miniproject2_ChatBot.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Miniproject 2: Chatbot\n",
    "\n",
    "## Introduction\n",
    "\n",
    "### Description\n",
    "\n",
    "Developing a model employing ANN on real-world data requires going through several major steps, each of which with \n",
    "important design choices that directly impact the final results. \n",
    "In this project, we guide you through these choices starting from a large database of \n",
    "[conversations](http://parl.ai/downloads/personachat/personachat.tgz) to a functional chatbot. \n",
    "\n",
    "### Prerequisites\n",
    "\n",
    "- You should have a running installation of [tensorflow](https://www.tensorflow.org/install/) and [keras](https://keras.io/).\n",
    "- You should know the concepts \"recurrent neural networks\", \"LSTM\", \"training and validation data\", \"overfitting\" and \"early stopping\".\n",
    "\n",
    "### What you will learn\n",
    "\n",
    "- You will be guided through a data processing procedure and understand the importance of design choices in ANN modeling\n",
    "- You will learn how to define recurrent neural networks in keras and fit them to data.\n",
    "- You will be guided through a prototyping procedure for the application of deep learning to a specific domain.\n",
    "- You will get in contact with concepts discussed in the lecture, like \"overfitting\", \"LSTM network\", and \"Generative model\".\n",
    "- You will learn to be more patient :) Some fits may take your computer quite a bit of time; run them over night and make sure you save (and load) your data and models.\n",
    "\n",
    "### Evaluation criteria\n",
    "\n",
    "The evaluation is (mostly) based on the figures you submit and your answer sentences. \n",
    "We will only do random tests of your code and not re-run the full notebook. \n",
    "Please ensure that your notebook is fully executed before handing it in. \n",
    "\n",
    "### Submission \n",
    "\n",
    "You should submit your notebook through the Moodle page submission tool. You should work in teams of two people and each member should submit the same notebook to Moodle.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Functions and imports\n",
    "\n",
    "For your convenience we import some libraries and provide some functions below. Fill in your names, sciper numbers and run the following cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "names = {'student_1': \"Doga Tekin\",\n",
    "        'student_2': \"Serif Soner Serbest\"}\n",
    "\n",
    "sciper = {'student_1': 293688, \n",
    "          'student_2': 294910}\n",
    "\n",
    "seed = sciper['student_1']+sciper['student_2']\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import os, sys\n",
    "import copy\n",
    "import pickle\n",
    "\n",
    "plt.rcParams['font.size'] = 28\n",
    "plt.style.use('ggplot')\n",
    "plt.rcParams[\"axes.grid\"] = False\n",
    "c = plt.rcParams['axes.prop_cycle'].by_key()['color']\n",
    "\n",
    "import keras\n",
    "from keras.models import Model, load_model\n",
    "from keras.layers import Input, Masking, TimeDistributed, Dense, Concatenate, Dropout, LSTM, GRU, SimpleRNN, Bidirectional, Embedding, BatchNormalization\n",
    "from keras.optimizers import Adam\n",
    "from keras.utils import np_utils\n",
    "from keras.preprocessing.sequence import pad_sequences\n",
    "from keras.callbacks import ModelCheckpoint, EarlyStopping\n",
    "\n",
    "\n",
    "def getRawDataFromFile(datapath=\"data/personachat/\", file=\"train_both_revised.txt\"):\n",
    "    \n",
    "    f = open(datapath+file)\n",
    "\n",
    "    conversations = []\n",
    "    current_conversation = []\n",
    "    \n",
    "    for l, line in enumerate(f):\n",
    "        #print(l, line)\n",
    "        if \"persona:\" in line:\n",
    "            if len(current_conversation) > 1:\n",
    "                conversations.append(current_conversation)\n",
    "            current_conversation = [] \n",
    "            continue\n",
    "\n",
    "        #remove numberings\n",
    "        processed_line = line.split(' ')\n",
    "        processed_line = \" \".join(processed_line[1:])\n",
    "        line = processed_line\n",
    "        #print(line)\n",
    "\n",
    "        conv = line.split('\\t')    \n",
    "        q = conv[0]\n",
    "        a = conv[1]\n",
    "        current_conversation.append(q)\n",
    "        current_conversation.append(a)\n",
    "    \n",
    "    return conversations "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Data visualization and preprocessing\n",
    "\n",
    "Here we will process and visualize the data.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parse raw data \n",
    "\n",
    "Download the dataset on http://parl.ai/downloads/personachat/personachat.tgz. Unpack it and add it to your project folder. Read and run the getRawDataFromFile function (if needed, modify the default path). It extracts the conversations.\n",
    "\n",
    "**Output** Display two randomly selected conversations. [1 pt]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['good evening . how are you doing ?',\n",
       "  'i am good . had some really good super hot wings tonight . you ?',\n",
       "  'sounds good ! and i am just peachy . enjoying scary movies tonight .',\n",
       "  'scary movies are awesome ! i watched so many when i worked at the theater',\n",
       "  'that would be a cool job . although i would like to be a designer some day .',\n",
       "  'that sounds cool . i want to photograph fashion someday with my new camera',\n",
       "  'do it . no dream is too big . i just moved to ny to pursue mine .',\n",
       "  'awesome ! do you think i should cut my long hair to make a fresh start ?',\n",
       "  'bold move . but you sound like you could use a fresh start .',\n",
       "  'yes ! i have been running nightly to try to get in shape',\n",
       "  'good luck ! i love to go shopping . that is my exercise .',\n",
       "  'sounds like a fun way to exercise',\n",
       "  'it is . also an expensive one . but fashion is my thing . so it is okay ,',\n",
       "  'you sound like a super cool person !'],\n",
       " ['hello . how are you ?',\n",
       "  'i am good . i have been working in my garden .',\n",
       "  'that is great . i am a mom and i stay at home .',\n",
       "  'oh , i only have one son .',\n",
       "  'me too ! we have a second child on the way . i am pregnant now .',\n",
       "  'congratulations ! it goes by so fast , my son is 40 .',\n",
       "  'i know . my husband works . he is an engineer .',\n",
       "  'oh . i am retired from the navy .',\n",
       "  'oh ok . i have a disorder . severe anxiety .',\n",
       "  'oh . i am sorry to hear that .',\n",
       "  'it is ok . i am getting treatment . do you have a good pension from the navy ?',\n",
       "  'so so . good enough to live on .',\n",
       "  'my dad is retired from postal service . disabled veteran .',\n",
       "  'oh . how old is he ?',\n",
       "  '72 . hey do you like art ? i love it .',\n",
       "  'i do sometimes . who is your favorite artist ?']]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conversations = getRawDataFromFile()\n",
    "\n",
    "list(np.random.choice(conversations, size=2, replace=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extract word tokens\n",
    "\n",
    "Let's start looking at our data. \n",
    "\n",
    "**Code** Compute the set of unique words (dictionary) in all sentences along with the number of occurences of each of these words. HINT: each word is separated by a space character, use the python string.split(' ') function to separate words. Consider punctuations as 'words'. [1 pt]\n",
    "\n",
    "**Figure** In a bar plot, show the first 75 most frequent words (x-axis) and their number of occurences (y-axis). [1 pt]\n",
    "\n",
    "**Figure** In another bar plot, show the 75 least frequent words (x-axis) and their number of occurences (y-axis). [1 pt] \n",
    "\n",
    "**Figure** In a log-log scale, plot the sorted word index (x-axis) vs their respective count (y-axis). [1 pt]\n",
    "\n",
    "**Question** Relate the sorted word count distribution with Zipf's law.\n",
    "Argue using the log-log plot. [1 pt]\n",
    "\n",
    "**Answer** *Zipf's law states that the frequenct of any word is inversely proportional to its rank in a corpus which can be observed as a line in log-log scale. We observed a almost linearly plot which conforms to Zipf's Law.*  \n",
    "\n",
    "**Question** How many words appear only once in the entire dataset? [1 pt]\n",
    "\n",
    "**Answer** *7080*\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "sentences = [sentence for conversation in conversations for sentence in conversation]\n",
    "\n",
    "words = ' '.join(sentences)\n",
    "dictionary = Counter(words.split(' '))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'count')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1224x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "frequent_words, count = zip(*dictionary.most_common(75))\n",
    "\n",
    "plt.figure(figsize=(17, 10))\n",
    "plt.bar(frequent_words, count)\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.title(\"Count of Most Frequent 75 Words\")\n",
    "plt.xlabel(\"wors\")\n",
    "plt.ylabel(\"count\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'count')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "frequent_words, count = zip(*dictionary.most_common(75))\n",
    "\n",
    "plt.figure(figsize=(17, 10))\n",
    "plt.bar(frequent_words, count)\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.title(\"Count of Most Frequent 75 Words\")\n",
    "plt.xlabel(\"word\")\n",
