Joke_Completion_Pure_TF2_Implementation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "TRSU0wfDCkrW"
   },
   "source": [
    "In this notebok we will try to create a model that generates jokes. What's different in this is that we will build everything from scratch using the TF2.0 API. \n",
    "\n",
    "The GPT2 model was pretrained on vast amount of data for days by HuggingFace. The model already knew the English language structures and context. We were only tweaking it for our own dataset. \n",
    "\n",
    "In this notebook we are creating our own model with random weights so we cannot expect the learning to be as accurate and fast as the GPT2 one. Yet it serves the purpose of providing a deeper understanding of how things are working inside the Transformer.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "TzbpWQz8-ugL"
   },
   "source": [
    "*Note:* The code for this notebook is mainly based on the code provided in the tutorial: https://github.com/tensorflow/docs/blob/master/site/en/tutorials/text/transformer.ipynb."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "szUF0_0td5TA"
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import tensorflow_datasets as tfds\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "_Re9xr5aDbuM"
   },
   "source": [
    "## Downloading the Data\n",
    "\n",
    "The dataset is available at https://www.kaggle.com/abhinavmoudgil95/short-jokes/data\n",
    "\n",
    "Sign In to Kaggle and begin the download process for **shortjokes.csv** file. Then copy the link address for the download file and update the _URL varaiable.\n",
    "\n",
    "Once done run all the cells in the notebook. Also, you can cancel the donwload process for the file. :)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "-iXcBqApCPC7"
   },
   "outputs": [],
   "source": [
    "_URL = 'https://storage.googleapis.com/kaggle-data-sets/781%2F1457%2Fcompressed%2Fshortjokes.csv.zip?GoogleAccessId=gcp-kaggle-com@kaggle-161607.iam.gserviceaccount.com&Expires=1591366620&Signature=B62abmVrT%2BrxZx98Vr4GcGpSGV8nTSmQVG7qBr771XvAfs7lQIgg0OQNGmhHAJ3BUlXTdTPh93RH45jtojG7qdec86oV%2FyXYk6HxyvDZgNzkC6vQsZv7A4WFt1Q9EZPBzhimECVrGwq5KwmwSDPntEYiZwQ9xHOvyefomUhLkUYpoi5xFbf%2BO79c2iAekgwbYLyAG%2BTNmVF4CGBCnlj%2F%2FqYT0anHefFnIfQ26msN3njQEq%2BMPLafSNAWGZYeZmvX8TAIZqkHz8VlgnpahK%2Bp3%2B0WUTWSTlii03q1JcAumVwB%2Fg2uByPlASohVNqqd1pkpLRpVuSsA%2BewR%2BdyGobhDw%3D%3D'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 71
    },
    "colab_type": "code",
    "id": "AWj0QsMBDeVu",
    "outputId": "27ca0221-be48-4beb-ed30-820c2926b03a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/kaggle-data-sets/781%2F1457%2Fcompressed%2Fshortjokes.csv.zip?GoogleAccessId=gcp-kaggle-com@kaggle-161607.iam.gserviceaccount.com&Expires=1591366620&Signature=B62abmVrT%2BrxZx98Vr4GcGpSGV8nTSmQVG7qBr771XvAfs7lQIgg0OQNGmhHAJ3BUlXTdTPh93RH45jtojG7qdec86oV%2FyXYk6HxyvDZgNzkC6vQsZv7A4WFt1Q9EZPBzhimECVrGwq5KwmwSDPntEYiZwQ9xHOvyefomUhLkUYpoi5xFbf%2BO79c2iAekgwbYLyAG%2BTNmVF4CGBCnlj%2F%2FqYT0anHefFnIfQ26msN3njQEq%2BMPLafSNAWGZYeZmvX8TAIZqkHz8VlgnpahK%2Bp3%2B0WUTWSTlii03q1JcAumVwB%2Fg2uByPlASohVNqqd1pkpLRpVuSsA%2BewR%2BdyGobhDw%3D%3D\n",
      "10305536/10299687 [==============================] - 0s 0us/step\n"
     ]
    }
   ],
   "source": [
    "path_to_zip = tf.keras.utils.get_file('shortjokes.csv.zip', origin=_URL, extract=True)\n",
    "\n",
    "FILE_PATH = os.path.join(os.path.dirname(path_to_zip), 'shortjokes.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "qXAViRJ0DqJC"
   },
   "source": [
    "## Preparing the Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "PwgJHtnnDxVO"
   },
   "source": [
    "### Extracting jokes list from CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "Vic4to7CDhHl",
    "outputId": "21f19748-57d6-4306-9ba6-e25734699be7"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Joke</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>[me narrating a documentary about narrators] \"...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Telling my daughter garlic is good for you. Go...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>I've been going through a really rough period ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>If I could have dinner with anyone, dead or al...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Two guys walk into a bar. The third guy ducks.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID                                               Joke\n",
       "0   1  [me narrating a documentary about narrators] \"...\n",
       "1   2  Telling my daughter garlic is good for you. Go...\n",
       "2   3  I've been going through a really rough period ...\n",
       "3   4  If I could have dinner with anyone, dead or al...\n",
       "4   5     Two guys walk into a bar. The third guy ducks."
