diff --git a/ch16/ch16_part1.ipynb b/ch16/ch16_part1.ipynb
index 82c1be9a..d014a277 100644
--- a/ch16/ch16_part1.ipynb
+++ b/ch16/ch16_part1.ipynb
@@ -37,11 +37,11 @@
       "Sebastian Raschka & Vahid Mirjalili \n",
       "last updated: 2019-11-03 \n",
       "\n",
-      "numpy 1.17.2\n",
-      "scipy 1.2.1\n",
-      "matplotlib 3.1.0\n",
+      "numpy 1.17.3\n",
+      "scipy 1.3.1\n",
+      "matplotlib 3.1.1\n",
       "tensorflow 2.0.0\n",
-      "tensorflow_datasets 1.3.0\n"
+      "tensorflow_datasets 1.2.0\n"
      ]
     }
    ],
@@ -52,7 +52,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -74,7 +74,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -84,7 +84,7 @@
        "<IPython.core.display.Image object>"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 3,
      "metadata": {
       "image/png": {
        "width": 700
@@ -106,7 +106,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -116,7 +116,7 @@
        "<IPython.core.display.Image object>"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 4,
      "metadata": {
       "image/png": {
        "width": 700
@@ -140,7 +140,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -150,7 +150,7 @@
        "<IPython.core.display.Image object>"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 5,
      "metadata": {
       "image/png": {
        "width": 700
@@ -165,7 +165,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -175,7 +175,7 @@
        "<IPython.core.display.Image object>"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 6,
      "metadata": {
       "image/png": {
        "width": 700
@@ -197,7 +197,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -207,7 +207,7 @@
        "<IPython.core.display.Image object>"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 7,
      "metadata": {
       "image/png": {
        "width": 700
@@ -222,7 +222,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -232,7 +232,7 @@
        "<IPython.core.display.Image object>"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 8,
      "metadata": {
       "image/png": {
        "width": 700
@@ -254,7 +254,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -264,7 +264,7 @@
        "<IPython.core.display.Image object>"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 9,
      "metadata": {
       "image/png": {
        "width": 700
@@ -279,7 +279,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -310,7 +310,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -379,7 +379,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -389,7 +389,7 @@
        "<IPython.core.display.Image object>"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 12,
      "metadata": {
       "image/png": {
        "width": 700
@@ -412,7 +412,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -422,7 +422,7 @@
        "<IPython.core.display.Image object>"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 13,
      "metadata": {
       "image/png": {
        "width": 700
@@ -449,7 +449,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -461,7 +461,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -476,7 +476,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -506,27 +506,27 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>49995</th>\n",
+       "      <td>49995</td>\n",
        "      <td>OK, lets start with the best. the building. al...</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>49996</th>\n",
+       "      <td>49996</td>\n",
        "      <td>The British 'heritage film' industry is out of...</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>49997</th>\n",
+       "      <td>49997</td>\n",
        "      <td>I don't even know where to begin on this one. ...</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>49998</th>\n",
+       "      <td>49998</td>\n",
        "      <td>Richard Tyler is a little boy who is scared of...</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>49999</th>\n",
+       "      <td>49999</td>\n",
        "      <td>I waited long to watch this movie. Also becaus...</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
@@ -543,7 +543,7 @@
        "49999  I waited long to watch this movie. Also becaus...          1"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -556,7 +556,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -591,7 +591,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -624,7 +624,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -652,7 +652,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -661,7 +661,7 @@
        "[232, 9, 270, 1123]"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -677,7 +677,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -696,7 +696,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -732,7 +732,7 @@
        " <tf.Tensor: id=43303, shape=(), dtype=int64, numpy=0>)"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -777,7 +777,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -814,7 +814,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -852,7 +852,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -862,7 +862,7 @@
        "<IPython.core.display.Image object>"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 25,
      "metadata": {
       "image/png": {
        "width": 700
@@ -877,7 +877,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -934,7 +934,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -979,7 +979,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
@@ -1023,7 +1023,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
@@ -1071,7 +1071,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
@@ -1095,27 +1095,27 @@
       "Non-trainable params: 0\n",
       "_________________________________________________________________\n",
       "Epoch 1/10\n",
-      "625/625 [==============================] - 268s 429ms/step - loss: 0.5440 - accuracy: 0.7143 - val_loss: 0.0000e+00 - val_accuracy: 0.0000e+00\n",
