chatbot_tutorial.py

# -*- coding: utf-8 -*-

"""
Chatbot Tutorial
================
**Author:** `Matthew Inkawhich <https://github.com/MatthewInkawhich>`_
"""


######################################################################
# In this tutorial, we explore a fun and interesting use-case of recurrent
# sequence-to-sequence models. We will train a simple chatbot using movie
# scripts from the `Cornell Movie-Dialogs
# Corpus <https://www.cs.cornell.edu/~cristian/Cornell_Movie-Dialogs_Corpus.html>`__.
#
# Conversational models are a hot topic in artificial intelligence
# research. Chatbots can be found in a variety of settings, including
# customer service applications and online helpdesks. These bots are often
# powered by retrieval-based models, which output predefined responses to
# questions of certain forms. In a highly restricted domain like a
# company’s IT helpdesk, these models may be sufficient, however, they are
# not robust enough for more general use-cases. Teaching a machine to
# carry out a meaningful conversation with a human in multiple domains is
# a research question that is far from solved. Recently, the deep learning
# boom has allowed for powerful generative models like Google’s `Neural
# Conversational Model <https://arxiv.org/abs/1506.05869>`__, which marks
# a large step towards multi-domain generative conversational models. In
# this tutorial, we will implement this kind of model in PyTorch.
#
# .. figure:: /_static/img/chatbot/bot.png
#    :align: center
#    :alt: bot
#
# .. code-block:: python
#
#    > hello?
#    Bot: hello .
#    > where am I?
#    Bot: you re in a hospital .
#    > who are you?
#    Bot: i m a lawyer .
#    > how are you doing?
#    Bot: i m fine .
#    > are you my friend?
#    Bot: no .
#    > you're under arrest
#    Bot: i m trying to help you !
#    > i'm just kidding
#    Bot: i m sorry .
#    > where are you from?
#    Bot: san francisco .
#    > it's time for me to leave
#    Bot: i know .
#    > goodbye
#    Bot: goodbye .
#
# **Tutorial Highlights**
#
# -  Handle loading and preprocessing of `Cornell Movie-Dialogs
#    Corpus <https://www.cs.cornell.edu/~cristian/Cornell_Movie-Dialogs_Corpus.html>`__
#    dataset
# -  Implement a sequence-to-sequence model with `Luong attention
#    mechanism(s) <https://arxiv.org/abs/1508.04025>`__
# -  Jointly train encoder and decoder models using mini-batches
# -  Implement greedy-search decoding module
# -  Interact with trained chatbot
#
# **Acknowledgments**
#
# This tutorial borrows code from the following sources:
#
# 1) Yuan-Kuei Wu’s pytorch-chatbot implementation:
#    https://github.com/ywk991112/pytorch-chatbot
#
# 2) Sean Robertson’s practical-pytorch seq2seq-translation example:
#    https://github.com/spro/practical-pytorch/tree/master/seq2seq-translation
#
# 3) FloydHub Cornell Movie Corpus preprocessing code:
#    https://github.com/floydhub/textutil-preprocess-cornell-movie-corpus
#


######################################################################
# Preparations
# ------------
#
# To get started, `download <https://zissou.infosci.cornell.edu/convokit/datasets/movie-corpus/movie-corpus.zip>`__ the Movie-Dialogs Corpus zip file.

# and put in a ``data/`` directory under the current directory.
#
# After that, let’s import some necessities.
#

import torch
from torch.jit import script, trace
import torch.nn as nn
from torch import optim
import torch.nn.functional as F
import csv
import random
import re
import os
import unicodedata
import codecs
from io import open
import itertools
import math
import json


# If the current `accelerator <https://pytorch.org/docs/stable/torch.html#accelerators>`__ is available,
# we will use it. Otherwise, we use the CPU.
device = torch.accelerator.current_accelerator().type if torch.accelerator.is_available() else "cpu"
print(f"Using {device} device")


######################################################################
# Load & Preprocess Data
# ----------------------
#
# The next step is to reformat our data file and load the data into
# structures that we can work with.
#
# The `Cornell Movie-Dialogs
# Corpus <https://www.cs.cornell.edu/~cristian/Cornell_Movie-Dialogs_Corpus.html>`__
# is a rich dataset of movie character dialog:
#
# -  220,579 conversational exchanges between 10,292 pairs of movie
#    characters
# -  9,035 characters from 617 movies
# -  304,713 total utterances
#
# This dataset is large and diverse, and there is a great variation of
# language formality, time periods, sentiment, etc. Our hope is that this
# diversity makes our model robust to many forms of inputs and queries.
#
# First, we’ll take a look at some lines of our datafile to see the
# original format.
#

corpus_name = "movie-corpus"
corpus = os.path.join("data", corpus_name)

def printLines(file, n=10):
    with open(file, 'rb') as datafile:
        lines = datafile.readlines()
    for line in lines[:n]:
        print(line)

printLines(os.path.join(corpus, "utterances.jsonl"))


######################################################################
# Create formatted data file
# ~~~~~~~~~~~~~~~~~~~~~~~~~~
#
# For convenience, we'll create a nicely formatted data file in which each line
# contains a tab-separated *query sentence* and a *response sentence* pair.
#
# The following functions facilitate the parsing of the raw
# ``utterances.jsonl`` data file.
#
# -  ``loadLinesAndConversations`` splits each line of the file into a dictionary of
#    lines with fields: ``lineID``, ``characterID``, and text and then groups them
#    into conversations with fields: ``conversationID``, ``movieID``, and lines.
# -  ``extractSentencePairs`` extracts pairs of sentences from
#    conversations
#

# Splits each line of the file to create lines and conversations
def loadLinesAndConversations(fileName):
    lines = {}
    conversations = {}
    with open(fileName, 'r', encoding='iso-8859-1') as f:
        for line in f:
            lineJson = json.loads(line)
            # Extract fields for line object
            lineObj = {}
            lineObj["lineID"] = lineJson["id"]
            lineObj["characterID"] = lineJson["speaker"]
            lineObj["text"] = lineJson["text"]
            lines[lineObj['lineID']] = lineObj