      ]
     },
     "execution_count": 4,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jokes = pd.read_csv(FILE_PATH)\n",
    "jokes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "fkWCcuoYD1Xm"
   },
   "outputs": [],
   "source": [
    "jokeslist = jokes['Joke'].to_list()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "O8_S-Mw9UQrU"
   },
   "source": [
    "### Creating the Tokenizer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "T1jmmoi5yya3"
   },
   "source": [
    "We first need to build our tokenizer from the corpus that we have."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "VsAsh_rFPdtO"
   },
   "outputs": [],
   "source": [
    "tokenizer = tfds.features.text.SubwordTextEncoder.build_from_corpus(\n",
    "      jokeslist,\n",
    "      target_vocab_size=50000\n",
    "  )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "qcVEJ1qPuyaj",
    "outputId": "15bbcc0e-d7b2-4405-b215-012b662dd107"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "48089"
      ]
     },
     "execution_count": 7,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenizer.vocab_size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Wk6gfaWUy996"
   },
   "source": [
    "We add 2 tokens -> The start token and end token to our vocab thus our VOCAB_SIZE will have 2 added elements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "VpRED-XFUbkv"
   },
   "outputs": [],
   "source": [
    "# Define start and end token to indicate the start and end of a sentence\n",
    "START_TOKEN, END_TOKEN = [tokenizer.vocab_size], [tokenizer.vocab_size + 1]\n",
    "\n",
    "# Vocabulary size plus start and end token\n",
    "VOCAB_SIZE = tokenizer.vocab_size + 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "9UT04G4DqUcW"
   },
   "outputs": [],
   "source": [
    "MAX_LENGTH = 64\n",
    "\n",
    "## This function tokenizes the sentences with our tokenzier and also pads it \n",
    "## so that each is of size MAX_LENGTH\n",
    "def tokenize(sentence_list):\n",
    "  \n",
    "  ## Add start token and end token to the tokenized output of each sentence\n",
    "  tokenized_sentence_list = [\n",
    "    START_TOKEN + tokenizer.encode(sentence) + END_TOKEN \\\n",
    "    for sentence in sentence_list\n",
    "  ]\n",
    "\n",
    "  padded_list = tf.keras.preprocessing.sequence.pad_sequences(\n",
    "      tokenized_sentence_list, maxlen = MAX_LENGTH, padding='post'\n",
    "  )\n",
    "\n",
    "  return padded_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 153
    },
    "colab_type": "code",
    "id": "eDqX_8byqwdt",
    "outputId": "dc30497b-e41a-423b-c61d-5d57a52927da"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([48089, 47924,    34, 15902,     1,  4310,    46, 42599, 47948,\n",
       "         658,     3,    89, 47872,    14,   110,    77,   171, 47872,\n",
       "          57,   719,  2820, 47906, 47872,    36,  2977, 47867, 48090,\n",
       "           0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
       "           0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
       "           0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
       "           0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
       "           0], dtype=int32)"
      ]
     },
     "execution_count": 10,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jokes_as_tokens = tokenize(jokeslist)\n",
    "\n",
    "jokes_as_tokens[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "YDD2CF7BW8a8"
   },
   "outputs": [],
   "source": [
    "# A utility method to create a tf.data dataset from a List of jokes\n",
    "def tokenslist_to_dataset(tokens_list, \n",
    "                  shuffle=True, batch_size=64):\n",
    "\n",
    "  ## In our dataset the y_true will be 1 unit shifted version of \n",
    "  ## the input data. Thus we ignore the last index in X and the zeroth \n",
    "  ## index in y\n",
    "  dataset = tf.data.Dataset.from_tensor_slices(( \n",
    "          tokens_list[:, :-1],\n",
    "          tokens_list[:, 1:]\n",
    "  ))\n",
    "\n",
    "  if shuffle:\n",
    "    dataset = dataset.shuffle(buffer_size=len(tokens_list))\n",
    "\n",
    "  dataset = dataset.batch(batch_size)\n",
    "\n",
    "  return dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "gidgErMJpoEG"
   },
   "outputs": [],
   "source": [
    "jokes_dataset = tokenslist_to_dataset(jokes_as_tokens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 238
    },
    "colab_type": "code",
    "id": "LQniu_e9tDSy",
    "outputId": "6c2b8a44-9700-4a0b-de8a-c003e2fa203f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor(\n",
      "[48089   943   157 47914     7    16    18   160   309    25   134 47872\n",
      "     9  1453     2  1987    12   334   305    10   258  2097   209   125\n",
      "    49  1557     1   503     4 47898     7  1364  3617  5843 48090     0\n",
      "     0     0     0     0     0     0     0     0     0     0     0     0\n",
      "     0     0     0     0     0     0     0     0     0     0     0     0\n",
      "     0     0     0], shape=(63,), dtype=int32) tf.Tensor(\n",
      "[  943   157 47914     7    16    18   160   309    25   134 47872     9\n",
      "  1453     2  1987    12   334   305    10   258  2097   209   125    49\n",
      "  1557     1   503     4 47898     7  1364  3617  5843 48090     0     0\n",
      "     0     0     0     0     0     0     0     0     0     0     0     0\n",
      "     0     0     0     0     0     0     0     0     0     0     0     0\n",
      "     0     0     0], shape=(63,), dtype=int32)\n"
