diff --git a/1_to_1_multi_layer.ipynb b/1_to_1_multi_layer.ipynb
index 70ef2c4..cd2c117 100644
--- a/1_to_1_multi_layer.ipynb
+++ b/1_to_1_multi_layer.ipynb
@@ -2,18 +2,9 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\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 pandas as pd\n",
     "import numpy as np\n",
@@ -33,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -46,26 +37,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'test': 1, 'a': 'b'}\n"
-     ]
-    }
-   ],
-   "source": [
-    "dic = {\"test\": 1, \"a\": \"b\"}\n",
-    "pkl.dump( dic, open( \"save.pkl\", \"wb\" ) )\n",
-    "print(pkl.load( open( \"save.pkl\", \"rb\" ) ))"
-   ]
+   "outputs": [],
+   "source": []
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -81,7 +60,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -112,7 +91,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -145,7 +124,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -157,7 +136,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -224,7 +203,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -291,7 +270,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -305,7 +284,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -341,7 +320,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -362,7 +341,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -407,7 +386,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -422,7 +401,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -458,7 +437,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -639,12 +618,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "def full_save(rnn):\n",
-    "    folder = \"./rnn_model_\" + str(rnn._)+ \"_\" + rnn.__ + \"_\" + str(len(rnn.ncells)) + \"l_\" + str(rnn.ncells).replace(\" \",\"\") + \"c/rnn_basic\"\n",
+    "    folder = \"./rnn_model_\" + str(rnn._)+ \"_\" + rnn.__ + \"_\" + str(rnn.ncells).replace(\" \",\"\") + \"c/rnn_basic\"\n",
     "    rnn.save(folder)\n",
     "    pkl_name = folder[2:-10] + \".pkl\"\n",
     "    \n",
@@ -694,13 +673,13 @@
     "    return rnn\n",
     "\n",
     "def get_rnn_folder(ncells, cell_type, activation):\n",
-    "    folder = \"./rnn_model_\" + cell_type + \"_\" + activation + \"_\" + str(len(ncells)) + \"l_\" + str(ncells).replace(\" \",\"\") + \"c/rnn_basic\"\n",
+    "    folder = \"./rnn_model_\" + cell_type + \"_\" + activation + \"_\" + str(ncells).replace(\" \",\"\") + \"c/rnn_basic\"\n",
     "    return folder"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -744,26 +723,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": null,
    "metadata": {
     "scrolled": true
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch number  5\n",
-      "Cost:  138389.9095210623 e-6\n",
-      "Patience:  0 / 200\n",
-      "Last checkpoint at: Epoch  5 \n",
-      "\n",
-      "\n",
-      "\n",
-      "Model saved in at:  ./rnn_model_lstm_leaky_relu_[50,40,30,20,10]c/rnn_basic\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "rnn.fit(minibatches, epochs = 5, print_step=5)\n",
     "full_save(rnn)"
@@ -771,29 +735,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": null,
    "metadata": {
     "scrolled": false
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "#Plot the loss\n",
+    "def plot_loss_list(loss_list= rnn.loss_list):\n",
+    "    plt.plot(rnn.loss_list)\n",
+    "    plt.xlabel(\"Epoch\")\n",
+    "    plt.ylabel(\"Cost\")\n",
+    "    plt.show()\n",
     "\n",
-    "plt.plot(rnn.loss_list)\n",
-    "plt.xlabel(\"Epoch\")\n",
-    "plt.ylabel(\"Cost\")\n",
-    "plt.show()"
+    "plot_loss_list()"
    ]
   },
   {
@@ -802,25 +757,17 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "#full_save(rnn)"
+    "full_save(rnn)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO:tensorflow:Restoring parameters from ./rnn_model_lstm_leaky_relu_5l_[50,40,30,20,10]c/rnn_basic\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "folder = get_rnn_folder(ncells = [50, 40, 30, 20, 10], cell_type = \"lstm\", activation = \"leaky_relu\")\n",
-    "rnn = full_load(folder)"
+    "#folder = get_rnn_folder(ncells = [50, 40, 30, 20, 10], cell_type = \"lstm\", activation = \"leaky_relu\")\n",
+    "#rnn = full_load(folder)"
    ]
   },
   {