+      "625/625 [==============================] - 383s 613ms/step - loss: 0.5173 - accuracy: 0.7376 - val_loss: 0.0000e+00 - val_accuracy: 0.0000e+00\n",
       "Epoch 2/10\n",
-      "625/625 [==============================] - 262s 419ms/step - loss: 0.2753 - accuracy: 0.8903 - val_loss: 0.3489 - val_accuracy: 0.8634\n",
+      "625/625 [==============================] - 369s 590ms/step - loss: 0.2747 - accuracy: 0.8955 - val_loss: 0.3982 - val_accuracy: 0.8516\n",
       "Epoch 3/10\n",
-      "625/625 [==============================] - 262s 420ms/step - loss: 0.1378 - accuracy: 0.9514 - val_loss: 0.4153 - val_accuracy: 0.8434\n",
+      "625/625 [==============================] - 369s 590ms/step - loss: 0.1528 - accuracy: 0.9478 - val_loss: 0.4270 - val_accuracy: 0.8588\n",
       "Epoch 4/10\n",
-      "625/625 [==============================] - 263s 420ms/step - loss: 0.0685 - accuracy: 0.9778 - val_loss: 0.4895 - val_accuracy: 0.8450\n",
+      "625/625 [==============================] - 369s 590ms/step - loss: 0.0958 - accuracy: 0.9674 - val_loss: 0.6082 - val_accuracy: 0.8358\n",
       "Epoch 5/10\n",
-      "625/625 [==============================] - 263s 420ms/step - loss: 0.0470 - accuracy: 0.9861 - val_loss: 0.5602 - val_accuracy: 0.8228\n",
+      "625/625 [==============================] - 368s 589ms/step - loss: 0.0551 - accuracy: 0.9836 - val_loss: 0.6766 - val_accuracy: 0.8468\n",
       "Epoch 6/10\n",
-      "625/625 [==============================] - 263s 420ms/step - loss: 0.0383 - accuracy: 0.9884 - val_loss: 0.6552 - val_accuracy: 0.8200\n",
+      "625/625 [==============================] - 368s 590ms/step - loss: 0.0454 - accuracy: 0.9858 - val_loss: 0.6429 - val_accuracy: 0.8398\n",
       "Epoch 7/10\n",
-      "625/625 [==============================] - 262s 420ms/step - loss: 0.0294 - accuracy: 0.9916 - val_loss: 0.6579 - val_accuracy: 0.8380\n",
+      "625/625 [==============================] - 369s 591ms/step - loss: 0.0418 - accuracy: 0.9870 - val_loss: 0.6093 - val_accuracy: 0.8388\n",
       "Epoch 8/10\n",
-      "625/625 [==============================] - 262s 420ms/step - loss: 0.0321 - accuracy: 0.9894 - val_loss: 0.8191 - val_accuracy: 0.8182\n",
+      "625/625 [==============================] - 369s 590ms/step - loss: 0.0288 - accuracy: 0.9908 - val_loss: 0.7021 - val_accuracy: 0.8356\n",
       "Epoch 9/10\n",
-      "625/625 [==============================] - 263s 420ms/step - loss: 0.0552 - accuracy: 0.9809 - val_loss: 0.7586 - val_accuracy: 0.8496\n",
+      "625/625 [==============================] - 368s 590ms/step - loss: 0.0308 - accuracy: 0.9909 - val_loss: 0.7601 - val_accuracy: 0.8392\n",
       "Epoch 10/10\n",
-      "625/625 [==============================] - 262s 420ms/step - loss: 0.0252 - accuracy: 0.9921 - val_loss: 0.7784 - val_accuracy: 0.8474\n",
-      "782/782 [==============================] - 100s 128ms/step - loss: 0.7809 - accuracy: 0.8437\n",
-      "Test Acc.: 84.37%\n"
+      "625/625 [==============================] - 369s 590ms/step - loss: 0.0205 - accuracy: 0.9931 - val_loss: 0.8158 - val_accuracy: 0.8388\n",
+      "782/782 [==============================] - 144s 185ms/step - loss: 0.8131 - accuracy: 0.8392\n",
+      "Test Acc.: 83.92%\n"
      ]
     }
    ],
@@ -1161,7 +1161,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1181,7 +1181,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1239,7 +1239,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1294,7 +1294,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
@@ -1302,7 +1302,7 @@
      "output_type": "stream",
      "text": [
       "Vocab-size: 58063\n",
-      "Model: \"sequential_6\"\n",
+      "Model: \"sequential_5\"\n",
       "_________________________________________________________________\n",
       "Layer (type)                 Output Shape              Param #   \n",
       "=================================================================\n",
@@ -1350,7 +1350,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
@@ -1358,25 +1358,25 @@
      "output_type": "stream",
      "text": [
       "Epoch 1/10\n",
-      "625/625 [==============================] - 54s 87ms/step - loss: 0.6997 - accuracy: 0.5013 - val_loss: 0.0000e+00 - val_accuracy: 0.0000e+00\n",
+      "625/625 [==============================] - 68s 109ms/step - loss: 0.7055 - accuracy: 0.5026 - val_loss: 0.0000e+00 - val_accuracy: 0.0000e+00\n",
       "Epoch 2/10\n",
-      "625/625 [==============================] - 52s 83ms/step - loss: 0.6741 - accuracy: 0.5641 - val_loss: 0.7000 - val_accuracy: 0.6032\n",
+      "625/625 [==============================] - 66s 106ms/step - loss: 0.7037 - accuracy: 0.5113 - val_loss: 0.6953 - val_accuracy: 0.5072\n",
       "Epoch 3/10\n",
-      "625/625 [==============================] - 52s 83ms/step - loss: 0.4976 - accuracy: 0.7637 - val_loss: 0.5935 - val_accuracy: 0.6918\n",
+      "625/625 [==============================] - 66s 106ms/step - loss: 0.6954 - accuracy: 0.5213 - val_loss: 0.6884 - val_accuracy: 0.5452\n",
       "Epoch 4/10\n",
-      "625/625 [==============================] - 52s 83ms/step - loss: 0.3362 - accuracy: 0.8600 - val_loss: 0.4991 - val_accuracy: 0.7818\n",
+      "625/625 [==============================] - 67s 106ms/step - loss: 0.6317 - accuracy: 0.6259 - val_loss: 0.6152 - val_accuracy: 0.6522\n",
       "Epoch 5/10\n",
-      "625/625 [==============================] - 52s 83ms/step - loss: 0.2168 - accuracy: 0.9157 - val_loss: 0.4592 - val_accuracy: 0.8118\n",
+      "625/625 [==============================] - 66s 106ms/step - loss: 0.4913 - accuracy: 0.7629 - val_loss: 0.7798 - val_accuracy: 0.6278\n",
       "Epoch 6/10\n",
-      "625/625 [==============================] - 52s 83ms/step - loss: 0.1549 - accuracy: 0.9422 - val_loss: 0.6382 - val_accuracy: 0.7686\n",