     ]
    }
   ],
   "source": [
    "for batchX, batchY in jokes_dataset:\n",
    "  print(batchX[0], batchY[0])\n",
    "  break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Ss0nKTSW0LGB"
   },
   "source": [
    "As can be seen we our dataset is correctly providing the shifted version of our X in our expected Y values. We can thus move on to build our model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ZPYOl-X2viLb"
   },
   "source": [
    "## Attention"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "JUKNyVPHvlIH"
   },
   "source": [
    "### Masked Self Attention\n",
    "\n",
    "The scaled dot-product attention function used by the transformer takes three inputs: Q (query), K (key), V (value). The equation used to calculate the attention weights is:\n",
    "\n",
    "$$\\Large{Attention(Q, K, V) = softmax_k(\\frac{QK^T}{\\sqrt{d_k}}) V} $$\n",
    "\n",
    "As the softmax normalization is done on the `key`, its values decide the amount of importance given to the `query`.\n",
    "\n",
    "The output represents the multiplication of the attention weights and the `value` vector. This ensures that the words we want to focus on are kept as is and the irrelevant words are flushed out.\n",
    "\n",
    "The dot-product attention is scaled by a factor of square root of the depth. This is done because for large values of depth, the dot product grows large in magnitude pushing the softmax function where it has small gradients resulting in a very hard softmax. \n",
    "\n",
    "The mask is multiplied with *-1e9 (close to negative infinity).* This is done because the mask is summed with the scaled matrix multiplication of `query` and `key` and is applied immediately before a softmax. The goal is to zero out these cells, and large negative inputs to softmax are near zero in the output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "txv7WHHjsdqO"
   },
   "outputs": [],
   "source": [
    "def masked_self_attention(query, key, value, mask):\n",
    "  \"\"\"Calculate the attention weights. \"\"\"\n",
    "  matmul_qk = tf.matmul(query, key, transpose_b=True)\n",
    "\n",
    "  # scale matmul_qk\n",
    "  depth = tf.cast(tf.shape(key)[-1], tf.float32)\n",
    "  logits = matmul_qk / tf.math.sqrt(depth)\n",
    "\n",
    "  # add the mask to zero out padding tokens\n",
    "  if mask is not None:\n",
    "    logits += (mask * -1e9)\n",
    "\n",
    "  # softmax is normalized on the last axis (seq_len_k)\n",
    "  attention_weights = tf.nn.softmax(logits, axis=-1)\n",
    "\n",
    "  output = tf.matmul(attention_weights, value)\n",
    "\n",
    "  return output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "iUbUFpK2vvqb"
   },
   "source": [
    "### Multi-head attention Layer\n",
    "\n",
    "\n",
    "Multi-head attention consists of four parts:\n",
    "* Linear layers and split into heads.\n",
    "* Masked Self attention.\n",
    "* Concatenation of heads.\n",
    "* Final linear layer.\n",
    "\n",
    "Basically, while single self attention can be used to capture a feature of language model using multiple heads could help understanding more correlations in the stucture of the grammar. This makes the generated sentences grammatically correct. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "L9eYssGIAG4h"
   },
   "outputs": [],
   "source": [
    "class MultiHeadAttention(tf.keras.layers.Layer):\n",
    "\n",
    "  def __init__(self, d_model, num_heads, name=\"multi_head_attention\"):\n",
    "    super(MultiHeadAttention, self).__init__(name=name)\n",
    "    self.num_heads = num_heads\n",
    "    self.d_model = d_model\n",
    "\n",
    "    assert d_model % self.num_heads == 0\n",
    "\n",
    "    self.depth = d_model // self.num_heads\n",
    "\n",
    "    self.query_dense = tf.keras.layers.Dense(units=d_model)\n",
    "    self.key_dense = tf.keras.layers.Dense(units=d_model)\n",
    "    self.value_dense = tf.keras.layers.Dense(units=d_model)\n",
    "\n",
    "    self.dense = tf.keras.layers.Dense(units=d_model)\n",
    "\n",
    "  def split_heads(self, inputs, batch_size):\n",
    "    inputs = tf.reshape(\n",
    "        inputs, shape=(batch_size, -1, self.num_heads, self.depth))\n",
    "    return tf.transpose(inputs, perm=[0, 2, 1, 3])\n",
    "\n",
    "  def call(self, inputs):\n",
    "    query, key, value, mask = inputs['query'], inputs['key'], inputs[\n",
    "        'value'], inputs['mask']\n",
    "    batch_size = tf.shape(query)[0]\n",
    "\n",
    "    # linear layers\n",
    "    query = self.query_dense(query)\n",
    "    key = self.key_dense(key)\n",
    "    value = self.value_dense(value)\n",
    "\n",
    "    # split heads\n",
    "    query = self.split_heads(query, batch_size)\n",
    "    key = self.split_heads(key, batch_size)\n",
    "    value = self.split_heads(value, batch_size)\n",
    "\n",
    "    # masked self attention\n",
    "    scaled_attention = masked_self_attention(query, key, value, mask)\n",
    "\n",
    "    scaled_attention = tf.transpose(scaled_attention, perm=[0, 2, 1, 3])\n",
    "\n",
    "    # concatenation of heads\n",
    "    concat_attention = tf.reshape(scaled_attention,\n",
    "                                  (batch_size, -1, self.d_model))\n",
    "\n",
    "    # final linear layer\n",
    "    outputs = self.dense(concat_attention)\n",
    "\n",
    "    return outputs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5-lueAT0wb1N"
   },
   "source": [
    "## Building the Transformer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Qr1pVKc-weEF"
