diff --git a/SenseGenModel.ipynb b/SenseGenModel.ipynb
index 58d708f..390bf4f 100644
--- a/SenseGenModel.ipynb
+++ b/SenseGenModel.ipynb
@@ -17,10 +17,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "%load_ext autoreload\n",
@@ -29,10 +27,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "import data_utils\n",
@@ -42,10 +38,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
@@ -54,22 +48,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
-    "import tensorflow as tf\n",
+    "import tensorflow.compat.v1 as tf\n",
+    "tf.disable_v2_behavior() \n",
     "import numpy as np"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "data = data_utils.load_training_data()"
@@ -84,10 +75,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "# To get reasonable outputs, should use something bigger than 1000 !\n",
@@ -97,1037 +86,15 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": []
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Tensor(\"mdn_model/add_1:0\", shape=(40, 72), dtype=float32)\n",
-      "Tensor(\"mdn_model/strided_slice:0\", shape=(40, 24), dtype=float32)\n",
-      "Tensor(\"mdn_model/Exp:0\", shape=(40, 24), dtype=float32)\n",
-      "Tensor(\"mdn_model/y:0\", shape=(4, 10, 1), dtype=float32)\n",
-      "Tensor(\"mdn_model_1/add_1:0\", shape=(1, 72), dtype=float32)\n",
-      "Tensor(\"mdn_model_1/strided_slice:0\", shape=(1, 24), dtype=float32)\n",
-      "Tensor(\"mdn_model_1/Exp:0\", shape=(1, 24), dtype=float32)\n",
-      "0   0.431827\n",
-      "1   0.233722\n",
-      "2   0.137326\n",
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-      "990   -1.71508\n",
-      "991   -1.79559\n",
-      "992   -1.76411\n",
-      "993   -1.80683\n",
-      "994   -1.81438\n",
-      "995   -1.78\n",
-      "996   -1.78637\n",
-      "997   -1.73508\n",
-      "998   -1.77904\n",
-      "999   -1.84538\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "model_utils.reset_session_and_model()\n",
     "with tf.Session() as sess:\n",
@@ -1161,41 +128,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Tensor(\"mdn_model/add_1:0\", shape=(1, 72), dtype=float32)\n",
-      "Tensor(\"mdn_model/strided_slice:0\", shape=(1, 24), dtype=float32)\n",
-      "Tensor(\"mdn_model/Exp:0\", shape=(1, 24), dtype=float32)\n",
-      "Tensor(\"mdn_model/y:0\", shape=(1, 1, 1), dtype=float32)\n",
-      "INFO:tensorflow:Restoring parameters from models/mdnmodel.ckpt-999\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.text.Text at 0x7f65466a3390>"
-      ]
-     },
-     "execution_count": 72,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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llBNUG7C6rqx7IjLQbiBBEIQYlBQuyNRNCUjwiRBx6SxhDEgz1u/E5npxEcvMJm9/fGuJ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\nTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f65b79cb5f8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "ckpt_path = 'models/mdnmodel.ckpt-999'\n",
     "seq_len = 2000\n",
@@ -1222,9 +157,14 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": []
   }
@@ -1245,7 +185,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.5"
+   "version": "3.9.4"
   }
  },
  "nbformat": 4,
diff --git a/SenseGenModel_ECGDataset.ipynb b/SenseGenModel_ECGDataset.ipynb
index dfcab68..1852532 100644
--- a/SenseGenModel_ECGDataset.ipynb
+++ b/SenseGenModel_ECGDataset.ipynb
@@ -2,20 +2,9 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'\\nAuthor: Moustafa Alzantot (malzantot@ucla.edu)\\nAll rights reserved Networked and Embedded Systems Lab (NESL), UCLA.\\nPermission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:\\nThe above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\\n'"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "\"\"\"\n",
     "Author: Moustafa Alzantot (malzantot@ucla.edu)\n",
@@ -28,7 +17,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -38,18 +27,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/malzantot/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
-      "  from ._conv import register_converters as _register_converters\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import data_utils\n",
     "import model_utils\n",
@@ -58,7 +38,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -68,17 +48,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "import tensorflow as tf\n",
+    "import tensorflow.compat.v1 as tf\n",
+    "tf.disable_v2_behavior() \n",
     "import numpy as np"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -94,7 +75,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -113,484 +94,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:tensorflow:From /home/malzantot/Nesl/sensegen/model.py:137: LSTMCell.__init__ (from tensorflow.python.ops.rnn_cell_impl) is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "This class is equivalent as tf.keras.layers.LSTMCell, and will be replaced by that in Tensorflow 2.0.\n",
-      "WARNING:tensorflow:From /home/malzantot/Nesl/sensegen/model.py:137: MultiRNNCell.__init__ (from tensorflow.python.ops.rnn_cell_impl) is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "This class is equivalent as tf.keras.layers.StackedRNNCells, and will be replaced by that in Tensorflow 2.0.\n",
-      "WARNING:tensorflow:From /home/malzantot/Nesl/sensegen/model.py:142: dynamic_rnn (from tensorflow.python.ops.rnn) is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "Please use `keras.layers.RNN(cell)`, which is equivalent to this API\n",
-      "WARNING:tensorflow:From /home/malzantot/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/tensor_array_ops.py:162: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "Colocations handled automatically by placer.\n",
-      "Tensor(\"mdn_model/add_1:0\", shape=(1280, 72), dtype=float32)\n",
-      "Tensor(\"mdn_model/strided_slice:0\", shape=(1280, 24), dtype=float32)\n",
-      "Tensor(\"mdn_model/Exp:0\", shape=(1280, 24), dtype=float32)\n",
-      "Tensor(\"mdn_model/y:0\", shape=(128, 10, 1), dtype=float32)\n",
-      "\n",
-      "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
-      "For more information, please see:\n",
-      "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
-      "  * https://github.com/tensorflow/addons\n",
-      "If you depend on functionality not listed there, please file an issue.\n",
-      "\n",
-      "WARNING:tensorflow:From /home/malzantot/Nesl/sensegen/model.py:169: Normal.__init__ (from tensorflow.python.ops.distributions.normal) is deprecated and will be removed after 2019-01-01.\n",
-      "Instructions for updating:\n",
-      "The TensorFlow Distributions library has moved to TensorFlow Probability (https://github.com/tensorflow/probability). You should update all references to use `tfp.distributions` instead of `tf.distributions`.\n",