+      "625/625 [==============================] - 66s 106ms/step - loss: 0.4252 - accuracy: 0.8094 - val_loss: 0.6105 - val_accuracy: 0.7432\n",
       "Epoch 7/10\n",
-      "625/625 [==============================] - 52s 83ms/step - loss: 0.0899 - accuracy: 0.9696 - val_loss: 0.6741 - val_accuracy: 0.7848\n",
+      "625/625 [==============================] - 66s 106ms/step - loss: 0.4100 - accuracy: 0.8122 - val_loss: 0.6718 - val_accuracy: 0.7016\n",
       "Epoch 8/10\n",
-      "625/625 [==============================] - 52s 83ms/step - loss: 0.0659 - accuracy: 0.9777 - val_loss: 0.6549 - val_accuracy: 0.8022\n",
+      "625/625 [==============================] - 66s 106ms/step - loss: 0.3749 - accuracy: 0.8281 - val_loss: 0.7762 - val_accuracy: 0.6348\n",
       "Epoch 9/10\n",
-      "625/625 [==============================] - 52s 83ms/step - loss: 0.0710 - accuracy: 0.9758 - val_loss: 0.6970 - val_accuracy: 0.7830\n",
+      "625/625 [==============================] - 66s 106ms/step - loss: 0.2711 - accuracy: 0.8813 - val_loss: 0.7503 - val_accuracy: 0.6892\n",
       "Epoch 10/10\n",
-      "625/625 [==============================] - 52s 83ms/step - loss: 0.0476 - accuracy: 0.9839 - val_loss: 0.7840 - val_accuracy: 0.8038\n"
+      "625/625 [==============================] - 66s 106ms/step - loss: 0.1665 - accuracy: 0.9367 - val_loss: 0.7172 - val_accuracy: 0.7454\n"
      ]
     }
    ],
@@ -1394,14 +1394,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "782/782 [==============================] - 28s 36ms/step - loss: 0.7896 - accuracy: 0.8070\n"
+      "782/782 [==============================] - 39s 50ms/step - loss: 0.7098 - accuracy: 0.7478\n"
      ]
     }
    ],
@@ -1411,14 +1411,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Test Acc.: 80.70%\n"
+      "Test Acc.: 74.78%\n"
      ]
     }
    ],
@@ -1437,7 +1437,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
@@ -1445,7 +1445,7 @@
      "output_type": "stream",
      "text": [
       "Vocab-size: 87007\n",
-      "Model: \"sequential_7\"\n",
+      "Model: \"sequential_6\"\n",
       "_________________________________________________________________\n",
       "Layer (type)                 Output Shape              Param #   \n",
       "=================================================================\n",
@@ -1490,7 +1490,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [
     {
@@ -1498,25 +1498,25 @@
      "output_type": "stream",
      "text": [
       "Epoch 1/10\n",
-      "625/625 [==============================] - 178s 285ms/step - loss: 0.6991 - accuracy: 0.5019 - val_loss: 0.0000e+00 - val_accuracy: 0.0000e+00\n",
+      "625/625 [==============================] - 225s 361ms/step - loss: 0.6993 - accuracy: 0.5034 - val_loss: 0.0000e+00 - val_accuracy: 0.0000e+00\n",
       "Epoch 2/10\n",
-      "625/625 [==============================] - 177s 283ms/step - loss: 0.6983 - accuracy: 0.4920 - val_loss: 0.6931 - val_accuracy: 0.5108\n",
+      "625/625 [==============================] - 225s 361ms/step - loss: 0.6993 - accuracy: 0.5006 - val_loss: 0.6998 - val_accuracy: 0.5010\n",
       "Epoch 3/10\n",
-      "625/625 [==============================] - 177s 283ms/step - loss: 0.6949 - accuracy: 0.4975 - val_loss: 0.6930 - val_accuracy: 0.5128\n",
+      "625/625 [==============================] - 226s 361ms/step - loss: 0.6968 - accuracy: 0.5034 - val_loss: 0.6960 - val_accuracy: 0.5004\n",
       "Epoch 4/10\n",
-      "625/625 [==============================] - 177s 283ms/step - loss: 0.6945 - accuracy: 0.5002 - val_loss: 0.6929 - val_accuracy: 0.5126\n",
+      "625/625 [==============================] - 225s 360ms/step - loss: 0.6958 - accuracy: 0.5038 - val_loss: 0.6953 - val_accuracy: 0.5002\n",
       "Epoch 5/10\n",
-      "625/625 [==============================] - 177s 283ms/step - loss: 0.6944 - accuracy: 0.5058 - val_loss: 0.6936 - val_accuracy: 0.5056\n",
+      "625/625 [==============================] - 226s 361ms/step - loss: 0.6952 - accuracy: 0.5045 - val_loss: 0.6947 - val_accuracy: 0.4952\n",
       "Epoch 6/10\n",
-      "625/625 [==============================] - 177s 283ms/step - loss: 0.6959 - accuracy: 0.5045 - val_loss: 0.6932 - val_accuracy: 0.5118\n",
+      "625/625 [==============================] - 226s 361ms/step - loss: 0.6945 - accuracy: 0.5080 - val_loss: 0.6945 - val_accuracy: 0.4956\n",
       "Epoch 7/10\n",
-      "625/625 [==============================] - 177s 283ms/step - loss: 0.6943 - accuracy: 0.5029 - val_loss: 0.6932 - val_accuracy: 0.5124\n",
+      "625/625 [==============================] - 225s 360ms/step - loss: 0.6940 - accuracy: 0.5109 - val_loss: 0.6945 - val_accuracy: 0.4960\n",
       "Epoch 8/10\n",
-      "625/625 [==============================] - 177s 283ms/step - loss: 0.6937 - accuracy: 0.5059 - val_loss: 0.6932 - val_accuracy: 0.5128\n",
+      "625/625 [==============================] - 225s 360ms/step - loss: 0.6936 - accuracy: 0.5110 - val_loss: 0.6963 - val_accuracy: 0.4968\n",
       "Epoch 9/10\n",
-      "625/625 [==============================] - 177s 283ms/step - loss: 0.6934 - accuracy: 0.5053 - val_loss: 0.6930 - val_accuracy: 0.5124\n",
+      "625/625 [==============================] - 226s 361ms/step - loss: 0.6934 - accuracy: 0.5101 - val_loss: 0.6948 - val_accuracy: 0.4960\n",
       "Epoch 10/10\n",
-      "625/625 [==============================] - 177s 283ms/step - loss: 0.6931 - accuracy: 0.5088 - val_loss: 0.6930 - val_accuracy: 0.5130\n"
+      "625/625 [==============================] - 225s 361ms/step - loss: 0.6935 - accuracy: 0.5096 - val_loss: 0.6936 - val_accuracy: 0.4986\n"
      ]
     }
    ],
@@ -1547,7 +1547,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
@@ -1584,1488 +1584,183 @@
       "    }\"\"\",\n",
       "    redistribution_info=,\n",
       ")\n",
-      "\n",