            # Extract fields for conversation object
            if lineJson["conversation_id"] not in conversations:
                convObj = {}
                convObj["conversationID"] = lineJson["conversation_id"]
                convObj["movieID"] = lineJson["meta"]["movie_id"]
                convObj["lines"] = [lineObj]
            else:
                convObj = conversations[lineJson["conversation_id"]]
                convObj["lines"].insert(0, lineObj)
            conversations[convObj["conversationID"]] = convObj

    return lines, conversations


# Extracts pairs of sentences from conversations
def extractSentencePairs(conversations):
    qa_pairs = []
    for conversation in conversations.values():
        # Iterate over all the lines of the conversation
        for i in range(len(conversation["lines"]) - 1):  # We ignore the last line (no answer for it)
            inputLine = conversation["lines"][i]["text"].strip()
            targetLine = conversation["lines"][i+1]["text"].strip()
            # Filter wrong samples (if one of the lists is empty)
            if inputLine and targetLine:
                qa_pairs.append([inputLine, targetLine])
    return qa_pairs


######################################################################
# Now we’ll call these functions and create the file. We’ll call it
# ``formatted_movie_lines.txt``.
#

# Define path to new file
datafile = os.path.join(corpus, "formatted_movie_lines.txt")

delimiter = '\t'
# Unescape the delimiter
delimiter = str(codecs.decode(delimiter, "unicode_escape"))

# Initialize lines dict and conversations dict
lines = {}
conversations = {}
# Load lines and conversations
print("\nProcessing corpus into lines and conversations...")
lines, conversations = loadLinesAndConversations(os.path.join(corpus, "utterances.jsonl"))

# Write new csv file
print("\nWriting newly formatted file...")
with open(datafile, 'w', encoding='utf-8') as outputfile:
    writer = csv.writer(outputfile, delimiter=delimiter, lineterminator='\n')
    for pair in extractSentencePairs(conversations):
        writer.writerow(pair)

# Print a sample of lines
print("\nSample lines from file:")
printLines(datafile)


######################################################################
# Load and trim data
# ~~~~~~~~~~~~~~~~~~
#
# Our next order of business is to create a vocabulary and load
# query/response sentence pairs into memory.
#
# Note that we are dealing with sequences of **words**, which do not have
# an implicit mapping to a discrete numerical space. Thus, we must create
# one by mapping each unique word that we encounter in our dataset to an
# index value.
#
# For this we define a ``Voc`` class, which keeps a mapping from words to
# indexes, a reverse mapping of indexes to words, a count of each word and
# a total word count. The class provides methods for adding a word to the
# vocabulary (``addWord``), adding all words in a sentence
# (``addSentence``) and trimming infrequently seen words (``trim``). More
# on trimming later.
#

# Default word tokens
PAD_token = 0  # Used for padding short sentences
SOS_token = 1  # Start-of-sentence token
EOS_token = 2  # End-of-sentence token

class Voc:
    def __init__(self, name):
        self.name = name
        self.trimmed = False
        self.word2index = {}
        self.word2count = {}
        self.index2word = {PAD_token: "PAD", SOS_token: "SOS", EOS_token: "EOS"}
        self.num_words = 3  # Count SOS, EOS, PAD

    def addSentence(self, sentence):
        for word in sentence.split(' '):
            self.addWord(word)

    def addWord(self, word):
        if word not in self.word2index:
            self.word2index[word] = self.num_words
            self.word2count[word] = 1
            self.index2word[self.num_words] = word
            self.num_words += 1
        else:
            self.word2count[word] += 1

    # Remove words below a certain count threshold
    def trim(self, min_count):
        if self.trimmed:
            return
        self.trimmed = True

        keep_words = []

        for k, v in self.word2count.items():
            if v >= min_count:
                keep_words.append(k)

        print('keep_words {} / {} = {:.4f}'.format(
            len(keep_words), len(self.word2index), len(keep_words) / len(self.word2index)
        ))

        # Reinitialize dictionaries
        self.word2index = {}
        self.word2count = {}
        self.index2word = {PAD_token: "PAD", SOS_token: "SOS", EOS_token: "EOS"}
        self.num_words = 3 # Count default tokens

        for word in keep_words:
            self.addWord(word)


######################################################################
# Now we can assemble our vocabulary and query/response sentence pairs.
# Before we are ready to use this data, we must perform some
# preprocessing.
#
# First, we must convert the Unicode strings to ASCII using
# ``unicodeToAscii``. Next, we should convert all letters to lowercase and
# trim all non-letter characters except for basic punctuation
# (``normalizeString``). Finally, to aid in training convergence, we will
# filter out sentences with length greater than the ``MAX_LENGTH``
# threshold (``filterPairs``).
#

MAX_LENGTH = 10  # Maximum sentence length to consider

# Turn a Unicode string to plain ASCII, thanks to
# https://stackoverflow.com/a/518232/2809427
def unicodeToAscii(s):
    return ''.join(
        c for c in unicodedata.normalize('NFD', s)
        if unicodedata.category(c) != 'Mn'
    )

# Lowercase, trim, and remove non-letter characters
def normalizeString(s):
    s = unicodeToAscii(s.lower().strip())
    s = re.sub(r"([.!?])", r" \1", s)
    s = re.sub(r"[^a-zA-Z.!?]+", r" ", s)
    s = re.sub(r"\s+", r" ", s).strip()
    return s

# Read query/response pairs and return a voc object
def readVocs(datafile, corpus_name):
    print("Reading lines...")
    # Read the file and split into lines
    lines = open(datafile, encoding='utf-8').\
        read().strip().split('\n')
    # Split every line into pairs and normalize
    pairs = [[normalizeString(s) for s in l.split('\t')] for l in lines]
    voc = Voc(corpus_name)
    return voc, pairs

# Returns True if both sentences in a pair 'p' are under the MAX_LENGTH threshold
def filterPair(p):
    # Input sequences need to preserve the last word for EOS token
    return len(p[0].split(' ')) < MAX_LENGTH and len(p[1].split(' ')) < MAX_LENGTH

# Filter pairs using the ``filterPair`` condition
def filterPairs(pairs):
    return [pair for pair in pairs if filterPair(pair)]

# Using the functions defined above, return a populated voc object and pairs list
def loadPrepareData(corpus, corpus_name, datafile, save_dir):
    print("Start preparing training data ...")
    voc, pairs = readVocs(datafile, corpus_name)
    print("Read {!s} sentence pairs".format(len(pairs)))
    pairs = filterPairs(pairs)
    print("Trimmed to {!s} sentence pairs".format(len(pairs)))
    print("Counting words...")
    for pair in pairs:
        voc.addSentence(pair[0])
        voc.addSentence(pair[1])
    print("Counted words:", voc.num_words)
    return voc, pairs