   },
   "source": [
    "### Masking"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "qI-7zz5bwgxw"
   },
   "source": [
    "Before moving on to other section lets handle how we will mask our tokens for training. There are two masking needed. \n",
    "\n",
    "First, we need to mask the padding token since the model shouldn't see it as input. \n",
    "\n",
    "Secondly, at any given instant we need to ensure that the model is only looking at the words that has occured before the position in focus while predicting the next word for that position. \n",
    "\n",
    "The reason this is necessary is very easy to understand. If we don't apply this mask then for the model the best option is to predict the next word from the list as the value for current position. So, this way model can reduce it's loss very quickily although it isn't learning anything subtatntial since in actual example we wont have the next word. So, the masking step is necessary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "hz63G0oTwU4I"
   },
   "outputs": [],
   "source": [
    "## This function set the mask flag = 1 for all the places where the value is 0\n",
    "## 0 is token with which keras as padded our tokens.\n",
    "def get_padding_mask(x):\n",
    "  mask = tf.cast(tf.math.equal(x, 0), tf.float32)\n",
    "  # (batch_size, 1, 1, sequence length)\n",
    "  return mask[:, tf.newaxis, tf.newaxis, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "KXDILd5dwp9t"
   },
   "source": [
    "Look-ahead mask to mask the future tokens in a sequence.\n",
    "We also mask out pad tokens.\n",
    "\n",
    "i.e. To predict the third word, only the first and second word will be used"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3bM5FvqqwmTE"
   },
   "outputs": [],
   "source": [
    "def get_look_ahead_mask(x):\n",
    "  seq_len = tf.shape(x)[1]\n",
    "  look_ahead_mask = 1 - tf.linalg.band_part(tf.ones((seq_len, seq_len)), -1, 0)\n",
    "\n",
    "  ## Once we have the upper triangular matrix filled with 1s we need to ensure \n",
    "  ## that if theres a padding within we mask that as well. So we take maximum i.e.\n",
    "  ## logical or of the two masks\n",
    "  padding_mask = get_padding_mask(x)\n",
    "  return tf.maximum(look_ahead_mask, padding_mask)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "0B4Ta6amwzb9"
   },
   "source": [
    "### Positional encoding\n",
    "\n",
    "Since this model doesn't contain any recurrence or convolution, positional encoding is added to give the model some information about the relative position of the words in the sentence. \n",
    "\n",
    "The positional encoding vector is added to the embedding vector. Embeddings represent a token in a d-dimensional space where tokens with similar meaning will be closer to each other. But the embeddings do not encode the relative position of words in a sentence. So after adding the positional encoding, words will be closer to each other based on the *similarity of their meaning and their position in the sentence*, in the d-dimensional space.\n",
    "\n",
    "The formula for calculating the positional encoding is as follows:\n",
    "\n",
    "$$\\Large{PE_{(pos, 2i)} = sin(pos / 10000^{2i / d_{model}})} $$\n",
    "$$\\Large{PE_{(pos, 2i+1)} = cos(pos / 10000^{2i / d_{model}})} $$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "sLdxfWo_ws9z"
   },
   "outputs": [],
   "source": [
    "class PositionalEncoding(tf.keras.layers.Layer):\n",
    "\n",
    "  def __init__(self, position, d_model):\n",
    "    super(PositionalEncoding, self).__init__()\n",
    "    self.pos_encoding = self.positional_encoding(position, d_model)\n",
    "\n",
    "  def get_angles(self, position, i, d_model):\n",
    "    angles = 1 / tf.pow(10000, (2 * (i // 2)) / tf.cast(d_model, tf.float32))\n",
    "    return position * angles\n",
    "\n",
    "  def positional_encoding(self, position, d_model):\n",
    "    angle_rads = self.get_angles(\n",
    "        position=tf.range(position, dtype=tf.float32)[:, tf.newaxis],\n",
    "        i=tf.range(d_model, dtype=tf.float32)[tf.newaxis, :],\n",
    "        d_model=d_model)\n",
    "    # apply sin to even index in the array\n",
    "    sines = tf.math.sin(angle_rads[:, 0::2])\n",
    "    # apply cos to odd index in the array\n",
    "    cosines = tf.math.cos(angle_rads[:, 1::2])\n",
    "\n",
    "    pos_encoding = tf.concat([sines, cosines], axis=-1)\n",
    "    pos_encoding = pos_encoding[tf.newaxis, ...]\n",
    "    return tf.cast(pos_encoding, tf.float32)\n",
    "\n",
    "  def call(self, inputs):\n",
    "    return inputs + self.pos_encoding[:, :tf.shape(inputs)[1], :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "obFTZLl28kyn"
   },
   "source": [
    "The reason we need postional encoding is let's say we need to want the model to finish the sentence \n",
    "\n",
    "`I am well` and `am I well`\n",
    "\n",
    "Both will have same representation in bag of words format but yet former needs to end with `.` and latter with `?`.\n",
    "\n",
    "This information of relative position can be added by using positional encodings on our word embeddings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 283
    },
    "colab_type": "code",
    "id": "WEHBsqqJw1Zz",
    "outputId": "d0c3b071-2f2a-4020-aa40-58beb65cb588"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_pos_encoding = PositionalEncoding(50, 512)\n",
    "\n",
    "plt.pcolormesh(sample_pos_encoding.pos_encoding.numpy()[0], cmap='RdBu')\n",
    "plt.xlabel('Depth')\n",