-      "\u001b[1mDownloading and preparing dataset imdb_reviews (80.23 MiB) to /home/raschka/tensorflow_datasets/imdb_reviews/plain_text/0.1.0...\u001b[0m\n"
+      "\n"
      ]
     },
     {
      "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "de79e74b2c204490bac94be76c59f0a5",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=1, bar_style='info', description='Dl Completed...', max=1, style=ProgressStyl…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "538b24048f2a4307b2570bbcd6b4ab00",
-       "version_major": 2,
-       "version_minor": 0
-      },
       "text/plain": [
-       "HBox(children=(IntProgress(value=1, bar_style='info', description='Dl Size...', max=1, style=ProgressStyle(des…"
+       "dict_keys(['test', 'train', 'unsupervised'])"
       ]
      },
+     "execution_count": 40,
      "metadata": {},
-     "output_type": "display_data"
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "imdb_bldr = tfds.builder('imdb_reviews')\n",
+    "print(imdb_bldr.info)\n",
+    "\n",
+    "imdb_bldr.download_and_prepare()\n",
+    "\n",
+    "datasets = imdb_bldr.as_dataset(shuffle_files=False)\n",
+    "\n",
+    "datasets.keys()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "imdb_train = datasets['train']\n",
+    "imdb_train = datasets['test']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### B -- Tokenizer and Encoder\n",
+    "\n",
+    " * `tfds.features.text.Tokenizer`: https://www.tensorflow.org/datasets/api_docs/python/tfds/features/text/Tokenizer\n",
+    " * `tfds.features.text.TokenTextEncoder`: https://www.tensorflow.org/datasets/api_docs/python/tfds/features/text/TokenTextEncoder\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\n",
-      "\n",
-      "\n",
-      "\n"
+      "<TokenTextEncoder vocab_size=6>\n",
+      "[2, 1, 4, 3]\n",
+      "[2, 1, 4, 3, 5, 5, 5, 5, 5, 5]\n"
      ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "5be14932e00141e296b58813c2aed3e3",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
+    }
+   ],
+   "source": [
+    "vocab_set = {'a', 'b', 'c', 'd'}\n",
+    "encoder = tfds.features.text.TokenTextEncoder(vocab_set)\n",
+    "print(encoder)\n",
+    "\n",
+    "print(encoder.encode(b'a b c d, , : .'))\n",
+    "\n",
+    "print(encoder.encode(b'a b c d e f g h i z'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### C -- Text Pre-processing with Keras "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\r"
+      "[[1, 2, 3, 4], [5, 6, 7, 8]]\n"
      ]
     },
     {
      "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "00f49d8f9d6148fea84e304e312c4a3f",
-       "version_major": 2,
-       "version_minor": 0
-      },
       "text/plain": [
-       "HBox(children=(IntProgress(value=0, description='Shuffling...', max=10, style=ProgressStyle(description_width=…"
+       "array([[0, 0, 0, 0, 0, 0, 1, 2, 3, 4],\n",
+       "       [0, 0, 0, 0, 0, 0, 5, 6, 7, 8]], dtype=int32)"
       ]
      },
+     "execution_count": 43,
      "metadata": {},
-     "output_type": "display_data"
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "TOP_K = 200\n",
+    "MAX_LEN = 10\n",
+    "\n",
+    "tokenizer = tf.keras.preprocessing.text.Tokenizer(num_words=TOP_K)\n",
+    "\n",
+    "tokenizer.fit_on_texts(['this is an example', 'je suis en forme '])\n",
+    "sequences = tokenizer.texts_to_sequences(['this is an example', 'je suis en forme '])\n",
+    "print(sequences)\n",
+    "\n",
+    "tf.keras.preprocessing.sequence.pad_sequences(sequences, maxlen=MAX_LEN)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "WARNING:tensorflow:From /home/raschka/miniconda3/lib/python3.7/site-packages/tensorflow_datasets/core/file_format_adapter.py:209: tf_record_iterator (from tensorflow.python.lib.io.tf_record) is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "Use eager execution and: \n",
-      "`tf.data.TFRecordDataset(path)`\n"
+      "25000\n",
+      "(25000, 500)\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "TOP_K = 20000\n",
+    "MAX_LEN = 500\n",
+    "\n",
+    "tokenizer = tf.keras.preprocessing.text.Tokenizer(num_words=TOP_K)\n",
+    "\n",
+    "tokenizer.fit_on_texts(\n",
+    "    [example['text'].numpy().decode('utf-8') \n",
+    "     for example in imdb_train])\n",
+    "\n",
+    "x_train = tokenizer.texts_to_sequences(\n",
+    "    [example['text'].numpy().decode('utf-8')\n",
+    "     for example in imdb_train])\n",
+    "\n",
+    "print(len(x_train))\n",
+    "\n",
+    "\n",
+    "x_train_padded = tf.keras.preprocessing.sequence.pad_sequences(\n",
+    "    x_train, maxlen=MAX_LEN)\n",
+    "\n",
+    "print(x_train_padded.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### D -- Embedding\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
     {
-     "name": "stderr",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
-      "WARNING:tensorflow:From /home/raschka/miniconda3/lib/python3.7/site-packages/tensorflow_datasets/core/file_format_adapter.py:209: tf_record_iterator (from tensorflow.python.lib.io.tf_record) is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "Use eager execution and: \n",