# Load/Assemble voc and pairs
save_dir = os.path.join("data", "save")
voc, pairs = loadPrepareData(corpus, corpus_name, datafile, save_dir)
# Print some pairs to validate
print("\npairs:")
for pair in pairs[:10]:
    print(pair)


######################################################################
# Another tactic that is beneficial to achieving faster convergence during
# training is trimming rarely used words out of our vocabulary. Decreasing
# the feature space will also soften the difficulty of the function that
# the model must learn to approximate. We will do this as a two-step
# process:
#
# 1) Trim words used under ``MIN_COUNT`` threshold using the ``voc.trim``
#    function.
#
# 2) Filter out pairs with trimmed words.
#

MIN_COUNT = 3    # Minimum word count threshold for trimming

def trimRareWords(voc, pairs, MIN_COUNT):
    # Trim words used under the MIN_COUNT from the voc
    voc.trim(MIN_COUNT)
    # Filter out pairs with trimmed words
    keep_pairs = []
    for pair in pairs:
        input_sentence = pair[0]
        output_sentence = pair[1]
        keep_input = True
        keep_output = True
        # Check input sentence
        for word in input_sentence.split(' '):
            if word not in voc.word2index:
                keep_input = False
                break
        # Check output sentence
        for word in output_sentence.split(' '):
            if word not in voc.word2index:
                keep_output = False
                break

        # Only keep pairs that do not contain trimmed word(s) in their input or output sentence
        if keep_input and keep_output:
            keep_pairs.append(pair)

    print("Trimmed from {} pairs to {}, {:.4f} of total".format(len(pairs), len(keep_pairs), len(keep_pairs) / len(pairs)))
    return keep_pairs


# Trim voc and pairs
pairs = trimRareWords(voc, pairs, MIN_COUNT)


######################################################################
# Prepare Data for Models
# -----------------------
#
# Although we have put a great deal of effort into preparing and massaging our
# data into a nice vocabulary object and list of sentence pairs, our models
# will ultimately expect numerical torch tensors as inputs. One way to
# prepare the processed data for the models can be found in the `seq2seq
# translation
# tutorial <https://pytorch.org/tutorials/intermediate/seq2seq_translation_tutorial.html>`__.
# In that tutorial, we use a batch size of 1, meaning that all we have to
# do is convert the words in our sentence pairs to their corresponding
# indexes from the vocabulary and feed this to the models.
#
# However, if you’re interested in speeding up training and/or would like
# to leverage GPU parallelization capabilities, you will need to train
# with mini-batches.
#
# Using mini-batches also means that we must be mindful of the variation
# of sentence length in our batches. To accommodate sentences of different
# sizes in the same batch, we will make our batched input tensor of shape
# *(max_length, batch_size)*, where sentences shorter than the
# *max_length* are zero padded after an *EOS_token*.
#
# If we simply convert our English sentences to tensors by converting
# words to their indexes(\ ``indexesFromSentence``) and zero-pad, our
# tensor would have shape *(batch_size, max_length)* and indexing the
# first dimension would return a full sequence across all time-steps.
# However, we need to be able to index our batch along time, and across
# all sequences in the batch. Therefore, we transpose our input batch
# shape to *(max_length, batch_size)*, so that indexing across the first
# dimension returns a time step across all sentences in the batch. We
# handle this transpose implicitly in the ``zeroPadding`` function.
#
# .. figure:: /_static/img/chatbot/seq2seq_batches.png
#    :align: center
#    :alt: batches
#
# The ``inputVar`` function handles the process of converting sentences to
# tensor, ultimately creating a correctly shaped zero-padded tensor. It
# also returns a tensor of ``lengths`` for each of the sequences in the
# batch which will be passed to our decoder later.
#
# The ``outputVar`` function performs a similar function to ``inputVar``,
# but instead of returning a ``lengths`` tensor, it returns a binary mask
# tensor and a maximum target sentence length. The binary mask tensor has
# the same shape as the output target tensor, but every element that is a
# *PAD_token* is 0 and all others are 1.
#
# ``batch2TrainData`` simply takes a bunch of pairs and returns the input
# and target tensors using the aforementioned functions.
#

def indexesFromSentence(voc, sentence):
    return [voc.word2index[word] for word in sentence.split(' ')] + [EOS_token]


def zeroPadding(l, fillvalue=PAD_token):
    return list(itertools.zip_longest(*l, fillvalue=fillvalue))

def binaryMatrix(l, value=PAD_token):
    m = []
    for i, seq in enumerate(l):
        m.append([])
        for token in seq:
            if token == PAD_token:
                m[i].append(0)
            else:
                m[i].append(1)
    return m

# Returns padded input sequence tensor and lengths
def inputVar(l, voc):
    indexes_batch = [indexesFromSentence(voc, sentence) for sentence in l]
    lengths = torch.tensor([len(indexes) for indexes in indexes_batch])
    padList = zeroPadding(indexes_batch)
    padVar = torch.LongTensor(padList)
    return padVar, lengths

# Returns padded target sequence tensor, padding mask, and max target length
def outputVar(l, voc):
    indexes_batch = [indexesFromSentence(voc, sentence) for sentence in l]
    max_target_len = max([len(indexes) for indexes in indexes_batch])
    padList = zeroPadding(indexes_batch)
    mask = binaryMatrix(padList)
    mask = torch.BoolTensor(mask)
    padVar = torch.LongTensor(padList)
    return padVar, mask, max_target_len

# Returns all items for a given batch of pairs
def batch2TrainData(voc, pair_batch):
    pair_batch.sort(key=lambda x: len(x[0].split(" ")), reverse=True)
    input_batch, output_batch = [], []
    for pair in pair_batch:
        input_batch.append(pair[0])
        output_batch.append(pair[1])
    inp, lengths = inputVar(input_batch, voc)
    output, mask, max_target_len = outputVar(output_batch, voc)
    return inp, lengths, output, mask, max_target_len


# Example for validation
small_batch_size = 5
batches = batch2TrainData(voc, [random.choice(pairs) for _ in range(small_batch_size)])
input_variable, lengths, target_variable, mask, max_target_len = batches

print("input_variable:", input_variable)
print("lengths:", lengths)
print("target_variable:", target_variable)
print("mask:", mask)
print("max_target_len:", max_target_len)