    "plt.ylabel(\"count\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'count')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "least_frequent_words, count = zip(*dictionary.most_common()[:-76:-1])\n",
    "\n",
    "plt.figure(figsize=(17, 10))\n",
    "plt.bar(least_frequent_words, count)\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.title(\"Count of Least Frequent 75 Words\")\n",
    "plt.xlabel(\"word\")\n",
    "plt.ylabel(\"count\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'count in log scale')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, sorted_counts = zip(*dictionary.most_common())\n",
    "\n",
    "plt.figure(figsize=(17, 10))\n",
    "plt.loglog(sorted_counts)\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.title(\"Count of words vs their sorted index in log-log scale\")\n",
    "plt.xlabel(\"indices in log scale\")\n",
    "plt.ylabel(\"count in log scale\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7080"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of words appear only once in the dataset\n",
    "sum(np.array(sorted_counts) == 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filtering\n",
    "\n",
    "We suggest to filter your data by removing sentences containing rare words. \n",
    "\n",
    "\n",
    "**Code** To achieve that, you should create a new dataset where sentences containing rare words (words that occur less than N times in the dataset) are removed. Keep at least 50'000 sentences (depending on your computing power, you can keep more). \n",
    "HINT: Start by updating the dictionary accordingly and then remove any sentence that contains at least a single word that is not in the dictionary of words. [2 pts]\n",
    "\n",
    "**Question**: How much did you reduce the number of unique words with your rare event suppression procedure? [1 pt]\n",
    "    \n",
    "**Answer**: *We aproximately reduce the unique_words with a rate of 1/12 which corresponds to 1644 words out of 18673*\n",
    "\n",
    "**Question**: How many sentences are in your filtered and original dataset? [1 pt]\n",
    "\n",
    "**Answer**: *There are 131424 sentences in original dataset and 67024 sentences in filtered dataset*\n",
    "\n",
    "**Question**: What is the impact on learning and generalization of removing sentences with rare words from your dataset? [2 pt]\n",
    "\n",
    "**Answer**: *In terms of learning, it provides cleaner and smaller dataset that allows to train faster. In terms of generalization, it removes context specific terms and allows network to respond in a more common language.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1644, 18673)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frequent_dictionary = {}\n",
    "count_threshold = 50\n",
    "\n",
    "for word in dictionary:\n",
    "    if dictionary[word] >= count_threshold: frequent_dictionary[word] = dictionary[word]\n",
    "        \n",
    "len(frequent_dictionary), len(dictionary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "filtered_sentences = []\n",
    "\n",
    "for sentence in sentences:\n",
    "    words = set(sentence.split(' '))\n",
    "    include = True\n",
    "    for word in words:\n",
    "        if not word in frequent_dictionary: include = False\n",
    "    if include: filtered_sentences.append(sentence)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(67024, 131424)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(filtered_sentences), len(sentences)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tokenization and padding\n",
    "\n",
    "Now you will transform our filtered data into a format that is understandable by an ANN. To achieve that, you should transform words to integers, where single integers in the range [1,size of the dictionary] are mapped to single words in your dictionary. This process is commonly named 'tokenization'. In addition, we will keep the value 0 to a specific artificial word 'PADD' that will be used to account for the variable length of sentences and add to each sentence a 'START' and an 'END' word. \n",
    "\n",
    "**Code** Start by adding the three artificial words to your dictionary (list of possible tokens) and then translate every sentences to a list of integers. \n",
    "HINT: use the Python List index() method. [2 pts]\n",
    "\n",
    "**Figure** Use the violinplot to show the density of tokenized sentences length. [1pt]\n",
    "\n",
    "**Code** From this figure, select a maximum number (=maxlen) of tokens for which most of the sentences have less. Padd (and eventually truncate) all sentences with the 'PADD' token (value 0 in the integer representation) until all tokenized sentences have the same length (maxlen).\n",
    "HINT: use the pad_sequences function from keras.preprocessing.sequence [2 pts]\n",
    "\n",
    "**Code** Check that you can recover the original sentence. Randomly select two sentences from your integer and padded representation and translate them back using your dictionary. [1 pt]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create tokenization dictionary that links words with their tokens\n",
    "word_to_token = {'PADD': 0, 'START': 1, 'END': 2}\n",
    "token_to_word = {0: 'PADD', 1: 'START', 2: 'END'}\n",
    "\n",
    "k = 3\n",
    "for word in frequent_dictionary:\n",
    "    word_to_token[word] = k\n",
    "    token_to_word[k] = word\n",
    "    k += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# transform dataset to tokenized dataset with 'START' and 'END' tokens\n",
    "\n",
    "tokenized_sentences = []\n",
    "\n",
    "for sentence in filtered_sentences:\n",
    "    token = [word_to_token['START']]\n",
    "    for word in sentence.split(' '):\n",
    "        token.append(word_to_token[word])\n",
    "    token.append(word_to_token['END'])\n",
    "    tokenized_sentences.append(token)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Violinplot of Tokenized Sentences')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot violinplot\n",
    "lengths_of_tokenized_sentences = [len(tokenized_sentence) for tokenized_sentence in tokenized_sentences]\n",
    "\n",
    "plt.figure(figsize=(17, 10))\n",
    "plt.violinplot(lengths_of_tokenized_sentences)\n",
    "plt.title(\"Violinplot of Tokenized Sentences\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pad the dataset\n",
    "maxlen = 25\n",
    "padded_tokenized_sentences=pad_sequences(tokenized_sentences, maxlen=maxlen, padding='post', truncating='post', value=word_to_token['PADD'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tokenlisttosentence(tokenlist):\n",
    "    sentence = ''\n",
    "    for token in tokenlist[1:]:\n",
    "        if token == word_to_token['END']: break\n",
    "        else: sentence = sentence + token_to_word[token] + ' '\n",
    "    return sentence[:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "original:  cool ! my name is mary . where are you from ? recovered:  cool ! my name is mary . where are you from ?\n",
      "original:  i am with you on that what ways have you had fun ? recovered:  i am with you on that what ways have you had fun ?\n"
     ]
    }
   ],
   "source": [
    "idx = np.random.randint(len(padded_tokenized_sentences))\n",
    "print(\"original: \", filtered_sentences[idx], \"recovered: \", tokenlisttosentence(padded_tokenized_sentences[idx]))\n",
    "idx = np.random.randint(len(padded_tokenized_sentences))\n",
    "print(\"original: \", filtered_sentences[idx], \"recovered: \", tokenlisttosentence(padded_tokenized_sentences[idx]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Saving\n",
    "\n",
    "Now is a good time to save your data (end of processing). An example code using the pickle library is shown below.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "#save\n",
    "# with open(\"data.pkl\", \"wb\") as file:\n",
    "#     pickle.dump([filtered_sentences, dictionary, tokens], file)\n",
    "    \n",
    "# #load\n",
    "# with open(\"data.pkl\", \"rb\") as file:\n",
    "#     [filtered_sentences, dictionary, tokens] = pickle.load(file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building and training generative models of language"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### RNN vs LSTM vs GRU \n",
    "\n",
    "Build, train and compare generative models of language based on RNNs with different recurrent units (SimpleRNN, GRU and LSTM). \n",
    "\n",
    "The target of the network will be to approximate the word transition probabilities Pr(word[n+1]|H[n]) with H[n]=f(word[:n]) being the hidden state of the network.  \n",
    "\n",
    "**code** You should complete the proposed model (using the Keras API rather than the Sequential model for more flexibility). Be sure to understand each line. The embedding layer allows to transform an integer to a dense vector. That would be our input to the recurrent network - each sentence is mapped to a sequence of vectors, each representing a single word. You can then design your own readout(s) and output layers. By default, use the proposed meta parameters. You can adapt them if you have more or less computing power (32 epochs should take around 30 minutes). [2 pts]\n",
    "\n",
    "**Question** How will your networks deal with the artificial word 'PADD' that you added at the end of each sentences  [2 pts]\n",
    "\n",
    "**Answer** We are masking 'PADD' tokens in embedding layer by activating mask_zero parameter. Hence, the network ignores PADDED timesteps and exclude them in trainning and error computations.\n",
    "\n",
    "**code** Then train three different networks with the same architecture but using different recurrent units (simpleRNN, GRU and LSTM). Save the learning history (training/validation loss and accuracy for each epoch) as well as the models. [1 pt]\n",
    "\n",
