char_rnn_generation_tutorial.py

# -*- coding: utf-8 -*-
"""
NLP From Scratch: Generating Names with a Character-Level RNN
*************************************************************
**Author**: `Sean Robertson <https://github.com/spro/practical-pytorch>`_

This is our second of three tutorials on "NLP From Scratch".
In the `first tutorial </intermediate/char_rnn_classification_tutorial>`
we used a RNN to classify names into their language of origin. This time
we'll turn around and generate names from languages.

::

    > python sample.py Russian RUS
    Rovakov
    Uantov
    Shavakov

    > python sample.py German GER
    Gerren
    Ereng
    Rosher

    > python sample.py Spanish SPA
    Salla
    Parer
    Allan

    > python sample.py Chinese CHI
    Chan
    Hang
    Iun

We are still hand-crafting a small RNN with a few linear layers. The big
difference is instead of predicting a category after reading in all the
letters of a name, we input a category and output one letter at a time.
Recurrently predicting characters to form language (this could also be
done with words or other higher order constructs) is often referred to
as a "language model".

**Recommended Reading:**

I assume you have at least installed PyTorch, know Python, and
understand Tensors:

-  https://pytorch.org/ For installation instructions
-  :doc:`/beginner/deep_learning_60min_blitz` to get started with PyTorch in general
-  :doc:`/beginner/pytorch_with_examples` for a wide and deep overview
-  :doc:`/beginner/former_torchies_tutorial` if you are former Lua Torch user

It would also be useful to know about RNNs and how they work:

-  `The Unreasonable Effectiveness of Recurrent Neural
   Networks <https://karpathy.github.io/2015/05/21/rnn-effectiveness/>`__
   shows a bunch of real life examples
-  `Understanding LSTM
   Networks <https://colah.github.io/posts/2015-08-Understanding-LSTMs/>`__
   is about LSTMs specifically but also informative about RNNs in
   general

I also suggest the previous tutorial, :doc:`/intermediate/char_rnn_classification_tutorial`


Preparing the Data
==================

.. Note::
   Download the data from
   `here <https://download.pytorch.org/tutorial/data.zip>`_
   and extract it to the current directory.

See the last tutorial for more detail of this process. In short, there
are a bunch of plain text files ``data/names/[Language].txt`` with a
name per line. We split lines into an array, convert Unicode to ASCII,
and end up with a dictionary ``{language: [names ...]}``.

"""
from __future__ import unicode_literals, print_function, division
from io import open
import glob
import os
import unicodedata
import string

all_letters = string.ascii_letters + " .,;'-"
n_letters = len(all_letters) + 1 # Plus EOS marker

def findFiles(path): return glob.glob(path)

# 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'
        and c in all_letters
    )

# Read a file and split into lines
def readLines(filename):
    with open(filename, encoding='utf-8') as some_file:
        return [unicodeToAscii(line.strip()) for line in some_file]

# Build the category_lines dictionary, a list of lines per category
category_lines = {}
all_categories = []
for filename in findFiles('data/names/*.txt'):
    category = os.path.splitext(os.path.basename(filename))[0]
    all_categories.append(category)
    lines = readLines(filename)
    category_lines[category] = lines

n_categories = len(all_categories)

if n_categories == 0:
    raise RuntimeError('Data not found. Make sure that you downloaded data '
        'from https://download.pytorch.org/tutorial/data.zip and extract it to '
        'the current directory.')

print('# categories:', n_categories, all_categories)
print(unicodeToAscii("O'Néàl"))


