diff --git a/tv-script-generation/.ipynb_checkpoints/dlnd_tv_script_generation-checkpoint.ipynb b/tv-script-generation/.ipynb_checkpoints/dlnd_tv_script_generation-checkpoint.ipynb
index de7b75f..2a55cc9 100644
--- a/tv-script-generation/.ipynb_checkpoints/dlnd_tv_script_generation-checkpoint.ipynb
+++ b/tv-script-generation/.ipynb_checkpoints/dlnd_tv_script_generation-checkpoint.ipynb
@@ -15,7 +15,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 1,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -47,7 +47,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 2,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -104,9 +104,37 @@
     "print('\\n'.join(text.split('\\n')[view_sentence_range[0]:view_sentence_range[1]]))"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "00:00.0 Host bridge: Intel Corporation 440FX - 82441FX PMC [Natoma] (rev 02)\r\n",
+      "00:01.0 ISA bridge: Intel Corporation 82371SB PIIX3 ISA [Natoma/Triton II]\r\n",
+      "00:01.1 IDE interface: Intel Corporation 82371SB PIIX3 IDE [Natoma/Triton II]\r\n",
+      "00:01.3 Bridge: Intel Corporation 82371AB/EB/MB PIIX4 ACPI (rev 01)\r\n",
+      "00:02.0 VGA compatible controller: Cirrus Logic GD 5446\r\n",
+      "00:03.0 VGA compatible controller: NVIDIA Corporation GK104GL [GRID K520] (rev a1)\r\n",
+      "00:1f.0 Unassigned class [ff80]: XenSource, Inc. Xen Platform Device (rev 01)\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!lspci"
+   ]
+  },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "#### Exploring Sentence Lengths \n",
     "\n",
@@ -115,9 +143,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 320,
+   "execution_count": 10,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [
     {
@@ -126,7 +156,7 @@
        "\n",
        "    <div class=\"bk-root\">\n",
        "        <a href=\"http://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
-       "        <span id=\"ac8cada1-716f-46a2-a8f6-437383d8c041\">Loading BokehJS ...</span>\n",
+       "        <span id=\"5d944cec-21f7-43a6-9d66-027ae50d0773\">Loading BokehJS ...</span>\n",
        "    </div>"
       ]
      },
@@ -174,7 +204,7 @@
        "\n",
        "  function display_loaded() {\n",
        "    if (window.Bokeh !== undefined) {\n",
-       "      document.getElementById(\"ac8cada1-716f-46a2-a8f6-437383d8c041\").textContent = \"BokehJS successfully loaded.\";\n",
+       "      document.getElementById(\"5d944cec-21f7-43a6-9d66-027ae50d0773\").textContent = \"BokehJS successfully loaded.\";\n",
        "    } else if (Date.now() < window._bokeh_timeout) {\n",
        "      setTimeout(display_loaded, 100)\n",
        "    }\n",
@@ -216,9 +246,9 @@
        "      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
        "      document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
        "    }\n",
-       "  };var element = document.getElementById(\"ac8cada1-716f-46a2-a8f6-437383d8c041\");\n",
+       "  };var element = document.getElementById(\"5d944cec-21f7-43a6-9d66-027ae50d0773\");\n",
        "  if (element == null) {\n",
-       "    console.log(\"Bokeh: ERROR: autoload.js configured with elementid 'ac8cada1-716f-46a2-a8f6-437383d8c041' but no matching script tag was found. \")\n",
+       "    console.log(\"Bokeh: ERROR: autoload.js configured with elementid '5d944cec-21f7-43a6-9d66-027ae50d0773' but no matching script tag was found. \")\n",
        "    return false;\n",
        "  }\n",
        "\n",
@@ -231,7 +261,7 @@
        "    \n",
        "    function(Bokeh) {\n",
        "      \n",
-       "      document.getElementById(\"ac8cada1-716f-46a2-a8f6-437383d8c041\").textContent = \"BokehJS is loading...\";\n",
+       "      document.getElementById(\"5d944cec-21f7-43a6-9d66-027ae50d0773\").textContent = \"BokehJS is loading...\";\n",
        "    },\n",
        "    function(Bokeh) {\n",
        "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.4.min.css\");\n",
@@ -254,7 +284,7 @@
        "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
        "      window._bokeh_failed_load = true;\n",
        "    } else if (force !== true) {\n",
-       "      var cell = $(document.getElementById(\"ac8cada1-716f-46a2-a8f6-437383d8c041\")).parents('.cell').data().cell;\n",
+       "      var cell = $(document.getElementById(\"5d944cec-21f7-43a6-9d66-027ae50d0773\")).parents('.cell').data().cell;\n",
        "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
        "    }\n",
        "\n",
@@ -281,7 +311,7 @@
        "\n",
        "\n",
        "    <div class=\"bk-root\">\n",
-       "        <div class=\"bk-plotdiv\" id=\"e1217a82-2456-48d8-b142-efd9a4b024a6\"></div>\n",
+       "        <div class=\"bk-plotdiv\" id=\"46433a97-e836-4db8-9dbd-6723adbab9ef\"></div>\n",
        "    </div>\n",
        "<script type=\"text/javascript\">\n",
        "  \n",
@@ -322,7 +352,7 @@
        "  \n",
        "    function display_loaded() {\n",
        "      if (window.Bokeh !== undefined) {\n",
-       "        document.getElementById(\"e1217a82-2456-48d8-b142-efd9a4b024a6\").textContent = \"BokehJS successfully loaded.\";\n",
+       "        document.getElementById(\"46433a97-e836-4db8-9dbd-6723adbab9ef\").textContent = \"BokehJS successfully loaded.\";\n",
        "      } else if (Date.now() < window._bokeh_timeout) {\n",
        "        setTimeout(display_loaded, 100)\n",
        "      }\n",
@@ -364,9 +394,9 @@
        "        console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
        "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
        "      }\n",
-       "    };var element = document.getElementById(\"e1217a82-2456-48d8-b142-efd9a4b024a6\");\n",
+       "    };var element = document.getElementById(\"46433a97-e836-4db8-9dbd-6723adbab9ef\");\n",
        "    if (element == null) {\n",
-       "      console.log(\"Bokeh: ERROR: autoload.js configured with elementid 'e1217a82-2456-48d8-b142-efd9a4b024a6' but no matching script tag was found. \")\n",
+       "      console.log(\"Bokeh: ERROR: autoload.js configured with elementid '46433a97-e836-4db8-9dbd-6723adbab9ef' but no matching script tag was found. \")\n",
        "      return false;\n",
        "    }\n",
        "  \n",
@@ -376,8 +406,8 @@
        "      function(Bokeh) {\n",
        "        (function() {\n",
        "          var fn = function() {\n",
-       "            var docs_json = {\"76b61ef9-fd40-4d57-9498-0ec23b58389e\":{\"roots\":{\"references\":[{\"attributes\":{\"callback\":null,\"column_names\":[\"width\",\"fill_alpha\",\"label\",\"x\",\"line_color\",\"y\",\"height\",\"line_alpha\",\"color\"],\"data\":{\"chart_index\":[\"(86.480000, 89.040000]\"],\"color\":[\"#f22c40\"],\"fill_alpha\":[0.8],\"height\":[0.0],\"label\":[\"(86.480000, 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+       "            var render_items = [{\"docid\":\"b315479b-aa1c-4b1d-a62f-5f3345d28e0e\",\"elementid\":\"46433a97-e836-4db8-9dbd-6723adbab9ef\",\"modelid\":\"197186d2-75e3-4931-8078-3a33f3d5ec66\"}];\n",
        "            \n",
        "            Bokeh.embed.embed_items(docs_json, render_items);\n",
        "          };\n",
@@ -402,7 +432,7 @@
        "        console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
        "        window._bokeh_failed_load = true;\n",
        "      } else if (force !== true) {\n",
-       "        var cell = $(document.getElementById(\"e1217a82-2456-48d8-b142-efd9a4b024a6\")).parents('.cell').data().cell;\n",
+       "        var cell = $(document.getElementById(\"46433a97-e836-4db8-9dbd-6723adbab9ef\")).parents('.cell').data().cell;\n",
        "        cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
        "      }\n",
        "  \n",
@@ -480,7 +510,7 @@
   },
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+   "execution_count": 14,
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@@ -546,7 +576,7 @@
   },
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@@ -600,7 +630,7 @@
   },
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@@ -628,7 +658,7 @@
   },
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@@ -658,7 +688,7 @@
   },
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@@ -702,7 +732,7 @@
   },
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@@ -755,7 +785,7 @@
   },
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@@ -812,7 +842,7 @@
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@@ -871,7 +901,7 @@
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@@ -928,7 +958,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 228,
+   "execution_count": 23,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -986,7 +1016,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 231,
+   "execution_count": 24,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -1117,11 +1147,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 455,
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 4, 2, 3])"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "t = np.array([1,2,3,4])\n",
+    "np.random.shuffle(t)\n",
+    "t"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "scrolled": true
    },
    "outputs": [
     {
@@ -1142,11 +1197,11 @@
     "    :return: Batches as a Numpy array\n",
     "    \"\"\"\n",
     "    \n",
-    "    slice_size = batch_size * seq_length\n",
+    "    slice_size = int(batch_size * seq_length)\n",
     "    n_batches = int(len(int_text)/slice_size)\n",
     "    \n",
     "    assert n_batches > 0, 'Maybe your batch size is too big ?'\n",
-    "    \n",
+    "\n",
     "    # input part\n",
     "    _inputs = np.array(int_text[:n_batches*slice_size])\n",
     "    \n",
@@ -1160,18 +1215,17 @@
     "    \n",
     "    # concat inputs and targets\n",
     "    batches = np.c_[inputs, targets]\n",
-    "    #print(batches.shape)\n",
+    "    \n",
     "    \n",
     "    # reshape into [2 * inputs]\n",
     "    batches = batches.reshape((2, batch_size, -1))\n",
-    "    #print('after reshape 2,batch_size,-1')\n",
     "    #print(batches)\n",
     "    \n",
     "    \n",
     "    # Shuffle the batches of sequences without disturbing each sequence order\n",
     "    # We are using transpose to bring the batch into axes 0 and be able to shuffle it\n",
     "    np.random.shuffle(np.transpose(batches, (1,2,0)))\n",
-    "    #print(batches)\n",
+    "    \n",
     "    \n",
     "    # Reshape into final batches output\n",
     "    batches = batches.reshape((-1, 2, batch_size, seq_length))\n",
@@ -1209,7 +1263,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 317,
+   "execution_count": 71,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -1222,12 +1276,12 @@
     "# Batch Size\n",
     "batch_size = 128\n",
     "# RNN Size\n",
-    "rnn_size = 70\n",
+    "rnn_size = 100\n",
     "# Sequence Length\n",
     "# Use the mean of sentences length as sequence length\n",
-    "seq_length = np.floor(s_lengths.mean())\n",
+    "seq_length = int(s_lengths.mean())\n",
     "# Learning Rate\n",
-    "learning_rate = 1e-3\n",
+    "learning_rate = 1e-2\n",
     "# Show stats for every n number of batches\n",
     "show_every_n_batches = 10\n",
     "\n",
@@ -1250,7 +1304,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 235,
+   "execution_count": 53,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -1299,7 +1353,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 238,
+   "execution_count": 36,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -1326,7 +1380,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 197,
+   "execution_count": 74,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -1337,508 +1391,116 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "5\n",
-      "Epoch   0 Batch    0/5   train_loss = 8.825\n",
-      "Epoch   2 Batch    0/5   train_loss = 6.441\n",
-      "Epoch   4 Batch    0/5   train_loss = 6.023\n",
-      "Epoch   6 Batch    0/5   train_loss = 5.927\n",
-      "Epoch   8 Batch    0/5   train_loss = 5.903\n",
-      "Epoch  10 Batch    0/5   train_loss = 5.883\n",
-      "Epoch  12 Batch    0/5   train_loss = 5.874\n",
-      "Epoch  14 Batch    0/5   train_loss = 5.858\n",
-      "Epoch  16 Batch    0/5   train_loss = 5.833\n",
-      "Epoch  18 Batch    0/5   train_loss = 5.794\n",
-      "Epoch  20 Batch    0/5   train_loss = 5.739\n",
-      "Epoch  22 Batch    0/5   train_loss = 5.682\n",
-      "Epoch  24 Batch    0/5   train_loss = 5.626\n",
-      "Epoch  26 Batch    0/5   train_loss = 5.572\n",
-      "Epoch  28 Batch    0/5   train_loss = 5.521\n",
-      "Epoch  30 Batch    0/5   train_loss = 5.471\n",
-      "Epoch  32 Batch    0/5   train_loss = 5.421\n",
-      "Epoch  34 Batch    0/5   train_loss = 5.365\n",