    "**Question** How can you use this network to approximate the word transition probabilities? What will be the inputs and targets of the network at each batch? Give the input/output tensor dimensions. [2 pts]\n",
    "\n",
    "**Answer**:\n",
    "This network gives word transition probability distribution of each time step by looking at all preceding words by learning from the actual word in that time step in the dataset.\n",
    "\n",
    "Inputs are padded tokenized sentences with the last word removed, which is a 67024 x 24 tensor.\n",
    "Targets are the one hot encoded tokens of sentences with the first word, removed which is 67024 x 24 x 1647 tensor.\n",
    "\n",
    "For each batch\n",
    "input = (batch_size, 24)\n",
    "output = (batch_size, 24, 1647)\n",
    "\n",
    "**Figure** Show the learning curves (training and validation loss) for the different recurrent units. [1 pt]\n",
    "\n",
    "**Figure** Show the learning curves (training and validation accuracy) for the different recurrent units. [1 pt]\n",
    "\n",
    "**Question:** Which recurrent unit yields the best validation accuracy? Which is the fastest learner? [1 pt]\n",
    "\n",
    "**Answer**: Although GRU and LSTM perform similarly, GRU has the best validation accuracy . GRU is the fastest learner as it is seen in comparison graph.\n",
    "\n",
    "**Question:** Do you observe an overfitting effect? Where and for which case? Give a possible explanation. [1 pt] \n",
    "\n",
    "**Answer**: We observe overfitting clearly in GRU and slightly in Simple RNN around 10-15 epoch. One possible explanation could be such that GRU and Simple RNN  learn faster than LSTM as it is seen in comparison graph so that they overfit faster since no precaution is undertaken for overfitting in these networks.\n",
    "\n",
    "**Question:** Suggest one option modifying your dataset to decrease overfitting. [1 pt]\n",
    "\n",
    "**Answer**: Increasing dataset size (# of sentences in the dataset)\n",
    "\n",
    "**Question:** Suggest one possible option modifying your network to decrease overfitting. [1 pt]\n",
    "\n",
    "**Answer**: Recurrent Dropout\n",
    "\n",
    "**Question:** Suggest one possible option modifying the training modalities to counter overfitting. [1 pt]   \n",
    "\n",
    "**Answer**: Early Stopping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Meta-parameters\n",
    "embedding_size = 128\n",
    "hidden_size = 64\n",
    "dropout = 0.\n",
    "recurrent_dropout = 0.\n",
    "\n",
    "batch_size = 64\n",
    "epochs = 32\n",
    "validation_split = 0.2\n",
    "\n",
    "dataset_cut = -1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "I = {}\n",
    "E = {}\n",
    "H = {}\n",
    "R = {}\n",
    "Y = {}\n",
    "models = {}\n",
    "logs = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.utils import to_categorical\n",
    "\n",
    "X = padded_tokenized_sentences\n",
    "T = to_categorical(X)\n",
    "maxlen=X.shape[1]\n",
    "tokens = list(token_to_word.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((67024, 25), (67024, 25, 1647))"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape, T.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RNN\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input (InputLayer)           (None, 24)                0         \n",
      "_________________________________________________________________\n",
      "embedding (Embedding)        (None, 24, 128)           210816    \n",
      "_________________________________________________________________\n",
      "simple_rnn_1 (SimpleRNN)     (None, 24, 64)            12352     \n",
      "_________________________________________________________________\n",
      "readout (TimeDistributed)    (None, 24, 128)           8320      \n",
      "_________________________________________________________________\n",
      "output (TimeDistributed)     (None, 24, 1647)          212463    \n",
      "=================================================================\n",
      "Total params: 443,951\n",
      "Trainable params: 443,951\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "GRU\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input (InputLayer)           (None, 24)                0         \n",
      "_________________________________________________________________\n",
      "embedding (Embedding)        (None, 24, 128)           210816    \n",
      "_________________________________________________________________\n",
      "gru_1 (GRU)                  (None, 24, 64)            37056     \n",
      "_________________________________________________________________\n",
      "readout (TimeDistributed)    (None, 24, 128)           8320      \n",
      "_________________________________________________________________\n",
      "output (TimeDistributed)     (None, 24, 1647)          212463    \n",
      "=================================================================\n",
      "Total params: 468,655\n",
      "Trainable params: 468,655\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "LSTM\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input (InputLayer)           (None, 24)                0         \n",
      "_________________________________________________________________\n",
      "embedding (Embedding)        (None, 24, 128)           210816    \n",
      "_________________________________________________________________\n",
      "lstm_1 (LSTM)                (None, 24, 64)            49408     \n",
      "_________________________________________________________________\n",
      "readout (TimeDistributed)    (None, 24, 128)           8320      \n",
      "_________________________________________________________________\n",
      "output (TimeDistributed)     (None, 24, 1647)          212463    \n",
      "=================================================================\n",
      "Total params: 481,007\n",
      "Trainable params: 481,007\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "def createModels():\n",
    "    modelnames = ['RNN', 'GRU', 'LSTM']\n",
    "    \n",
    "    for modelname in modelnames:\n",
    "        print(modelname)\n",
    "        I[modelname] = Input(shape=(maxlen-1,), name=\"input\")\n",
    "        E[modelname] = Embedding(len(tokens), embedding_size, mask_zero=True, name=\"embedding\")\n",
    "        if modelname == 'RNN':\n",
    "            H[modelname] = SimpleRNN(units=hidden_size, return_sequences=True, dropout=dropout, recurrent_dropout=recurrent_dropout)(E[modelname](I[modelname]))  #... Recurrent layer(s)\n",
    "        elif modelname == 'GRU':\n",
    "            H[modelname] = GRU(units=hidden_size, return_sequences=True, dropout=dropout, recurrent_dropout=recurrent_dropout)(E[modelname](I[modelname]))  #... Recurrent layer(s)\n",
    "        elif modelname == 'LSTM':\n",
    "            H[modelname] = LSTM(units=hidden_size, return_sequences=True, dropout=dropout, recurrent_dropout=recurrent_dropout)(E[modelname](I[modelname]))  #... Recurrent layer(s)\n",
    "        else:\n",
    "            print('something went wrong ...')\n",
    "        R[modelname] = TimeDistributed(Dense(embedding_size, activation='relu'), name='readout')(H[modelname])  #... Readout\n",
    "        Y[modelname] = TimeDistributed(Dense(len(tokens), activation='softmax'), name='output')(R[modelname])  #... Output\n",
    "        \n",
    "        models[modelname] = Model(inputs = [I[modelname]], outputs = [Y[modelname]])\n",
    "        models[modelname].compile(\n",
    "            loss='categorical_crossentropy', \n",
    "            optimizer=Adam(),\n",
    "            metrics=['acc'])\n",
    "        models[modelname].summary()\n",
    "\n",
    "createModels()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def trainModel(modelname):\n",
    "    logs[modelname] = models[modelname].fit({'input': X[:dataset_cut,:-1]}, {'output': T[:dataset_cut,1:]}, \n",
    "                                        epochs=epochs, \n",
    "                                        validation_split=validation_split, \n",
    "                                        batch_size=batch_size).history\n",
    "    #save\n",
    "    with open(str(modelname)+\"model_\"+str(embedding_size)+'_'+str(hidden_size)+\"_log.pkl\", \"wb\") as file:\n",
    "        pickle.dump(logs[modelname], file)\n",
    "    models[modelname].save( str(modelname)+\"model_\"+str(embedding_size)+'_'+str(hidden_size))\n",
    "    \n",
    "#trainModel('RNN')\n",
    "#trainModel('GRU')\n",
    "#trainModel('LSTM')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "#load models\n",
    "with open(\"RNNmodel_\"+str(embedding_size)+'_'+str(hidden_size)+\"_log.pkl\", \"rb\") as file:\n",
    "    RNNmodel_log = pickle.load(file)\n",
    "RNNmodel = load_model(\"RNNmodel_\"+str(embedding_size)+'_'+str(hidden_size))\n",
    "\n",
    "with open(\"GRUmodel_\"+str(embedding_size)+'_'+str(hidden_size)+\"_log.pkl\", \"rb\") as file:\n",
    "    GRUmodel_log = pickle.load(file)\n",
    "GRUmodel = load_model(\"GRUmodel_\"+str(embedding_size)+'_'+str(hidden_size))\n",
    "\n",
    "with open(\"LSTMmodel_\"+str(embedding_size)+'_'+str(hidden_size)+\"_log.pkl\", \"rb\") as file:\n",
    "    LSTMmodel_log = pickle.load(file)\n",
    "LSTMmodel = load_model(\"LSTMmodel_\"+str(embedding_size)+'_'+str(hidden_size))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot loss and accuracy curves and compare models\n",
    "def plot_curves(model_log, title=''):\n",
    "    t = range(1, len(model_log['loss'])+1)\n",
    "\n",
    "    fig, axs = plt.subplots(1, 2, figsize=(15,4))\n",
    "    axs[0].plot(t, model_log['loss'], label='Training Loss')\n",
    "    axs[0].plot(t, model_log['val_loss'], label='Validation Loss')\n",
    "    axs[0].set_xlabel('Epochs')\n",