######################################################################
# Define Models
# -------------
#
# Seq2Seq Model
# ~~~~~~~~~~~~~
#
# The brains of our chatbot is a sequence-to-sequence (seq2seq) model. The
# goal of a seq2seq model is to take a variable-length sequence as an
# input, and return a variable-length sequence as an output using a
# fixed-sized model.
#
# `Sutskever et al. <https://arxiv.org/abs/1409.3215>`__ discovered that
# by using two separate recurrent neural nets together, we can accomplish
# this task. One RNN acts as an **encoder**, which encodes a variable
# length input sequence to a fixed-length context vector. In theory, this
# context vector (the final hidden layer of the RNN) will contain semantic
# information about the query sentence that is input to the bot. The
# second RNN is a **decoder**, which takes an input word and the context
# vector, and returns a guess for the next word in the sequence and a
# hidden state to use in the next iteration.
#
# .. figure:: /_static/img/chatbot/seq2seq_ts.png
#    :align: center
#    :alt: model
#
# Image source:
# https://jeddy92.github.io/JEddy92.github.io/ts_seq2seq_intro/
#


######################################################################
# Encoder
# ~~~~~~~
#
# The encoder RNN iterates through the input sentence one token
# (e.g. word) at a time, at each time step outputting an “output” vector
# and a “hidden state” vector. The hidden state vector is then passed to
# the next time step, while the output vector is recorded. The encoder
# transforms the context it saw at each point in the sequence into a set
# of points in a high-dimensional space, which the decoder will use to
# generate a meaningful output for the given task.
#
# At the heart of our encoder is a multi-layered Gated Recurrent Unit,
# invented by `Cho et al. <https://arxiv.org/pdf/1406.1078v3.pdf>`__ in
# 2014. We will use a bidirectional variant of the GRU, meaning that there
# are essentially two independent RNNs: one that is fed the input sequence
# in normal sequential order, and one that is fed the input sequence in
# reverse order. The outputs of each network are summed at each time step.
# Using a bidirectional GRU will give us the advantage of encoding both
# past and future contexts.
#
# Bidirectional RNN:
#
# .. figure:: /_static/img/chatbot/RNN-bidirectional.png
#    :width: 70%
#    :align: center
#    :alt: rnn_bidir
#
# Image source: https://colah.github.io/posts/2015-09-NN-Types-FP/
#
# Note that an ``embedding`` layer is used to encode our word indices in
# an arbitrarily sized feature space. For our models, this layer will map
# each word to a feature space of size *hidden_size*. When trained, these
# values should encode semantic similarity between similar meaning words.
#
# Finally, if passing a padded batch of sequences to an RNN module, we
# must pack and unpack padding around the RNN pass using
# ``nn.utils.rnn.pack_padded_sequence`` and
# ``nn.utils.rnn.pad_packed_sequence`` respectively.
#
# **Computation Graph:**
#
#    1) Convert word indexes to embeddings.
#    2) Pack padded batch of sequences for RNN module.
#    3) Forward pass through GRU.
#    4) Unpack padding.
#    5) Sum bidirectional GRU outputs.
#    6) Return output and final hidden state.
#
# **Inputs:**
#
# -  ``input_seq``: batch of input sentences; shape=\ *(max_length,
#    batch_size)*
# -  ``input_lengths``: list of sentence lengths corresponding to each
#    sentence in the batch; shape=\ *(batch_size)*
# -  ``hidden``: hidden state; shape=\ *(n_layers x num_directions,
#    batch_size, hidden_size)*
#
# **Outputs:**
#
# -  ``outputs``: output features from the last hidden layer of the GRU
#    (sum of bidirectional outputs); shape=\ *(max_length, batch_size,
#    hidden_size)*
# -  ``hidden``: updated hidden state from GRU; shape=\ *(n_layers x
#    num_directions, batch_size, hidden_size)*
#
#

class EncoderRNN(nn.Module):
    def __init__(self, hidden_size, embedding, n_layers=1, dropout=0):
        super(EncoderRNN, self).__init__()
        self.n_layers = n_layers
        self.hidden_size = hidden_size
        self.embedding = embedding

        # Initialize GRU; the input_size and hidden_size parameters are both set to 'hidden_size'
        #   because our input size is a word embedding with number of features == hidden_size
        self.gru = nn.GRU(hidden_size, hidden_size, n_layers,
                          dropout=(0 if n_layers == 1 else dropout), bidirectional=True)

    def forward(self, input_seq, input_lengths, hidden=None):
        # Convert word indexes to embeddings
        embedded = self.embedding(input_seq)
        # Pack padded batch of sequences for RNN module
        packed = nn.utils.rnn.pack_padded_sequence(embedded, input_lengths)
        # Forward pass through GRU
        outputs, hidden = self.gru(packed, hidden)
        # Unpack padding
        outputs, _ = nn.utils.rnn.pad_packed_sequence(outputs)
        # Sum bidirectional GRU outputs
        outputs = outputs[:, :, :self.hidden_size] + outputs[:, : ,self.hidden_size:]
        # Return output and final hidden state
        return outputs, hidden


######################################################################
# Decoder
# ~~~~~~~
#
# The decoder RNN generates the response sentence in a token-by-token
# fashion. It uses the encoder’s context vectors, and internal hidden
# states to generate the next word in the sequence. It continues
# generating words until it outputs an *EOS_token*, representing the end
# of the sentence. A common problem with a vanilla seq2seq decoder is that
# if we rely solely on the context vector to encode the entire input
# sequence’s meaning, it is likely that we will have information loss.
# This is especially the case when dealing with long input sequences,
# greatly limiting the capability of our decoder.
#
# To combat this, `Bahdanau et al. <https://arxiv.org/abs/1409.0473>`__
# created an “attention mechanism” that allows the decoder to pay
# attention to certain parts of the input sequence, rather than using the
# entire fixed context at every step.
#
# At a high level, attention is calculated using the decoder’s current
# hidden state and the encoder’s outputs. The output attention weights
# have the same shape as the input sequence, allowing us to multiply them
# by the encoder outputs, giving us a weighted sum which indicates the
# parts of encoder output to pay attention to. `Sean
# Robertson’s <https://github.com/spro>`__ figure describes this very
# well:
#
# .. figure:: /_static/img/chatbot/attn2.png
#    :align: center
#    :alt: attn2
#
# `Luong et al. <https://arxiv.org/abs/1508.04025>`__ improved upon
# Bahdanau et al.’s groundwork by creating “Global attention”. The key
# difference is that with “Global attention”, we consider all of the
# encoder’s hidden states, as opposed to Bahdanau et al.’s “Local
# attention”, which only considers the encoder’s hidden state from the
# current time step. Another difference is that with “Global attention”,
# we calculate attention weights, or energies, using the hidden state of
# the decoder from the current time step only. Bahdanau et al.’s attention
# calculation requires knowledge of the decoder’s state from the previous
# time step. Also, Luong et al. provides various methods to calculate the
# attention energies between the encoder output and decoder output which
# are called “score functions”:
#
# .. figure:: /_static/img/chatbot/scores.png
#    :width: 60%
#    :align: center
#    :alt: scores
#
# where :math:`h_t` = current target decoder state and :math:`\bar{h}_s` =
# all encoder states.
#
# Overall, the Global attention mechanism can be summarized by the
# following figure. Note that we will implement the “Attention Layer” as a
# separate ``nn.Module`` called ``Attn``. The output of this module is a
# softmax normalized weights tensor of shape *(batch_size, 1,
# max_length)*.
#
# .. figure:: /_static/img/chatbot/global_attn.png
#    :align: center
#    :width: 60%
#    :alt: global_attn
#