-      "Epoch  36 Batch    0/5   train_loss = 5.304\n",
-      "Epoch  38 Batch    0/5   train_loss = 5.244\n",
-      "Epoch  40 Batch    0/5   train_loss = 5.185\n",
-      "Epoch  42 Batch    0/5   train_loss = 5.124\n",
-      "Epoch  44 Batch    0/5   train_loss = 5.063\n",
-      "Epoch  46 Batch    0/5   train_loss = 5.003\n",
-      "Epoch  48 Batch    0/5   train_loss = 4.945\n",
-      "Epoch  50 Batch    0/5   train_loss = 4.891\n",
-      "Epoch  52 Batch    0/5   train_loss = 4.841\n",
-      "Epoch  54 Batch    0/5   train_loss = 4.794\n",
-      "Epoch  56 Batch    0/5   train_loss = 4.751\n",
-      "Epoch  58 Batch    0/5   train_loss = 4.710\n",
-      "Epoch  60 Batch    0/5   train_loss = 4.669\n",
-      "Epoch  62 Batch    0/5   train_loss = 4.638\n",
-      "Epoch  64 Batch    0/5   train_loss = 4.638\n",
-      "Epoch  66 Batch    0/5   train_loss = 4.589\n",
-      "Epoch  68 Batch    0/5   train_loss = 4.537\n",
-      "Epoch  70 Batch    0/5   train_loss = 4.501\n",
-      "Epoch  72 Batch    0/5   train_loss = 4.469\n",
-      "Epoch  74 Batch    0/5   train_loss = 4.436\n",
-      "Epoch  76 Batch    0/5   train_loss = 4.405\n",
-      "Epoch  78 Batch    0/5   train_loss = 4.375\n",
-      "Epoch  80 Batch    0/5   train_loss = 4.344\n",
-      "Epoch  82 Batch    0/5   train_loss = 4.363\n",
-      "Epoch  84 Batch    0/5   train_loss = 4.311\n",
-      "Epoch  86 Batch    0/5   train_loss = 4.274\n",
-      "Epoch  88 Batch    0/5   train_loss = 4.240\n",
-      "Epoch  90 Batch    0/5   train_loss = 4.211\n",
-      "Epoch  92 Batch    0/5   train_loss = 4.182\n",
-      "Epoch  94 Batch    0/5   train_loss = 4.155\n",
-      "Epoch  96 Batch    0/5   train_loss = 4.135\n",
-      "Epoch  98 Batch    0/5   train_loss = 4.107\n",
-      "Epoch 100 Batch    0/5   train_loss = 4.093\n",
-      "Epoch 102 Batch    0/5   train_loss = 4.053\n",
-      "Epoch 104 Batch    0/5   train_loss = 4.030\n",
-      "Epoch 106 Batch    0/5   train_loss = 4.002\n",
-      "Epoch 108 Batch    0/5   train_loss = 3.978\n",
-      "Epoch 110 Batch    0/5   train_loss = 3.951\n",
-      "Epoch 112 Batch    0/5   train_loss = 3.928\n",
-      "Epoch 114 Batch    0/5   train_loss = 3.902\n",
-      "Epoch 116 Batch    0/5   train_loss = 3.884\n",
-      "Epoch 118 Batch    0/5   train_loss = 3.862\n",
-      "Epoch 120 Batch    0/5   train_loss = 3.840\n",
-      "Epoch 122 Batch    0/5   train_loss = 3.814\n",
-      "Epoch 124 Batch    0/5   train_loss = 3.803\n",
-      "Epoch 126 Batch    0/5   train_loss = 3.775\n",
-      "Epoch 128 Batch    0/5   train_loss = 3.738\n",
-      "Epoch 130 Batch    0/5   train_loss = 3.714\n",
-      "Epoch 132 Batch    0/5   train_loss = 3.690\n",
-      "Epoch 134 Batch    0/5   train_loss = 3.665\n",
-      "Epoch 136 Batch    0/5   train_loss = 3.642\n",
-      "Epoch 138 Batch    0/5   train_loss = 3.619\n",
-      "Epoch 140 Batch    0/5   train_loss = 3.596\n",
-      "Epoch 142 Batch    0/5   train_loss = 3.577\n",
-      "Epoch 144 Batch    0/5   train_loss = 3.588\n",
-      "Epoch 146 Batch    0/5   train_loss = 3.561\n",
-      "Epoch 148 Batch    0/5   train_loss = 3.537\n",
-      "Epoch 150 Batch    0/5   train_loss = 3.494\n",
-      "Epoch 152 Batch    0/5   train_loss = 3.475\n",
-      "Epoch 154 Batch    0/5   train_loss = 3.444\n",
-      "Epoch 156 Batch    0/5   train_loss = 3.431\n",
-      "Epoch 158 Batch    0/5   train_loss = 3.403\n",
-      "Epoch 160 Batch    0/5   train_loss = 3.393\n",
-      "Epoch 162 Batch    0/5   train_loss = 3.371\n",
-      "Epoch 164 Batch    0/5   train_loss = 3.352\n",
-      "Epoch 166 Batch    0/5   train_loss = 3.323\n",
-      "Epoch 168 Batch    0/5   train_loss = 3.328\n",
-      "Epoch 170 Batch    0/5   train_loss = 3.281\n",
-      "Epoch 172 Batch    0/5   train_loss = 3.261\n",
-      "Epoch 174 Batch    0/5   train_loss = 3.238\n",
-      "Epoch 176 Batch    0/5   train_loss = 3.216\n",
-      "Epoch 178 Batch    0/5   train_loss = 3.197\n",
-      "Epoch 180 Batch    0/5   train_loss = 3.172\n",
-      "Epoch 182 Batch    0/5   train_loss = 3.169\n",
-      "Epoch 184 Batch    0/5   train_loss = 3.140\n",
-      "Epoch 186 Batch    0/5   train_loss = 3.136\n",
-      "Epoch 188 Batch    0/5   train_loss = 3.145\n",
-      "Epoch 190 Batch    0/5   train_loss = 3.106\n",
-      "Epoch 192 Batch    0/5   train_loss = 3.069\n",
-      "Epoch 194 Batch    0/5   train_loss = 3.038\n",
-      "Epoch 196 Batch    0/5   train_loss = 3.019\n",
-      "Epoch 198 Batch    0/5   train_loss = 2.995\n",
-      "Epoch 200 Batch    0/5   train_loss = 2.979\n",
-      "Epoch 202 Batch    0/5   train_loss = 2.960\n",
-      "Epoch 204 Batch    0/5   train_loss = 2.943\n",
-      "Epoch 206 Batch    0/5   train_loss = 2.963\n",
-      "Epoch 208 Batch    0/5   train_loss = 2.917\n",
-      "Epoch 210 Batch    0/5   train_loss = 2.898\n",
-      "Epoch 212 Batch    0/5   train_loss = 2.867\n",
-      "Epoch 214 Batch    0/5   train_loss = 2.863\n",
-      "Epoch 216 Batch    0/5   train_loss = 2.834\n",
-      "Epoch 218 Batch    0/5   train_loss = 2.809\n",
-      "Epoch 220 Batch    0/5   train_loss = 2.797\n",
-      "Epoch 222 Batch    0/5   train_loss = 2.774\n",
-      "Epoch 224 Batch    0/5   train_loss = 2.759\n",
-      "Epoch 226 Batch    0/5   train_loss = 2.732\n",
-      "Epoch 228 Batch    0/5   train_loss = 2.742\n",
-      "Epoch 230 Batch    0/5   train_loss = 2.704\n",
-      "Epoch 232 Batch    0/5   train_loss = 2.703\n",
-      "Epoch 234 Batch    0/5   train_loss = 2.663\n",
-      "Epoch 236 Batch    0/5   train_loss = 2.672\n",
-      "Epoch 238 Batch    0/5   train_loss = 2.638\n",
-      "Epoch 240 Batch    0/5   train_loss = 2.620\n",
-      "Epoch 242 Batch    0/5   train_loss = 2.595\n",
-      "Epoch 244 Batch    0/5   train_loss = 2.585\n",
-      "Epoch 246 Batch    0/5   train_loss = 2.563\n",
-      "Epoch 248 Batch    0/5   train_loss = 2.539\n",
-      "Epoch 250 Batch    0/5   train_loss = 2.534\n",
-      "Epoch 252 Batch    0/5   train_loss = 2.517\n",
-      "Epoch 254 Batch    0/5   train_loss = 2.497\n",
-      "Epoch 256 Batch    0/5   train_loss = 2.475\n",
-      "Epoch 258 Batch    0/5   train_loss = 2.463\n",
-      "Epoch 260 Batch    0/5   train_loss = 2.478\n",
-      "Epoch 262 Batch    0/5   train_loss = 2.450\n",
-      "Epoch 264 Batch    0/5   train_loss = 2.436\n",
-      "Epoch 266 Batch    0/5   train_loss = 2.417\n",
-      "Epoch 268 Batch    0/5   train_loss = 2.384\n",
-      "Epoch 270 Batch    0/5   train_loss = 2.363\n",
-      "Epoch 272 Batch    0/5   train_loss = 2.340\n",
-      "Epoch 274 Batch    0/5   train_loss = 2.323\n",
-      "Epoch 276 Batch    0/5   train_loss = 2.314\n",
-      "Epoch 278 Batch    0/5   train_loss = 2.302\n",
-      "Epoch 280 Batch    0/5   train_loss = 2.300\n",
-      "Epoch 282 Batch    0/5   train_loss = 2.300\n",
-      "Epoch 284 Batch    0/5   train_loss = 2.283\n",
-      "Epoch 286 Batch    0/5   train_loss = 2.246\n",
-      "Epoch 288 Batch    0/5   train_loss = 2.246\n",
-      "Epoch 290 Batch    0/5   train_loss = 2.210\n",
-      "Epoch 292 Batch    0/5   train_loss = 2.203\n",
-      "Epoch 294 Batch    0/5   train_loss = 2.185\n",
-      "Epoch 296 Batch    0/5   train_loss = 2.170\n",
-      "Epoch 298 Batch    0/5   train_loss = 2.150\n",
-      "Epoch 300 Batch    0/5   train_loss = 2.130\n",
-      "Epoch 302 Batch    0/5   train_loss = 2.132\n",
-      "Epoch 304 Batch    0/5   train_loss = 2.113\n",
-      "Epoch 306 Batch    0/5   train_loss = 2.083\n",
-      "Epoch 308 Batch    0/5   train_loss = 2.073\n",
-      "Epoch 310 Batch    0/5   train_loss = 2.060\n",
-      "Epoch 312 Batch    0/5   train_loss = 2.072\n",
-      "Epoch 314 Batch    0/5   train_loss = 2.081\n",
-      "Epoch 316 Batch    0/5   train_loss = 2.031\n",
-      "Epoch 318 Batch    0/5   train_loss = 2.007\n",
-      "Epoch 320 Batch    0/5   train_loss = 2.001\n",
-      "Epoch 322 Batch    0/5   train_loss = 1.987\n",
-      "Epoch 324 Batch    0/5   train_loss = 1.978\n",
-      "Epoch 326 Batch    0/5   train_loss = 1.963\n",
-      "Epoch 328 Batch    0/5   train_loss = 1.952\n",
-      "Epoch 330 Batch    0/5   train_loss = 1.932\n",
-      "Epoch 332 Batch    0/5   train_loss = 1.918\n",
-      "Epoch 334 Batch    0/5   train_loss = 1.898\n",
-      "Epoch 336 Batch    0/5   train_loss = 1.885\n",
-      "Epoch 338 Batch    0/5   train_loss = 1.872\n",
-      "Epoch 340 Batch    0/5   train_loss = 1.864\n",
-      "Epoch 342 Batch    0/5   train_loss = 1.867\n",
-      "Epoch 344 Batch    0/5   train_loss = 1.848\n",
-      "Epoch 346 Batch    0/5   train_loss = 1.821\n",
-      "Epoch 348 Batch    0/5   train_loss = 1.814\n",
-      "Epoch 350 Batch    0/5   train_loss = 1.788\n",
-      "Epoch 352 Batch    0/5   train_loss = 1.806\n",
-      "Epoch 354 Batch    0/5   train_loss = 1.790\n",
-      "Epoch 356 Batch    0/5   train_loss = 1.761\n",
-      "Epoch 358 Batch    0/5   train_loss = 1.745\n",
-      "Epoch 360 Batch    0/5   train_loss = 1.735\n",
-      "Epoch 362 Batch    0/5   train_loss = 1.718\n",
-      "Epoch 364 Batch    0/5   train_loss = 1.747\n",
-      "Epoch 366 Batch    0/5   train_loss = 1.726\n",
-      "Epoch 368 Batch    0/5   train_loss = 1.753\n",
-      "Epoch 370 Batch    0/5   train_loss = 1.703\n",
-      "Epoch 372 Batch    0/5   train_loss = 1.662\n",
-      "Epoch 374 Batch    0/5   train_loss = 1.643\n",
-      "Epoch 376 Batch    0/5   train_loss = 1.624\n",
-      "Epoch 378 Batch    0/5   train_loss = 1.617\n",
-      "Epoch 380 Batch    0/5   train_loss = 1.598\n",
-      "Epoch 382 Batch    0/5   train_loss = 1.613\n",
-      "Epoch 384 Batch    0/5   train_loss = 1.601\n",
-      "Epoch 386 Batch    0/5   train_loss = 1.584\n",
-      "Epoch 388 Batch    0/5   train_loss = 1.569\n",
-      "Epoch 390 Batch    0/5   train_loss = 1.557\n",
-      "Epoch 392 Batch    0/5   train_loss = 1.534\n",
-      "Epoch 394 Batch    0/5   train_loss = 1.534\n",
-      "Epoch 396 Batch    0/5   train_loss = 1.520\n",
-      "Epoch 398 Batch    0/5   train_loss = 1.547\n",
-      "Epoch 400 Batch    0/5   train_loss = 1.545\n",
-      "Epoch 402 Batch    0/5   train_loss = 1.521\n",
-      "Epoch 404 Batch    0/5   train_loss = 1.486\n",
-      "Epoch 406 Batch    0/5   train_loss = 1.469\n",
-      "Epoch 408 Batch    0/5   train_loss = 1.458\n",
-      "Epoch 410 Batch    0/5   train_loss = 1.442\n",
-      "Epoch 412 Batch    0/5   train_loss = 1.431\n",
-      "Epoch 414 Batch    0/5   train_loss = 1.410\n",
-      "Epoch 416 Batch    0/5   train_loss = 1.411\n",
-      "Epoch 418 Batch    0/5   train_loss = 1.412\n",
-      "Epoch 420 Batch    0/5   train_loss = 1.398\n",
-      "Epoch 422 Batch    0/5   train_loss = 1.417\n",
-      "Epoch 424 Batch    0/5   train_loss = 1.381\n",
-      "Epoch 426 Batch    0/5   train_loss = 1.355\n",
-      "Epoch 428 Batch    0/5   train_loss = 1.354\n",
-      "Epoch 430 Batch    0/5   train_loss = 1.338\n",
-      "Epoch 432 Batch    0/5   train_loss = 1.321\n",
-      "Epoch 434 Batch    0/5   train_loss = 1.326\n",
-      "Epoch 436 Batch    0/5   train_loss = 1.324\n",
-      "Epoch 438 Batch    0/5   train_loss = 1.314\n",
-      "Epoch 440 Batch    0/5   train_loss = 1.292\n",
-      "Epoch 442 Batch    0/5   train_loss = 1.279\n",
-      "Epoch 444 Batch    0/5   train_loss = 1.259\n",
-      "Epoch 446 Batch    0/5   train_loss = 1.283\n",
-      "Epoch 448 Batch    0/5   train_loss = 1.274\n",
-      "Epoch 450 Batch    0/5   train_loss = 1.251\n",
-      "Epoch 452 Batch    0/5   train_loss = 1.279\n",
-      "Epoch 454 Batch    0/5   train_loss = 1.249\n",
-      "Epoch 456 Batch    0/5   train_loss = 1.214\n",
-      "Epoch 458 Batch    0/5   train_loss = 1.196\n",
-      "Epoch 460 Batch    0/5   train_loss = 1.185\n",
-      "Epoch 462 Batch    0/5   train_loss = 1.174\n",
-      "Epoch 464 Batch    0/5   train_loss = 1.158\n",
-      "Epoch 466 Batch    0/5   train_loss = 1.195\n",
-      "Epoch 468 Batch    0/5   train_loss = 1.158\n",
-      "Epoch 470 Batch    0/5   train_loss = 1.145\n",
-      "Epoch 472 Batch    0/5   train_loss = 1.160\n",
-      "Epoch 474 Batch    0/5   train_loss = 1.123\n",
-      "Epoch 476 Batch    0/5   train_loss = 1.118\n",
-      "Epoch 478 Batch    0/5   train_loss = 1.103\n",
-      "Epoch 480 Batch    0/5   train_loss = 1.088\n",
-      "Epoch 482 Batch    0/5   train_loss = 1.089\n",
-      "Epoch 484 Batch    0/5   train_loss = 1.094\n",
-      "Epoch 486 Batch    0/5   train_loss = 1.092\n",
-      "Epoch 488 Batch    0/5   train_loss = 1.106\n",
-      "Epoch 490 Batch    0/5   train_loss = 1.053\n",