    "    axs[0].set_ylabel('Loss')\n",
    "    axs[0].legend()\n",
    "\n",
    "    axs[1].plot(t, model_log['acc'], label='Training Accuracy')\n",
    "    axs[1].plot(t, model_log['val_acc'], label='Validation Accuracy')\n",
    "    axs[1].set_xlabel('Epochs')\n",
    "    axs[1].set_ylabel('Accuracy')\n",
    "    axs[1].legend()\n",
    "    \n",
    "    plt.suptitle(title);\n",
    "    \n",
    "def compare(logs, names):\n",
    "    fig, axs = plt.subplots(1, 2, figsize=(15,4))\n",
    "    \n",
    "    for log, name in zip(logs, names):\n",
    "        axs[0].plot(log['val_loss'], label=name)\n",
    "        axs[0].set_xlabel('Epochs')\n",
    "        axs[0].set_ylabel('Loss')\n",
    "        axs[0].legend()\n",
    "        \n",
    "        axs[1].plot(log['val_acc'], label=name)\n",
    "        axs[1].set_xlabel('Epochs')\n",
    "        axs[1].set_ylabel('Accuracy')\n",
    "        axs[1].legend()\n",
    "\n",
    "plot_curves(RNNmodel_log, 'Simple RNN')\n",
    "plot_curves(GRUmodel_log, 'GRU')\n",
    "plot_curves(LSTMmodel_log, 'LSTM')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "compare(logs=[RNNmodel_log, GRUmodel_log, LSTMmodel_log], names=['Simple RNN', 'GRU', 'LSTM'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Optimal network size\n",
    "\n",
    "Compare the learning curves for three networks with 64 (previous exercise), 128 and 256 GRUs (single layer) and one with two hidden layers of 64 GRUs. \n",
    "\n",
    "**Code** Build and train the networks. Apply EarlyStopping (monitor='val_acc', min_delta=0.001, patience=2). Use transfer learning, do not train from scratch your embedding layer, rather re-use the embedding layer from your best performing network in the last exercise. [4 pts]\n",
    "\n",
    "**Figure** Show the learning curves (training and validation loss) for the four models. [1 pt]\n",
    "\n",
    "**Figure** Show the learning curves (training and validation accuracy) for the four models. [1 pt]\n",
    "\n",
    "**Question** List and briefly explain the differences in the learning curves for the different models? [2 pts]\n",
    "\n",
    "**Answer** GRU with 256 hidden units converged the fastest and achieved the highest performance. The two layer model was slower to converge because it had more layers. GRU with 128 units performed similarly but slightly worse than 256 in terms of validation accuracy and convergence speed.\n",
    "\n",
    "**Question** What effect had EarlyStopping? Give one advantage and one drawback. [2 pts]\n",
    "\n",
    "**Answer** It had the effect of stopping training on all our networks around 10 epochs. One advantage is that it stops training when the network starts to overfit, so we end up with a more generalizable network. One drawback is that it's hard to detect when the network actually starts to overfit, so it might end up stopping too early before the network converges.\n",
    "\n",
    "**Question** What is your best model? Why? [1 pt]\n",
    "\n",
    "**Answer** GRU 256 is our best model because it has the highest validation accuracy and convergence speed. This is because it has more representation power since it has more units than the 128 unit one and it is faster because it has only one layer instead of two like the other model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "trained_embedding_weights = GRUmodel.get_weights()[0]\n",
    "early_stopping = EarlyStopping(monitor='val_acc', min_delta=0.001, patience=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GRU128\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input (InputLayer)           (None, 24)                0         \n",
      "_________________________________________________________________\n",
      "embedding (Embedding)        (None, 24, 128)           210816    \n",
      "_________________________________________________________________\n",
      "gru_15 (GRU)                 (None, 24, 128)           98688     \n",
      "_________________________________________________________________\n",
      "readout (TimeDistributed)    (None, 24, 128)           16512     \n",
      "_________________________________________________________________\n",
      "output (TimeDistributed)     (None, 24, 1647)          212463    \n",
      "=================================================================\n",
      "Total params: 538,479\n",
      "Trainable params: 538,479\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "GRU256\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input (InputLayer)           (None, 24)                0         \n",
      "_________________________________________________________________\n",
      "embedding (Embedding)        (None, 24, 128)           210816    \n",
      "_________________________________________________________________\n",
      "gru_16 (GRU)                 (None, 24, 256)           295680    \n",
      "_________________________________________________________________\n",
      "readout (TimeDistributed)    (None, 24, 128)           32896     \n",
      "_________________________________________________________________\n",
      "output (TimeDistributed)     (None, 24, 1647)          212463    \n",
      "=================================================================\n",
      "Total params: 751,855\n",
      "Trainable params: 751,855\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "GRU64\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input (InputLayer)           (None, 24)                0         \n",
      "_________________________________________________________________\n",
      "embedding (Embedding)        (None, 24, 128)           210816    \n",
      "_________________________________________________________________\n",
      "gru_17 (GRU)                 (None, 24, 64)            37056     \n",
      "_________________________________________________________________\n",
      "gru_18 (GRU)                 (None, 24, 64)            24768     \n",
      "_________________________________________________________________\n",
      "readout (TimeDistributed)    (None, 24, 128)           8320      \n",
      "_________________________________________________________________\n",
      "output (TimeDistributed)     (None, 24, 1647)          212463    \n",
      "=================================================================\n",
      "Total params: 493,423\n",
      "Trainable params: 493,423\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "def createGRUModels():\n",
    "    modelnames = ['GRU128', 'GRU256', 'GRU64']\n",
    "    \n",
    "    for modelname in modelnames:\n",
    "        print(modelname)\n",
    "        I[modelname] = Input(shape=(maxlen-1,), name=\"input\")\n",
    "        E[modelname] = Embedding(len(tokens), embedding_size, weights=[trained_embedding_weights], mask_zero=True, name=\"embedding\")\n",
    "        if modelname == 'GRU128':\n",
    "            H['GRU128'] = GRU(units=128, return_sequences=True, dropout=dropout, recurrent_dropout=recurrent_dropout)(E['GRU128'](I['GRU128']))  #... Recurrent layer(s)\n",
    "        elif modelname == 'GRU256':\n",
    "            H['GRU256'] = GRU(units=256, return_sequences=True, dropout=dropout, recurrent_dropout=recurrent_dropout)(E['GRU256'](I['GRU256']))  #... Recurrent layer(s)\n",
    "        elif modelname == 'GRU64':\n",
    "            GRU_first_layer = GRU(units=64, return_sequences=True, dropout=dropout, recurrent_dropout=recurrent_dropout)(E['GRU64'](I['GRU64']))  #... Recurrent layer(s)\n",
    "            H['GRU64'] = GRU(units=64, return_sequences=True, dropout=dropout, recurrent_dropout=recurrent_dropout)(GRU_first_layer)  #... Recurrent layer(s)\n",
    "\n",
    "        else:\n",
    "            print('something went wrong ...')\n",
    "        R[modelname] = TimeDistributed(Dense(embedding_size, activation='relu'), name='readout')(H[modelname])  #... Readout\n",
    "        Y[modelname] = TimeDistributed(Dense(len(tokens), activation='softmax'), name='output')(R[modelname])  #... Output\n",
    "        \n",
    "        models[modelname] = Model(inputs = [I[modelname]], outputs = [Y[modelname]])\n",
    "        models[modelname].compile(\n",
    "            loss='categorical_crossentropy', \n",
    "            optimizer=Adam(),\n",
    "            metrics=['acc'])\n",
    "        models[modelname].summary()\n",
    "\n",
    "createGRUModels()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def trainModelWithEarlystopping(modelname, early_stopping):\n",
    "    logs[modelname] = models[modelname].fit({'input': X[:dataset_cut,:-1]}, {'output': T[:dataset_cut,1:]}, \n",
    "                                        epochs=epochs, \n",
    "                                        validation_split=validation_split, \n",
    "                                        batch_size=batch_size,\n",
    "                                        callbacks=[early_stopping]).history\n",
    "    #save\n",
    "    with open(str(modelname)+\"model_\"+str(embedding_size)+'_'+str(hidden_size)+\"_log.pkl\", \"wb\") as file:\n",
    "        pickle.dump(logs[modelname], file)\n",
    "    models[modelname].save( str(modelname)+\"model_\"+str(embedding_size)+'_'+str(hidden_size))\n",
    "\n",
    "#trainModelWithEarlystopping('GRU128', early_stopping)\n",
    "#trainModelWithEarlystopping('GRU256', early_stopping)\n",
    "#trainModelWithEarlystopping('GRU64', early_stopping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "#load models\n",
    "with open(\"GRU128model_\"+str(embedding_size)+'_'+str(128)+\"_log.pkl\", \"rb\") as file:\n",
    "    GRU128model_log = pickle.load(file)\n",
    "GRU128model = load_model(\"GRU128model_\"+str(embedding_size)+'_'+str(128))\n",
    "\n",