# Luong attention layer
class Attn(nn.Module):
    def __init__(self, method, hidden_size):
        super(Attn, self).__init__()
        self.method = method
        if self.method not in ['dot', 'general', 'concat']:
            raise ValueError(self.method, "is not an appropriate attention method.")
        self.hidden_size = hidden_size
        if self.method == 'general':
            self.attn = nn.Linear(self.hidden_size, hidden_size)
        elif self.method == 'concat':
            self.attn = nn.Linear(self.hidden_size * 2, hidden_size)
            self.v = nn.Parameter(torch.FloatTensor(hidden_size))

    def dot_score(self, hidden, encoder_output):
        return torch.sum(hidden * encoder_output, dim=2)

    def general_score(self, hidden, encoder_output):
        energy = self.attn(encoder_output)
        return torch.sum(hidden * energy, dim=2)

    def concat_score(self, hidden, encoder_output):
        energy = self.attn(torch.cat((hidden.expand(encoder_output.size(0), -1, -1), encoder_output), 2)).tanh()
        return torch.sum(self.v * energy, dim=2)

    def forward(self, hidden, encoder_outputs):
        # Calculate the attention weights (energies) based on the given method
        if self.method == 'general':
            attn_energies = self.general_score(hidden, encoder_outputs)
        elif self.method == 'concat':
            attn_energies = self.concat_score(hidden, encoder_outputs)
        elif self.method == 'dot':
            attn_energies = self.dot_score(hidden, encoder_outputs)

        # Transpose max_length and batch_size dimensions
        attn_energies = attn_energies.t()

        # Return the softmax normalized probability scores (with added dimension)
        return F.softmax(attn_energies, dim=1).unsqueeze(1)


######################################################################
# Now that we have defined our attention submodule, we can implement the
# actual decoder model. For the decoder, we will manually feed our batch
# one time step at a time. This means that our embedded word tensor and
# GRU output will both have shape *(1, batch_size, hidden_size)*.
#
# **Computation Graph:**
#
#    1) Get embedding of current input word.
#    2) Forward through unidirectional GRU.
#    3) Calculate attention weights from the current GRU output from (2).
#    4) Multiply attention weights to encoder outputs to get new "weighted sum" context vector.
#    5) Concatenate weighted context vector and GRU output using Luong eq. 5.
#    6) Predict next word using Luong eq. 6 (without softmax).
#    7) Return output and final hidden state.
#
# **Inputs:**
#
# -  ``input_step``: one time step (one word) of input sequence batch;
#    shape=\ *(1, batch_size)*
# -  ``last_hidden``: final hidden layer of GRU; shape=\ *(n_layers x
#    num_directions, batch_size, hidden_size)*
# -  ``encoder_outputs``: encoder model’s output; shape=\ *(max_length,
#    batch_size, hidden_size)*
#
# **Outputs:**
#
# -  ``output``: softmax normalized tensor giving probabilities of each
#    word being the correct next word in the decoded sequence;
#    shape=\ *(batch_size, voc.num_words)*
# -  ``hidden``: final hidden state of GRU; shape=\ *(n_layers x
#    num_directions, batch_size, hidden_size)*
#

class LuongAttnDecoderRNN(nn.Module):
    def __init__(self, attn_model, embedding, hidden_size, output_size, n_layers=1, dropout=0.1):
        super(LuongAttnDecoderRNN, self).__init__()

        # Keep for reference
        self.attn_model = attn_model
        self.hidden_size = hidden_size
        self.output_size = output_size
        self.n_layers = n_layers
        self.dropout = dropout

        # Define layers
        self.embedding = embedding
        self.embedding_dropout = nn.Dropout(dropout)
        self.gru = nn.GRU(hidden_size, hidden_size, n_layers, dropout=(0 if n_layers == 1 else dropout))
        self.concat = nn.Linear(hidden_size * 2, hidden_size)
        self.out = nn.Linear(hidden_size, output_size)

        self.attn = Attn(attn_model, hidden_size)

    def forward(self, input_step, last_hidden, encoder_outputs):
        # Note: we run this one step (word) at a time
        # Get embedding of current input word
        embedded = self.embedding(input_step)
        embedded = self.embedding_dropout(embedded)
        # Forward through unidirectional GRU
        rnn_output, hidden = self.gru(embedded, last_hidden)
        # Calculate attention weights from the current GRU output
        attn_weights = self.attn(rnn_output, encoder_outputs)
        # Multiply attention weights to encoder outputs to get new "weighted sum" context vector
        context = attn_weights.bmm(encoder_outputs.transpose(0, 1))
        # Concatenate weighted context vector and GRU output using Luong eq. 5
        rnn_output = rnn_output.squeeze(0)
        context = context.squeeze(1)
        concat_input = torch.cat((rnn_output, context), 1)
        concat_output = torch.tanh(self.concat(concat_input))
        # Predict next word using Luong eq. 6
        output = self.out(concat_output)
        output = F.softmax(output, dim=1)
        # Return output and final hidden state
        return output, hidden