-      "Epoch 492 Batch    0/5   train_loss = 1.052\n",
-      "Epoch 494 Batch    0/5   train_loss = 1.046\n",
-      "Epoch 496 Batch    0/5   train_loss = 1.030\n",
-      "Epoch 498 Batch    0/5   train_loss = 1.021\n",
-      "Epoch 500 Batch    0/5   train_loss = 1.020\n",
-      "Epoch 502 Batch    0/5   train_loss = 1.046\n",
-      "Epoch 504 Batch    0/5   train_loss = 1.040\n",
-      "Epoch 506 Batch    0/5   train_loss = 1.026\n",
-      "Epoch 508 Batch    0/5   train_loss = 0.982\n",
-      "Epoch 510 Batch    0/5   train_loss = 0.969\n",
-      "Epoch 512 Batch    0/5   train_loss = 0.962\n",
-      "Epoch 514 Batch    0/5   train_loss = 0.946\n",
-      "Epoch 516 Batch    0/5   train_loss = 0.941\n",
-      "Epoch 518 Batch    0/5   train_loss = 0.951\n",
-      "Epoch 520 Batch    0/5   train_loss = 0.945\n",
-      "Epoch 522 Batch    0/5   train_loss = 0.952\n",
-      "Epoch 524 Batch    0/5   train_loss = 0.931\n",
-      "Epoch 526 Batch    0/5   train_loss = 0.905\n",
-      "Epoch 528 Batch    0/5   train_loss = 0.893\n",
-      "Epoch 530 Batch    0/5   train_loss = 0.881\n",
-      "Epoch 532 Batch    0/5   train_loss = 0.882\n",
-      "Epoch 534 Batch    0/5   train_loss = 0.871\n",
-      "Epoch 536 Batch    0/5   train_loss = 0.904\n",
-      "Epoch 538 Batch    0/5   train_loss = 0.893\n",
-      "Epoch 540 Batch    0/5   train_loss = 0.884\n",
-      "Epoch 542 Batch    0/5   train_loss = 0.864\n",
-      "Epoch 544 Batch    0/5   train_loss = 0.854\n",
-      "Epoch 546 Batch    0/5   train_loss = 0.854\n",
-      "Epoch 548 Batch    0/5   train_loss = 0.836\n",
-      "Epoch 550 Batch    0/5   train_loss = 0.816\n",
-      "Epoch 552 Batch    0/5   train_loss = 0.829\n",
-      "Epoch 554 Batch    0/5   train_loss = 0.813\n",
-      "Epoch 556 Batch    0/5   train_loss = 0.798\n",
-      "Epoch 558 Batch    0/5   train_loss = 0.808\n",
-      "Epoch 560 Batch    0/5   train_loss = 0.789\n",
-      "Epoch 562 Batch    0/5   train_loss = 0.791\n",
-      "Epoch 564 Batch    0/5   train_loss = 0.779\n",
-      "Epoch 566 Batch    0/5   train_loss = 0.765\n",
-      "Epoch 568 Batch    0/5   train_loss = 0.746\n",
-      "Epoch 570 Batch    0/5   train_loss = 0.746\n",
-      "Epoch 572 Batch    0/5   train_loss = 0.733\n",
-      "Epoch 574 Batch    0/5   train_loss = 0.733\n",
-      "Epoch 576 Batch    0/5   train_loss = 0.752\n",
-      "Epoch 578 Batch    0/5   train_loss = 0.727\n",
-      "Epoch 580 Batch    0/5   train_loss = 0.712\n",
-      "Epoch 582 Batch    0/5   train_loss = 0.711\n",
-      "Epoch 584 Batch    0/5   train_loss = 0.708\n",
-      "Epoch 586 Batch    0/5   train_loss = 0.695\n",
-      "Epoch 588 Batch    0/5   train_loss = 0.699\n",
-      "Epoch 590 Batch    0/5   train_loss = 0.688\n",
-      "Epoch 592 Batch    0/5   train_loss = 0.682\n",
-      "Epoch 594 Batch    0/5   train_loss = 0.703\n",
-      "Epoch 596 Batch    0/5   train_loss = 0.681\n",
-      "Epoch 598 Batch    0/5   train_loss = 0.672\n",
-      "Epoch 600 Batch    0/5   train_loss = 0.678\n",
-      "Epoch 602 Batch    0/5   train_loss = 0.657\n",
-      "Epoch 604 Batch    0/5   train_loss = 0.652\n",
-      "Epoch 606 Batch    0/5   train_loss = 0.627\n",
-      "Epoch 608 Batch    0/5   train_loss = 0.623\n",
-      "Epoch 610 Batch    0/5   train_loss = 0.633\n",
-      "Epoch 612 Batch    0/5   train_loss = 0.608\n",
-      "Epoch 614 Batch    0/5   train_loss = 0.614\n",
-      "Epoch 616 Batch    0/5   train_loss = 0.620\n",
-      "Epoch 618 Batch    0/5   train_loss = 0.610\n",
-      "Epoch 620 Batch    0/5   train_loss = 0.596\n",
-      "Epoch 622 Batch    0/5   train_loss = 0.596\n",
-      "Epoch 624 Batch    0/5   train_loss = 0.605\n",
-      "Epoch 626 Batch    0/5   train_loss = 0.574\n",
-      "Epoch 628 Batch    0/5   train_loss = 0.581\n",
-      "Epoch 630 Batch    0/5   train_loss = 0.571\n",
-      "Epoch 632 Batch    0/5   train_loss = 0.563\n",
-      "Epoch 634 Batch    0/5   train_loss = 0.582\n",
-      "Epoch 636 Batch    0/5   train_loss = 0.579\n",
-      "Epoch 638 Batch    0/5   train_loss = 0.562\n",
-      "Epoch 640 Batch    0/5   train_loss = 0.549\n",
-      "Epoch 642 Batch    0/5   train_loss = 0.540\n",
-      "Epoch 644 Batch    0/5   train_loss = 0.520\n",
-      "Epoch 646 Batch    0/5   train_loss = 0.515\n",
-      "Epoch 648 Batch    0/5   train_loss = 0.509\n",
-      "Epoch 650 Batch    0/5   train_loss = 0.509\n",
-      "Epoch 652 Batch    0/5   train_loss = 0.527\n",
-      "Epoch 654 Batch    0/5   train_loss = 0.524\n",
-      "Epoch 656 Batch    0/5   train_loss = 0.509\n",
-      "Epoch 658 Batch    0/5   train_loss = 0.523\n",
-      "Epoch 660 Batch    0/5   train_loss = 0.502\n",
-      "Epoch 662 Batch    0/5   train_loss = 0.477\n",
-      "Epoch 664 Batch    0/5   train_loss = 0.473\n",
-      "Epoch 666 Batch    0/5   train_loss = 0.463\n",
-      "Epoch 668 Batch    0/5   train_loss = 0.457\n",
-      "Epoch 670 Batch    0/5   train_loss = 0.455\n",
-      "Epoch 672 Batch    0/5   train_loss = 0.459\n",
-      "Epoch 674 Batch    0/5   train_loss = 0.475\n",
-      "Epoch 676 Batch    0/5   train_loss = 0.471\n",
-      "Epoch 678 Batch    0/5   train_loss = 0.455\n",
-      "Epoch 680 Batch    0/5   train_loss = 0.443\n",
-      "Epoch 682 Batch    0/5   train_loss = 0.456\n",
-      "Epoch 684 Batch    0/5   train_loss = 0.440\n",
-      "Epoch 686 Batch    0/5   train_loss = 0.421\n",
-      "Epoch 688 Batch    0/5   train_loss = 0.413\n",
-      "Epoch 690 Batch    0/5   train_loss = 0.405\n",
-      "Epoch 692 Batch    0/5   train_loss = 0.401\n",
-      "Epoch 694 Batch    0/5   train_loss = 0.404\n",
-      "Epoch 696 Batch    0/5   train_loss = 0.400\n",
-      "Epoch 698 Batch    0/5   train_loss = 0.428\n",
-      "Epoch 700 Batch    0/5   train_loss = 0.451\n",
-      "Epoch 702 Batch    0/5   train_loss = 0.426\n",
-      "Epoch 704 Batch    0/5   train_loss = 0.410\n",
-      "Epoch 706 Batch    0/5   train_loss = 0.422\n",
-      "Epoch 708 Batch    0/5   train_loss = 0.398\n",
-      "Epoch 710 Batch    0/5   train_loss = 0.377\n",
-      "Epoch 712 Batch    0/5   train_loss = 0.368\n",
-      "Epoch 714 Batch    0/5   train_loss = 0.358\n",
-      "Epoch 716 Batch    0/5   train_loss = 0.352\n",
-      "Epoch 718 Batch    0/5   train_loss = 0.349\n",
-      "Epoch 720 Batch    0/5   train_loss = 0.344\n",
-      "Epoch 722 Batch    0/5   train_loss = 0.346\n",
-      "Epoch 724 Batch    0/5   train_loss = 0.345\n",
-      "Epoch 726 Batch    0/5   train_loss = 0.337\n",
-      "Epoch 728 Batch    0/5   train_loss = 0.345\n",
-      "Epoch 730 Batch    0/5   train_loss = 0.348\n",
-      "Epoch 732 Batch    0/5   train_loss = 0.358\n",
-      "Epoch 734 Batch    0/5   train_loss = 0.346\n",
-      "Epoch 736 Batch    0/5   train_loss = 0.337\n",
-      "Epoch 738 Batch    0/5   train_loss = 0.329\n",
-      "Epoch 740 Batch    0/5   train_loss = 0.320\n",
-      "Epoch 742 Batch    0/5   train_loss = 0.323\n",
-      "Epoch 744 Batch    0/5   train_loss = 0.316\n",
-      "Epoch 746 Batch    0/5   train_loss = 0.304\n",
-      "Epoch 748 Batch    0/5   train_loss = 0.299\n",
-      "Epoch 750 Batch    0/5   train_loss = 0.292\n",
-      "Epoch 752 Batch    0/5   train_loss = 0.288\n",
-      "Epoch 754 Batch    0/5   train_loss = 0.289\n",
-      "Epoch 756 Batch    0/5   train_loss = 0.284\n",
-      "Epoch 758 Batch    0/5   train_loss = 0.290\n",
-      "Epoch 760 Batch    0/5   train_loss = 0.304\n",
-      "Epoch 762 Batch    0/5   train_loss = 0.311\n",
-      "Epoch 764 Batch    0/5   train_loss = 0.405\n",
-      "Epoch 766 Batch    0/5   train_loss = 0.390\n",
-      "Epoch 768 Batch    0/5   train_loss = 0.344\n",
-      "Epoch 770 Batch    0/5   train_loss = 0.320\n",
-      "Epoch 772 Batch    0/5   train_loss = 0.280\n",
-      "Epoch 774 Batch    0/5   train_loss = 0.265\n",
-      "Epoch 776 Batch    0/5   train_loss = 0.258\n",
-      "Epoch 778 Batch    0/5   train_loss = 0.252\n",
-      "Epoch 780 Batch    0/5   train_loss = 0.247\n",
-      "Epoch 782 Batch    0/5   train_loss = 0.243\n",
-      "Epoch 784 Batch    0/5   train_loss = 0.240\n",
-      "Epoch 786 Batch    0/5   train_loss = 0.237\n",
-      "Epoch 788 Batch    0/5   train_loss = 0.233\n",
-      "Epoch 790 Batch    0/5   train_loss = 0.231\n",
-      "Epoch 792 Batch    0/5   train_loss = 0.229\n",
-      "Epoch 794 Batch    0/5   train_loss = 0.225\n",
-      "Epoch 796 Batch    0/5   train_loss = 0.230\n",
-      "Epoch 798 Batch    0/5   train_loss = 0.226\n",
-      "Epoch 800 Batch    0/5   train_loss = 0.222\n",
-      "Epoch 802 Batch    0/5   train_loss = 0.237\n",
-      "Epoch 804 Batch    0/5   train_loss = 0.225\n",
-      "Epoch 806 Batch    0/5   train_loss = 0.225\n",
-      "Epoch 808 Batch    0/5   train_loss = 0.245\n",
-      "Epoch 810 Batch    0/5   train_loss = 0.227\n",
-      "Epoch 812 Batch    0/5   train_loss = 0.210\n",
-      "Epoch 814 Batch    0/5   train_loss = 0.206\n",
-      "Epoch 816 Batch    0/5   train_loss = 0.202\n",
-      "Epoch 818 Batch    0/5   train_loss = 0.198\n",
-      "Epoch 820 Batch    0/5   train_loss = 0.195\n",
-      "Epoch 822 Batch    0/5   train_loss = 0.192\n",
-      "Epoch 824 Batch    0/5   train_loss = 0.189\n",
-      "Epoch 826 Batch    0/5   train_loss = 0.189\n",
-      "Epoch 828 Batch    0/5   train_loss = 0.187\n",
-      "Epoch 830 Batch    0/5   train_loss = 0.186\n",
-      "Epoch 832 Batch    0/5   train_loss = 0.187\n",
-      "Epoch 834 Batch    0/5   train_loss = 0.189\n",
-      "Epoch 836 Batch    0/5   train_loss = 0.189\n",
-      "Epoch 838 Batch    0/5   train_loss = 0.197\n",
-      "Epoch 840 Batch    0/5   train_loss = 0.207\n",
-      "Epoch 842 Batch    0/5   train_loss = 0.196\n",
-      "Epoch 844 Batch    0/5   train_loss = 0.187\n",
-      "Epoch 846 Batch    0/5   train_loss = 0.197\n",
-      "Epoch 848 Batch    0/5   train_loss = 0.189\n",
-      "Epoch 850 Batch    0/5   train_loss = 0.176\n",
-      "Epoch 852 Batch    0/5   train_loss = 0.171\n",
-      "Epoch 854 Batch    0/5   train_loss = 0.164\n",
-      "Epoch 856 Batch    0/5   train_loss = 0.161\n",
-      "Epoch 858 Batch    0/5   train_loss = 0.157\n",
-      "Epoch 860 Batch    0/5   train_loss = 0.154\n",
-      "Epoch 862 Batch    0/5   train_loss = 0.152\n",
-      "Epoch 864 Batch    0/5   train_loss = 0.150\n",
-      "Epoch 866 Batch    0/5   train_loss = 0.148\n",
-      "Epoch 868 Batch    0/5   train_loss = 0.146\n",
-      "Epoch 870 Batch    0/5   train_loss = 0.145\n",
-      "Epoch 872 Batch    0/5   train_loss = 0.145\n",
-      "Epoch 874 Batch    0/5   train_loss = 0.142\n",
-      "Epoch 876 Batch    0/5   train_loss = 0.143\n",
-      "Epoch 878 Batch    0/5   train_loss = 0.159\n",
-      "Epoch 880 Batch    0/5   train_loss = 0.145\n",
-      "Epoch 882 Batch    0/5   train_loss = 0.161\n",
-      "Epoch 884 Batch    0/5   train_loss = 0.211\n",
-      "Epoch 886 Batch    0/5   train_loss = 0.196\n",
-      "Epoch 888 Batch    0/5   train_loss = 0.335\n",
-      "Epoch 890 Batch    0/5   train_loss = 0.325\n",
-      "Epoch 892 Batch    0/5   train_loss = 0.279\n",
-      "Epoch 894 Batch    0/5   train_loss = 0.244\n",
-      "Epoch 896 Batch    0/5   train_loss = 0.214\n",
-      "Epoch 898 Batch    0/5   train_loss = 0.174\n",
-      "Epoch 900 Batch    0/5   train_loss = 0.147\n",
-      "Epoch 902 Batch    0/5   train_loss = 0.138\n",
-      "Epoch 904 Batch    0/5   train_loss = 0.131\n",
-      "Epoch 906 Batch    0/5   train_loss = 0.128\n",
-      "Epoch 908 Batch    0/5   train_loss = 0.125\n",
-      "Epoch 910 Batch    0/5   train_loss = 0.123\n",
-      "Epoch 912 Batch    0/5   train_loss = 0.121\n",
-      "Epoch 914 Batch    0/5   train_loss = 0.119\n",
-      "Epoch 916 Batch    0/5   train_loss = 0.117\n",
-      "Epoch 918 Batch    0/5   train_loss = 0.116\n",
-      "Epoch 920 Batch    0/5   train_loss = 0.114\n",
-      "Epoch 922 Batch    0/5   train_loss = 0.113\n",
-      "Epoch 924 Batch    0/5   train_loss = 0.112\n",
-      "Epoch 926 Batch    0/5   train_loss = 0.111\n",
-      "Epoch 928 Batch    0/5   train_loss = 0.109\n",
-      "Epoch 930 Batch    0/5   train_loss = 0.108\n",
-      "Epoch 932 Batch    0/5   train_loss = 0.107\n",
-      "Epoch 934 Batch    0/5   train_loss = 0.106\n",
-      "Epoch 936 Batch    0/5   train_loss = 0.105\n",
-      "Epoch 938 Batch    0/5   train_loss = 0.103\n",
-      "Epoch 940 Batch    0/5   train_loss = 0.102\n",
-      "Epoch 942 Batch    0/5   train_loss = 0.101\n",
-      "Epoch 944 Batch    0/5   train_loss = 0.100\n",