    "with open(\"GRU256model_\"+str(embedding_size)+'_'+str(64)+\"_log.pkl\", \"rb\") as file:\n",
    "    GRU256model_log = pickle.load(file)\n",
    "GRU256model = load_model(\"GRU256model_\"+str(embedding_size)+'_'+str(64))\n",
    "\n",
    "with open(\"GRU64model_\"+str(embedding_size)+'_'+str(64)+\"_log.pkl\", \"rb\") as file:\n",
    "    GRU64model_log = pickle.load(file)\n",
    "GU64model = load_model(\"GRU64model_\"+str(embedding_size)+'_'+str(64))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_curves(GRU128model_log, '128 Units')\n",
    "plot_curves(GRU256model_log, '256 Units')\n",
    "plot_curves(GRU64model_log, '2 Layers 64 Units')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "compare(logs=[GRU128model_log, GRU256model_log, GRU64model_log],names=['GRU128', 'GRU256', 'GRU64*2'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate sentences\n",
    "\n",
    "Now you will generate new sentences from your best performing model.\n",
    "\n",
    "**Code** To achieve that, use the provided sample function below to generate new sentences from your model. You should start by constructing a sentence that starts with the 'START' artificial word and all other words being the 'PADD' artificial word. Then sample the first word from the corresponding probabilities given by your model. Add this word to the sentence and continue like this until you sample the 'END' artificial word or the maximum sentence length. [2 pts]\n",
    "\n",
    "**Code** Generate 10 sentences for different sampling temperature in [0., 0.25, 0.5, 0.75, 1., 1.5., 2.]. [1 pt]\n",
    "\n",
    "**7 Figures** For each temperature, use matplotlib imshow to plot the probablities of every word in one generated sentence (and only these words) at each time step. y-axis should be the words that are present in the sentence. x-axis the timesteps and the imshow value the probabilities given by the model for all words in the sentence at each timestep. Use the a colormap where 0 is white, e.g. cmap='Greys'. [2 pts]\n",
    "\n",
    "**Code** Finally, seed your model with two different beginnings of max 4 words and let it generate 10 possible continuations (use sampling temperature of 1.). [2 pts]\n",
    "\n",
    "**Question** What is the effect of sampling temperature on the generated sentences? [1 pt]\n",
    "\n",
    "**Answer** Temperature controls the randomness of predictions. On smaller values, network is more confident and conservative to sample from unlikely candidates. On larger values, network is more creative by selecting diverse words and also it makes more mistakes.\n",
    "\n",
    "\n",
    "\n",
    "**Question** In terms of sampling a probability distribution, what does a sampling temperature of 0 corresponds to? [1 pt] \n",
    "\n",
    "**Answer** As temperature goes to 0, the probability of the word with the highest output in the layer tends to 1 while others tends to zero.\n",
    "\n",
    "**Question** In terms of sampling a probability distribution, what does a sampling temperature of 1 corresponds to? [1 pt] \n",
    "\n",
    "**Answer** When the temperature is 1, we compute the softmax directly on the unscaled outputs of output layer.\n",
    "\n",
    "**Question** In terms of sampling a probability distribution, what does a very high sampling temperature corresponds to? [1 pt]\n",
    "\n",
    "**Answer** As sampling temperature increases result of softmax gets closer to the uniform distribution because of the scaled exponential function. \n",
    "\n",
    "**Question** Based on the plotted word probabilities, explain how a sentence is generated. [2 pts]\n",
    "\n",
    "**Answer** According to input sentence, network gives word transition probability distribution of each time step by looking at all preceding words. By iterating over each time step, and adding the randomly selected word from the distribution to the sentence, complete sentence is generated at the end of the iteration (or by obtaining 'END' token).\n",
    "\n",
    "**Question** Do you observe timesteps with more than one word with non-zero probability? How do these probable words relate in terms of language? [1 pt]\n",
    "\n",
    "**Answer** Yes. Indeed networks models the nature of the language nicely since there are many possible words to continue a sentence at any given moment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sample(preds, temperature=1.):\n",
    "    # helper function to sample an index from a probability array\n",
    "    if temperature == 0.:\n",
    "        return np.argmax(preds)\n",
    "    preds = np.asarray(preds).astype('float64')\n",
    "    # assign very low probability to zeros to not get divide by zero error \n",
    "    ######################################################################\n",
    "    preds[preds == 0] = 1e-100\n",
    "    ######################################################################\n",
    "    preds = np.log(preds) / temperature\n",
    "    exp_preds = np.exp(preds)\n",
    "    preds = exp_preds / np.sum(exp_preds)\n",
    "    probas = np.random.multinomial(1, preds, 1)\n",
    "    return np.argmax(probas)\n",
    "\n",
    "def generate(model, temperature = 1., print_sentence = True):\n",
    "    token_list = np.zeros((1, maxlen - 1)).astype('int32')\n",
    "    next_token = 1 # 'START'\n",
    "    token_list[0,0] = next_token\n",
    "    i = 1\n",
    "    while not next_token == word_to_token['END'] and not i == maxlen - 1:\n",
    "        preds = model.predict(token_list)\n",
    "        next_token = sample(preds.squeeze()[i - 1, :], temperature)\n",
    "        token_list[0, i] = next_token\n",
    "        if i == 1: pred_list = preds.squeeze()[i - 1, :]\n",
    "        else: pred_list = np.vstack((pred_list, preds.squeeze()[i - 1, :]))\n",
    "        i += 1\n",
    "        \n",
    "    \n",
    "    sentence = tokenlisttosentence(token_list.squeeze())\n",
    "    #print(\"token list:\", token_list)\n",
    "    if print_sentence: print(\"sentence:\", sentence)\n",
    "    \n",
    "    return token_list, sentence, pred_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "temperature: 0.0\n",
      "sentence: i am a teacher , i am a college student\n",
      "sentence: i am a teacher , i am a college student\n",
      "sentence: i am a teacher , i am a college student\n",
      "sentence: i am a teacher , i am a college student\n",
      "sentence: i am a teacher , i am a college student\n",
      "sentence: i am a teacher , i am a college student\n",
      "sentence: i am a teacher , i am a college student\n",
      "sentence: i am a teacher , i am a college student\n",
      "sentence: i am a teacher , i am a college student\n",
      "sentence: i am a teacher , i am a college student\n",
      "---\n",
      "temperature: 0.25\n",
      "sentence: i do not have any pets\n",
      "sentence: that is cool . i am a waitress at a local restaurant .\n",
      "sentence: i am doing good . how are you ?\n",
      "sentence: i am good . just got back from a run .\n",
      "sentence: i am good . just got back from a long walk with my dog .\n",
      "sentence: i am doing well , how are you ?\n",
      "sentence: i have a cat , he is a cop .\n",
      "sentence: i am good . just got back from a run .\n",
      "sentence: i am doing well , how are you ?\n",
      "sentence: i am a student , i am a college student\n",
      "---\n",
      "temperature: 0.5\n",
      "sentence: i am a waitress at the moment .\n",
      "sentence: i am a nurse . what about you ?\n",
      "sentence: i am not sure what i want to do .\n",
      "sentence: i am fine . i am just hanging out .\n",
      "sentence: i am good . just got back from a run .\n",
      "sentence: i am a teacher . what do you do for fun\n",
      "sentence: i am from texas , how about you ?\n",
      "sentence: i am good just got back from a run .\n",
      "sentence: that is cool . where do you work ?\n",
      "sentence: i am doing well . how are you ?\n",
      "---\n",
      "temperature: 0.75\n",
      "sentence: no , just taking some time for a living .\n",
      "sentence: yes but i like being outside .\n",
      "sentence: hi , how are you today ?\n",
      "sentence: i am good do you work\n",
      "sentence: i am doing well , just working and am a doctor .\n",
      "sentence: awesome i am just watching a movie\n",
      "sentence: hi how are you ?\n",
      "sentence: i like to run i am healthy\n",
      "sentence: you should try the one !\n",
      "sentence: hello there , i am doing well , how are you ?\n",
      "---\n",
      "temperature: 1.0\n",
      "sentence: you are welcome . i hope to know you i would watch .\n",
      "sentence: i cannot say i have any 3 girls\n",
      "sentence: all me , especially reading . my family used to say i think i am very different too .\n",
      "sentence: oh good ! congrats . i m just hanging out here on country , education game of thrones .\n",
      "sentence: where has your friend growing up ?\n",
      "sentence: oh ok we have a bummer\n",
      "sentence: hello ! how can you tell me about you ?\n",
      "sentence: that is fantastic ! what kind of music do you cook ?\n",
      "sentence: that is like a pretty cool name , and you read ? i am an elementary school teacher .\n",
      "sentence: yeah , i have no siblings . its 18 years old and me\n",
      "---\n",
      "temperature: 1.5\n",
      "sentence: singing in what were how before i mean ?\n",
      "sentence: she does get by far in friends grocery because i get gross funny mostly at least may be famous !\n",
      "sentence: yes sweets most know them gives jokes\n",
      "sentence: oh . talk shoes would like some fish as well ! guess it is too orange .\n",
      "sentence: fine you should ! next holidays are the gardening its blue ! ! ! i have seen that\n",