-      "WARNING:tensorflow:From /home/malzantot/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/distributions/normal.py:160: Distribution.__init__ (from tensorflow.python.ops.distributions.distribution) is deprecated and will be removed after 2019-01-01.\n",
-      "Instructions for updating:\n",
-      "The TensorFlow Distributions library has moved to TensorFlow Probability (https://github.com/tensorflow/probability). You should update all references to use `tfp.distributions` instead of `tf.distributions`.\n",
-      "WARNING:tensorflow:From /home/malzantot/Nesl/sensegen/model.py:171: calling reduce_sum_v1 (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "keep_dims is deprecated, use keepdims instead\n",
-      "WARNING:tensorflow:From /home/malzantot/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "Use tf.cast instead.\n",
-      "Tensor(\"mdn_model_1/add_1:0\", shape=(1, 72), dtype=float32)\n",
-      "Tensor(\"mdn_model_1/strided_slice:0\", shape=(1, 24), dtype=float32)\n",
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-      "337   -1.5268233\n",
-      "338   -1.526803\n",
-      "339   -1.5323822\n",
-      "340   -1.5249627\n",
-      "341   -1.5312594\n",
-      "342   -1.5224974\n",
-      "343   -1.5228763\n",
-      "344   -1.5323776\n",
-      "345   -1.5249143\n",
-      "346   -1.5284789\n",
-      "347   -1.5270176\n",
-      "348   -1.5300974\n",
-      "349   -1.5293187\n",
-      "350   -1.5279602\n",
-      "351   -1.5197133\n",
-      "352   -1.5292267\n",
-      "353   -1.5182769\n",
-      "354   -1.5220273\n",
-      "355   -1.5293491\n",
-      "356   -1.5247715\n",
-      "357   -1.5237035\n",
-      "358   -1.5304126\n",
-      "359   -1.5284162\n",
-      "360   -1.5320547\n",
-      "361   -1.519982\n",
-      "362   -1.5288659\n",
-      "363   -1.5269455\n",
-      "364   -1.5200175\n",
-      "365   -1.525511\n",
-      "366   -1.525453\n",
-      "367   -1.5255369\n",
-      "368   -1.5223013\n",
-      "369   -1.531625\n",
-      "370   -1.5324117\n",
-      "371   -1.5296425\n",
-      "372   -1.5276752\n",
-      "373   -1.5329815\n",
-      "374   -1.5297412\n",
-      "375   -1.5290095\n",
-      "376   -1.5315715\n",
-      "377   -1.5318837\n",
-      "378   -1.5297729\n",
-      "379   -1.5215596\n",
-      "380   -1.5360425\n",
-      "381   -1.5321892\n",
-      "382   -1.532932\n",
-      "383   -1.5289149\n",
-      "384   -1.5318658\n",
-      "385   -1.5302466\n",
-      "386   -1.5342908\n",
-      "387   -1.5274019\n",
-      "388   -1.5325809\n",
-      "389   -1.5345662\n",
-      "390   -1.5339288\n",
-      "391   -1.5312002\n",
-      "392   -1.5296836\n",
-      "393   -1.5252416\n",
-      "394   -1.5309366\n",
-      "395   -1.5332506\n",
-      "396   -1.5334619\n",
-      "397   -1.5359544\n",
-      "398   -1.5284281\n",
-      "399   -1.5376202\n",
-      "400   -1.5310094\n",
-      "401   -1.5351522\n",
-      "402   -1.5362556\n",
-      "403   -1.5293115\n",
-      "404   -1.5338084\n",
-      "405   -1.5322225\n",
-      "406   -1.5319463\n",
-      "407   -1.5354942\n",
-      "408   -1.5331061\n",
-      "409   -1.5368834\n",
-      "410   -1.5373237\n",
-      "411   -1.5329795\n",
-      "412   -1.5356042\n",
-      "413   -1.5374467\n",
-      "414   -1.5344105\n",
-      "415   -1.5393586\n",
-      "416   -1.5312672\n",
-      "417   -1.5361015\n",
-      "418   -1.5382856\n",
-      "419   -1.5382527\n",
-      "420   -1.5330033\n",
-      "421   -1.5371863\n",
-      "422   -1.5346358\n",
-      "423   -1.531743\n",
-      "424   -1.53284\n",
-      "425   -1.5348405\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "model_utils.reset_session_and_model()\n",
     "with tf.Session() as sess:\n",
@@ -625,41 +129,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Tensor(\"mdn_model/add_1:0\", shape=(1, 72), dtype=float32)\n",
-      "Tensor(\"mdn_model/strided_slice:0\", shape=(1, 24), dtype=float32)\n",
-      "Tensor(\"mdn_model/Exp:0\", shape=(1, 24), dtype=float32)\n",
-      "Tensor(\"mdn_model/y:0\", shape=(1, 1, 1), dtype=float32)\n",
-      "INFO:tensorflow:Restoring parameters from ./models/ecg_mdnmodel.ckpt-999\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.5,1,'Fake data')"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1008x576 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "ckpt_path = './models/ecg_mdnmodel.ckpt-999'\n",
     "seq_len = 1200\n",
@@ -685,7 +157,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -725,7 +197,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.5"
+   "version": "3.9.4"
   }
  },
  "nbformat": 4,
diff --git a/TestRNNModel.ipynb b/TestRNNModel.ipynb
index e0e1512..0441702 100644
--- a/TestRNNModel.ipynb
+++ b/TestRNNModel.ipynb
@@ -2,10 +2,8 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "%load_ext autoreload\n",
@@ -14,10 +12,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "import data_utils\n",
@@ -27,10 +23,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
@@ -39,19 +33,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
-    "import tensorflow as tf\n",
+    "import tensorflow.compat.v1 as tf\n",
+    "tf.disable_v2_behavior() \n",
     "import numpy as np"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -60,10 +53,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "# To get reasonable outputs, should use something bigger than 150 !\n",
@@ -72,1022 +63,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
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-      "999   0.00901097\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "model_utils.reset_session_and_model()\n",
     "with tf.Session() as sess:\n",
@@ -1112,30 +90,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f5a1e52fef0>]"
-      ]
-     },
-     "execution_count": 54,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
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z+mLa2Hhf2Q26GSJgvlBJm01pz2IWPeP1uEhPYJR7dij6Ucx6xkTlO+SYTrhm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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f5905ad1278>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "plt.plot(sample_preds)"
    ]
@@ -1143,9 +100,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": []
   }
@@ -1166,7 +121,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.1"
+   "version": "3.9.4"
   }
  },
  "nbformat": 4,
diff --git a/download_ecg_dataset.sh b/download_ecg_dataset.sh
old mode 100644
new mode 100755
diff --git a/model.py b/model.py
index 288a8c7..d7e3536 100644
--- a/model.py
+++ b/model.py
@@ -10,7 +10,8 @@
 """
 