-      "Epoch 946 Batch    0/5   train_loss = 0.099\n",
-      "Epoch 948 Batch    0/5   train_loss = 0.098\n",
-      "Epoch 950 Batch    0/5   train_loss = 0.097\n",
-      "Epoch 952 Batch    0/5   train_loss = 0.096\n",
-      "Epoch 954 Batch    0/5   train_loss = 0.095\n",
-      "Epoch 956 Batch    0/5   train_loss = 0.094\n",
-      "Epoch 958 Batch    0/5   train_loss = 0.094\n",
-      "Epoch 960 Batch    0/5   train_loss = 0.093\n",
-      "Epoch 962 Batch    0/5   train_loss = 0.092\n",
-      "Epoch 964 Batch    0/5   train_loss = 0.091\n",
-      "Epoch 966 Batch    0/5   train_loss = 0.090\n",
-      "Epoch 968 Batch    0/5   train_loss = 0.089\n",
-      "Epoch 970 Batch    0/5   train_loss = 0.088\n",
-      "Epoch 972 Batch    0/5   train_loss = 0.088\n",
-      "Epoch 974 Batch    0/5   train_loss = 0.087\n",
-      "Epoch 976 Batch    0/5   train_loss = 0.086\n",
-      "Epoch 978 Batch    0/5   train_loss = 0.085\n",
-      "Epoch 980 Batch    0/5   train_loss = 0.084\n",
-      "Epoch 982 Batch    0/5   train_loss = 0.083\n",
-      "Epoch 984 Batch    0/5   train_loss = 0.083\n",
-      "Epoch 986 Batch    0/5   train_loss = 0.082\n",
-      "Epoch 988 Batch    0/5   train_loss = 0.081\n",
-      "Epoch 990 Batch    0/5   train_loss = 0.080\n",
-      "Epoch 992 Batch    0/5   train_loss = 0.080\n",
-      "Epoch 994 Batch    0/5   train_loss = 0.079\n",
-      "Epoch 996 Batch    0/5   train_loss = 0.078\n",
-      "Epoch 998 Batch    0/5   train_loss = 0.078\n",
-      "Model Trained and Saved\n"
+      "Epoch   0 Batch    0/49   train_loss = 8.823\n",
+      "Epoch   0 Batch   10/49   train_loss = 6.394\n",
+      "Epoch   0 Batch   20/49   train_loss = 6.288\n",
+      "Epoch   0 Batch   30/49   train_loss = 6.552\n",
+      "Epoch   0 Batch   40/49   train_loss = 6.792\n",
+      "Epoch   1 Batch    1/49   train_loss = 6.129\n",
+      "Epoch   1 Batch   11/49   train_loss = 6.013\n",
+      "Epoch   1 Batch   21/49   train_loss = 6.131\n",
+      "Epoch   1 Batch   31/49   train_loss = 6.094\n",
+      "Epoch   1 Batch   41/49   train_loss = 6.035\n",
+      "Epoch   2 Batch    2/49   train_loss = 5.961\n",
+      "Epoch   2 Batch   12/49   train_loss = 6.199\n",
+      "Epoch   2 Batch   22/49   train_loss = 6.037\n",
+      "Epoch   2 Batch   32/49   train_loss = 6.311\n",
+      "Epoch   2 Batch   42/49   train_loss = 6.187\n",
+      "Epoch   3 Batch    3/49   train_loss = 6.182\n",
+      "Epoch   3 Batch   13/49   train_loss = 6.023\n",
+      "Epoch   3 Batch   23/49   train_loss = 5.968\n",
+      "Epoch   3 Batch   33/49   train_loss = 5.981\n",
+      "Epoch   3 Batch   43/49   train_loss = 6.098\n",
+      "Epoch   4 Batch    4/49   train_loss = 6.104\n",
+      "Epoch   4 Batch   14/49   train_loss = 5.956\n",
+      "Epoch   4 Batch   24/49   train_loss = 6.170\n",
+      "Epoch   4 Batch   34/49   train_loss = 6.176\n",
+      "Epoch   4 Batch   44/49   train_loss = 6.136\n",
+      "Epoch   5 Batch    5/49   train_loss = 6.161\n",
+      "Epoch   5 Batch   15/49   train_loss = 6.067\n",
+      "Epoch   5 Batch   25/49   train_loss = 6.108\n",
+      "Epoch   5 Batch   35/49   train_loss = 6.007\n",
+      "Epoch   5 Batch   45/49   train_loss = 6.007\n",
+      "Epoch   6 Batch    6/49   train_loss = 5.833\n",
+      "Epoch   6 Batch   16/49   train_loss = 6.090\n",
+      "Epoch   6 Batch   26/49   train_loss = 6.225\n",
+      "Epoch   6 Batch   36/49   train_loss = 6.081\n",
+      "Epoch   6 Batch   46/49   train_loss = 6.062\n",
+      "Epoch   7 Batch    7/49   train_loss = 6.026\n",
+      "Epoch   7 Batch   17/49   train_loss = 5.683\n",
+      "Epoch   7 Batch   27/49   train_loss = 6.055\n",
+      "Epoch   7 Batch   37/49   train_loss = 5.834\n",
+      "Epoch   7 Batch   47/49   train_loss = 6.057\n",
+      "Epoch   8 Batch    8/49   train_loss = 5.782\n",
+      "Epoch   8 Batch   18/49   train_loss = 5.966\n",
+      "Epoch   8 Batch   28/49   train_loss = 6.036\n",
+      "Epoch   8 Batch   38/49   train_loss = 6.007\n",
+      "Epoch   8 Batch   48/49   train_loss = 6.105\n",
+      "Epoch   9 Batch    9/49   train_loss = 5.654\n",
+      "Epoch   9 Batch   19/49   train_loss = 5.723\n",
+      "Epoch   9 Batch   29/49   train_loss = 5.926\n",
+      "Epoch   9 Batch   39/49   train_loss = 5.998\n",
+      "Epoch  10 Batch    0/49   train_loss = 5.719\n",
+      "Epoch  10 Batch   10/49   train_loss = 5.906\n",
+      "Epoch  10 Batch   20/49   train_loss = 5.788\n",
+      "Epoch  10 Batch   30/49   train_loss = 5.764\n",
+      "Epoch  10 Batch   40/49   train_loss = 5.994\n",
+      "Epoch  11 Batch    1/49   train_loss = 5.807\n",
+      "Epoch  11 Batch   11/49   train_loss = 5.796\n",
+      "Epoch  11 Batch   21/49   train_loss = 5.863\n",
+      "Epoch  11 Batch   31/49   train_loss = 5.897\n",
+      "Epoch  11 Batch   41/49   train_loss = 5.779\n",
+      "Epoch  12 Batch    2/49   train_loss = 5.720\n",
+      "Epoch  12 Batch   12/49   train_loss = 5.869\n",
+      "Epoch  12 Batch   22/49   train_loss = 5.764\n",
+      "Epoch  12 Batch   32/49   train_loss = 5.961\n",
+      "Epoch  12 Batch   42/49   train_loss = 5.880\n",
+      "Epoch  13 Batch    3/49   train_loss = 5.819\n",
+      "Epoch  13 Batch   13/49   train_loss = 5.741\n",
+      "Epoch  13 Batch   23/49   train_loss = 5.707\n",
+      "Epoch  13 Batch   33/49   train_loss = 5.683\n",
+      "Epoch  13 Batch   43/49   train_loss = 5.723\n",
+      "Epoch  14 Batch    4/49   train_loss = 5.754\n",
+      "Epoch  14 Batch   14/49   train_loss = 5.721\n",
+      "Epoch  14 Batch   24/49   train_loss = 5.777\n",
+      "Epoch  14 Batch   34/49   train_loss = 5.893\n",
+      "Epoch  14 Batch   44/49   train_loss = 5.771\n",
+      "Epoch  15 Batch    5/49   train_loss = 5.785\n",
+      "Epoch  15 Batch   15/49   train_loss = 5.753\n",
+      "Epoch  15 Batch   25/49   train_loss = 5.703\n",
+      "Epoch  15 Batch   35/49   train_loss = 5.638\n",
+      "Epoch  15 Batch   45/49   train_loss = 5.638\n",
+      "Epoch  16 Batch    6/49   train_loss = 5.463\n",
+      "Epoch  16 Batch   16/49   train_loss = 5.703\n",
+      "Epoch  16 Batch   26/49   train_loss = 5.689\n",
+      "Epoch  16 Batch   36/49   train_loss = 5.740\n",
+      "Epoch  16 Batch   46/49   train_loss = 5.701\n",
+      "Epoch  17 Batch    7/49   train_loss = 5.635\n",
+      "Epoch  17 Batch   17/49   train_loss = 5.377\n",
+      "Epoch  17 Batch   27/49   train_loss = 5.638\n",
+      "Epoch  17 Batch   37/49   train_loss = 5.471\n",
+      "Epoch  17 Batch   47/49   train_loss = 5.639\n",
+      "Epoch  18 Batch    8/49   train_loss = 5.518\n",
+      "Epoch  18 Batch   18/49   train_loss = 5.474\n",
+      "Epoch  18 Batch   28/49   train_loss = 5.561\n",
+      "Epoch  18 Batch   38/49   train_loss = 5.609\n",
+      "Epoch  18 Batch   48/49   train_loss = 5.630\n"
+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-74-396c263e7e73>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     16\u001b[0m                 \u001b[0minitial_state\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m                 lr: learning_rate}\n\u001b[0;32m---> 18\u001b[0;31m             \u001b[0mtrain_loss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcost\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfinal_state\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_op\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     19\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m             \u001b[0;31m# Show every <show_every_n_batches> batches\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/home/carnd/anaconda3/envs/dl/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    765\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    766\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 767\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    768\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    769\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/home/carnd/anaconda3/envs/dl/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    963\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    964\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m--> 965\u001b[0;31m                              feed_dict_string, options, run_metadata)\n\u001b[0m\u001b[1;32m    966\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    967\u001b[0m       \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/home/carnd/anaconda3/envs/dl/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1013\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1014\u001b[0m       return self._do_call(_run_fn, self._session, feed_dict, fetch_list,\n\u001b[0;32m-> 1015\u001b[0;31m                            target_list, options, run_metadata)\n\u001b[0m\u001b[1;32m   1016\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1017\u001b[0m       return self._do_call(_prun_fn, self._session, handle, feed_dict,\n",
+      "\u001b[0;32m/home/carnd/anaconda3/envs/dl/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m   1020\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1021\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1022\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1023\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1024\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/home/carnd/anaconda3/envs/dl/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(session, feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m   1002\u001b[0m         return tf_session.TF_Run(session, options,\n\u001b[1;32m   1003\u001b[0m                                  \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1004\u001b[0;31m                                  status, run_metadata)\n\u001b[0m\u001b[1;32m   1005\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1006\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
      ]
     }
    ],
@@ -1992,6 +1654,62 @@
     "tests.test_get_tensors(get_tensors)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 511,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(3,)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 511,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "t = np.array([1,2,3])\n",
+    "print(t.shape)\n",
+    "np.extract(t==3, t)[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 458,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 458,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -2005,7 +1723,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 274,
+   "execution_count": 472,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -2016,7 +1734,20 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Tests Passed\n"
+      "0.8\n"
+     ]
+    },
+    {
+     "ename": "IndexError",
+     "evalue": "only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-472-ec56654dfaa2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0mDON\u001b[0m\u001b[0;31m'\u001b[0m\u001b[0mT\u001b[0m \u001b[0mMODIFY\u001b[0m \u001b[0mANYTHING\u001b[0m \u001b[0mIN\u001b[0m \u001b[0mTHIS\u001b[0m \u001b[0mCELL\u001b[0m \u001b[0mTHAT\u001b[0m \u001b[0mIS\u001b[0m \u001b[0mBELOW\u001b[0m \u001b[0mTHIS\u001b[0m \u001b[0mLINE\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m \"\"\"\n\u001b[0;32m---> 18\u001b[0;31m \u001b[0mtests\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtest_pick_word\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpick_word\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m/home/spike/ml/udacity/nd101/deep-learning-modified/tv-script-generation/problem_unittests.py\u001b[0m in \u001b[0;36mtest_pick_word\u001b[0;34m(pick_word)\u001b[0m\n\u001b[1;32m    283\u001b[0m         \u001b[0mtest_int_to_vocab\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mword_i\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mword\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mword_i\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mword\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'this'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'is'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'a'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'test'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    284\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 285\u001b[0;31m         \u001b[0mpred_word\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpick_word\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_probabilities\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_int_to_vocab\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    286\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    287\u001b[0m         \u001b[0;31m# Check type\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-472-ec56654dfaa2>\u001b[0m in \u001b[0;36mpick_word\u001b[0;34m(probabilities, int_to_vocab)\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchoice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprobabilities\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mprobabilities\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mint_to_vocab\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mprobabilities\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchoice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprobabilities\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mprobabilities\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mIndexError\u001b[0m: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices"