      "sentence: yea , sad money to look this right sorry\n",
      "sentence: game ! gosh . he has always light tuna take guys stick after christmas .\n",
      "sentence: are you pretty good for all my firm\n",
      "sentence: anything he hates me which gives me longer work too happy .\n",
      "sentence: oh okay , how about cancer sound to dangerous funny a lot\n",
      "---\n",
      "temperature: 2.0\n",
      "sentence: i walk from sewing trees and about whatever pets big pay\n",
      "sentence: clients really you spend strange and the youngest visit politics different animals\n",
      "sentence: romance you and doing play violin right you . especially cleaning university with the pool 30\n",
      "sentence: shopping a whole house ? do your time job i will adventure since making nuts mexican !\n",
      "sentence: when does dark laugh usually easy ever visit saving pass truck\n",
      "sentence: superhero shows to old places actually is forget basketball smell pick out dragon living lol . lol there\n",
      "sentence: sick better sometimes then ! hope beat these work are alright\n",
      "sentence: we know american food she french wine it usually are better iron doctors when taste not science\n",
      "sentence: lucky moving finding makes people training . fried water alot not yet\n",
      "sentence: really canada tell you real sports a hobby . its out there night here almost car gaming\n",
      "---\n"
     ]
    }
   ],
   "source": [
    "# generate 10 sentences for each temperature\n",
    "for temperature in  [0., 0.25, 0.5, 0.75, 1., 1.5, 2.]:\n",
    "    print('temperature:', temperature)\n",
    "    for i in range(10):\n",
    "        generate(GRU256model, temperature)\n",
    "    print('---')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "temperature: 0.0\n",
      "sentence: i am a teacher , i am a college student\n",
      "temperature: 0.25\n",
      "sentence: i am good . just got back from a run .\n",
      "temperature: 0.5\n",
      "sentence: i am a baker , what about you ?\n",
      "temperature: 0.75\n",
      "sentence: you are busy ! it is just a lot of work .\n",
      "temperature: 1.0\n",
      "sentence: i have traveled and play before . do you work with your family at national parks ?\n",
      "temperature: 1.5\n",
      "sentence: 14 hours i hope a huge goal in before , thanks any vacation !\n",
      "temperature: 2.0\n",
      "sentence: eh big good team coach your highschool just get nice going out\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# generate heatmap for sentences for each temperature\n",
    "\n",
    "temperatures = [0., 0.25, 0.5, 0.75, 1., 1.5, 2.]\n",
    "for i, temperature in enumerate(temperatures):\n",
    "    print('temperature:', temperature)\n",
    "    tl, s, pl = generate(GRU256model, temperature)\n",
    "    word_num = len(s.split()) + 1\n",
    "    x = pl[:,tl[:,1:word_num + 1]]\n",
    "    \n",
    "    plt.figure()\n",
    "    plt.imshow(x.squeeze(), cmap='Greys')\n",
    "    #plt.gca().invert_xaxis()\n",
    "    plt.yticks(np.arange(word_num), s.split());\n",
    "    plt.title(temperature)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sentence: i am doing good , thanks for asking .\n",
      "sentence: that would be a good idea . i do not have pets either\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "('i am doing good', 'that would be a')"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# generate two beginings with temperature 0.75\n",
    "'''selected two nice beginning by running the cells several times'''\n",
    "tl1, s1, pl1 = generate(GRU256model, 0.75)\n",
    "tl2, s2, pl2 = generate(GRU256model, 0.75)\n",
    "\n",
    "token_list_1 = tl1[:,:5]\n",
    "token_list_2 = tl2[:,:5]\n",
    "\n",
    "sentence1 = ' '.join(s1.split()[:4])\n",
    "sentence2 = ' '.join(s2.split()[:4])\n",
    "\n",
    "sentence1, sentence2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "input: i am doing good\n",
      "sentence: i am doing good and yourself ?\n",
      "sentence: i am doing good eating down for dinner ?\n",
      "sentence: i am doing good just playing soccer , not really a lot of chinese food , what do you do for a living\n",
      "sentence: i am doing good . not at all\n",
      "sentence: i am doing good thanks for asking\n",
      "sentence: i am doing good . what about you ?\n",
      "sentence: i am doing good just reading a book\n",
      "sentence: i am doing good too . just relaxing . what are you up to today ?\n",
      "sentence: i am doing good . just came in from my job .\n",
      "sentence: i am doing good , do you like the color pink ?\n",
      "---\n",
      "input: that would be a\n",
      "sentence: that would be a cool name . do you have any hobbies ?\n",
      "sentence: that would be a good way to be more soon\n",
      "sentence: that would be a awesome league .\n",
      "sentence: that would be a good idea . she plays sports . and you ?\n",
      "sentence: that would be a annoying do i think i fell .\n",
      "sentence: that would be a weird body . i have 5 kids\n",
      "sentence: that would be a amazing cook . want to teach me play ?\n",
      "sentence: that would be a little far in life . i do not know anyone\n",
      "sentence: that would be a good career .\n",
      "sentence: that would be a hard job . do you like being in the military ?\n"
     ]
    }
   ],
   "source": [
    "# complete the missing sentences decided above\n",
    "def complete(model, tokens):\n",
    "    _, i = tokens.shape\n",
    "    next_token = tokens[:,-1]\n",
    "    token_list = np.hstack((tokens, np.zeros((1,24 - i))))\n",
    "    while not next_token == word_to_token['END'] and not i == maxlen - 1:\n",
    "        preds = model.predict(token_list)\n",
    "        next_token = sample(preds.squeeze()[i - 1, :], temperature=1)\n",
    "        token_list[0, i] = next_token\n",
    "        i += 1\n",
    "\n",
    "    sentence = tokenlisttosentence(token_list.squeeze())\n",
    "    #print(\"token list:\", token_list)\n",
    "    print(\"sentence:\", sentence)\n",
    "\n",
    "print('input:',sentence1)\n",
    "for i in range(10):\n",
    "    complete(GRU256model, token_list_1)\n",
    "print('---')\n",
    "print('input:',sentence2)\n",
    "for i in range(10):\n",
    "    complete(GRU256model, token_list_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Word embedding visualization\n",
    "\n",
    "Here, you are asked to visualize the embedding layer. \n",
    "\n",
    "**Code** To do that, project in 2D the embedding vectors for different words. Use t-SNE, a projection that conserve the neighborhood relationships between vectors. HINT: Build a Keras model that takes as input a list of words and outputs a list of vector embeddings as learned by your best performing model. Use t-SNE dimensionality reduction (from sklearn.manifold import TSNE). [2 pts]\n",
    "\n",
    "**Figure** Plot the projection of the first 200 most frequent words in a 2D plot. On the plot, write the words. [2 pt] \n",
    "\n",
    "**Question** Do you observe clusters of words with similar meaning or role in language? Report three of them here. [1 pt]\n",
    "\n",
    "**Answer** Yes, for example: (do, did, does), (will, would, can, could, should, must), (is, was, are).\n",
    "\n",
    "**Question** Why is having similar vector representation for similar words a good approach for such models? Explain using the example clusters from before and argue in terms of prediction accuracy and/or generalization. [2 pts]\n",
    "\n",
    "**Answer** Neural network fed by similar vector representations outputs similar probability distributions for similar words. This enables network to represent distributions of word more correctly in terms of grammar and semantics with limited number of parameters since clusters of words carry grammatical end semantic information with themselves. E.g. (cats, dogs, animals) are both plural objects and carry similar semantics which can be replacable in a sentence. In terms of generalization, having the knowledge that two words are similar allows the network to generate sentences that it did not see in the training dataset by exploiting these similarities. E.g. with the (do, did, does) cluster above, the network can generate a sentence like \"He did ...\" even though it only saw sentences like \"He does\" in the training data.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from sklearn.manifold import TSNE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_200 = list(zip(*dictionary.most_common(200)))[0]\n",
    "top_200_tokens = [word_to_token[word] for word in top_200]\n",
    "X = to_categorical(top_200_tokens, num_classes=1647)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "embedding_matrix = GRUmodel.get_weights()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "embedded = X @ embedding_matrix\n",
    "projected = TSNE(n_components=2).fit_transform(embedded)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 9))\n",
    "plt.title('Projection of the First 200 Most Frequent Words on 2D')\n",
    "plt.scatter(projected[:,0], projected[:,1])\n",
    "for i, word in enumerate(top_200):\n",
    "    plt.annotate(word, (projected[i,0], projected[i,1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Chatbot\n",
    "\n",
    "Finally, you will construct a model with which you can chat. The network will take as input a sentence and output a response.\n",
    "\n",
    "**Code** For that, you should go back to your original data and construct a new dataset containing pairs of sentences, where each pair is a sentence and its answer. Be careful to not include any pair of sentences that contains words not present in your filtered dictionary. [2 pts]\n",
    "\n",