-      "`tf.data.TFRecordDataset(path)`\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "38d1a86661954cae828ad256d40a92bc",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=1, bar_style='info', description='Reading...', max=1, style=ProgressStyle(des…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ccc6d64908494ca69c31c17ed9d3fc8a",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=0, description='Writing...', max=2500, style=ProgressStyle(description_width=…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9cdb75bbe50c44d7b98ddee86990a056",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=1, bar_style='info', description='Reading...', max=1, style=ProgressStyle(des…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f629460148724702a0dc07f39a1036fb",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=0, description='Writing...', max=2500, style=ProgressStyle(description_width=…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "5d2bc744109141e8af87a090b7f0bd79",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=1, bar_style='info', description='Reading...', max=1, style=ProgressStyle(des…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f65100cfcff9421891c3aca5f92e6e35",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=0, description='Writing...', max=2500, style=ProgressStyle(description_width=…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b0357aa53a274fc6aba4e9383937ce32",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=1, bar_style='info', description='Reading...', max=1, style=ProgressStyle(des…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c9b5a917709f4ca58804e20b6e57763e",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=0, description='Writing...', max=2500, style=ProgressStyle(description_width=…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "aa9565276fec427083574782b7a978b7",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=1, bar_style='info', description='Reading...', max=1, style=ProgressStyle(des…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7ca461ea11be4af184f432cbb612e5f2",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=0, description='Writing...', max=2500, style=ProgressStyle(description_width=…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "636e631bbd594aebb0c33111e0ee7d7d",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=1, bar_style='info', description='Reading...', max=1, style=ProgressStyle(des…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0932df82a331465c8d82cc69dbb0521f",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=0, description='Writing...', max=2500, style=ProgressStyle(description_width=…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "56b715c451f24fa9bbfe8d7214e89d0e",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=1, bar_style='info', description='Reading...', max=1, style=ProgressStyle(des…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d2312c56af214db181801c1a35437553",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=0, description='Writing...', max=2500, style=ProgressStyle(description_width=…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3d058093762f40beb363ee7c3e282609",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=1, bar_style='info', description='Reading...', max=1, style=ProgressStyle(des…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3a9ecefeac964b07bf14b89c21575735",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=0, description='Writing...', max=2500, style=ProgressStyle(description_width=…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e641fc32d8cd43a985f4aa8467c3f1c8",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=1, bar_style='info', description='Reading...', max=1, style=ProgressStyle(des…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "fdf16f70917a4b47b7840b4b6186df4d",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=0, description='Writing...', max=2500, style=ProgressStyle(description_width=…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c2cdeaf540764609a896ec59e38feaaa",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=1, bar_style='info', description='Reading...', max=1, style=ProgressStyle(des…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b0ca0e9241b540139ce759425c71890d",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=0, description='Writing...', max=2500, style=ProgressStyle(description_width=…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\r"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "11bd659783ec45e98be7caba5c11ef18",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\r"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "cbb812579b0e446dafefdf5eccdc8612",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=0, description='Shuffling...', max=10, style=ProgressStyle(description_width=…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "5b93a9d9b9c549be95ea73d507693ba5",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=1, bar_style='info', description='Reading...', max=1, style=ProgressStyle(des…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f32dae170cd449a69ec862d20641133a",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=0, description='Writing...', max=2500, style=ProgressStyle(description_width=…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "fab06d6b0b3c469d8f4051bd96e9aa71",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=1, bar_style='info', description='Reading...', max=1, style=ProgressStyle(des…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "71d2f0d35e4e495e8eb6eaa45ec28c32",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=0, description='Writing...', max=2500, style=ProgressStyle(description_width=…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d03e6d401cfc4d09a784b3fedc9c4df9",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=1, bar_style='info', description='Reading...', max=1, style=ProgressStyle(des…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9f158c0c614348029a1573a5ee738f49",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=0, description='Writing...', max=2500, style=ProgressStyle(description_width=…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "77c83c93259f48cd96bdc3b9f999ac12",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=1, bar_style='info', description='Reading...', max=1, style=ProgressStyle(des…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8ec339e2dfa34ae5936d0f2967ce4ed1",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=0, description='Writing...', max=2500, style=ProgressStyle(description_width=…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7f21b47ffde14506bb446928510dad38",