      ]
     }
    ],
@@ -2029,9 +1760,10 @@
     "    :return: String of the predicted word\n",
     "    \"\"\"\n",
     "    \n",
-    "    word = int_to_vocab[np.argmax(probabilities)]\n",
+    "    word_p = np.random.choice(probabilities,p=probabilities)\n",
+    "    word = np.extract(probabilities==word_p, probabilities)\n",
     "    \n",
-    "    return word\n",
+    "    return int_to_vocab[probabilities[np.random.choice(probabilities,p=probabilities)]]\n",
     "\n",
     "\n",
     "\"\"\"\n",
@@ -2146,7 +1878,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.5.2"
   },
   "toc": {
    "colors": {
diff --git a/tv-script-generation/__pycache__/problem_unittests.cpython-35.pyc b/tv-script-generation/__pycache__/problem_unittests.cpython-35.pyc
index f0ca97b..0e2a09b 100644
Binary files a/tv-script-generation/__pycache__/problem_unittests.cpython-35.pyc and b/tv-script-generation/__pycache__/problem_unittests.cpython-35.pyc differ
diff --git a/tv-script-generation/dlnd_tv_script_generation.ipynb b/tv-script-generation/dlnd_tv_script_generation.ipynb
index ac665fa..2a55cc9 100644
--- a/tv-script-generation/dlnd_tv_script_generation.ipynb
+++ b/tv-script-generation/dlnd_tv_script_generation.ipynb
@@ -15,7 +15,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 1,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -47,7 +47,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 2,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -104,9 +104,37 @@
     "print('\\n'.join(text.split('\\n')[view_sentence_range[0]:view_sentence_range[1]]))"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "00:00.0 Host bridge: Intel Corporation 440FX - 82441FX PMC [Natoma] (rev 02)\r\n",
+      "00:01.0 ISA bridge: Intel Corporation 82371SB PIIX3 ISA [Natoma/Triton II]\r\n",
+      "00:01.1 IDE interface: Intel Corporation 82371SB PIIX3 IDE [Natoma/Triton II]\r\n",
+      "00:01.3 Bridge: Intel Corporation 82371AB/EB/MB PIIX4 ACPI (rev 01)\r\n",
+      "00:02.0 VGA compatible controller: Cirrus Logic GD 5446\r\n",
+      "00:03.0 VGA compatible controller: NVIDIA Corporation GK104GL [GRID K520] (rev a1)\r\n",
+      "00:1f.0 Unassigned class [ff80]: XenSource, Inc. Xen Platform Device (rev 01)\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!lspci"
+   ]
+  },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "#### Exploring Sentence Lengths \n",
     "\n",
@@ -115,9 +143,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 320,
+   "execution_count": 10,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [
     {
@@ -126,7 +156,7 @@
        "\n",
        "    <div class=\"bk-root\">\n",
        "        <a href=\"http://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
-       "        <span id=\"ac8cada1-716f-46a2-a8f6-437383d8c041\">Loading BokehJS ...</span>\n",
+       "        <span id=\"5d944cec-21f7-43a6-9d66-027ae50d0773\">Loading BokehJS ...</span>\n",
        "    </div>"
       ]
      },
@@ -174,7 +204,7 @@
        "\n",
        "  function display_loaded() {\n",
        "    if (window.Bokeh !== undefined) {\n",
-       "      document.getElementById(\"ac8cada1-716f-46a2-a8f6-437383d8c041\").textContent = \"BokehJS successfully loaded.\";\n",
+       "      document.getElementById(\"5d944cec-21f7-43a6-9d66-027ae50d0773\").textContent = \"BokehJS successfully loaded.\";\n",
        "    } else if (Date.now() < window._bokeh_timeout) {\n",
        "      setTimeout(display_loaded, 100)\n",
        "    }\n",
@@ -216,9 +246,9 @@
        "      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
        "      document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
        "    }\n",
-       "  };var element = document.getElementById(\"ac8cada1-716f-46a2-a8f6-437383d8c041\");\n",
+       "  };var element = document.getElementById(\"5d944cec-21f7-43a6-9d66-027ae50d0773\");\n",
        "  if (element == null) {\n",
-       "    console.log(\"Bokeh: ERROR: autoload.js configured with elementid 'ac8cada1-716f-46a2-a8f6-437383d8c041' but no matching script tag was found. \")\n",
+       "    console.log(\"Bokeh: ERROR: autoload.js configured with elementid '5d944cec-21f7-43a6-9d66-027ae50d0773' but no matching script tag was found. \")\n",
        "    return false;\n",
        "  }\n",
        "\n",
@@ -231,7 +261,7 @@
        "    \n",
        "    function(Bokeh) {\n",
        "      \n",
-       "      document.getElementById(\"ac8cada1-716f-46a2-a8f6-437383d8c041\").textContent = \"BokehJS is loading...\";\n",
+       "      document.getElementById(\"5d944cec-21f7-43a6-9d66-027ae50d0773\").textContent = \"BokehJS is loading...\";\n",
        "    },\n",
        "    function(Bokeh) {\n",
        "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.4.min.css\");\n",
@@ -254,7 +284,7 @@
        "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
        "      window._bokeh_failed_load = true;\n",
        "    } else if (force !== true) {\n",
-       "      var cell = $(document.getElementById(\"ac8cada1-716f-46a2-a8f6-437383d8c041\")).parents('.cell').data().cell;\n",
+       "      var cell = $(document.getElementById(\"5d944cec-21f7-43a6-9d66-027ae50d0773\")).parents('.cell').data().cell;\n",
        "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
        "    }\n",
        "\n",
@@ -281,7 +311,7 @@
        "\n",
        "\n",
        "    <div class=\"bk-root\">\n",
-       "        <div class=\"bk-plotdiv\" id=\"e1217a82-2456-48d8-b142-efd9a4b024a6\"></div>\n",
+       "        <div class=\"bk-plotdiv\" id=\"46433a97-e836-4db8-9dbd-6723adbab9ef\"></div>\n",
        "    </div>\n",
        "<script type=\"text/javascript\">\n",
        "  \n",
@@ -322,7 +352,7 @@
        "  \n",
        "    function display_loaded() {\n",
        "      if (window.Bokeh !== undefined) {\n",
-       "        document.getElementById(\"e1217a82-2456-48d8-b142-efd9a4b024a6\").textContent = \"BokehJS successfully loaded.\";\n",
+       "        document.getElementById(\"46433a97-e836-4db8-9dbd-6723adbab9ef\").textContent = \"BokehJS successfully loaded.\";\n",
        "      } else if (Date.now() < window._bokeh_timeout) {\n",
        "        setTimeout(display_loaded, 100)\n",
        "      }\n",
@@ -364,9 +394,9 @@
        "        console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
        "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
        "      }\n",
-       "    };var element = document.getElementById(\"e1217a82-2456-48d8-b142-efd9a4b024a6\");\n",
+       "    };var element = document.getElementById(\"46433a97-e836-4db8-9dbd-6723adbab9ef\");\n",
        "    if (element == null) {\n",
-       "      console.log(\"Bokeh: ERROR: autoload.js configured with elementid 'e1217a82-2456-48d8-b142-efd9a4b024a6' but no matching script tag was found. \")\n",
+       "      console.log(\"Bokeh: ERROR: autoload.js configured with elementid '46433a97-e836-4db8-9dbd-6723adbab9ef' but no matching script tag was found. \")\n",
        "      return false;\n",
        "    }\n",
        "  \n",
@@ -376,8 +406,8 @@
        "      function(Bokeh) {\n",
        "        (function() {\n",
        "          var fn = function() {\n",
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+       "            var render_items = [{\"docid\":\"b315479b-aa1c-4b1d-a62f-5f3345d28e0e\",\"elementid\":\"46433a97-e836-4db8-9dbd-6723adbab9ef\",\"modelid\":\"197186d2-75e3-4931-8078-3a33f3d5ec66\"}];\n",
        "            \n",
        "            Bokeh.embed.embed_items(docs_json, render_items);\n",
        "          };\n",
@@ -402,7 +432,7 @@
        "        console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
        "        window._bokeh_failed_load = true;\n",
        "      } else if (force !== true) {\n",
-       "        var cell = $(document.getElementById(\"e1217a82-2456-48d8-b142-efd9a4b024a6\")).parents('.cell').data().cell;\n",
+       "        var cell = $(document.getElementById(\"46433a97-e836-4db8-9dbd-6723adbab9ef\")).parents('.cell').data().cell;\n",
        "        cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
        "      }\n",
        "  \n",
@@ -480,7 +510,7 @@
   },
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+   "execution_count": 14,
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@@ -546,7 +576,7 @@
   },
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@@ -600,7 +630,7 @@
   },
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@@ -628,7 +658,7 @@
   },
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@@ -658,7 +688,7 @@
   },
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@@ -702,7 +732,7 @@
   },
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@@ -755,7 +785,7 @@
   },
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@@ -812,7 +842,7 @@
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@@ -871,7 +901,7 @@
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@@ -928,7 +958,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 228,
+   "execution_count": 23,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -986,7 +1016,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 231,
+   "execution_count": 24,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -1117,11 +1147,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 455,
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 4, 2, 3])"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "t = np.array([1,2,3,4])\n",
+    "np.random.shuffle(t)\n",
+    "t"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "scrolled": true
    },
    "outputs": [
     {
@@ -1142,11 +1197,11 @@
     "    :return: Batches as a Numpy array\n",
     "    \"\"\"\n",
     "    \n",
-    "    slice_size = batch_size * seq_length\n",
+    "    slice_size = int(batch_size * seq_length)\n",
     "    n_batches = int(len(int_text)/slice_size)\n",
     "    \n",
     "    assert n_batches > 0, 'Maybe your batch size is too big ?'\n",
-    "    \n",
+    "\n",
     "    # input part\n",
     "    _inputs = np.array(int_text[:n_batches*slice_size])\n",
     "    \n",
@@ -1160,18 +1215,17 @@
     "    \n",
     "    # concat inputs and targets\n",
     "    batches = np.c_[inputs, targets]\n",
-    "    #print(batches.shape)\n",
+    "    \n",
     "    \n",
     "    # reshape into [2 * inputs]\n",
     "    batches = batches.reshape((2, batch_size, -1))\n",
-    "    #print('after reshape 2,batch_size,-1')\n",
     "    #print(batches)\n",
     "    \n",
     "    \n",
     "    # Shuffle the batches of sequences without disturbing each sequence order\n",
     "    # We are using transpose to bring the batch into axes 0 and be able to shuffle it\n",
     "    np.random.shuffle(np.transpose(batches, (1,2,0)))\n",
-    "    #print(batches)\n",
+    "    \n",
     "    \n",
     "    # Reshape into final batches output\n",
     "    batches = batches.reshape((-1, 2, batch_size, seq_length))\n",
@@ -1209,7 +1263,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 317,
+   "execution_count": 71,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -1222,12 +1276,12 @@
     "# Batch Size\n",
     "batch_size = 128\n",
     "# RNN Size\n",
-    "rnn_size = 70\n",
+    "rnn_size = 100\n",
     "# Sequence Length\n",
     "# Use the mean of sentences length as sequence length\n",
-    "seq_length = np.floor(s_lengths.mean())\n",
+    "seq_length = int(s_lengths.mean())\n",
     "# Learning Rate\n",
-    "learning_rate = 1e-3\n",
+    "learning_rate = 1e-2\n",
     "# Show stats for every n number of batches\n",
     "show_every_n_batches = 10\n",
     "\n",
@@ -1250,7 +1304,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 235,
+   "execution_count": 53,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -1299,7 +1353,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 238,
+   "execution_count": 36,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -1326,7 +1380,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 197,
