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Generating Names with a Character-Level RNN

Author: Sean Robertson

In the last 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:

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

I also suggest the previous tutorial, Classifying Names with a Character-Level RNN

Preparing the Data

Note

Download the data from here 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 http://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):
    lines = open(filename, encoding='utf-8').read().strip().split('\n')
    return [unicodeToAscii(line) for line in lines]

# 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"))

Out:

# categories: 18 ['Italian', 'German', 'Portuguese', 'Chinese', 'Greek', 'Polish', 'French', 'English', 'Spanish', 'Arabic', 'Czech', 'Russian', 'Irish', 'Dutch', 'Scottish', 'Vietnamese', 'Korean', 'Japanese']
O'Neal

Creating the Network

This network extends the last tutorial’s RNN 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 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.

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”).

The category tensor is a one-hot tensor 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_(-learning_rate, p.grad.data)

    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

Out:

0m 18s (5000 5%) 2.4301
0m 34s (10000 10%) 2.0946
0m 50s (15000 15%) 2.7818
1m 6s (20000 20%) 2.5621
1m 22s (25000 25%) 2.7732
1m 38s (30000 30%) 3.7447
1m 54s (35000 35%) 3.3865
2m 9s (40000 40%) 2.9855
2m 25s (45000 45%) 2.6154
2m 41s (50000 50%) 2.9450
2m 57s (55000 55%) 2.4140
3m 13s (60000 60%) 2.0269
3m 29s (65000 65%) 2.4112
3m 45s (70000 70%) 3.3427
4m 1s (75000 75%) 1.7840
4m 17s (80000 80%) 2.4763
4m 33s (85000 85%) 2.8541
4m 49s (90000 90%) 2.3952
5m 5s (95000 95%) 2.1057
5m 21s (100000 100%) 0.7924

Plotting the Losses

Plotting the historical loss from all_losses shows the network learning:

import matplotlib.pyplot as plt
import matplotlib.ticker as ticker

plt.figure()
plt.plot(all_losses)
../_images/sphx_glr_char_rnn_generation_tutorial_001.png

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')

Out:

Rovakin
Uantonovev
Sharino
Ganter
Erenger
Rongen
Sander
Paner
Allan
Chan
Han
Iin

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

Total running time of the script: ( 5 minutes 21.204 seconds)

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