######################################################################
# Define Training Procedure
# -------------------------
#
# Masked loss
# ~~~~~~~~~~~
#
# Since we are dealing with batches of padded sequences, we cannot simply
# consider all elements of the tensor when calculating loss. We define
# ``maskNLLLoss`` to calculate our loss based on our decoder’s output
# tensor, the target tensor, and a binary mask tensor describing the
# padding of the target tensor. This loss function calculates the average
# negative log likelihood of the elements that correspond to a *1* in the
# mask tensor.
#

def maskNLLLoss(inp, target, mask):
    nTotal = mask.sum()
    crossEntropy = -torch.log(torch.gather(inp, 1, target.view(-1, 1)).squeeze(1))
    loss = crossEntropy.masked_select(mask).mean()
    loss = loss.to(device)
    return loss, nTotal.item()


######################################################################
# Single training iteration
# ~~~~~~~~~~~~~~~~~~~~~~~~~
#
# The ``train`` function contains the algorithm for a single training
# iteration (a single batch of inputs).
#
# We will use a couple of clever tricks to aid in convergence:
#
# -  The first trick is using **teacher forcing**. This means that at some
#    probability, set by ``teacher_forcing_ratio``, we use the current
#    target word as the decoder’s next input rather than using the
#    decoder’s current guess. This technique acts as training wheels for
#    the decoder, aiding in more efficient training. However, teacher
#    forcing can lead to model instability during inference, as the
#    decoder may not have a sufficient chance to truly craft its own
#    output sequences during training. Thus, we must be mindful of how we
#    are setting the ``teacher_forcing_ratio``, and not be fooled by fast
#    convergence.
#
# -  The second trick that we implement is **gradient clipping**. This is
#    a commonly used technique for countering the “exploding gradient”
#    problem. In essence, by clipping or thresholding gradients to a
#    maximum value, we prevent the gradients from growing exponentially
#    and either overflow (NaN), or overshoot steep cliffs in the cost
#    function.
#
# .. figure:: /_static/img/chatbot/grad_clip.png
#    :align: center
#    :width: 60%
#    :alt: grad_clip
#
# Image source: Goodfellow et al. *Deep Learning*. 2016. https://www.deeplearningbook.org/
#
# **Sequence of Operations:**
#
#    1) Forward pass entire input batch through encoder.
#    2) Initialize decoder inputs as SOS_token, and hidden state as the encoder's final hidden state.
#    3) Forward input batch sequence through decoder one time step at a time.
#    4) If teacher forcing: set next decoder input as the current target; else: set next decoder input as current decoder output.
#    5) Calculate and accumulate loss.
#    6) Perform backpropagation.
#    7) Clip gradients.
#    8) Update encoder and decoder model parameters.
#
#
# .. Note ::
#
#   PyTorch’s RNN modules (``RNN``, ``LSTM``, ``GRU``) can be used like any
#   other non-recurrent layers by simply passing them the entire input
#   sequence (or batch of sequences). We use the ``GRU`` layer like this in
#   the ``encoder``. The reality is that under the hood, there is an
#   iterative process looping over each time step calculating hidden states.
#   Alternatively, you can run these modules one time-step at a time. In
#   this case, we manually loop over the sequences during the training
#   process like we must do for the ``decoder`` model. As long as you
#   maintain the correct conceptual model of these modules, implementing
#   sequential models can be very straightforward.
#
#


def train(input_variable, lengths, target_variable, mask, max_target_len, encoder, decoder, embedding,
          encoder_optimizer, decoder_optimizer, batch_size, clip, max_length=MAX_LENGTH):

    # Zero gradients
    encoder_optimizer.zero_grad()
    decoder_optimizer.zero_grad()

    # Set device options
    input_variable = input_variable.to(device)
    target_variable = target_variable.to(device)
    mask = mask.to(device)
    # Lengths for RNN packing should always be on the CPU
    lengths = lengths.to("cpu")

    # Initialize variables
    loss = 0
    print_losses = []
    n_totals = 0

    # Forward pass through encoder
    encoder_outputs, encoder_hidden = encoder(input_variable, lengths)

    # Create initial decoder input (start with SOS tokens for each sentence)
    decoder_input = torch.LongTensor([[SOS_token for _ in range(batch_size)]])
    decoder_input = decoder_input.to(device)

    # Set initial decoder hidden state to the encoder's final hidden state
    decoder_hidden = encoder_hidden[:decoder.n_layers]

    # Determine if we are using teacher forcing this iteration
    use_teacher_forcing = True if random.random() < teacher_forcing_ratio else False

    # Forward batch of sequences through decoder one time step at a time
    if use_teacher_forcing:
        for t in range(max_target_len):
            decoder_output, decoder_hidden = decoder(
                decoder_input, decoder_hidden, encoder_outputs
            )
            # Teacher forcing: next input is current target
            decoder_input = target_variable[t].view(1, -1)
            # Calculate and accumulate loss
            mask_loss, nTotal = maskNLLLoss(decoder_output, target_variable[t], mask[t])
            loss += mask_loss
            print_losses.append(mask_loss.item() * nTotal)
            n_totals += nTotal
    else:
        for t in range(max_target_len):
            decoder_output, decoder_hidden = decoder(
                decoder_input, decoder_hidden, encoder_outputs
            )
            # No teacher forcing: next input is decoder's own current output
            _, topi = decoder_output.topk(1)
            decoder_input = torch.LongTensor([[topi[i][0] for i in range(batch_size)]])
            decoder_input = decoder_input.to(device)
            # Calculate and accumulate loss
            mask_loss, nTotal = maskNLLLoss(decoder_output, target_variable[t], mask[t])
            loss += mask_loss
            print_losses.append(mask_loss.item() * nTotal)
            n_totals += nTotal

    # Perform backpropagation
    loss.backward()

    # Clip gradients: gradients are modified in place
    _ = nn.utils.clip_grad_norm_(encoder.parameters(), clip)
    _ = nn.utils.clip_grad_norm_(decoder.parameters(), clip)

    # Adjust model weights
    encoder_optimizer.step()
    decoder_optimizer.step()

    return sum(print_losses) / n_totals


######################################################################
# Training iterations
# ~~~~~~~~~~~~~~~~~~~
#
# It is finally time to tie the full training procedure together with the
# data. The ``trainIters`` function is responsible for running
# ``n_iterations`` of training given the passed models, optimizers, data,
# etc. This function is quite self explanatory, as we have done the heavy
# lifting with the ``train`` function.
#
# One thing to note is that when we save our model, we save a tarball
# containing the encoder and decoder ``state_dicts`` (parameters), the
# optimizers’ ``state_dicts``, the loss, the iteration, etc. Saving the model
# in this way will give us the ultimate flexibility with the checkpoint.
# After loading a checkpoint, we will be able to use the model parameters
# to run inference, or we can continue training right where we left off.
#

def trainIters(model_name, voc, pairs, encoder, decoder, encoder_optimizer, decoder_optimizer, embedding, encoder_n_layers, decoder_n_layers, save_dir, n_iteration, batch_size, print_every, save_every, clip, corpus_name, loadFilename):

