirement already satisfied: pyparsing<3.1,>=2.3.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from matplotlib) (3.0.9)\n",
+      "Requirement already satisfied: python-dateutil>=2.7 in c:\\programdata\\anaconda3\\lib\\site-packages (from matplotlib) (2.8.2)\n",
+      "Requirement already satisfied: CFFI>=1.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from sounddevice>=0.4.4->mediapipe) (1.15.1)\n",
+      "Requirement already satisfied: wheel<1.0,>=0.23.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from astunparse>=1.6.0->tensorflow-intel==2.12.0->tensorflow==2.12.0) (0.38.4)\n",
+      "Requirement already satisfied: pycparser in c:\\programdata\\anaconda3\\lib\\site-packages (from CFFI>=1.0->sounddevice>=0.4.4->mediapipe) (2.21)\n",
+      "Requirement already satisfied: ml-dtypes>=0.2.0 in c:\\users\\ashok\\appdata\\roaming\\python\\python311\\site-packages (from jax>=0.3.15->tensorflow-intel==2.12.0->tensorflow==2.12.0) (0.3.1)\n",
+      "Requirement already satisfied: scipy>=1.9 in c:\\programdata\\anaconda3\\lib\\site-packages (from jax>=0.3.15->tensorflow-intel==2.12.0->tensorflow==2.12.0) (1.11.1)\n",
+      "Requirement already satisfied: google-auth<3,>=1.6.3 in c:\\users\\ashok\\appdata\\roaming\\python\\python311\\site-packages (from tensorboard<2.13,>=2.12->tensorflow-intel==2.12.0->tensorflow==2.12.0) (2.23.4)\n",
+      "Requirement already satisfied: google-auth-oauthlib<1.1,>=0.5 in c:\\users\\ashok\\appdata\\roaming\\python\\python311\\site-packages (from tensorboard<2.13,>=2.12->tensorflow-intel==2.12.0->tensorflow==2.12.0) (1.0.0)\n",
+      "Requirement already satisfied: markdown>=2.6.8 in c:\\programdata\\anaconda3\\lib\\site-packages (from tensorboard<2.13,>=2.12->tensorflow-intel==2.12.0->tensorflow==2.12.0) (3.4.1)\n",
+      "Requirement already satisfied: requests<3,>=2.21.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from tensorboard<2.13,>=2.12->tensorflow-intel==2.12.0->tensorflow==2.12.0) (2.31.0)\n",
+      "Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in c:\\users\\ashok\\appdata\\roaming\\python\\python311\\site-packages (from tensorboard<2.13,>=2.12->tensorflow-intel==2.12.0->tensorflow==2.12.0) (0.7.2)\n",
+      "Requirement already satisfied: werkzeug>=1.0.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from tensorboard<2.13,>=2.12->tensorflow-intel==2.12.0->tensorflow==2.12.0) (2.2.3)\n",
+      "Requirement already satisfied: cachetools<6.0,>=2.0.0 in c:\\users\\ashok\\appdata\\roaming\\python\\python311\\site-packages (from google-auth<3,>=1.6.3->tensorboard<2.13,>=2.12->tensorflow-intel==2.12.0->tensorflow==2.12.0) (5.3.2)\n",
+      "Requirement already satisfied: pyasn1-modules>=0.2.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from google-auth<3,>=1.6.3->tensorboard<2.13,>=2.12->tensorflow-intel==2.12.0->tensorflow==2.12.0) (0.2.8)\n",
+      "Requirement already satisfied: rsa<5,>=3.1.4 in c:\\users\\ashok\\appdata\\roaming\\python\\python311\\site-packages (from google-auth<3,>=1.6.3->tensorboard<2.13,>=2.12->tensorflow-intel==2.12.0->tensorflow==2.12.0) (4.9)\n",
+      "Requirement already satisfied: requests-oauthlib>=0.7.0 in c:\\users\\ashok\\appdata\\roaming\\python\\python311\\site-packages (from google-auth-oauthlib<1.1,>=0.5->tensorboard<2.13,>=2.12->tensorflow-intel==2.12.0->tensorflow==2.12.0) (1.3.1)\n",
+      "Requirement already satisfied: charset-normalizer<4,>=2 in c:\\programdata\\anaconda3\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard<2.13,>=2.12->tensorflow-intel==2.12.0->tensorflow==2.12.0) (2.0.4)\n",
+      "Requirement already satisfied: idna<4,>=2.5 in c:\\programdata\\anaconda3\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard<2.13,>=2.12->tensorflow-intel==2.12.0->tensorflow==2.12.0) (3.4)\n",
+      "Requirement already satisfied: urllib3<3,>=1.21.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard<2.13,>=2.12->tensorflow-intel==2.12.0->tensorflow==2.12.0) (1.26.16)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in c:\\programdata\\anaconda3\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard<2.13,>=2.12->tensorflow-intel==2.12.0->tensorflow==2.12.0) (2023.7.22)\n",
+      "Requirement already satisfied: MarkupSafe>=2.1.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from werkzeug>=1.0.1->tensorboard<2.13,>=2.12->tensorflow-intel==2.12.0->tensorflow==2.12.0) (2.1.1)\n",
+      "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in c:\\programdata\\anaconda3\\lib\\site-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.13,>=2.12->tensorflow-intel==2.12.0->tensorflow==2.12.0) (0.4.8)\n",
+      "Requirement already satisfied: oauthlib>=3.0.0 in c:\\users\\ashok\\appdata\\roaming\\python\\python311\\site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<1.1,>=0.5->tensorboard<2.13,>=2.12->tensorflow-intel==2.12.0->tensorflow==2.12.0) (3.2.2)\n"
+     ]
+    }
+   ],
    "source": [
-    "!pip install tensorflow==2.4.1 tensorflow-gpu==2.4.1 opencv-python mediapipe sklearn matplotlib"