    "plt.xlim((0, 512))\n",
    "plt.ylabel('Position')\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "qibkF0rF7XDQ"
   },
   "source": [
    "### Do we need an Encoder??"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "qETYnZ-O7bKP"
   },
   "source": [
    "Generally, when we search for a transformer model we see that the architecture consists of both an encoder and a decoder. The reason is that the architectures are proposed for language conversion, like French to English etc. \n",
    "\n",
    "In such cases we have an Encoder that gets the original langauge as input and learns how to encode the language such that it can be understood by the decoder. This information is then passed on to the decoder which uses it to understand the semantic of the text to be generated. \n",
    "\n",
    "Thus, the decoder by itself is simply language text generator for target language and encoder is the semantic extractor for source language. \n",
    "\n",
    "Since in our case we don't need to pass any semantic knowledge to the decoder and only need to generate the text we will be skipping the encoder part in our transformer."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "O3OFhE3nxcE3"
   },
   "source": [
    "### Decoder Layer\n",
    "\n",
    "Each decoder layer consists of sublayers:\n",
    "\n",
    "1.   Masked multi-head attention (with look ahead mask and padding mask)\n",
    "2.   2 dense layers followed by dropout\n",
    "\n",
    "Each of these sublayers has a residual connection around it followed by a layer normalization. The output of each sublayer is `LayerNorm(x + Sublayer(x))`. The normalization is done on the `d_model` (last) axis.\n",
    "\n",
    "As `query` receives the output from decoder's first attention block, and `key` receives the encoder output, the attention weights represent the importance given to the decoder's input based on the encoder's output. In other words, the decoder predicts the next word by looking at the encoder output and self-attending to its own output. See the demonstration above in the scaled dot product attention section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "D-yZI6EpxXW8"
   },
   "outputs": [],
   "source": [
    "def decoder_layer(units, d_model, num_heads, dropout, name=\"decoder_layer\"):\n",
    "  inputs = tf.keras.Input(shape=(None, d_model), name=\"inputs\")\n",
    "  look_ahead_mask = tf.keras.Input(\n",
    "      shape=(1, None, None), name=\"look_ahead_mask\")\n",
    "\n",
    "  attention1 = MultiHeadAttention(\n",
    "      d_model, num_heads, name=\"attention_1\")(inputs={\n",
    "          'query': inputs,\n",
    "          'key': inputs,\n",
    "          'value': inputs,\n",
    "          'mask': look_ahead_mask\n",
    "      })\n",
    "  attention2 = tf.keras.layers.LayerNormalization(\n",
    "      epsilon=1e-6)(attention1 + inputs)\n",
    "\n",
    "\n",
    "  outputs = tf.keras.layers.Dense(units=units, activation='relu')(attention2)\n",
    "  outputs = tf.keras.layers.Dense(units=d_model)(outputs)\n",
    "  outputs = tf.keras.layers.Dropout(rate=dropout)(outputs)\n",
    "  outputs = tf.keras.layers.LayerNormalization(\n",
    "      epsilon=1e-6)(outputs + attention2)\n",
    "\n",
    "  return tf.keras.Model(\n",
    "      inputs=[inputs, look_ahead_mask],\n",
    "      outputs=outputs,\n",
    "      name=name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 976
    },
    "colab_type": "code",
    "id": "JD_tEmh2xfYX",
    "outputId": "c6004f75-00d9-44f1-a3d8-0c1b0b087740"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 21,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_decoder_layer = decoder_layer(\n",
    "    units=512,\n",
    "    d_model=128,\n",
    "    num_heads=4,\n",
    "    dropout=0.3,\n",
    "    name=\"sample_decoder_layer\")\n",
    "\n",
    "tf.keras.utils.plot_model(\n",
    "    sample_decoder_layer, to_file='decoder_layer.png', show_shapes=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "rnMaBMtjxmaJ"
   },
   "source": [
    "### Decoder\n",
    "\n",
    "The Decoder consists of:\n",
    "1.   Output Embedding\n",
    "2.   Positional Encoding\n",
    "3.   N decoder layers\n",
    "\n",
    "The target is put through an embedding which is summed with the positional encoding. The output of this summation is the input to the decoder layers. The output of the decoder is the input to the final linear layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "IXKtQBQqxhED"
   },
   "outputs": [],
   "source": [
    "def decoder(vocab_size,\n",
    "            num_layers,\n",
    "            units,\n",
    "            d_model,\n",
    "            num_heads,\n",
    "            dropout,\n",
    "            name='Language_Decoder'):\n",
    "  inputs = tf.keras.Input(shape=(None,), name='inputs')\n",
    "  look_ahead_mask = tf.keras.Input(\n",
    "      shape=(1, None, None), name='look_ahead_mask')\n",
    "  \n",
    "  embeddings = tf.keras.layers.Embedding(vocab_size, d_model)(inputs)\n",
    "  embeddings *= tf.math.sqrt(tf.cast(d_model, tf.float32))\n",
    "  embeddings = PositionalEncoding(vocab_size, d_model)(embeddings)\n",
    "\n",
    "  outputs = tf.keras.layers.Dropout(rate=dropout)(embeddings)\n",
    "\n",
    "  for i in range(num_layers):\n",
    "    outputs = decoder_layer(\n",
    "        units=units,\n",
    "        d_model=d_model,\n",
    "        num_heads=num_heads,\n",
    "        dropout=dropout,\n",
    "        name='decoder_layer_{}'.format(i),\n",
    "    )(inputs=[outputs,  look_ahead_mask])\n",