######################################################################
# Creating the Network
# ====================
#
# This network extends `the last tutorial's RNN <#Creating-the-Network>`__
# with an extra argument for the category tensor, which is concatenated
# along with the others. The category tensor is a one-hot vector just like
# the letter input.
#
# We will interpret the output as the probability of the next letter. When
# sampling, the most likely output letter is used as the next input
# letter.
#
# I added a second linear layer ``o2o`` (after combining hidden and
# output) to give it more muscle to work with. There's also a dropout
# layer, which `randomly zeros parts of its
# input <https://arxiv.org/abs/1207.0580>`__ with a given probability
# (here 0.1) and is usually used to fuzz inputs to prevent overfitting.
# Here we're using it towards the end of the network to purposely add some
# chaos and increase sampling variety.
#
# .. figure:: https://i.imgur.com/jzVrf7f.png
#    :alt:
#
#

import torch
import torch.nn as nn

class RNN(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(RNN, self).__init__()
        self.hidden_size = hidden_size

        self.i2h = nn.Linear(n_categories + input_size + hidden_size, hidden_size)
        self.i2o = nn.Linear(n_categories + input_size + hidden_size, output_size)
        self.o2o = nn.Linear(hidden_size + output_size, output_size)
        self.dropout = nn.Dropout(0.1)
        self.softmax = nn.LogSoftmax(dim=1)

    def forward(self, category, input, hidden):
        input_combined = torch.cat((category, input, hidden), 1)
        hidden = self.i2h(input_combined)
        output = self.i2o(input_combined)
        output_combined = torch.cat((hidden, output), 1)
        output = self.o2o(output_combined)
        output = self.dropout(output)
        output = self.softmax(output)
        return output, hidden

    def initHidden(self):
        return torch.zeros(1, self.hidden_size)


######################################################################
# Training
# =========
# Preparing for Training
# ----------------------
#
# First of all, helper functions to get random pairs of (category, line):
#

import random

# Random item from a list
def randomChoice(l):
    return l[random.randint(0, len(l) - 1)]

# Get a random category and random line from that category
def randomTrainingPair():
    category = randomChoice(all_categories)
    line = randomChoice(category_lines[category])
    return category, line


######################################################################
# For each timestep (that is, for each letter in a training word) the
# inputs of the network will be
# ``(category, current letter, hidden state)`` and the outputs will be
# ``(next letter, next hidden state)``. So for each training set, we'll
# need the category, a set of input letters, and a set of output/target
# letters.
#
# Since we are predicting the next letter from the current letter for each
# timestep, the letter pairs are groups of consecutive letters from the
# line - e.g. for ``"ABCD<EOS>"`` we would create ("A", "B"), ("B", "C"),
# ("C", "D"), ("D", "EOS").
#
# .. figure:: https://i.imgur.com/JH58tXY.png
#    :alt:
#
# The category tensor is a `one-hot
# tensor <https://en.wikipedia.org/wiki/One-hot>`__ of size
# ``<1 x n_categories>``. When training we feed it to the network at every
# timestep - this is a design choice, it could have been included as part
# of initial hidden state or some other strategy.
#

# One-hot vector for category
def categoryTensor(category):
    li = all_categories.index(category)
    tensor = torch.zeros(1, n_categories)
    tensor[0][li] = 1
    return tensor

# One-hot matrix of first to last letters (not including EOS) for input
def inputTensor(line):
    tensor = torch.zeros(len(line), 1, n_letters)
    for li in range(len(line)):
        letter = line[li]
        tensor[li][0][all_letters.find(letter)] = 1
    return tensor

# LongTensor of second letter to end (EOS) for target
def targetTensor(line):
    letter_indexes = [all_letters.find(line[li]) for li in range(1, len(line))]
    letter_indexes.append(n_letters - 1) # EOS
    return torch.LongTensor(letter_indexes)


######################################################################
# For convenience during training we'll make a ``randomTrainingExample``
# function that fetches a random (category, line) pair and turns them into
# the required (category, input, target) tensors.
#

# Make category, input, and target tensors from a random category, line pair
def randomTrainingExample():
    category, line = randomTrainingPair()
    category_tensor = categoryTensor(category)
    input_line_tensor = inputTensor(line)
    target_line_tensor = targetTensor(line)
    return category_tensor, input_line_tensor, target_line_tensor