    # Load batches for each iteration
    training_batches = [batch2TrainData(voc, [random.choice(pairs) for _ in range(batch_size)])
                      for _ in range(n_iteration)]

    # Initializations
    print('Initializing ...')
    start_iteration = 1
    print_loss = 0
    if loadFilename:
        start_iteration = checkpoint['iteration'] + 1

    # Training loop
    print("Training...")
    for iteration in range(start_iteration, n_iteration + 1):
        training_batch = training_batches[iteration - 1]
        # Extract fields from batch
        input_variable, lengths, target_variable, mask, max_target_len = training_batch

        # Run a training iteration with batch
        loss = train(input_variable, lengths, target_variable, mask, max_target_len, encoder,
                     decoder, embedding, encoder_optimizer, decoder_optimizer, batch_size, clip)
        print_loss += loss

        # Print progress
        if iteration % print_every == 0:
            print_loss_avg = print_loss / print_every
            print("Iteration: {}; Percent complete: {:.1f}%; Average loss: {:.4f}".format(iteration, iteration / n_iteration * 100, print_loss_avg))
            print_loss = 0

        # Save checkpoint
        if (iteration % save_every == 0):
            directory = os.path.join(save_dir, model_name, corpus_name, '{}-{}_{}'.format(encoder_n_layers, decoder_n_layers, hidden_size))
            if not os.path.exists(directory):
                os.makedirs(directory)
            torch.save({
                'iteration': iteration,
                'en': encoder.state_dict(),
                'de': decoder.state_dict(),
                'en_opt': encoder_optimizer.state_dict(),
                'de_opt': decoder_optimizer.state_dict(),
                'loss': loss,
                'voc_dict': voc.__dict__,
                'embedding': embedding.state_dict()
            }, os.path.join(directory, '{}_{}.tar'.format(iteration, 'checkpoint')))


######################################################################
# Define Evaluation
# -----------------
#
# After training a model, we want to be able to talk to the bot ourselves.
# First, we must define how we want the model to decode the encoded input.
#
# Greedy decoding
# ~~~~~~~~~~~~~~~
#
# Greedy decoding is the decoding method that we use during training when
# we are **NOT** using teacher forcing. In other words, for each time
# step, we simply choose the word from ``decoder_output`` with the highest
# softmax value. This decoding method is optimal on a single time-step
# level.
#
# To facilitate the greedy decoding operation, we define a
# ``GreedySearchDecoder`` class. When run, an object of this class takes
# an input sequence (``input_seq``) of shape *(input_seq length, 1)*, a
# scalar input length (``input_length``) tensor, and a ``max_length`` to
# bound the response sentence length. The input sentence is evaluated
# using the following computational graph:
#
# **Computation Graph:**
#
#    1) Forward input through encoder model.
#    2) Prepare encoder's final hidden layer to be first hidden input to the decoder.
#    3) Initialize decoder's first input as SOS_token.
#    4) Initialize tensors to append decoded words to.
#    5) Iteratively decode one word token at a time:
#        a) Forward pass through decoder.
#        b) Obtain most likely word token and its softmax score.
#        c) Record token and score.
#        d) Prepare current token to be next decoder input.
#    6) Return collections of word tokens and scores.
#

class GreedySearchDecoder(nn.Module):
    def __init__(self, encoder, decoder):
        super(GreedySearchDecoder, self).__init__()
        self.encoder = encoder
        self.decoder = decoder

    def forward(self, input_seq, input_length, max_length):
        # Forward input through encoder model
        encoder_outputs, encoder_hidden = self.encoder(input_seq, input_length)
        # Prepare encoder's final hidden layer to be first hidden input to the decoder
        decoder_hidden = encoder_hidden[:self.decoder.n_layers]
        # Initialize decoder input with SOS_token
        decoder_input = torch.ones(1, 1, device=device, dtype=torch.long) * SOS_token
        # Initialize tensors to append decoded words to
        all_tokens = torch.zeros([0], device=device, dtype=torch.long)
        all_scores = torch.zeros([0], device=device)
        # Iteratively decode one word token at a time
        for _ in range(max_length):
            # Forward pass through decoder
            decoder_output, decoder_hidden = self.decoder(decoder_input, decoder_hidden, encoder_outputs)
            # Obtain most likely word token and its softmax score
            decoder_scores, decoder_input = torch.max(decoder_output, dim=1)
            # Record token and score
            all_tokens = torch.cat((all_tokens, decoder_input), dim=0)
            all_scores = torch.cat((all_scores, decoder_scores), dim=0)
            # Prepare current token to be next decoder input (add a dimension)
            decoder_input = torch.unsqueeze(decoder_input, 0)
        # Return collections of word tokens and scores
        return all_tokens, all_scores


######################################################################
# Evaluate my text
# ~~~~~~~~~~~~~~~~
#
# Now that we have our decoding method defined, we can write functions for
# evaluating a string input sentence. The ``evaluate`` function manages
# the low-level process of handling the input sentence. We first format
# the sentence as an input batch of word indexes with *batch_size==1*. We
# do this by converting the words of the sentence to their corresponding
# indexes, and transposing the dimensions to prepare the tensor for our
# models. We also create a ``lengths`` tensor which contains the length of
# our input sentence. In this case, ``lengths`` is scalar because we are
# only evaluating one sentence at a time (batch_size==1). Next, we obtain
# the decoded response sentence tensor using our ``GreedySearchDecoder``
# object (``searcher``). Finally, we convert the response’s indexes to
# words and return the list of decoded words.
#
# ``evaluateInput`` acts as the user interface for our chatbot. When
# called, an input text field will spawn in which we can enter our query
# sentence. After typing our input sentence and pressing *Enter*, our text
# is normalized in the same way as our training data, and is ultimately
# fed to the ``evaluate`` function to obtain a decoded output sentence. We
# loop this process, so we can keep chatting with our bot until we enter
# either “q” or “quit”.
#
# Finally, if a sentence is entered that contains a word that is not in
# the vocabulary, we handle this gracefully by printing an error message
# and prompting the user to enter another sentence.
#