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=1, bar_style='info', description='Reading...', max=1, style=ProgressStyle(des…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "941941e809f7481db75aaac0db3611d1",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=0, description='Writing...', max=2500, style=ProgressStyle(description_width=…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f9f58fc3f4344318b7bf62710b51d924",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=1, bar_style='info', description='Reading...', max=1, style=ProgressStyle(des…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c52c15d2303a4d6cb4ab4a64018d69c5",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=0, description='Writing...', max=2500, style=ProgressStyle(description_width=…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3a8d6944bff74d36bcd2e17639e7f64b",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=1, bar_style='info', description='Reading...', max=1, style=ProgressStyle(des…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "47763715394b49618453989273c15a38",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=0, description='Writing...', max=2500, style=ProgressStyle(description_width=…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c50a3c53780b4e4297a078ada405734a",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=1, bar_style='info', description='Reading...', max=1, style=ProgressStyle(des…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b175e9e1fc074ccb9df408b1d8c7481a",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=0, description='Writing...', max=2500, style=ProgressStyle(description_width=…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "45291f47ad2a48f79c707485c2d8994e",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=1, bar_style='info', description='Reading...', max=1, style=ProgressStyle(des…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a2ca9a9a1a044a30bc234e4b763640d3",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=0, description='Writing...', max=2500, style=ProgressStyle(description_width=…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a6323fb1eb2847e7b15778840a4df2b1",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=1, bar_style='info', description='Reading...', max=1, style=ProgressStyle(des…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4402375d75d541ecb87ec1581d0087c0",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=0, description='Writing...', max=2500, style=ProgressStyle(description_width=…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\r"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "885b9473e3d544a3b6b97a2e6cd84210",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\r"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c332b6392ce14b63aedf87ab9ba548d9",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=0, description='Shuffling...', max=20, style=ProgressStyle(description_width=…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9f134ac7d9794013acd4de7fd85df3c8",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=1, bar_style='info', description='Reading...', max=1, style=ProgressStyle(des…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "703bd98b621c4db8b492b1009ba6b8f2",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=0, description='Writing...', max=2500, style=ProgressStyle(description_width=…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6795e1b6cc794f6f971b2436787a51f6",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=1, bar_style='info', description='Reading...', max=1, style=ProgressStyle(des…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "bb3d851c2a63462c8856c6c58aa1809e",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=0, description='Writing...', max=2500, style=ProgressStyle(description_width=…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ebe4b3cc57ce4f87a7095e271c481429",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=1, bar_style='info', description='Reading...', max=1, style=ProgressStyle(des…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6e9a9b4e0f01488eaf79dcb62906fd28",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=0, description='Writing...', max=2500, style=ProgressStyle(description_width=…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2e89ff3e960f4fae9ada9990cbf53fc1",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=1, bar_style='info', description='Reading...', max=1, style=ProgressStyle(des…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "fe268d74efdb4cee9e6283285b390050",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=0, description='Writing...', max=2500, style=ProgressStyle(description_width=…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "5f10167829704d9aafb8b4908f8598fd",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=1, bar_style='info', description='Reading...', max=1, style=ProgressStyle(des…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a030df39d3b847649964e0efda3914e1",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=0, description='Writing...', max=2500, style=ProgressStyle(description_width=…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0f5f4d2d423c46e4aa5267bc9394e9e2",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=1, bar_style='info', description='Reading...', max=1, style=ProgressStyle(des…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e2ebfe1b9d2a407081a6159adb873613",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=0, description='Writing...', max=2500, style=ProgressStyle(description_width=…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2b37cd9626124d669a42162466e49b9b",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=1, bar_style='info', description='Reading...', max=1, style=ProgressStyle(des…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "22bf09a24a2d4972b82c2caf15bd15bc",