    "\n",
    "  return tf.keras.Model(\n",
    "      inputs=[inputs, look_ahead_mask],\n",
    "      outputs=outputs,\n",
    "      name=name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "Ma9U6QkUxobt",
    "outputId": "f25badd1-57a0-4d33-adea-dc872a3f1515"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 23,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_decoder = decoder(\n",
    "    vocab_size=8192,\n",
    "    num_layers=8,\n",
    "    units=512,\n",
    "    d_model=128,\n",
    "    num_heads=4,\n",
    "    dropout=0.3,\n",
    "    name=\"sample_decoder\")\n",
    "\n",
    "tf.keras.utils.plot_model(\n",
    "    sample_decoder, to_file='decoder.png', show_shapes=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5JrF4kgTxvq2"
   },
   "source": [
    "### Transformer\n",
    "\n",
    "Transformer consists of the encoder, decoder and a final linear layer. The output of the decoder is the input to the linear layer and its output is returned."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "n-nltntMxqR0"
   },
   "outputs": [],
   "source": [
    "def transformer(vocab_size,\n",
    "                num_layers,\n",
    "                units,\n",
    "                d_model,\n",
    "                num_heads,\n",
    "                dropout,\n",
    "                name=\"transformer\"):\n",
    "  inputs = tf.keras.Input(shape=(None,), name=\"inputs\")\n",
    "\n",
    "  # mask the future tokens for decoder inputs at the 1st attention block\n",
    "  look_ahead_mask = tf.keras.layers.Lambda(\n",
    "      get_look_ahead_mask,\n",
    "      output_shape=(1, None, None),\n",
    "      name='look_ahead_mask')(inputs)\n",
    " \n",
    "\n",
    "  dec_outputs = decoder(\n",
    "      vocab_size=vocab_size,\n",
    "      num_layers=num_layers,\n",
    "      units=units,\n",
    "      d_model=d_model,\n",
    "      num_heads=num_heads,\n",
    "      dropout=dropout,\n",
    "  )(inputs=[inputs, look_ahead_mask])\n",
    "\n",
    "  outputs = tf.keras.layers.Dense(units=vocab_size, name=\"outputs\")(dec_outputs)\n",
    "\n",
    "  return tf.keras.Model(inputs=inputs, outputs=outputs, name=name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 422
    },
    "colab_type": "code",
    "id": "5melqfM_x1YW",
    "outputId": "2a39451a-3622-422d-a535-f003b8901f3e"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 25,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_transformer = transformer(\n",
    "    vocab_size=VOCAB_SIZE,\n",
    "    num_layers=4,\n",
    "    units=512,\n",
    "    d_model=128,\n",
    "    num_heads=4,\n",
    "    dropout=0.3,\n",
    "    name=\"sample_transformer\")\n",
    "\n",
    "tf.keras.utils.plot_model(\n",
    "    sample_transformer, to_file='transformer.png', show_shapes=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "peddXlj5yASD"
   },
   "source": [
    "## Train model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "seTP0sIEyC3X"
   },
   "source": [
    "### Initialize model\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Uosdh-E8x4L6"
   },
   "outputs": [],
   "source": [
    "tf.keras.backend.clear_session()\n",
    "\n",
    "# Hyper-parameters\n",
    "NUM_LAYERS = 8\n",
    "D_MODEL = 256\n",
    "NUM_HEADS = 8\n",
    "UNITS = 512\n",
    "DROPOUT = 0.1\n",
    "\n",
    "model = transformer(\n",
    "    vocab_size=VOCAB_SIZE,\n",
    "    num_layers=NUM_LAYERS,\n",
    "    units=UNITS,\n",
    "    d_model=D_MODEL,\n",
    "    num_heads=NUM_HEADS,\n",
    "    dropout=DROPOUT)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Rks7RGSmySer"
   },
   "source": [
    "### Loss function\n",
    "\n",
    "Since the target sequences are padded, it is important to apply a padding mask when calculating the loss."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Bef3vpH-yMKE"
   },
   "outputs": [],
   "source": [
    "def loss_function(y_true, y_pred):\n",
    "  y_true = tf.reshape(y_true, shape=(-1, MAX_LENGTH - 1))\n",
    "  \n",
    "  loss = tf.keras.losses.SparseCategoricalCrossentropy(\n",
    "      from_logits=True, reduction='none')(y_true, y_pred)\n",
    "\n",
    "  mask = tf.cast(tf.not_equal(y_true, 0), tf.float32)\n",
    "  loss = tf.multiply(loss, mask)\n",
    "\n",
    "  return tf.reduce_mean(loss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3axt8miVyWoP"
   },
   "source": [
    "### Custom learning rate\n",
    "\n",
    "Use the Adam optimizer with a custom learning rate scheduler according to the formula in the [paper](https://arxiv.org/abs/1706.03762).\n",
    "\n",
    "$$\\Large{lrate = d_{model}^{-0.5} * min(step{\\_}num^{-0.5}, step{\\_}num * warmup{\\_}steps^{-1.5})}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "zq2WdiRgyR0Y"
   },
   "outputs": [],
   "source": [
    "class CustomSchedule(tf.keras.optimizers.schedules.LearningRateSchedule):\n",
    "\n",
    "  def __init__(self, d_model, warmup_steps=4000):\n",
    "    super(CustomSchedule, self).__init__()\n",
    "\n",
    "    self.d_model = d_model\n",
    "    self.d_model = tf.cast(self.d_model, tf.float32)\n",
    "\n",
    "    self.warmup_steps = warmup_steps\n",
    "\n",
    "  def __call__(self, step):\n",
    "    arg1 = tf.math.rsqrt(step)\n",
    "    arg2 = step * (self.warmup_steps**-1.5)\n",
    "\n",
    "    return tf.math.rsqrt(self.d_model) * tf.math.minimum(arg1, arg2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 296
    },
    "colab_type": "code",
    "id": "6QicjRygyYSR",
    "outputId": "877783dc-e708-48d9-dd0b-94a1055ba2cb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Train Step')"
