Remove incomplete RNN example.

samples/core/guide/autograph.ipynb

@@ -711,7 +711,7 @@
         "\n",
         "<!--TODO(markdaoust) link to full examples  or these referenced models.-->\n",
         "\n",
-        "The easiest way is to `@autograph.convert()` the `call` method. See the [keras guide](https://tensorflow.org/guide/keras#build_advanced_models) for details on how to build on these classes. \n",
+        "The easiest way to use autograph is keras layers and models is to `@autograph.convert()` the `call` method. See the [keras guide](https://tensorflow.org/guide/keras#build_advanced_models) for details on how to build on these classes. \n",
         "\n",
         "Here is a simple example of the [stocastic network depth](https://arxiv.org/abs/1603.09382) technique :"
       ]
@@ -857,102 +857,6 @@
       "execution_count": 0,
       "outputs": []
     },
-    {
-      "metadata": {
-        "id": "cpUD21HQWcOq",
-        "colab_type": "text"
-      },
-      "cell_type": "markdown",
-      "source": [
-        "### RNN Cells\n",
-        "\n",
-        "The [standard approach](https://www.tensorflow.org/api_docs/python/tf/keras/layers/RNN) to custom RNN cells has the same issues that are solved by autograph.\n",
-        "\n",
-        "Implementing RNN cells with `autograph` is not much different from implementing them [under eager execution](https://colab.sandbox.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/contrib/eager/python/examples/nmt_with_attention/nmt_with_attention.ipynb).\n",
-        "\n",
-        "To implement the prediction step in a keras model you could say:\n",
-        "\n"
-      ]
-    },
-    {
-      "metadata": {
-        "id": "798S1r-sJGfR",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "cell_type": "code",
-      "source": [
-        "class BahdanauAttention(tf.keras.Model):\n",
-        "  def __init__(self, units):\n",
-        "    super(BahdanauAttention, self).__init__()\n",
-        "    self.W1 = tf.keras.layers.Dense(units)\n",
-        "    self.W2 = tf.keras.layers.Dense(units)\n",
-        "    self.V = tf.keras.layers.Dense(1)\n",
-        "  \n",
-        "  def call(self, features, hidden):\n",
-        "    hidden_with_time_axis = tf.expand_dims(hidden, 1)\n",
-        "    score = tf.nn.tanh(self.W1(features) + self.W2(hidden_with_time_axis))\n",
-        "    attention_weights = tf.nn.softmax(self.V(score), axis=1)\n",
-        "    context_vector = attention_weights * features\n",
-        "    context_vector = tf.reduce_sum(context_vector, axis=1)\n",
-        "    return context_vector, attention_weights"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "metadata": {
-        "id": "qwH-QnmlGV6c",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "cell_type": "code",
-      "source": [
-        "class Decoder(tf.keras.Model):\n",
-        "    def __init__(self, vocab_size, embedding_dim, dec_units):\n",
-        "        super(Decoder, self).__init__()\n",
-        "        self.dec_units = dec_units\n",
-        "        self.embedding = layers.Embedding(vocab_size, embedding_dim)\n",
-        "        self.gru = layers.GRU(self.dec_units)\n",
-        "        self.fc = tf.keras.layers.Dense(vocab_size)\n",
-        "        self.attention = BahdanauAttention(self.dec_units)\n",
-        "        \n",
-        "    def call(self, enc_output):\n",
-        "        results = tf.keras\n",
-        "        hidden_with_time_axis = tf.expand_dims(hidden, 1)\n",
-        "        score = tf.nn.tanh(self.W1(enc_output) + self.W2(hidden_with_time_axis))\n",
-        "        \n",
-        "        # attention_weights shape == (batch_size, max_length, 1)\n",
-        "        # we get 1 at the last axis because we are applying score to self.V\n",
-        "        attention_weights = tf.nn.softmax(self.V(score), axis=1)\n",
-        "        \n",
-        "        # context_vector shape after sum == (batch_size, hidden_size)\n",
-        "        context_vector = attention_weights * enc_output\n",
-        "        context_vector = tf.reduce_sum(context_vector, axis=1)\n",
-        "        \n",
-        "        # x shape after passing through embedding == (batch_size, 1, embedding_dim)\n",
-        "        x = self.embedding(x)\n",
-        "        \n",
-        "        # x shape after concatenation == (batch_size, 1, embedding_dim + hidden_size)\n",
-        "        x = tf.concat([tf.expand_dims(context_vector, 1), x], axis=-1)\n",
-        "        \n",
-        "        # passing the concatenated vector to the GRU\n",
-        "        output, state = self.gru(x)\n",
-        "        \n",
-        "        # output shape == (batch_size * max_length, hidden_size)\n",
-        "        output = tf.reshape(output, (-1, output.shape[2]))\n",
-        "        \n",
-        "        # output shape == (batch_size * max_length, vocab)\n",
-        "        x = self.fc(output)\n",
-        "        \n",
-        "        return x, state, attention_weights\n",
-        "        \n",
-        "    def initialize_hidden_state(self):\n",
-        "        return tf.zeros((self.batch_sz, self.dec_units))"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
     {
       "metadata": {
         "id": "4LfnJjm0Bm0B",