+    "!pip install tensorflow==2.12.0 opencv-python mediapipe sklearn matplotlib"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -39,7 +107,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -49,7 +117,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -64,12 +132,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
     "def draw_landmarks(image, results):\n",
-    "    mp_drawing.draw_landmarks(image, results.face_landmarks, mp_holistic.FACE_CONNECTIONS) # Draw face connections\n",
+    "    mp_drawing.draw_landmarks(image, results.face_landmarks, mp_holistic.FACEMESH_CONTOURS) # Draw face connections\n",
     "    mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_holistic.POSE_CONNECTIONS) # Draw pose connections\n",
     "    mp_drawing.draw_landmarks(image, results.left_hand_landmarks, mp_holistic.HAND_CONNECTIONS) # Draw left hand connections\n",
     "    mp_drawing.draw_landmarks(image, results.right_hand_landmarks, mp_holistic.HAND_CONNECTIONS) # Draw right hand connections"
@@ -77,44 +145,7716 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
     "def draw_styled_landmarks(image, results):\n",
     "    # Draw face connections\n",
-    "    mp_drawing.draw_landmarks(image, results.face_landmarks, mp_holistic.FACE_CONNECTIONS, \n",
+    "    mp_drawing.draw_landmarks(image, results.face_landmarks, mp_holistic.FACEMESH_CONTOURS, \n",
     "                             mp_drawing.DrawingSpec(color=(80,110,10), thickness=1, circle_radius=1), \n",
     "                             mp_drawing.DrawingSpec(color=(80,256,121), thickness=1, circle_radius=1)\n",
     "                             ) \n",
     "    # Draw pose connections\n",
     "    mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_holistic.POSE_CONNECTIONS,\n",
-    "                             mp_drawing.DrawingSpec(color=(80,22,10), thickness=2, circle_radius=4), \n",
-    "                             mp_drawing.DrawingSpec(color=(80,44,121), thickness=2, circle_radius=2)\n",
+    "                             mp_drawing.DrawingSpec(color=(80,110,10), thickness=2, circle_radius=2), \n",
+    "                             mp_drawing.DrawingSpec(color=(80,256,121), thickness=2, circle_radius=2)\n",
     "                             ) \n",
     "    # Draw left hand connections\n",
     "    mp_drawing.draw_landmarks(image, results.left_hand_landmarks, mp_holistic.HAND_CONNECTIONS, \n",
-    "                             mp_drawing.DrawingSpec(color=(121,22,76), thickness=2, circle_radius=4), \n",
-    "                             mp_drawing.DrawingSpec(color=(121,44,250), thickness=2, circle_radius=2)\n",
+    "                             mp_drawing.DrawingSpec(color=(80,110,10), thickness=1, circle_radius=2), \n",
+    "                             mp_drawing.DrawingSpec(color=(80,256,121), thickness=1, circle_radius=2)\n",
     "                             ) \n",
     "    # Draw right hand connections  \n",
     "    mp_drawing.draw_landmarks(image, results.right_hand_landmarks, mp_holistic.HAND_CONNECTIONS, \n",
-    "                             mp_drawing.DrawingSpec(color=(245,117,66), thickness=2, circle_radius=4), \n",
-    "                             mp_drawing.DrawingSpec(color=(245,66,230), thickness=2, circle_radius=2)\n",
+    "                             mp_drawing.DrawingSpec(color=(80,110,10), thickness=1, circle_radius=2), \n",
+    "                             mp_drawing.DrawingSpec(color=(80,256,121), thickness=1, circle_radius=2)\n",
     "                             ) "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 17,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
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+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
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@@ -578,7 +8318,7 @@
   },
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+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1298,9 +9038,9 @@
  ],
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-   "display_name": "action",
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@@ -1312,7 +9052,7 @@
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--- /dev/null
+++ b/generate_beep.py
@@ -0,0 +1,26 @@
+import numpy as np
+from scipy.io.wavfile import write
+
+# Parameters for the beep
+fs = 44100            # Sample rate (Hz)
+duration = 0.1        # Duration in seconds (100 ms)
+frequency = 440       # Frequency in Hz (A4 note)
+
+# Generate time samples
+t = np.linspace(0, duration, int(fs * duration), endpoint=False)
+
+# Create the sine wave with amplitude 0.5 (to avoid clipping)
+amplitude = 0.5
+audio = amplitude * np.sin(2 * np.pi * frequency * t)
+
+# Apply a fade-out envelope for smoothness
+fade = np.linspace(1, 0, audio.shape[0])
+audio = audio * fade
+
+# Convert to 16-bit PCM format
+audio_int16 = np.int16(audio * 32767)
+
+# Write to a WAV file
+write("beep.wav", fs, audio_int16)
+
+print("beep.wav has been created.")