######################################################################
# Training the Network
# --------------------
#
# In contrast to classification, where only the last output is used, we
# are making a prediction at every step, so we are calculating loss at
# every step.
#
# The magic of autograd allows you to simply sum these losses at each step
# and call backward at the end.
#

criterion = nn.NLLLoss()

learning_rate = 0.0005

def train(category_tensor, input_line_tensor, target_line_tensor):
    target_line_tensor.unsqueeze_(-1)
    hidden = rnn.initHidden()

    rnn.zero_grad()

    loss = 0

    for i in range(input_line_tensor.size(0)):
        output, hidden = rnn(category_tensor, input_line_tensor[i], hidden)
        l = criterion(output, target_line_tensor[i])
        loss += l

    loss.backward()

    for p in rnn.parameters():
        p.data.add_(p.grad.data, alpha=-learning_rate)

    return output, loss.item() / input_line_tensor.size(0)


######################################################################
# To keep track of how long training takes I am adding a
# ``timeSince(timestamp)`` function which returns a human readable string:
#

import time
import math

def timeSince(since):
    now = time.time()
    s = now - since
    m = math.floor(s / 60)
    s -= m * 60
    return '%dm %ds' % (m, s)


######################################################################
# Training is business as usual - call train a bunch of times and wait a
# few minutes, printing the current time and loss every ``print_every``
# examples, and keeping store of an average loss per ``plot_every`` examples
# in ``all_losses`` for plotting later.
#

rnn = RNN(n_letters, 128, n_letters)

n_iters = 100000
print_every = 5000
plot_every = 500
all_losses = []
total_loss = 0 # Reset every plot_every iters

start = time.time()

for iter in range(1, n_iters + 1):
    output, loss = train(*randomTrainingExample())
    total_loss += loss

    if iter % print_every == 0:
        print('%s (%d %d%%) %.4f' % (timeSince(start), iter, iter / n_iters * 100, loss))

    if iter % plot_every == 0:
        all_losses.append(total_loss / plot_every)
        total_loss = 0


######################################################################
# Plotting the Losses
# -------------------
#
# Plotting the historical loss from all\_losses shows the network
# learning:
#

import matplotlib.pyplot as plt

plt.figure()
plt.plot(all_losses)


######################################################################
# Sampling the Network
# ====================
#
# To sample we give the network a letter and ask what the next one is,
# feed that in as the next letter, and repeat until the EOS token.
#
# -  Create tensors for input category, starting letter, and empty hidden
#    state
# -  Create a string ``output_name`` with the starting letter
# -  Up to a maximum output length,
#
#    -  Feed the current letter to the network
#    -  Get the next letter from highest output, and next hidden state
#    -  If the letter is EOS, stop here
#    -  If a regular letter, add to ``output_name`` and continue
#
# -  Return the final name
#
# .. Note::
#    Rather than having to give it a starting letter, another
#    strategy would have been to include a "start of string" token in
#    training and have the network choose its own starting letter.
#

max_length = 20

# Sample from a category and starting letter
def sample(category, start_letter='A'):
    with torch.no_grad():  # no need to track history in sampling
        category_tensor = categoryTensor(category)
        input = inputTensor(start_letter)
        hidden = rnn.initHidden()

        output_name = start_letter

        for i in range(max_length):
            output, hidden = rnn(category_tensor, input[0], hidden)
            topv, topi = output.topk(1)
            topi = topi[0][0]
            if topi == n_letters - 1:
                break
            else:
                letter = all_letters[topi]
                output_name += letter
            input = inputTensor(letter)

        return output_name

# Get multiple samples from one category and multiple starting letters
def samples(category, start_letters='ABC'):
    for start_letter in start_letters:
        print(sample(category, start_letter))

samples('Russian', 'RUS')

samples('German', 'GER')

samples('Spanish', 'SPA')

samples('Chinese', 'CHI')


######################################################################
# Exercises
# =========
#
# -  Try with a different dataset of category -> line, for example:
#
#    -  Fictional series -> Character name
#    -  Part of speech -> Word
#    -  Country -> City
#
# -  Use a "start of sentence" token so that sampling can be done without
#    choosing a start letter
# -  Get better results with a bigger and/or better shaped network
#
#    -  Try the nn.LSTM and nn.GRU layers
#    -  Combine multiple of these RNNs as a higher level network
#