    "**Code** You should then tokenize, padd, truncate each sentence. Only the answers need the 'START' and 'END' artificial words. [1 pt]\n",
    "\n",
    "We provide you with a possible model, you are welcome to change it. This model uses an LSTM layer to encode the first sentence (the context). The final state of this LSTM layer is transfered to initialize the state of a decoder LSTM layer from which the answer sentence will be generated. \n",
    "\n",
    "**Code** Train your chatbot model on your dataset. [1 pt]\n",
    "\n",
    "**Code** Adapt your sentence generation code from before so that you can generate an answer given a context sentence from your model. [2 pts] \n",
    "\n",
    "**Code** After training, randomly select 10 context-answers pairs from your data and show both the real answer (the one from the data) and the generated one for two different sampling temperatures (e.g. 0.5 and 1.0). [2 pts]\n",
    "\n",
    "**Question** How similar are the generated answers and the real ones? Does your model provide probable answers (given the dataset)? Report here one good and one bad example. [2 pts]\n",
    "\n",
    "**Answer** Generally temperature 0.5 gives more similar answers than temperature 1.0. The answers are very similar for certain questions and very different for others.\n",
    "\n",
    "Good example:\n",
    "Context: hello , how are you doing ?\n",
    "Real answer: hi , i am good how about you ?\n",
    "Chatbot 0.5: i am good . how are you ?\n",
    "\n",
    "Bad example:\n",
    "Context: my least favorite food is tacos . but i absolutely love chocolate milk\n",
    "Real answer: oh no where is your hair ? have any animals ?\n",
    "Chatbot 0.5: i like to be a girl , but i love the cold weather\n",
    "\n",
    "**Question** Which sampling temperature gives better answers? why? [2 pts]\n",
    "\n",
    "**Answer** 0.5 gives better answers because it is less likely to choose unlikely words that don't make sense, which happens often with temperature 1.\n",
    "\n",
    "**Question** Would it be good if your model was able to reproduce exactly each real answer? Why? [1 pt]\n",
    "\n",
    "**Answer** No, since it would not be generalizable and basically act as a memorized table instead of a chatbot.\n",
    "\n",
    "**Code** Entertain yourself with your model. Write some code to chat with your bot, let it discuss with itself, ... be creative! [2 **bonus** pts]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generate dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "contexts = []\n",
    "answers = []\n",
    "\n",
    "for conversation in conversations:\n",
    "    for i in range(len(conversation)-1):\n",
    "        context = conversation[i]\n",
    "        answer = conversation[i+1]\n",
    "        \n",
    "        pair = context + ' ' + answer\n",
    "        \n",
    "        words = set(pair.split(' '))\n",
    "   \n",
    "        include = True\n",
    "        for word in words:\n",
    "            if not word in frequent_dictionary: \n",
    "                include = False\n",
    "\n",
    "        if include: \n",
    "            contexts.append(context)\n",
    "            answers.append(answer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def tokenize_sentences(filtered_sentences, is_answer):\n",
    "    # transform dataset to tokenized dataset with 'START' and 'END' tokens\n",
    "    tokenized_sentences = []\n",
    "\n",
    "    for sentence in filtered_sentences:\n",
    "        if is_answer:\n",
    "            token = [word_to_token['START']]\n",
    "        else:\n",
    "            token = []\n",
    "        for word in sentence.split(' '):\n",
    "            token.append(word_to_token[word])\n",
    "        if is_answer:\n",
    "            token.append(word_to_token['END'])\n",
    "            \n",
    "        tokenized_sentences.append(token)\n",
    "        \n",
    "    # plot violinplot\n",
    "    lengths_of_tokenized_sentences = [len(tokenized_sentence) for tokenized_sentence in tokenized_sentences]\n",
    "\n",
    "    plt.figure(figsize=(17, 10))\n",
    "    plt.violinplot(lengths_of_tokenized_sentences)\n",
    "    plt.title(\"Violinplot of Tokenized Sentences\")\n",
    "    \n",
    "    return tokenized_sentences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tokenized_contexts = tokenize_sentences(contexts, is_answer=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tokenized_answers = tokenize_sentences(answers, is_answer=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pad the dataset\n",
    "maxlen = 25\n",
    "padded_tokenized_contexts = pad_sequences(tokenized_contexts, maxlen=maxlen-2, padding='post', truncating='post', value=word_to_token['PADD'])\n",
    "padded_tokenized_answers = pad_sequences(tokenized_answers, maxlen=maxlen, padding='post', truncating='post', value=word_to_token['PADD'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "answers_input = padded_tokenized_answers[:,:-1]\n",
    "answers_output = to_categorical(padded_tokenized_answers[:,1:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "E['GRU'] = GRUmodel.layers[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_answer (InputLayer)       (None, 24)           0                                            \n",
      "__________________________________________________________________________________________________\n",
      "input_context (InputLayer)      (None, 23)           0                                            \n",
      "__________________________________________________________________________________________________\n",
      "embedding (Embedding)           multiple             210816      input_context[0][0]              \n",
      "                                                                 input_answer[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "encoder (LSTM)                  [(None, 64), (None,  49408       embedding[1][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "decoder (LSTM)                  (None, 24, 64)       49408       embedding[2][0]                  \n",
      "                                                                 encoder[0][1]                    \n",
      "                                                                 encoder[0][2]                    \n",
      "__________________________________________________________________________________________________\n",
      "readout (TimeDistributed)       (None, 24, 128)      8320        decoder[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "output (TimeDistributed)        (None, 24, 1647)     212463      readout[0][0]                    \n",
      "==================================================================================================\n",
      "Total params: 530,415\n",
      "Trainable params: 530,415\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "context = Input(shape=(maxlen-2,), name=\"input_context\")\n",
    "shared_embedding = E['GRU']\n",
    "context_embedding = shared_embedding(context)\n",
    "\n",
    "encoder_y, encoder_h, encoder_c = LSTM(hidden_size, \n",
    "            return_sequences=False,\n",
    "            return_state=True,\n",
    "            stateful=False,\n",
    "            dropout=dropout,\n",
    "            recurrent_dropout=recurrent_dropout,\n",
    "            go_backwards=True,\n",
    "            name=\"encoder\")(context_embedding)\n",
    "\n",
    "answer = Input(shape=(maxlen-1,), name=\"input_answer\")\n",
    "answer_embedding = shared_embedding(answer)\n",
    "\n",
    "decoder_input = answer_embedding\n",
    "decoder = LSTM(hidden_size, \n",
    "            return_sequences=True,\n",
    "            stateful=False,\n",
    "            dropout=dropout,\n",
    "            recurrent_dropout=recurrent_dropout,\n",
    "            name=\"decoder\")(answer_embedding, initial_state=[encoder_h, encoder_c])\n",
    "# decoder2 = LSTM(hidden_size, \n",
    "#             return_sequences=True,\n",
    "#             stateful=False,\n",
    "#             dropout=dropout,\n",
    "#             recurrent_dropout=recurrent_dropout,\n",
    "#             name=\"decoder2\")(decoder)\n",
    "\n",
    "R = TimeDistributed(Dense(embedding_size, activation='relu'), name='readout')(decoder)\n",
    "Y = TimeDistributed(Dense(len(tokens), activation='softmax'), name='output')(R)\n",
    "\n",
    "Chatbot = Model(inputs = [context, answer], outputs = [Y])\n",
    "Chatbot.compile(\n",
    "    loss='categorical_crossentropy', \n",
    "    optimizer=Adam(),\n",
    "    metrics=['acc'])\n",
    "Chatbot.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 26652 samples, validate on 6663 samples\n",
      "Epoch 1/32\n",
      "26652/26652 [==============================] - 56s 2ms/step - loss: 4.2131 - acc: 0.2647 - val_loss: 3.5148 - val_acc: 0.3439\n",
      "Epoch 2/32\n",
      "26652/26652 [==============================] - 54s 2ms/step - loss: 3.3368 - acc: 0.3641 - val_loss: 3.2334 - val_acc: 0.3760\n",
      "Epoch 3/32\n",
      "26652/26652 [==============================] - 53s 2ms/step - loss: 3.1394 - acc: 0.3830 - val_loss: 3.1235 - val_acc: 0.3852\n",
      "Epoch 4/32\n",
      "26652/26652 [==============================] - 53s 2ms/step - loss: 3.0321 - acc: 0.3916 - val_loss: 3.0619 - val_acc: 0.3898\n",
      "Epoch 5/32\n",
      "26652/26652 [==============================] - 53s 2ms/step - loss: 2.9574 - acc: 0.3986 - val_loss: 3.0143 - val_acc: 0.3962\n",
      "Epoch 6/32\n",
      "26652/26652 [==============================] - 51s 2ms/step - loss: 2.8987 - acc: 0.4046 - val_loss: 2.9872 - val_acc: 0.3981\n",
      "Epoch 7/32\n",
      "26652/26652 [==============================] - 52s 2ms/step - loss: 2.8483 - acc: 0.4095 - val_loss: 2.9594 - val_acc: 0.4019\n",