      ]
     },
     "execution_count": 29,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_learning_rate = CustomSchedule(d_model=128)\n",
    "\n",
    "plt.plot(sample_learning_rate(tf.range(200000, dtype=tf.float32)))\n",
    "plt.ylabel(\"Learning Rate\")\n",
    "plt.xlabel(\"Train Step\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "YN9Ji8-4yeZR"
   },
   "source": [
    "### Compile Model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "7HeJVxR7yZfd"
   },
   "outputs": [],
   "source": [
    "learning_rate = CustomSchedule(D_MODEL)\n",
    "\n",
    "optimizer = tf.keras.optimizers.Adam(\n",
    "    learning_rate, beta_1=0.9, beta_2=0.98, epsilon=1e-9)\n",
    "\n",
    "def accuracy(y_true, y_pred):\n",
    "  # ensure labels have shape (batch_size, MAX_LENGTH - 1)\n",
    "  y_true = tf.reshape(y_true, shape=(-1, MAX_LENGTH - 1))\n",
    "  return tf.keras.metrics.sparse_categorical_accuracy(y_true, y_pred)\n",
    "\n",
    "model.compile(optimizer=optimizer, loss=loss_function, metrics=[accuracy])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 306
    },
    "colab_type": "code",
    "id": "sXlGfg-OzD9J",
    "outputId": "2ccd1812-aadf-4f5b-8205-006ba7f2d632"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"transformer\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "inputs (InputLayer)             [(None, None)]       0                                            \n",
      "__________________________________________________________________________________________________\n",
      "look_ahead_mask (Lambda)        (None, 1, None, None 0           inputs[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "Language_Decoder (Model)        (None, None, 256)    16528128    inputs[0][0]                     \n",
      "                                                                 look_ahead_mask[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "outputs (Dense)                 (None, None, 48091)  12359387    Language_Decoder[1][0]           \n",
      "==================================================================================================\n",
      "Total params: 28,887,515\n",
      "Trainable params: 28,887,515\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "fRQ0RloFkuKp"
   },
   "source": [
    "## Checkpointing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "F2xeI2azktbY"
   },
   "outputs": [],
   "source": [
    "# Directory where the checkpoints will be saved\n",
    "checkpoint_dir = './training_checkpoints'\n",
    "# Name of the checkpoint files\n",
    "checkpoint_prefix = os.path.join(checkpoint_dir, \"ckpt_{epoch}\")\n",
    "\n",
    "checkpoint_callback=tf.keras.callbacks.ModelCheckpoint(\n",
    "    filepath=checkpoint_prefix,\n",
    "    save_weights_only=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 374
    },
    "colab_type": "code",
    "id": "IhAvAbIgzOR3",
    "outputId": "58ea7f23-e7d9-4624-8093-bfbd9fc0e8bc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "3620/3620 [==============================] - 1306s 361ms/step - loss: 2.2078 - accuracy: 0.0763\n",
      "Epoch 2/10\n",
      "3620/3620 [==============================] - 1298s 358ms/step - loss: 1.7876 - accuracy: 0.1056\n",
      "Epoch 3/10\n",
      "3620/3620 [==============================] - 1298s 359ms/step - loss: 1.6789 - accuracy: 0.1155\n",
      "Epoch 4/10\n",
      "3620/3620 [==============================] - 1299s 359ms/step - loss: 1.6185 - accuracy: 0.1218\n",
      "Epoch 5/10\n",
      "3620/3620 [==============================] - 1322s 365ms/step - loss: 1.5837 - accuracy: 0.1264\n",
      "Epoch 6/10\n",
      "3620/3620 [==============================] - 1327s 367ms/step - loss: 1.5570 - accuracy: 0.1301\n",
      "Epoch 7/10\n",
      "3620/3620 [==============================] - 1328s 367ms/step - loss: 1.5329 - accuracy: 0.1333\n",
      "Epoch 8/10\n",
      "3620/3620 [==============================] - 1321s 365ms/step - loss: 1.5124 - accuracy: 0.1359\n",
      "Epoch 9/10\n",
      "3620/3620 [==============================] - 1312s 363ms/step - loss: 1.4943 - accuracy: 0.1384\n",
      "Epoch 10/10\n",
      "3620/3620 [==============================] - 1311s 362ms/step - loss: 1.4781 - accuracy: 0.1406\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f46363530f0>"
      ]
     },
     "execution_count": 33,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "EPOCHS = 10\n",
    "\n",
    "model.fit(jokes_dataset, epochs=EPOCHS, callbacks = [checkpoint_callback])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "6Tr0WnBB7PU_"
   },
   "source": [
    "## Inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "GwtHRKm5oaJQ"
   },
   "outputs": [],
   "source": [
    "def exploit_best_token_while_exploring(probabilites, exploration_len=5):\n",
    "    ## Get the top k probabilites indices where k is exploration_len \n",
    "    top_indices = np.argpartition(probabilites, -exploration_len)\\\n",
    "                            [-exploration_len:]\n",
    "\n",
    "    ## Getting top proabilities value\n",
    "    top_probabilities = probabilites[top_indices]\n",
    "    \n",
    "    ## Normalizing it so that they sum is 1\n",
    "    top_probabilities = top_probabilities / np.sum(top_probabilities)\n",
    "    ## Although we choose next token randomly for our options we \n",
    "    ## pass the probabilities associated with each to account for the\n",
    "    ## model's confidence for the token also. \n",