def evaluate(encoder, decoder, searcher, voc, sentence, max_length=MAX_LENGTH):
    ### Format input sentence as a batch
    # words -> indexes
    indexes_batch = [indexesFromSentence(voc, sentence)]
    # Create lengths tensor
    lengths = torch.tensor([len(indexes) for indexes in indexes_batch])
    # Transpose dimensions of batch to match models' expectations
    input_batch = torch.LongTensor(indexes_batch).transpose(0, 1)
    # Use appropriate device
    input_batch = input_batch.to(device)
    lengths = lengths.to("cpu")
    # Decode sentence with searcher
    tokens, scores = searcher(input_batch, lengths, max_length)
    # indexes -> words
    decoded_words = [voc.index2word[token.item()] for token in tokens]
    return decoded_words


def evaluateInput(encoder, decoder, searcher, voc):
    input_sentence = ''
    while(1):
        try:
            # Get input sentence
            input_sentence = input('> ')
            # Check if it is quit case
            if input_sentence == 'q' or input_sentence == 'quit': break
            # Normalize sentence
            input_sentence = normalizeString(input_sentence)
            # Evaluate sentence
            output_words = evaluate(encoder, decoder, searcher, voc, input_sentence)
            # Format and print response sentence
            output_words[:] = [x for x in output_words if not (x == 'EOS' or x == 'PAD')]
            print('Bot:', ' '.join(output_words))

        except KeyError:
            print("Error: Encountered unknown word.")


######################################################################
# Run Model
# ---------
#
# Finally, it is time to run our model!
#
# Regardless of whether we want to train or test the chatbot model, we
# must initialize the individual encoder and decoder models. In the
# following block, we set our desired configurations, choose to start from
# scratch or set a checkpoint to load from, and build and initialize the
# models. Feel free to play with different model configurations to
# optimize performance.
#

# Configure models
model_name = 'cb_model'
attn_model = 'dot'
#``attn_model = 'general'``
#``attn_model = 'concat'``
hidden_size = 500
encoder_n_layers = 2
decoder_n_layers = 2
dropout = 0.1
batch_size = 64

# Set checkpoint to load from; set to None if starting from scratch
loadFilename = None
checkpoint_iter = 4000

#############################################################
# Sample code to load from a checkpoint:
#
# .. code-block:: python
#
#    loadFilename = os.path.join(save_dir, model_name, corpus_name,
#                        '{}-{}_{}'.format(encoder_n_layers, decoder_n_layers, hidden_size),
#                        '{}_checkpoint.tar'.format(checkpoint_iter))

# Load model if a ``loadFilename`` is provided
if loadFilename:
    # If loading on same machine the model was trained on
    checkpoint = torch.load(loadFilename)
    # If loading a model trained on GPU to CPU
    #checkpoint = torch.load(loadFilename, map_location=torch.device('cpu'))
    encoder_sd = checkpoint['en']
    decoder_sd = checkpoint['de']
    encoder_optimizer_sd = checkpoint['en_opt']
    decoder_optimizer_sd = checkpoint['de_opt']
    embedding_sd = checkpoint['embedding']
    voc.__dict__ = checkpoint['voc_dict']


print('Building encoder and decoder ...')
# Initialize word embeddings
embedding = nn.Embedding(voc.num_words, hidden_size)
if loadFilename:
    embedding.load_state_dict(embedding_sd)
# Initialize encoder & decoder models
encoder = EncoderRNN(hidden_size, embedding, encoder_n_layers, dropout)
decoder = LuongAttnDecoderRNN(attn_model, embedding, hidden_size, voc.num_words, decoder_n_layers, dropout)
if loadFilename:
    encoder.load_state_dict(encoder_sd)
    decoder.load_state_dict(decoder_sd)
# Use appropriate device
encoder = encoder.to(device)
decoder = decoder.to(device)
print('Models built and ready to go!')


######################################################################
# Run Training
# ~~~~~~~~~~~~
#
# Run the following block if you want to train the model.
#
# First we set training parameters, then we initialize our optimizers, and
# finally we call the ``trainIters`` function to run our training
# iterations.
#

# Configure training/optimization
clip = 50.0
teacher_forcing_ratio = 1.0
learning_rate = 0.0001
decoder_learning_ratio = 5.0
n_iteration = 4000
print_every = 1
save_every = 500

# Ensure dropout layers are in train mode
encoder.train()
decoder.train()

# Initialize optimizers
print('Building optimizers ...')
encoder_optimizer = optim.Adam(encoder.parameters(), lr=learning_rate)
decoder_optimizer = optim.Adam(decoder.parameters(), lr=learning_rate * decoder_learning_ratio)
if loadFilename:
    encoder_optimizer.load_state_dict(encoder_optimizer_sd)
    decoder_optimizer.load_state_dict(decoder_optimizer_sd)

# If you have an accelerator, configure it to call
for state in encoder_optimizer.state.values():
    for k, v in state.items():
        if isinstance(v, torch.Tensor):
            state[k] = v.to(device)

for state in decoder_optimizer.state.values():
    for k, v in state.items():
        if isinstance(v, torch.Tensor):
            state[k] = v.to(device)

# Run training iterations
print("Starting Training!")
trainIters(model_name, voc, pairs, encoder, decoder, encoder_optimizer, decoder_optimizer,
           embedding, encoder_n_layers, decoder_n_layers, save_dir, n_iteration, batch_size,
           print_every, save_every, clip, corpus_name, loadFilename)


######################################################################
# Run Evaluation
# ~~~~~~~~~~~~~~
#
# To chat with your model, run the following block.
#

# Set dropout layers to ``eval`` mode
encoder.eval()
decoder.eval()

# Initialize search module
searcher = GreedySearchDecoder(encoder, decoder)

# Begin chatting (uncomment and run the following line to begin)
# evaluateInput(encoder, decoder, searcher, voc)


######################################################################
# Conclusion
# ----------
#
# That’s all for this one, folks. Congratulations, you now know the
# fundamentals to building a generative chatbot model! If you’re
# interested, you can try tailoring the chatbot’s behavior by tweaking the
# model and training parameters and customizing the data that you train
# the model on.
#
# Check out the other tutorials for more cool deep learning applications
# in PyTorch!
#