+   "execution_count": 74,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -1337,508 +1391,116 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "5\n",
-      "Epoch   0 Batch    0/5   train_loss = 8.825\n",
-      "Epoch   2 Batch    0/5   train_loss = 6.441\n",
-      "Epoch   4 Batch    0/5   train_loss = 6.023\n",
-      "Epoch   6 Batch    0/5   train_loss = 5.927\n",
-      "Epoch   8 Batch    0/5   train_loss = 5.903\n",
-      "Epoch  10 Batch    0/5   train_loss = 5.883\n",
-      "Epoch  12 Batch    0/5   train_loss = 5.874\n",
-      "Epoch  14 Batch    0/5   train_loss = 5.858\n",
-      "Epoch  16 Batch    0/5   train_loss = 5.833\n",
-      "Epoch  18 Batch    0/5   train_loss = 5.794\n",
-      "Epoch  20 Batch    0/5   train_loss = 5.739\n",
-      "Epoch  22 Batch    0/5   train_loss = 5.682\n",
-      "Epoch  24 Batch    0/5   train_loss = 5.626\n",
-      "Epoch  26 Batch    0/5   train_loss = 5.572\n",
-      "Epoch  28 Batch    0/5   train_loss = 5.521\n",
-      "Epoch  30 Batch    0/5   train_loss = 5.471\n",
-      "Epoch  32 Batch    0/5   train_loss = 5.421\n",
-      "Epoch  34 Batch    0/5   train_loss = 5.365\n",
-      "Epoch  36 Batch    0/5   train_loss = 5.304\n",
-      "Epoch  38 Batch    0/5   train_loss = 5.244\n",
-      "Epoch  40 Batch    0/5   train_loss = 5.185\n",
-      "Epoch  42 Batch    0/5   train_loss = 5.124\n",
-      "Epoch  44 Batch    0/5   train_loss = 5.063\n",
-      "Epoch  46 Batch    0/5   train_loss = 5.003\n",
-      "Epoch  48 Batch    0/5   train_loss = 4.945\n",
-      "Epoch  50 Batch    0/5   train_loss = 4.891\n",
-      "Epoch  52 Batch    0/5   train_loss = 4.841\n",
-      "Epoch  54 Batch    0/5   train_loss = 4.794\n",
-      "Epoch  56 Batch    0/5   train_loss = 4.751\n",
-      "Epoch  58 Batch    0/5   train_loss = 4.710\n",
-      "Epoch  60 Batch    0/5   train_loss = 4.669\n",
-      "Epoch  62 Batch    0/5   train_loss = 4.638\n",
-      "Epoch  64 Batch    0/5   train_loss = 4.638\n",
-      "Epoch  66 Batch    0/5   train_loss = 4.589\n",
-      "Epoch  68 Batch    0/5   train_loss = 4.537\n",
-      "Epoch  70 Batch    0/5   train_loss = 4.501\n",
-      "Epoch  72 Batch    0/5   train_loss = 4.469\n",
-      "Epoch  74 Batch    0/5   train_loss = 4.436\n",
-      "Epoch  76 Batch    0/5   train_loss = 4.405\n",
-      "Epoch  78 Batch    0/5   train_loss = 4.375\n",
-      "Epoch  80 Batch    0/5   train_loss = 4.344\n",
-      "Epoch  82 Batch    0/5   train_loss = 4.363\n",
-      "Epoch  84 Batch    0/5   train_loss = 4.311\n",
-      "Epoch  86 Batch    0/5   train_loss = 4.274\n",
-      "Epoch  88 Batch    0/5   train_loss = 4.240\n",
-      "Epoch  90 Batch    0/5   train_loss = 4.211\n",
-      "Epoch  92 Batch    0/5   train_loss = 4.182\n",
-      "Epoch  94 Batch    0/5   train_loss = 4.155\n",
-      "Epoch  96 Batch    0/5   train_loss = 4.135\n",
-      "Epoch  98 Batch    0/5   train_loss = 4.107\n",
-      "Epoch 100 Batch    0/5   train_loss = 4.093\n",
-      "Epoch 102 Batch    0/5   train_loss = 4.053\n",
-      "Epoch 104 Batch    0/5   train_loss = 4.030\n",
-      "Epoch 106 Batch    0/5   train_loss = 4.002\n",
-      "Epoch 108 Batch    0/5   train_loss = 3.978\n",
-      "Epoch 110 Batch    0/5   train_loss = 3.951\n",
-      "Epoch 112 Batch    0/5   train_loss = 3.928\n",
-      "Epoch 114 Batch    0/5   train_loss = 3.902\n",
-      "Epoch 116 Batch    0/5   train_loss = 3.884\n",
-      "Epoch 118 Batch    0/5   train_loss = 3.862\n",
-      "Epoch 120 Batch    0/5   train_loss = 3.840\n",
-      "Epoch 122 Batch    0/5   train_loss = 3.814\n",
-      "Epoch 124 Batch    0/5   train_loss = 3.803\n",
-      "Epoch 126 Batch    0/5   train_loss = 3.775\n",
-      "Epoch 128 Batch    0/5   train_loss = 3.738\n",
-      "Epoch 130 Batch    0/5   train_loss = 3.714\n",
-      "Epoch 132 Batch    0/5   train_loss = 3.690\n",
-      "Epoch 134 Batch    0/5   train_loss = 3.665\n",
-      "Epoch 136 Batch    0/5   train_loss = 3.642\n",
-      "Epoch 138 Batch    0/5   train_loss = 3.619\n",
-      "Epoch 140 Batch    0/5   train_loss = 3.596\n",
-      "Epoch 142 Batch    0/5   train_loss = 3.577\n",
-      "Epoch 144 Batch    0/5   train_loss = 3.588\n",
-      "Epoch 146 Batch    0/5   train_loss = 3.561\n",
-      "Epoch 148 Batch    0/5   train_loss = 3.537\n",
-      "Epoch 150 Batch    0/5   train_loss = 3.494\n",
-      "Epoch 152 Batch    0/5   train_loss = 3.475\n",
-      "Epoch 154 Batch    0/5   train_loss = 3.444\n",
-      "Epoch 156 Batch    0/5   train_loss = 3.431\n",
-      "Epoch 158 Batch    0/5   train_loss = 3.403\n",
-      "Epoch 160 Batch    0/5   train_loss = 3.393\n",
-      "Epoch 162 Batch    0/5   train_loss = 3.371\n",
-      "Epoch 164 Batch    0/5   train_loss = 3.352\n",
-      "Epoch 166 Batch    0/5   train_loss = 3.323\n",
-      "Epoch 168 Batch    0/5   train_loss = 3.328\n",
-      "Epoch 170 Batch    0/5   train_loss = 3.281\n",
-      "Epoch 172 Batch    0/5   train_loss = 3.261\n",
-      "Epoch 174 Batch    0/5   train_loss = 3.238\n",
-      "Epoch 176 Batch    0/5   train_loss = 3.216\n",
-      "Epoch 178 Batch    0/5   train_loss = 3.197\n",
-      "Epoch 180 Batch    0/5   train_loss = 3.172\n",
-      "Epoch 182 Batch    0/5   train_loss = 3.169\n",
-      "Epoch 184 Batch    0/5   train_loss = 3.140\n",
-      "Epoch 186 Batch    0/5   train_loss = 3.136\n",
-      "Epoch 188 Batch    0/5   train_loss = 3.145\n",
-      "Epoch 190 Batch    0/5   train_loss = 3.106\n",
-      "Epoch 192 Batch    0/5   train_loss = 3.069\n",
-      "Epoch 194 Batch    0/5   train_loss = 3.038\n",
-      "Epoch 196 Batch    0/5   train_loss = 3.019\n",
-      "Epoch 198 Batch    0/5   train_loss = 2.995\n",
-      "Epoch 200 Batch    0/5   train_loss = 2.979\n",
-      "Epoch 202 Batch    0/5   train_loss = 2.960\n",
-      "Epoch 204 Batch    0/5   train_loss = 2.943\n",
-      "Epoch 206 Batch    0/5   train_loss = 2.963\n",
-      "Epoch 208 Batch    0/5   train_loss = 2.917\n",
-      "Epoch 210 Batch    0/5   train_loss = 2.898\n",
-      "Epoch 212 Batch    0/5   train_loss = 2.867\n",
-      "Epoch 214 Batch    0/5   train_loss = 2.863\n",
-      "Epoch 216 Batch    0/5   train_loss = 2.834\n",
-      "Epoch 218 Batch    0/5   train_loss = 2.809\n",
-      "Epoch 220 Batch    0/5   train_loss = 2.797\n",
-      "Epoch 222 Batch    0/5   train_loss = 2.774\n",
-      "Epoch 224 Batch    0/5   train_loss = 2.759\n",
-      "Epoch 226 Batch    0/5   train_loss = 2.732\n",
-      "Epoch 228 Batch    0/5   train_loss = 2.742\n",
-      "Epoch 230 Batch    0/5   train_loss = 2.704\n",
-      "Epoch 232 Batch    0/5   train_loss = 2.703\n",
-      "Epoch 234 Batch    0/5   train_loss = 2.663\n",
-      "Epoch 236 Batch    0/5   train_loss = 2.672\n",
-      "Epoch 238 Batch    0/5   train_loss = 2.638\n",
-      "Epoch 240 Batch    0/5   train_loss = 2.620\n",
-      "Epoch 242 Batch    0/5   train_loss = 2.595\n",
-      "Epoch 244 Batch    0/5   train_loss = 2.585\n",
-      "Epoch 246 Batch    0/5   train_loss = 2.563\n",
-      "Epoch 248 Batch    0/5   train_loss = 2.539\n",
-      "Epoch 250 Batch    0/5   train_loss = 2.534\n",
-      "Epoch 252 Batch    0/5   train_loss = 2.517\n",
-      "Epoch 254 Batch    0/5   train_loss = 2.497\n",
-      "Epoch 256 Batch    0/5   train_loss = 2.475\n",
-      "Epoch 258 Batch    0/5   train_loss = 2.463\n",
-      "Epoch 260 Batch    0/5   train_loss = 2.478\n",
-      "Epoch 262 Batch    0/5   train_loss = 2.450\n",
-      "Epoch 264 Batch    0/5   train_loss = 2.436\n",
-      "Epoch 266 Batch    0/5   train_loss = 2.417\n",
-      "Epoch 268 Batch    0/5   train_loss = 2.384\n",
-      "Epoch 270 Batch    0/5   train_loss = 2.363\n",
-      "Epoch 272 Batch    0/5   train_loss = 2.340\n",
-      "Epoch 274 Batch    0/5   train_loss = 2.323\n",
-      "Epoch 276 Batch    0/5   train_loss = 2.314\n",
-      "Epoch 278 Batch    0/5   train_loss = 2.302\n",
-      "Epoch 280 Batch    0/5   train_loss = 2.300\n",
-      "Epoch 282 Batch    0/5   train_loss = 2.300\n",
-      "Epoch 284 Batch    0/5   train_loss = 2.283\n",
-      "Epoch 286 Batch    0/5   train_loss = 2.246\n",
-      "Epoch 288 Batch    0/5   train_loss = 2.246\n",
-      "Epoch 290 Batch    0/5   train_loss = 2.210\n",
-      "Epoch 292 Batch    0/5   train_loss = 2.203\n",
-      "Epoch 294 Batch    0/5   train_loss = 2.185\n",
-      "Epoch 296 Batch    0/5   train_loss = 2.170\n",
-      "Epoch 298 Batch    0/5   train_loss = 2.150\n",
-      "Epoch 300 Batch    0/5   train_loss = 2.130\n",
-      "Epoch 302 Batch    0/5   train_loss = 2.132\n",
-      "Epoch 304 Batch    0/5   train_loss = 2.113\n",
-      "Epoch 306 Batch    0/5   train_loss = 2.083\n",
-      "Epoch 308 Batch    0/5   train_loss = 2.073\n",
-      "Epoch 310 Batch    0/5   train_loss = 2.060\n",
-      "Epoch 312 Batch    0/5   train_loss = 2.072\n",
-      "Epoch 314 Batch    0/5   train_loss = 2.081\n",
-      "Epoch 316 Batch    0/5   train_loss = 2.031\n",
-      "Epoch 318 Batch    0/5   train_loss = 2.007\n",
-      "Epoch 320 Batch    0/5   train_loss = 2.001\n",
-      "Epoch 322 Batch    0/5   train_loss = 1.987\n",
-      "Epoch 324 Batch    0/5   train_loss = 1.978\n",
-      "Epoch 326 Batch    0/5   train_loss = 1.963\n",
-      "Epoch 328 Batch    0/5   train_loss = 1.952\n",
-      "Epoch 330 Batch    0/5   train_loss = 1.932\n",
-      "Epoch 332 Batch    0/5   train_loss = 1.918\n",
-      "Epoch 334 Batch    0/5   train_loss = 1.898\n",
-      "Epoch 336 Batch    0/5   train_loss = 1.885\n",
-      "Epoch 338 Batch    0/5   train_loss = 1.872\n",
-      "Epoch 340 Batch    0/5   train_loss = 1.864\n",
-      "Epoch 342 Batch    0/5   train_loss = 1.867\n",
-      "Epoch 344 Batch    0/5   train_loss = 1.848\n",
-      "Epoch 346 Batch    0/5   train_loss = 1.821\n",
-      "Epoch 348 Batch    0/5   train_loss = 1.814\n",
-      "Epoch 350 Batch    0/5   train_loss = 1.788\n",
-      "Epoch 352 Batch    0/5   train_loss = 1.806\n",
-      "Epoch 354 Batch    0/5   train_loss = 1.790\n",
-      "Epoch 356 Batch    0/5   train_loss = 1.761\n",
-      "Epoch 358 Batch    0/5   train_loss = 1.745\n",
-      "Epoch 360 Batch    0/5   train_loss = 1.735\n",
-      "Epoch 362 Batch    0/5   train_loss = 1.718\n",
-      "Epoch 364 Batch    0/5   train_loss = 1.747\n",
-      "Epoch 366 Batch    0/5   train_loss = 1.726\n",
-      "Epoch 368 Batch    0/5   train_loss = 1.753\n",
-      "Epoch 370 Batch    0/5   train_loss = 1.703\n",
-      "Epoch 372 Batch    0/5   train_loss = 1.662\n",
-      "Epoch 374 Batch    0/5   train_loss = 1.643\n",
-      "Epoch 376 Batch    0/5   train_loss = 1.624\n",
-      "Epoch 378 Batch    0/5   train_loss = 1.617\n",
-      "Epoch 380 Batch    0/5   train_loss = 1.598\n",
-      "Epoch 382 Batch    0/5   train_loss = 1.613\n",
-      "Epoch 384 Batch    0/5   train_loss = 1.601\n",
-      "Epoch 386 Batch    0/5   train_loss = 1.584\n",
-      "Epoch 388 Batch    0/5   train_loss = 1.569\n",
-      "Epoch 390 Batch    0/5   train_loss = 1.557\n",
-      "Epoch 392 Batch    0/5   train_loss = 1.534\n",
-      "Epoch 394 Batch    0/5   train_loss = 1.534\n",
-      "Epoch 396 Batch    0/5   train_loss = 1.520\n",
-      "Epoch 398 Batch    0/5   train_loss = 1.547\n",
-      "Epoch 400 Batch    0/5   train_loss = 1.545\n",
-      "Epoch 402 Batch    0/5   train_loss = 1.521\n",
-      "Epoch 404 Batch    0/5   train_loss = 1.486\n",
-      "Epoch 406 Batch    0/5   train_loss = 1.469\n",
-      "Epoch 408 Batch    0/5   train_loss = 1.458\n",
-      "Epoch 410 Batch    0/5   train_loss = 1.442\n",
-      "Epoch 412 Batch    0/5   train_loss = 1.431\n",
-      "Epoch 414 Batch    0/5   train_loss = 1.410\n",
-      "Epoch 416 Batch    0/5   train_loss = 1.411\n",
-      "Epoch 418 Batch    0/5   train_loss = 1.412\n",
-      "Epoch 420 Batch    0/5   train_loss = 1.398\n",
-      "Epoch 422 Batch    0/5   train_loss = 1.417\n",
-      "Epoch 424 Batch    0/5   train_loss = 1.381\n",
-      "Epoch 426 Batch    0/5   train_loss = 1.355\n",
-      "Epoch 428 Batch    0/5   train_loss = 1.354\n",
-      "Epoch 430 Batch    0/5   train_loss = 1.338\n",
-      "Epoch 432 Batch    0/5   train_loss = 1.321\n",
-      "Epoch 434 Batch    0/5   train_loss = 1.326\n",
-      "Epoch 436 Batch    0/5   train_loss = 1.324\n",
-      "Epoch 438 Batch    0/5   train_loss = 1.314\n",
-      "Epoch 440 Batch    0/5   train_loss = 1.292\n",
-      "Epoch 442 Batch    0/5   train_loss = 1.279\n",
-      "Epoch 444 Batch    0/5   train_loss = 1.259\n",
-      "Epoch 446 Batch    0/5   train_loss = 1.283\n",