 import numpy as np
-import tensorflow as tf
+import tensorflow.compat.v1 as tf
+tf.disable_v2_behavior() 
 
 import model_utils
 
@@ -155,6 +156,7 @@ def _build_model(self):
         mu_ = gmm_params[:, : self.num_mixtures]
         sigma_= gmm_params[:,  self.num_mixtures: 2* self.num_mixtures]
         pi_ = gmm_params[:,  2*self.num_mixtures:]
+        print(pi_)
         self.mu = mu_
         self.sigma = tf.exp(sigma_ / 2.0)
         self.pi = tf.nn.softmax(pi_)
@@ -166,7 +168,8 @@ def _build_model(self):
             self.optimizer = tf.train.AdamOptimizer(self.learning_rate)
             #self.loss = tf.reduce_mean(tf.squared_difference(self.preds, self.y_holder))
             print(self.y_holder)
-            mixture_p = tf.contrib.distributions.Normal(self.mu, self.sigma).prob(tf.reshape(self.y_holder,(-1,1)))
+            
+            mixture_p = tf.compat.v1.distributions.Normal(self.mu, self.sigma).prob(tf.reshape(self.y_holder,(-1,1)))
             mixture_p = tf.multiply(self.pi, mixture_p)
             output_p = tf.reduce_sum(mixture_p, reduction_indices=1, keep_dims=True)
             log_output_p = tf.log(output_p)
@@ -212,6 +215,7 @@ def predict(self,sess, seq_len=1000):
                     self.init_state: cur_state
                 }
             )
+
             # chose one
             select_mixture = np.random.choice(self.num_mixtures, p=pi_[0])
             #new_pred_  = np.random.normal(loc=mu_[0
diff --git a/model_utils.py b/model_utils.py
index 01f28d1..185eca9 100644
--- a/model_utils.py
+++ b/model_utils.py
@@ -9,7 +9,8 @@
 THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
 """
 
-import tensorflow as tf
+import tensorflow.compat.v1 as tf
+tf.disable_v2_behavior() 
 
 
 def reset_session_and_model():