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=0, description='Writing...', max=2500, style=ProgressStyle(description_width=…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "eeed7bd1d3074be6baaabc7bad706565",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=1, bar_style='info', description='Reading...', max=1, style=ProgressStyle(des…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "fc32c7db4618406384f739c816bd9cfd",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=0, description='Writing...', max=2500, style=ProgressStyle(description_width=…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2810227adcf54e479c0eb3a3b8d13b4f",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=1, bar_style='info', description='Reading...', max=1, style=ProgressStyle(des…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "5252aa813ea04294a5fceec21aeed1ea",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=0, description='Writing...', max=2500, style=ProgressStyle(description_width=…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "169e22f658ac41d4809753405ccb3df2",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=1, bar_style='info', description='Reading...', max=1, style=ProgressStyle(des…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "861198bcff3842f1a35544fd982505de",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=0, description='Writing...', max=2500, style=ProgressStyle(description_width=…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8fad9d16feb3446191b7a0d8430b5886",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=1, bar_style='info', description='Reading...', max=1, style=ProgressStyle(des…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "915c8888fa5b4a24ba3ec935f2924955",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=0, description='Writing...', max=2500, style=ProgressStyle(description_width=…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0f378c95e1d448a3b0fab61759b91d69",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=1, bar_style='info', description='Reading...', max=1, style=ProgressStyle(des…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c68480a4635e4b7993649df9f50c173f",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=0, description='Writing...', max=2500, style=ProgressStyle(description_width=…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1d1cc390ed3d4dfd86de7743a39f1e59",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=1, bar_style='info', description='Reading...', max=1, style=ProgressStyle(des…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b19fe83393624220a6dace6c965e2830",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=0, description='Writing...', max=2500, style=ProgressStyle(description_width=…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c30986d29dc64281b62f35fce1ffdbfb",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=1, bar_style='info', description='Reading...', max=1, style=ProgressStyle(des…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9916f0608c454c558fe349c8a308388b",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=0, description='Writing...', max=2500, style=ProgressStyle(description_width=…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0027ed54de6e4010be6d833d38303ebd",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=1, bar_style='info', description='Reading...', max=1, style=ProgressStyle(des…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f395c711d04f46fc92a97153f2fd0688",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=0, description='Writing...', max=2500, style=ProgressStyle(description_width=…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ea071488d6464996981af2af51c043d3",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=1, bar_style='info', description='Reading...', max=1, style=ProgressStyle(des…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0dd6e0e5e15647d9acc9d8a7096e5ecb",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=0, description='Writing...', max=2500, style=ProgressStyle(description_width=…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0832cbbf73a94d5ab281b4cb26eba9d6",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=1, bar_style='info', description='Reading...', max=1, style=ProgressStyle(des…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "592be5c9161d444d8b81b8be08a879e5",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=0, description='Writing...', max=2500, style=ProgressStyle(description_width=…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "5537626415c94ceea6db81b5043cb861",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=1, bar_style='info', description='Reading...', max=1, style=ProgressStyle(des…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3fc229b5336f4b81943586079469dff9",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=0, description='Writing...', max=2500, style=ProgressStyle(description_width=…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e336e5505bdc48eb91ce493b942f098a",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=1, bar_style='info', description='Reading...', max=1, style=ProgressStyle(des…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4daf1ba7e9bf4cb4b21948197d8b3e7f",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=0, description='Writing...', max=2500, style=ProgressStyle(description_width=…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1d89a6af1797401ba8c9b64755bf5de6",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=1, bar_style='info', description='Reading...', max=1, style=ProgressStyle(des…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4a147ea88311404488d6601531ba5350",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntProgress(value=0, description='Writing...', max=2500, style=ProgressStyle(description_width=…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[1mDataset imdb_reviews downloaded and prepared to /home/raschka/tensorflow_datasets/imdb_reviews/plain_text/0.1.0. Subsequent calls will reuse this data.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "dict_keys(['test', 'train', 'unsupervised'])"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "imdb_bldr = tfds.builder('imdb_reviews')\n",