-      "Epoch 448 Batch    0/5   train_loss = 1.274\n",
-      "Epoch 450 Batch    0/5   train_loss = 1.251\n",
-      "Epoch 452 Batch    0/5   train_loss = 1.279\n",
-      "Epoch 454 Batch    0/5   train_loss = 1.249\n",
-      "Epoch 456 Batch    0/5   train_loss = 1.214\n",
-      "Epoch 458 Batch    0/5   train_loss = 1.196\n",
-      "Epoch 460 Batch    0/5   train_loss = 1.185\n",
-      "Epoch 462 Batch    0/5   train_loss = 1.174\n",
-      "Epoch 464 Batch    0/5   train_loss = 1.158\n",
-      "Epoch 466 Batch    0/5   train_loss = 1.195\n",
-      "Epoch 468 Batch    0/5   train_loss = 1.158\n",
-      "Epoch 470 Batch    0/5   train_loss = 1.145\n",
-      "Epoch 472 Batch    0/5   train_loss = 1.160\n",
-      "Epoch 474 Batch    0/5   train_loss = 1.123\n",
-      "Epoch 476 Batch    0/5   train_loss = 1.118\n",
-      "Epoch 478 Batch    0/5   train_loss = 1.103\n",
-      "Epoch 480 Batch    0/5   train_loss = 1.088\n",
-      "Epoch 482 Batch    0/5   train_loss = 1.089\n",
-      "Epoch 484 Batch    0/5   train_loss = 1.094\n",
-      "Epoch 486 Batch    0/5   train_loss = 1.092\n",
-      "Epoch 488 Batch    0/5   train_loss = 1.106\n",
-      "Epoch 490 Batch    0/5   train_loss = 1.053\n",
-      "Epoch 492 Batch    0/5   train_loss = 1.052\n",
-      "Epoch 494 Batch    0/5   train_loss = 1.046\n",
-      "Epoch 496 Batch    0/5   train_loss = 1.030\n",
-      "Epoch 498 Batch    0/5   train_loss = 1.021\n",
-      "Epoch 500 Batch    0/5   train_loss = 1.020\n",
-      "Epoch 502 Batch    0/5   train_loss = 1.046\n",
-      "Epoch 504 Batch    0/5   train_loss = 1.040\n",
-      "Epoch 506 Batch    0/5   train_loss = 1.026\n",
-      "Epoch 508 Batch    0/5   train_loss = 0.982\n",
-      "Epoch 510 Batch    0/5   train_loss = 0.969\n",
-      "Epoch 512 Batch    0/5   train_loss = 0.962\n",
-      "Epoch 514 Batch    0/5   train_loss = 0.946\n",
-      "Epoch 516 Batch    0/5   train_loss = 0.941\n",
-      "Epoch 518 Batch    0/5   train_loss = 0.951\n",
-      "Epoch 520 Batch    0/5   train_loss = 0.945\n",
-      "Epoch 522 Batch    0/5   train_loss = 0.952\n",
-      "Epoch 524 Batch    0/5   train_loss = 0.931\n",
-      "Epoch 526 Batch    0/5   train_loss = 0.905\n",
-      "Epoch 528 Batch    0/5   train_loss = 0.893\n",
-      "Epoch 530 Batch    0/5   train_loss = 0.881\n",
-      "Epoch 532 Batch    0/5   train_loss = 0.882\n",
-      "Epoch 534 Batch    0/5   train_loss = 0.871\n",
-      "Epoch 536 Batch    0/5   train_loss = 0.904\n",
-      "Epoch 538 Batch    0/5   train_loss = 0.893\n",
-      "Epoch 540 Batch    0/5   train_loss = 0.884\n",
-      "Epoch 542 Batch    0/5   train_loss = 0.864\n",
-      "Epoch 544 Batch    0/5   train_loss = 0.854\n",
-      "Epoch 546 Batch    0/5   train_loss = 0.854\n",
-      "Epoch 548 Batch    0/5   train_loss = 0.836\n",
-      "Epoch 550 Batch    0/5   train_loss = 0.816\n",
-      "Epoch 552 Batch    0/5   train_loss = 0.829\n",
-      "Epoch 554 Batch    0/5   train_loss = 0.813\n",
-      "Epoch 556 Batch    0/5   train_loss = 0.798\n",
-      "Epoch 558 Batch    0/5   train_loss = 0.808\n",
-      "Epoch 560 Batch    0/5   train_loss = 0.789\n",
-      "Epoch 562 Batch    0/5   train_loss = 0.791\n",
-      "Epoch 564 Batch    0/5   train_loss = 0.779\n",
-      "Epoch 566 Batch    0/5   train_loss = 0.765\n",
-      "Epoch 568 Batch    0/5   train_loss = 0.746\n",
-      "Epoch 570 Batch    0/5   train_loss = 0.746\n",
-      "Epoch 572 Batch    0/5   train_loss = 0.733\n",
-      "Epoch 574 Batch    0/5   train_loss = 0.733\n",
-      "Epoch 576 Batch    0/5   train_loss = 0.752\n",
-      "Epoch 578 Batch    0/5   train_loss = 0.727\n",
-      "Epoch 580 Batch    0/5   train_loss = 0.712\n",
-      "Epoch 582 Batch    0/5   train_loss = 0.711\n",
-      "Epoch 584 Batch    0/5   train_loss = 0.708\n",
-      "Epoch 586 Batch    0/5   train_loss = 0.695\n",
-      "Epoch 588 Batch    0/5   train_loss = 0.699\n",
-      "Epoch 590 Batch    0/5   train_loss = 0.688\n",
-      "Epoch 592 Batch    0/5   train_loss = 0.682\n",
-      "Epoch 594 Batch    0/5   train_loss = 0.703\n",
-      "Epoch 596 Batch    0/5   train_loss = 0.681\n",
-      "Epoch 598 Batch    0/5   train_loss = 0.672\n",
-      "Epoch 600 Batch    0/5   train_loss = 0.678\n",
-      "Epoch 602 Batch    0/5   train_loss = 0.657\n",
-      "Epoch 604 Batch    0/5   train_loss = 0.652\n",
-      "Epoch 606 Batch    0/5   train_loss = 0.627\n",
-      "Epoch 608 Batch    0/5   train_loss = 0.623\n",
-      "Epoch 610 Batch    0/5   train_loss = 0.633\n",
-      "Epoch 612 Batch    0/5   train_loss = 0.608\n",
-      "Epoch 614 Batch    0/5   train_loss = 0.614\n",
-      "Epoch 616 Batch    0/5   train_loss = 0.620\n",
-      "Epoch 618 Batch    0/5   train_loss = 0.610\n",
-      "Epoch 620 Batch    0/5   train_loss = 0.596\n",
-      "Epoch 622 Batch    0/5   train_loss = 0.596\n",
-      "Epoch 624 Batch    0/5   train_loss = 0.605\n",
-      "Epoch 626 Batch    0/5   train_loss = 0.574\n",
-      "Epoch 628 Batch    0/5   train_loss = 0.581\n",
-      "Epoch 630 Batch    0/5   train_loss = 0.571\n",
-      "Epoch 632 Batch    0/5   train_loss = 0.563\n",
-      "Epoch 634 Batch    0/5   train_loss = 0.582\n",
-      "Epoch 636 Batch    0/5   train_loss = 0.579\n",
-      "Epoch 638 Batch    0/5   train_loss = 0.562\n",
-      "Epoch 640 Batch    0/5   train_loss = 0.549\n",
-      "Epoch 642 Batch    0/5   train_loss = 0.540\n",
-      "Epoch 644 Batch    0/5   train_loss = 0.520\n",
-      "Epoch 646 Batch    0/5   train_loss = 0.515\n",
-      "Epoch 648 Batch    0/5   train_loss = 0.509\n",
-      "Epoch 650 Batch    0/5   train_loss = 0.509\n",
-      "Epoch 652 Batch    0/5   train_loss = 0.527\n",
-      "Epoch 654 Batch    0/5   train_loss = 0.524\n",
-      "Epoch 656 Batch    0/5   train_loss = 0.509\n",
-      "Epoch 658 Batch    0/5   train_loss = 0.523\n",
-      "Epoch 660 Batch    0/5   train_loss = 0.502\n",
-      "Epoch 662 Batch    0/5   train_loss = 0.477\n",
-      "Epoch 664 Batch    0/5   train_loss = 0.473\n",
-      "Epoch 666 Batch    0/5   train_loss = 0.463\n",
-      "Epoch 668 Batch    0/5   train_loss = 0.457\n",
-      "Epoch 670 Batch    0/5   train_loss = 0.455\n",
-      "Epoch 672 Batch    0/5   train_loss = 0.459\n",
-      "Epoch 674 Batch    0/5   train_loss = 0.475\n",
-      "Epoch 676 Batch    0/5   train_loss = 0.471\n",
-      "Epoch 678 Batch    0/5   train_loss = 0.455\n",
-      "Epoch 680 Batch    0/5   train_loss = 0.443\n",
-      "Epoch 682 Batch    0/5   train_loss = 0.456\n",
-      "Epoch 684 Batch    0/5   train_loss = 0.440\n",
-      "Epoch 686 Batch    0/5   train_loss = 0.421\n",
-      "Epoch 688 Batch    0/5   train_loss = 0.413\n",
-      "Epoch 690 Batch    0/5   train_loss = 0.405\n",
-      "Epoch 692 Batch    0/5   train_loss = 0.401\n",
-      "Epoch 694 Batch    0/5   train_loss = 0.404\n",
-      "Epoch 696 Batch    0/5   train_loss = 0.400\n",
-      "Epoch 698 Batch    0/5   train_loss = 0.428\n",
-      "Epoch 700 Batch    0/5   train_loss = 0.451\n",
-      "Epoch 702 Batch    0/5   train_loss = 0.426\n",
-      "Epoch 704 Batch    0/5   train_loss = 0.410\n",
-      "Epoch 706 Batch    0/5   train_loss = 0.422\n",
-      "Epoch 708 Batch    0/5   train_loss = 0.398\n",
-      "Epoch 710 Batch    0/5   train_loss = 0.377\n",
-      "Epoch 712 Batch    0/5   train_loss = 0.368\n",
-      "Epoch 714 Batch    0/5   train_loss = 0.358\n",
-      "Epoch 716 Batch    0/5   train_loss = 0.352\n",
-      "Epoch 718 Batch    0/5   train_loss = 0.349\n",
-      "Epoch 720 Batch    0/5   train_loss = 0.344\n",
-      "Epoch 722 Batch    0/5   train_loss = 0.346\n",
-      "Epoch 724 Batch    0/5   train_loss = 0.345\n",
-      "Epoch 726 Batch    0/5   train_loss = 0.337\n",
-      "Epoch 728 Batch    0/5   train_loss = 0.345\n",
-      "Epoch 730 Batch    0/5   train_loss = 0.348\n",
-      "Epoch 732 Batch    0/5   train_loss = 0.358\n",
-      "Epoch 734 Batch    0/5   train_loss = 0.346\n",
-      "Epoch 736 Batch    0/5   train_loss = 0.337\n",
-      "Epoch 738 Batch    0/5   train_loss = 0.329\n",
-      "Epoch 740 Batch    0/5   train_loss = 0.320\n",
-      "Epoch 742 Batch    0/5   train_loss = 0.323\n",
-      "Epoch 744 Batch    0/5   train_loss = 0.316\n",
-      "Epoch 746 Batch    0/5   train_loss = 0.304\n",
-      "Epoch 748 Batch    0/5   train_loss = 0.299\n",
-      "Epoch 750 Batch    0/5   train_loss = 0.292\n",
-      "Epoch 752 Batch    0/5   train_loss = 0.288\n",
-      "Epoch 754 Batch    0/5   train_loss = 0.289\n",
-      "Epoch 756 Batch    0/5   train_loss = 0.284\n",
-      "Epoch 758 Batch    0/5   train_loss = 0.290\n",
-      "Epoch 760 Batch    0/5   train_loss = 0.304\n",
-      "Epoch 762 Batch    0/5   train_loss = 0.311\n",
-      "Epoch 764 Batch    0/5   train_loss = 0.405\n",
-      "Epoch 766 Batch    0/5   train_loss = 0.390\n",
-      "Epoch 768 Batch    0/5   train_loss = 0.344\n",
-      "Epoch 770 Batch    0/5   train_loss = 0.320\n",
-      "Epoch 772 Batch    0/5   train_loss = 0.280\n",
-      "Epoch 774 Batch    0/5   train_loss = 0.265\n",
-      "Epoch 776 Batch    0/5   train_loss = 0.258\n",
-      "Epoch 778 Batch    0/5   train_loss = 0.252\n",
-      "Epoch 780 Batch    0/5   train_loss = 0.247\n",
-      "Epoch 782 Batch    0/5   train_loss = 0.243\n",
-      "Epoch 784 Batch    0/5   train_loss = 0.240\n",
-      "Epoch 786 Batch    0/5   train_loss = 0.237\n",
-      "Epoch 788 Batch    0/5   train_loss = 0.233\n",
-      "Epoch 790 Batch    0/5   train_loss = 0.231\n",
-      "Epoch 792 Batch    0/5   train_loss = 0.229\n",
-      "Epoch 794 Batch    0/5   train_loss = 0.225\n",
-      "Epoch 796 Batch    0/5   train_loss = 0.230\n",
-      "Epoch 798 Batch    0/5   train_loss = 0.226\n",
-      "Epoch 800 Batch    0/5   train_loss = 0.222\n",
-      "Epoch 802 Batch    0/5   train_loss = 0.237\n",
-      "Epoch 804 Batch    0/5   train_loss = 0.225\n",
-      "Epoch 806 Batch    0/5   train_loss = 0.225\n",
-      "Epoch 808 Batch    0/5   train_loss = 0.245\n",
-      "Epoch 810 Batch    0/5   train_loss = 0.227\n",
-      "Epoch 812 Batch    0/5   train_loss = 0.210\n",
-      "Epoch 814 Batch    0/5   train_loss = 0.206\n",
-      "Epoch 816 Batch    0/5   train_loss = 0.202\n",
-      "Epoch 818 Batch    0/5   train_loss = 0.198\n",
-      "Epoch 820 Batch    0/5   train_loss = 0.195\n",
-      "Epoch 822 Batch    0/5   train_loss = 0.192\n",
-      "Epoch 824 Batch    0/5   train_loss = 0.189\n",
-      "Epoch 826 Batch    0/5   train_loss = 0.189\n",
-      "Epoch 828 Batch    0/5   train_loss = 0.187\n",
-      "Epoch 830 Batch    0/5   train_loss = 0.186\n",
-      "Epoch 832 Batch    0/5   train_loss = 0.187\n",
-      "Epoch 834 Batch    0/5   train_loss = 0.189\n",
-      "Epoch 836 Batch    0/5   train_loss = 0.189\n",
-      "Epoch 838 Batch    0/5   train_loss = 0.197\n",
-      "Epoch 840 Batch    0/5   train_loss = 0.207\n",
-      "Epoch 842 Batch    0/5   train_loss = 0.196\n",
-      "Epoch 844 Batch    0/5   train_loss = 0.187\n",
-      "Epoch 846 Batch    0/5   train_loss = 0.197\n",
-      "Epoch 848 Batch    0/5   train_loss = 0.189\n",
-      "Epoch 850 Batch    0/5   train_loss = 0.176\n",
-      "Epoch 852 Batch    0/5   train_loss = 0.171\n",
-      "Epoch 854 Batch    0/5   train_loss = 0.164\n",
-      "Epoch 856 Batch    0/5   train_loss = 0.161\n",
-      "Epoch 858 Batch    0/5   train_loss = 0.157\n",
-      "Epoch 860 Batch    0/5   train_loss = 0.154\n",
-      "Epoch 862 Batch    0/5   train_loss = 0.152\n",
-      "Epoch 864 Batch    0/5   train_loss = 0.150\n",
-      "Epoch 866 Batch    0/5   train_loss = 0.148\n",
-      "Epoch 868 Batch    0/5   train_loss = 0.146\n",
-      "Epoch 870 Batch    0/5   train_loss = 0.145\n",
-      "Epoch 872 Batch    0/5   train_loss = 0.145\n",
-      "Epoch 874 Batch    0/5   train_loss = 0.142\n",
-      "Epoch 876 Batch    0/5   train_loss = 0.143\n",
-      "Epoch 878 Batch    0/5   train_loss = 0.159\n",
-      "Epoch 880 Batch    0/5   train_loss = 0.145\n",
-      "Epoch 882 Batch    0/5   train_loss = 0.161\n",
-      "Epoch 884 Batch    0/5   train_loss = 0.211\n",
-      "Epoch 886 Batch    0/5   train_loss = 0.196\n",
-      "Epoch 888 Batch    0/5   train_loss = 0.335\n",
-      "Epoch 890 Batch    0/5   train_loss = 0.325\n",
-      "Epoch 892 Batch    0/5   train_loss = 0.279\n",
-      "Epoch 894 Batch    0/5   train_loss = 0.244\n",
-      "Epoch 896 Batch    0/5   train_loss = 0.214\n",
-      "Epoch 898 Batch    0/5   train_loss = 0.174\n",
-      "Epoch 900 Batch    0/5   train_loss = 0.147\n",