      "Epoch 8/32\n",
      "26652/26652 [==============================] - 52s 2ms/step - loss: 2.8036 - acc: 0.4140 - val_loss: 2.9407 - val_acc: 0.4044\n",
      "Epoch 9/32\n",
      "26652/26652 [==============================] - 53s 2ms/step - loss: 2.7637 - acc: 0.4180 - val_loss: 2.9264 - val_acc: 0.4041\n",
      "Epoch 10/32\n",
      "26652/26652 [==============================] - 54s 2ms/step - loss: 2.7266 - acc: 0.4220 - val_loss: 2.9171 - val_acc: 0.4069\n",
      "Epoch 11/32\n",
      "26652/26652 [==============================] - 52s 2ms/step - loss: 2.6926 - acc: 0.4260 - val_loss: 2.9121 - val_acc: 0.4075\n",
      "Epoch 12/32\n",
      "26652/26652 [==============================] - 53s 2ms/step - loss: 2.6617 - acc: 0.4296 - val_loss: 2.9034 - val_acc: 0.4061\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/keras/engine/network.py:877: UserWarning: Layer decoder was passed non-serializable keyword arguments: {'initial_state': [<tf.Tensor 'encoder/while/Exit_3:0' shape=(?, 64) dtype=float32>, <tf.Tensor 'encoder/while/Exit_4:0' shape=(?, 64) dtype=float32>]}. They will not be included in the serialized model (and thus will be missing at deserialization time).\n",
      "  '. They will not be included '\n"
     ]
    }
   ],
   "source": [
    "early_stopping = EarlyStopping(monitor='val_acc', min_delta=0.001, patience=2)\n",
    "\n",
    "chatbot_log = Chatbot.fit({'input_context': padded_tokenized_contexts,\n",
    "                           'input_answer': answers_input}, \n",
    "                           {'output': answers_output}, \n",
    "                           epochs=epochs, \n",
    "                           validation_split=validation_split, \n",
    "                           batch_size=batch_size,\n",
    "                           callbacks=[early_stopping]).history\n",
    "#save\n",
    "with open(\"chatbot_log.pkl\", \"wb\") as file:\n",
    "    pickle.dump(chatbot_log, file)\n",
    "Chatbot.save(\"chatbot\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Chatbot model cannot be loaded from the saved file properly due to a serializability issue regarding the initial state."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_answer(model, context, temperature = 1.):\n",
    "    tokenized_context = [word_to_token[word] for word in context.split(' ')]\n",
    "    padded_context = pad_sequences([tokenized_context], maxlen=maxlen-2, padding='post', truncating='post', value=word_to_token['PADD'])\n",
    "    \n",
    "    token_list = np.zeros((1, maxlen - 1)).astype('int32')\n",
    "    next_token = 1 # 'START'\n",
    "    token_list[0,0] = next_token\n",
    "    i = 1\n",
    "    while not next_token == word_to_token['END'] and not i == maxlen - 1:\n",
    "        preds = model.predict({'input_context': padded_context, 'input_answer': token_list})\n",
    "        next_token = sample(preds.squeeze()[i - 1, :], temperature)\n",
    "        token_list[0, i] = next_token\n",
    "        if i == 1: pred_list = preds.squeeze()[i - 1, :]\n",
    "        else: pred_list = np.vstack((pred_list, preds.squeeze()[i - 1, :]))\n",
    "        i += 1\n",
    "        \n",
    "    answer = tokenlisttosentence(token_list.squeeze())\n",
    "    \n",
    "    return token_list, answer, pred_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comparison of real answers and the Chatbot answers for 10 randomly selected context sentences:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [],
   "source": [
    "indices = np.random.choice(range(len(contexts)), size=10, replace=False)\n",
    "random_contexts = np.array(contexts)[indices]\n",
    "random_answers = np.array(answers)[indices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Context: i am fine . i just got done in my garden . what are you doing ?\n",
      "Real answer: just sewing i love to do that\n",
      "Chatbot 0.5: i am good . how are you ?\n",
      "Chatbot 1.0: hey ! i am getting a stay at home at the factory\n",
      "---\n",
      "Context: maybe i will travel to your farm ; i love seeing new places\n",
      "Real answer: yes ! want to move to the city and get away from the farm\n",
      "Chatbot 0.5: i love to sing but i am a teacher .\n",
      "Chatbot 1.0: i understand , a concert may\n",
      "---\n",
      "Context: cool , college is where i go , i am a student\n",
      "Real answer: me too , but i also have to work\n",
      "Chatbot 0.5: what do you do for work ?\n",
      "Chatbot 1.0: that sounds awesome ! i just have two cats to my clients\n",
      "---\n",
      "Context: my least favorite food is tacos . but i absolutely love chocolate milk\n",
      "Real answer: oh no where is your hair ? have any animals ?\n",
      "Chatbot 0.5: i like to be a girl , but i love the cold weather\n",
      "Chatbot 1.0: a little blue , imagine dragons , thanks ! we concert to australia haha\n",
      "---\n",
      "Context: no i am an only child . i spend a lot of time helping my mom .\n",
      "Real answer: what do you help your mom ?\n",
      "Chatbot 0.5: i have a cat and i have been married with my husband .\n",
      "Chatbot 1.0: oh , that is funny . lol , what do you do for work ?\n",
      "---\n",
      "Context: yea i get to be outside all the time and not in an office\n",
      "Real answer: i hear you i think sometimes that is the only way i know how to live\n",
      "Chatbot 0.5: what kind of books do you like ?\n",
      "Chatbot 1.0: you do not have anyone else do you like beer ?\n",
      "---\n",
      "Context: oh , i work a fair bit at a doctors office .\n",
      "Real answer: hey me to ! what do you do ?\n",
      "Chatbot 0.5: what do you do for a living ?\n",
      "Chatbot 1.0: what do you i do like to sing\n",
      "---\n",
      "Context: hello , how are you doing ?\n",
      "Real answer: hi , i am good how about you ?\n",
      "Chatbot 0.5: i am good . how are you ?\n",
      "Chatbot 1.0: oh how is yours ? i am currently worked out and walk from a band .\n",
      "---\n",
      "Context: when i was younger i did\n",
      "Real answer: any reason you do not do it anymore ?\n",
      "Chatbot 0.5: i am sorry to hear that .\n",
      "Chatbot 1.0: that is sweet , if it blind you hit the way around me .\n",
      "---\n",
      "Context: hi how are you today ?\n",
      "Real answer: i am great how are you\n",
      "Chatbot 0.5: i am good and i am a little tired\n",
      "Chatbot 1.0: i am good evening how are you ?\n",
      "---\n"
     ]
    }
   ],
   "source": [
    "for context, real_answer in zip(random_contexts, random_answers):\n",
    "    _, answer_05, _ = generate_answer(Chatbot, context, temperature=0.5)\n",
    "    _, answer_1, _ = generate_answer(Chatbot, context, temperature=1)\n",
    "    print('Context:', context)    \n",
    "    print('Real answer:', real_answer)    \n",
    "    print('Chatbot 0.5:', answer_05)\n",
    "    print('Chatbot 1.0:', answer_1)\n",
    "    print('---')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Chat with the bot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "You: hi .\n",
      "Bot: hey ! tell me about yourself ?\n",
      "You: i am a student . you tell me about yourself .\n",
      "Bot: i am a student , i work at a lot . i love to read\n",
      "You: how are you ?\n",
      "Bot: i am doing great ! just got back from a run .\n",
      "You: what do you like to do for fun ?\n",
      "Bot: i like to play it , but i like to cook\n",
      "You: nice to meet you .\n",
      "Bot: i am a teacher , but i am hoping to go to the beach .\n",
      "You: goodbye\n",
      "Bot: I don't understand you!\n",
      "You: bye\n",
      "Bot: I don't understand you!\n",
      "You: farewell\n",
      "Bot: I don't understand you!\n",
      "You: see you\n",
      "Bot: i love the rolling stones music\n",
      "You: me too !\n",
      "Bot: i would like to be in the city\n",
      "You: me too !\n",
      "Bot: what do you like to do for fun ?\n",
      "You: i like cooking .\n",
      "Bot: i love going to it , i would love to drive it a lot .\n",
      "You: exit\n",
      "Bot: I don't understand you!\n"
     ]
    }
   ],
   "source": [
    "context = ''\n",
    "\n",
    "while context != 'exit':\n",
    "    context = input(prompt=\"You: \")\n",
    "    \n",
    "    words = context.split(' ')\n",
    "    if not all(word in word_to_token.keys() for word in words):\n",
    "        print(\"Bot: I don't understand you!\")\n",
    "    else:\n",
    "        _, answer, _ = generate_answer(Chatbot, context, temperature=0.5)\n",
    "        print(\"Bot:\", answer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bot chats with itself:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bot 1:  i am a student , and i am in school\n",
      "Bot 2:  oh , that is cool . i love it .\n",
      "Bot 1:  i like to watch netflix\n",
      "Bot 2:  i love to travel much . do you like sports ?\n",
      "Bot 1:  i have a dog . i live in the city .\n",
      "Bot 2:  i love the beach too .\n",
      "Bot 1:  what do you do for a living ?\n",
      "Bot 2:  i am a teacher and i am a teacher\n",
      "Bot 1:  well that is cool . i love animals . i have 2 kids .\n",
      "Bot 2:  i love dogs , i am a little teacher .\n"
     ]
    }
   ],
   "source": [
    "# generate a sentence from GRU256\n",
    "_, context, _ = generate(GRU256model, temperature=0.1, print_sentence=False)\n",
    "\n",
    "for i in range(5):\n",
    "    print('Bot 1: ', context)\n",
    "    _, answer, _ = generate_answer(Chatbot, context, temperature=0.5)\n",
    "    print('Bot 2: ', answer)\n",
    "    _, context,_ = generate_answer(Chatbot, answer, temperature=0.5)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  }
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