-    "print(imdb_bldr.info)\n",
-    "\n",
-    "imdb_bldr.download_and_prepare()\n",
-    "\n",
-    "datasets = imdb_bldr.as_dataset(shuffle_files=False)\n",
-    "\n",
-    "datasets.keys()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "imdb_train = datasets['train']\n",
-    "imdb_train = datasets['test']"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### B -- Tokenizer and Encoder\n",
-    "\n",
-    " * `tfds.features.text.Tokenizer`: https://www.tensorflow.org/datasets/api_docs/python/tfds/features/text/Tokenizer\n",
-    " * `tfds.features.text.TokenTextEncoder`: https://www.tensorflow.org/datasets/api_docs/python/tfds/features/text/TokenTextEncoder\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<TokenTextEncoder vocab_size=6>\n",
-      "[4, 3, 2, 1]\n",
-      "[4, 3, 2, 1, 5, 5, 5, 5, 5, 5]\n"
-     ]
-    }
-   ],
-   "source": [
-    "vocab_set = {'a', 'b', 'c', 'd'}\n",
-    "encoder = tfds.features.text.TokenTextEncoder(vocab_set)\n",
-    "print(encoder)\n",
-    "\n",
-    "print(encoder.encode(b'a b c d, , : .'))\n",
-    "\n",
-    "print(encoder.encode(b'a b c d e f g h i z'))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### C -- Text Pre-processing with Keras "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[1, 2, 3, 4], [5, 6, 7, 8]]\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "array([[0, 0, 0, 0, 0, 0, 1, 2, 3, 4],\n",
-       "       [0, 0, 0, 0, 0, 0, 5, 6, 7, 8]], dtype=int32)"
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "TOP_K = 200\n",
-    "MAX_LEN = 10\n",
-    "\n",
-    "tokenizer = tf.keras.preprocessing.text.Tokenizer(num_words=TOP_K)\n",
-    "\n",
-    "tokenizer.fit_on_texts(['this is an example', 'je suis en forme '])\n",
-    "sequences = tokenizer.texts_to_sequences(['this is an example', 'je suis en forme '])\n",
-    "print(sequences)\n",
-    "\n",
-    "tf.keras.preprocessing.sequence.pad_sequences(sequences, maxlen=MAX_LEN)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "25000\n",
-      "(25000, 500)\n"
-     ]
-    }
-   ],
-   "source": [
-    "TOP_K = 20000\n",
-    "MAX_LEN = 500\n",
-    "\n",
-    "tokenizer = tf.keras.preprocessing.text.Tokenizer(num_words=TOP_K)\n",
-    "\n",
-    "tokenizer.fit_on_texts(\n",
-    "    [example['text'].numpy().decode('utf-8') \n",
-    "     for example in imdb_train])\n",
-    "\n",
-    "x_train = tokenizer.texts_to_sequences(\n",
-    "    [example['text'].numpy().decode('utf-8')\n",
-    "     for example in imdb_train])\n",
-    "\n",
-    "print(len(x_train))\n",
-    "\n",
-    "\n",
-    "x_train_padded = tf.keras.preprocessing.sequence.pad_sequences(\n",
-    "    x_train, maxlen=MAX_LEN)\n",
-    "\n",
-    "print(x_train_padded.shape)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### D -- Embedding\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[-0.0208060984 0.0142502077 0.0475785471 -0.00649005175]\n",
-      " [-0.00420691818 -0.0375086069 -0.00477621704 0.00311584398]\n",
-      " [0.028728161 -0.0440448038 -0.0428906195 -0.019158531]\n",
-      " [-0.0248817336 0.0408470519 -0.00285203382 -0.0257614851]\n",
-      " [0.0443614833 0.00331580639 0.043055404 -0.011118304]\n",
-      " [-0.0281324144 0.00720113516 0.0192188732 -0.0186921246]]\n",
-      "TensorShape([6, 4])\n",
-      "[[-0.0208060984 0.0142502077 0.0475785471 -0.00649005175]]\n"
+      "[[-0.0208060984 0.0142502077 0.0475785471 -0.00649005175]\n",
+      " [-0.00420691818 -0.0375086069 -0.00477621704 0.00311584398]\n",
+      " [0.028728161 -0.0440448038 -0.0428906195 -0.019158531]\n",
+      " [-0.0248817336 0.0408470519 -0.00285203382 -0.0257614851]\n",
+      " [0.0443614833 0.00331580639 0.043055404 -0.011118304]\n",
+      " [-0.0281324144 0.00720113516 0.0192188732 -0.0186921246]]\n",
+      "TensorShape([6, 4])\n",
+      "[[-0.0208060984 0.0142502077 0.0475785471 -0.00649005175]]\n"
      ]
     }
    ],
@@ -3104,7 +1799,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [
     {
@@ -3112,7 +1807,7 @@
      "output_type": "stream",
      "text": [
       "[NbConvertApp] Converting notebook ch16_part1.ipynb to script\n",
-      "[NbConvertApp] Writing 16576 bytes to ch16_part1.py\n"
+      "[NbConvertApp] Writing 17277 bytes to ch16_part1.py\n"
      ]
     }
    ],
diff --git a/ch16/ch16_part1.py b/ch16/ch16_part1.py
index 0a4e6cf8..19fdf42a 100644
--- a/ch16/ch16_part1.py
+++ b/ch16/ch16_part1.py
@@ -31,22 +31,57 @@
 
 
 
+
+
+
+
 # # Introducing sequential data
 # 
 # ## Modeling sequential data⁠—order matters
 # 
 # ## Representing sequences
 # 
+# 
+
+
+
+
+
 # ## The different categories of sequence modeling
 
+
+
+
+
 # # RNNs for modeling sequences
 # 
 # ## Understanding the RNN looping mechanism
 # 
+
+
+
+
+
+
+
+
+
 # ## Computing activations in an RNN
 # 
+
+
+
+
+
+
+
+
+
 # ## Hidden-recurrence vs. output-recurrence
-# 
+
+
+
+
 
 
 
@@ -98,9 +133,19 @@
 
 
 # ## The challenges of learning long-range interactions
+# 
+
+
+
+
+
 # 
 # ## Long Short-Term Memory cells 
 
+
+
+
+
 # # Implementing RNNs for sequence modeling in TensorFlow
 # 
 # ## Project one: predicting the sentiment of IMDb movie reviews
@@ -261,7 +306,7 @@ def encode_map_fn(text, label):
     32, padded_shapes=([-1],[]))
 
 
-# #### Embedding layers for sentence encoding
+# ### Embedding layers for sentence encoding
 # 
 # 
 #  * `input_dim`: number of words, i.e. maximum integer index + 1.
@@ -281,6 +326,10 @@ def encode_map_fn(text, label):
 
 
 
+
+
+
+
 model = tf.keras.Sequential()
 
 model.add(Embedding(input_dim=100,