-      "Epoch 902 Batch    0/5   train_loss = 0.138\n",
-      "Epoch 904 Batch    0/5   train_loss = 0.131\n",
-      "Epoch 906 Batch    0/5   train_loss = 0.128\n",
-      "Epoch 908 Batch    0/5   train_loss = 0.125\n",
-      "Epoch 910 Batch    0/5   train_loss = 0.123\n",
-      "Epoch 912 Batch    0/5   train_loss = 0.121\n",
-      "Epoch 914 Batch    0/5   train_loss = 0.119\n",
-      "Epoch 916 Batch    0/5   train_loss = 0.117\n",
-      "Epoch 918 Batch    0/5   train_loss = 0.116\n",
-      "Epoch 920 Batch    0/5   train_loss = 0.114\n",
-      "Epoch 922 Batch    0/5   train_loss = 0.113\n",
-      "Epoch 924 Batch    0/5   train_loss = 0.112\n",
-      "Epoch 926 Batch    0/5   train_loss = 0.111\n",
-      "Epoch 928 Batch    0/5   train_loss = 0.109\n",
-      "Epoch 930 Batch    0/5   train_loss = 0.108\n",
-      "Epoch 932 Batch    0/5   train_loss = 0.107\n",
-      "Epoch 934 Batch    0/5   train_loss = 0.106\n",
-      "Epoch 936 Batch    0/5   train_loss = 0.105\n",
-      "Epoch 938 Batch    0/5   train_loss = 0.103\n",
-      "Epoch 940 Batch    0/5   train_loss = 0.102\n",
-      "Epoch 942 Batch    0/5   train_loss = 0.101\n",
-      "Epoch 944 Batch    0/5   train_loss = 0.100\n",
-      "Epoch 946 Batch    0/5   train_loss = 0.099\n",
-      "Epoch 948 Batch    0/5   train_loss = 0.098\n",
-      "Epoch 950 Batch    0/5   train_loss = 0.097\n",
-      "Epoch 952 Batch    0/5   train_loss = 0.096\n",
-      "Epoch 954 Batch    0/5   train_loss = 0.095\n",
-      "Epoch 956 Batch    0/5   train_loss = 0.094\n",
-      "Epoch 958 Batch    0/5   train_loss = 0.094\n",
-      "Epoch 960 Batch    0/5   train_loss = 0.093\n",
-      "Epoch 962 Batch    0/5   train_loss = 0.092\n",
-      "Epoch 964 Batch    0/5   train_loss = 0.091\n",
-      "Epoch 966 Batch    0/5   train_loss = 0.090\n",
-      "Epoch 968 Batch    0/5   train_loss = 0.089\n",
-      "Epoch 970 Batch    0/5   train_loss = 0.088\n",
-      "Epoch 972 Batch    0/5   train_loss = 0.088\n",
-      "Epoch 974 Batch    0/5   train_loss = 0.087\n",
-      "Epoch 976 Batch    0/5   train_loss = 0.086\n",
-      "Epoch 978 Batch    0/5   train_loss = 0.085\n",
-      "Epoch 980 Batch    0/5   train_loss = 0.084\n",
-      "Epoch 982 Batch    0/5   train_loss = 0.083\n",
-      "Epoch 984 Batch    0/5   train_loss = 0.083\n",
-      "Epoch 986 Batch    0/5   train_loss = 0.082\n",
-      "Epoch 988 Batch    0/5   train_loss = 0.081\n",
-      "Epoch 990 Batch    0/5   train_loss = 0.080\n",
-      "Epoch 992 Batch    0/5   train_loss = 0.080\n",
-      "Epoch 994 Batch    0/5   train_loss = 0.079\n",
-      "Epoch 996 Batch    0/5   train_loss = 0.078\n",
-      "Epoch 998 Batch    0/5   train_loss = 0.078\n",
-      "Model Trained and Saved\n"
+      "Epoch   0 Batch    0/49   train_loss = 8.823\n",
+      "Epoch   0 Batch   10/49   train_loss = 6.394\n",
+      "Epoch   0 Batch   20/49   train_loss = 6.288\n",
+      "Epoch   0 Batch   30/49   train_loss = 6.552\n",
+      "Epoch   0 Batch   40/49   train_loss = 6.792\n",
+      "Epoch   1 Batch    1/49   train_loss = 6.129\n",
+      "Epoch   1 Batch   11/49   train_loss = 6.013\n",
+      "Epoch   1 Batch   21/49   train_loss = 6.131\n",
+      "Epoch   1 Batch   31/49   train_loss = 6.094\n",
+      "Epoch   1 Batch   41/49   train_loss = 6.035\n",
+      "Epoch   2 Batch    2/49   train_loss = 5.961\n",
+      "Epoch   2 Batch   12/49   train_loss = 6.199\n",
+      "Epoch   2 Batch   22/49   train_loss = 6.037\n",
+      "Epoch   2 Batch   32/49   train_loss = 6.311\n",
+      "Epoch   2 Batch   42/49   train_loss = 6.187\n",
+      "Epoch   3 Batch    3/49   train_loss = 6.182\n",
+      "Epoch   3 Batch   13/49   train_loss = 6.023\n",
+      "Epoch   3 Batch   23/49   train_loss = 5.968\n",
+      "Epoch   3 Batch   33/49   train_loss = 5.981\n",
+      "Epoch   3 Batch   43/49   train_loss = 6.098\n",
+      "Epoch   4 Batch    4/49   train_loss = 6.104\n",
+      "Epoch   4 Batch   14/49   train_loss = 5.956\n",
+      "Epoch   4 Batch   24/49   train_loss = 6.170\n",
+      "Epoch   4 Batch   34/49   train_loss = 6.176\n",
+      "Epoch   4 Batch   44/49   train_loss = 6.136\n",
+      "Epoch   5 Batch    5/49   train_loss = 6.161\n",
+      "Epoch   5 Batch   15/49   train_loss = 6.067\n",
+      "Epoch   5 Batch   25/49   train_loss = 6.108\n",
+      "Epoch   5 Batch   35/49   train_loss = 6.007\n",
+      "Epoch   5 Batch   45/49   train_loss = 6.007\n",
+      "Epoch   6 Batch    6/49   train_loss = 5.833\n",
+      "Epoch   6 Batch   16/49   train_loss = 6.090\n",
+      "Epoch   6 Batch   26/49   train_loss = 6.225\n",
+      "Epoch   6 Batch   36/49   train_loss = 6.081\n",
+      "Epoch   6 Batch   46/49   train_loss = 6.062\n",
+      "Epoch   7 Batch    7/49   train_loss = 6.026\n",
+      "Epoch   7 Batch   17/49   train_loss = 5.683\n",
+      "Epoch   7 Batch   27/49   train_loss = 6.055\n",
+      "Epoch   7 Batch   37/49   train_loss = 5.834\n",
+      "Epoch   7 Batch   47/49   train_loss = 6.057\n",
+      "Epoch   8 Batch    8/49   train_loss = 5.782\n",
+      "Epoch   8 Batch   18/49   train_loss = 5.966\n",
+      "Epoch   8 Batch   28/49   train_loss = 6.036\n",
+      "Epoch   8 Batch   38/49   train_loss = 6.007\n",
+      "Epoch   8 Batch   48/49   train_loss = 6.105\n",
+      "Epoch   9 Batch    9/49   train_loss = 5.654\n",
+      "Epoch   9 Batch   19/49   train_loss = 5.723\n",
+      "Epoch   9 Batch   29/49   train_loss = 5.926\n",
+      "Epoch   9 Batch   39/49   train_loss = 5.998\n",
+      "Epoch  10 Batch    0/49   train_loss = 5.719\n",
+      "Epoch  10 Batch   10/49   train_loss = 5.906\n",
+      "Epoch  10 Batch   20/49   train_loss = 5.788\n",
+      "Epoch  10 Batch   30/49   train_loss = 5.764\n",
+      "Epoch  10 Batch   40/49   train_loss = 5.994\n",
+      "Epoch  11 Batch    1/49   train_loss = 5.807\n",
+      "Epoch  11 Batch   11/49   train_loss = 5.796\n",
+      "Epoch  11 Batch   21/49   train_loss = 5.863\n",
+      "Epoch  11 Batch   31/49   train_loss = 5.897\n",
+      "Epoch  11 Batch   41/49   train_loss = 5.779\n",
+      "Epoch  12 Batch    2/49   train_loss = 5.720\n",
+      "Epoch  12 Batch   12/49   train_loss = 5.869\n",
+      "Epoch  12 Batch   22/49   train_loss = 5.764\n",
+      "Epoch  12 Batch   32/49   train_loss = 5.961\n",
+      "Epoch  12 Batch   42/49   train_loss = 5.880\n",
+      "Epoch  13 Batch    3/49   train_loss = 5.819\n",
+      "Epoch  13 Batch   13/49   train_loss = 5.741\n",
+      "Epoch  13 Batch   23/49   train_loss = 5.707\n",
+      "Epoch  13 Batch   33/49   train_loss = 5.683\n",
+      "Epoch  13 Batch   43/49   train_loss = 5.723\n",
+      "Epoch  14 Batch    4/49   train_loss = 5.754\n",
+      "Epoch  14 Batch   14/49   train_loss = 5.721\n",
+      "Epoch  14 Batch   24/49   train_loss = 5.777\n",
+      "Epoch  14 Batch   34/49   train_loss = 5.893\n",
+      "Epoch  14 Batch   44/49   train_loss = 5.771\n",
+      "Epoch  15 Batch    5/49   train_loss = 5.785\n",
+      "Epoch  15 Batch   15/49   train_loss = 5.753\n",
+      "Epoch  15 Batch   25/49   train_loss = 5.703\n",
+      "Epoch  15 Batch   35/49   train_loss = 5.638\n",
+      "Epoch  15 Batch   45/49   train_loss = 5.638\n",
+      "Epoch  16 Batch    6/49   train_loss = 5.463\n",
+      "Epoch  16 Batch   16/49   train_loss = 5.703\n",
+      "Epoch  16 Batch   26/49   train_loss = 5.689\n",
+      "Epoch  16 Batch   36/49   train_loss = 5.740\n",
+      "Epoch  16 Batch   46/49   train_loss = 5.701\n",
+      "Epoch  17 Batch    7/49   train_loss = 5.635\n",
+      "Epoch  17 Batch   17/49   train_loss = 5.377\n",
+      "Epoch  17 Batch   27/49   train_loss = 5.638\n",
+      "Epoch  17 Batch   37/49   train_loss = 5.471\n",
+      "Epoch  17 Batch   47/49   train_loss = 5.639\n",
+      "Epoch  18 Batch    8/49   train_loss = 5.518\n",
+      "Epoch  18 Batch   18/49   train_loss = 5.474\n",
+      "Epoch  18 Batch   28/49   train_loss = 5.561\n",
+      "Epoch  18 Batch   38/49   train_loss = 5.609\n",
+      "Epoch  18 Batch   48/49   train_loss = 5.630\n"
+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-74-396c263e7e73>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     16\u001b[0m                 \u001b[0minitial_state\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m                 lr: learning_rate}\n\u001b[0;32m---> 18\u001b[0;31m             \u001b[0mtrain_loss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcost\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfinal_state\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_op\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     19\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m             \u001b[0;31m# Show every <show_every_n_batches> batches\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/home/carnd/anaconda3/envs/dl/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    765\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    766\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 767\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    768\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    769\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/home/carnd/anaconda3/envs/dl/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    963\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    964\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m--> 965\u001b[0;31m                              feed_dict_string, options, run_metadata)\n\u001b[0m\u001b[1;32m    966\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    967\u001b[0m       \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/home/carnd/anaconda3/envs/dl/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1013\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1014\u001b[0m       return self._do_call(_run_fn, self._session, feed_dict, fetch_list,\n\u001b[0;32m-> 1015\u001b[0;31m                            target_list, options, run_metadata)\n\u001b[0m\u001b[1;32m   1016\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1017\u001b[0m       return self._do_call(_prun_fn, self._session, handle, feed_dict,\n",
+      "\u001b[0;32m/home/carnd/anaconda3/envs/dl/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m   1020\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1021\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1022\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1023\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1024\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/home/carnd/anaconda3/envs/dl/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(session, feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m   1002\u001b[0m         return tf_session.TF_Run(session, options,\n\u001b[1;32m   1003\u001b[0m                                  \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1004\u001b[0;31m                                  status, run_metadata)\n\u001b[0m\u001b[1;32m   1005\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1006\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
      ]
     }
    ],
@@ -1997,6 +1659,8 @@
    "execution_count": 511,
    "metadata": {
     "collapsed": false,
+    "deletable": true,
+    "editable": true,
     "scrolled": true
    },
    "outputs": [
@@ -2028,7 +1692,9 @@
    "cell_type": "code",
    "execution_count": 458,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [
     {
@@ -2212,7 +1878,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.5.2"
   },
   "toc": {
    "colors": {
diff --git a/tv-script-generation/logs/1/events.out.tfevents.1490895533.ip-172-31-18-64 b/tv-script-generation/logs/1/events.out.tfevents.1490895533.ip-172-31-18-64
index e4b0945..1dfde1c 100644
Binary files a/tv-script-generation/logs/1/events.out.tfevents.1490895533.ip-172-31-18-64 and b/tv-script-generation/logs/1/events.out.tfevents.1490895533.ip-172-31-18-64 differ
diff --git a/tv-script-generation/logs/1/events.out.tfevents.1490999151.ip-172-31-18-64 b/tv-script-generation/logs/1/events.out.tfevents.1490999151.ip-172-31-18-64
new file mode 100644
index 0000000..b1fe5c8
Binary files /dev/null and b/tv-script-generation/logs/1/events.out.tfevents.1490999151.ip-172-31-18-64 differ
diff --git a/tv-script-generation/preprocess.p b/tv-script-generation/preprocess.p
index d4b2507..724c7e3 100644
--- a/tv-script-generation/preprocess.p
+++ b/tv-script-generation/preprocess.p
@@ -1,3 +1,3 @@
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-oid sha256:0c4525fb720ac816b5ee0720ef52e698cdab40204342e87096c2b4356b9829a8
-size 387442
+oid sha256:7a45b708bb1df01d93b2e4d8b431caea08c5b72568a78543621d4aa868d94646
+size 391194