    "    choice = np.random.choice(exploration_len, 1, p = top_probabilities)\n",
    "\n",
    "    ## Return next token id based on choice\n",
    "    next_token_id = int(top_indices[choice][0])\n",
    "    return next_token_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ITxSVWNzz-Tx"
   },
   "outputs": [],
   "source": [
    "def complete_joke(sentence, MAX_LEN = 64):\n",
    "  sentence = tf.expand_dims(\n",
    "      START_TOKEN + tokenizer.encode(sentence), axis=0)\n",
    "  \n",
    "\n",
    "  for pos in range(MAX_LEN):\n",
    "    predictions = model(sentence, training=False)\n",
    "\n",
    "    # select the last word from the seq_len dimension\n",
    "    predictions = predictions[0, -1]\n",
    "    softmax_logits = tf.nn.softmax(predictions, axis=0).numpy()\n",
    "\n",
    "    ## Depending on whether we are in initial or final stages of joke\n",
    "    ## we determine how many options we should explore to make sure we have\n",
    "    ## variety in jokes generated\n",
    "    if pos == 0:\n",
    "      # If we are predicting first word we need maximum exploration\n",
    "      exploration_len = 50\n",
    "\n",
    "    elif pos < 4:\n",
    "      # The next three words have exploration length 15\n",
    "      exploration_len = 15\n",
    "\n",
    "    else:\n",
    "      # As we move further we narrow our exploration length\n",
    "      exploration_len = 4\n",
    "\n",
    "    predicted_id = exploit_best_token_while_exploring(softmax_logits, \n",
    "                                                         exploration_len)\n",
    "    \n",
    "    # return the result if the predicted_id is equal to the end token\n",
    "    if tf.equal(predicted_id, END_TOKEN[0]) or tf.equal(predicted_id, START_TOKEN[0]):\n",
    "      return tokenizer.decode(tf.squeeze(sentence[:, 1:]).numpy())\n",
    "\n",
    "    # concatenated the predicted_id to the output which is given to the decoder\n",
    "    # as its input in enxt iteration.\n",
    "    sentence = tf.concat([sentence,tf.ones((1,1), dtype = tf.int32)*predicted_id], axis=-1)\n",
    "\n",
    "  ## If we did not get end token it means no joke is formed and we try again\n",
    "  return complete_joke(sentence)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "XBoLlGRsbkUN",
    "outputId": "2731a2b1-7a89-42cc-ac89-75a25013409f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'It is good to be so good at making a joke. I just came up with \"You know, you have the biggest dick.\"'"
      ]
     },
     "execution_count": 44,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "complete_joke(\"It is \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "XBoLlGRsbkUN"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'It is hard to explain puns to kleptomaniacs Because they always take things literally'"
      ]
     },
     "execution_count": 0,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "complete_joke(\"It is \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "QmeD5Fq27Bze",
    "outputId": "780c0ef5-0e1e-453c-b594-474d6eb4218e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'The joke is /r/r/jokes is [removed]'"
      ]
     },
     "execution_count": 0,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "complete_joke(\"The joke is\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "p9oK0wCqqalF",
    "outputId": "dac858b1-96fd-41fe-f8af-96ee5e636360"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"Life is not a box of chocolates... It's a joke.\""
      ]
     },
     "execution_count": 0,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "complete_joke(\"Life is not \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "mrARoeBPAIHc",
    "outputId": "67a5f221-2657-4309-a0ae-f780c8bfcf57"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Windows is bad. The number 7 was a joke.'"
      ]
     },
     "execution_count": 0,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "complete_joke(\"Windows is \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "TgzruoVNAjnW",
    "outputId": "d30ed655-df26-4cc4-e800-49e211c75dd5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"Am I the only one, the most important way to make a good joke? I don't know if it's a joke. I just want to be a joke here. I just made it up.\""
      ]
     },
     "execution_count": 0,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "complete_joke(\"Am I the \")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "JGbGo7gEOmPl"
   },
   "source": [
    "## Conclusion\n",
    "\n",
    "Given that we have not used transfer learning and built it from scratch the model seems to performs reasonably well imho. More training and deeper networks could yield even better result."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ogKMpNDSQU3o"
   },
   "source": [
    "You might be wondering how the jokes are looking reasonably well even when accuracy is only around 13%. The reason for this is that accuracy is not a good metric to judge language generation. Lets say we have only 2 texts in our dataset.\n",
    "\n",
    "`It is good.`\n",
    "\n",
    "`It is bad.`\n",
    "\n",
    "\n",
    "Now lets say we are training our model for `It is`. Even if the model predicts `good` and `bad` as the top two probable outcomes which will ensure the loss will be low, but since only one of them will be having the highest probability, the accuracy can be only `50 %`. Thus with two texts having the same beginning string we have an upper bound of 50% for accuracy."
   ]
  }
 ],
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