Note

Click here to download the full example code

Classifying Names with a Character-Level RNN¶

We will be building and training a basic character-level RNN to classify words. A character-level RNN reads words as a series of characters - outputting a prediction and “hidden state” at each step, feeding its previous hidden state into each next step. We take the final prediction to be the output, i.e. which class the word belongs to.

Specifically, we’ll train on a few thousand surnames from 18 languages of origin, and predict which language a name is from based on the spelling:

$ python predict.py Hinton
(-0.47) Scottish
(-1.52) English
(-3.57) Irish

$ python predict.py Schmidhuber
(-0.19) German
(-2.48) Czech
(-2.68) Dutch

Recommended Reading:

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

https://pytorch.org/ For installation instructions
Deep Learning with PyTorch: A 60 Minute Blitz to get started with PyTorch in general
Learning PyTorch with Examples for a wide and deep overview
PyTorch for Former Torch Users 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 shows a bunch of real life examples
Understanding LSTM Networks is about LSTMs specifically but also informative about RNNs in general

Preparing the Data¶

Note

Download the data from here and extract it to the current directory.

Included in the data/names directory are 18 text files named as “[Language].txt”. Each file contains a bunch of names, one name per line, mostly romanized (but we still need to convert from Unicode to ASCII).

We’ll end up with a dictionary of lists of names per language, {language: [names ...]}. The generic variables “category” and “line” (for language and name in our case) are used for later extensibility.

from __future__ import unicode_literals, print_function, division
from io import open
import glob
import os

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

print(findFiles('data/names/*.txt'))

import unicodedata
import string

all_letters = string.ascii_letters + " .,;'"
n_letters = len(all_letters)

# 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
    )

print(unicodeToAscii('Ślusàrski'))

# Build the category_lines dictionary, a list of names per language
category_lines = {}
all_categories = []

# 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]

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)

Out:

['data/names/Portuguese.txt', 'data/names/German.txt', 'data/names/English.txt', 'data/names/French.txt', 'data/names/Arabic.txt', 'data/names/Spanish.txt', 'data/names/Chinese.txt', 'data/names/Greek.txt', 'data/names/Russian.txt', 'data/names/Scottish.txt', 'data/names/Polish.txt', 'data/names/Italian.txt', 'data/names/Korean.txt', 'data/names/Czech.txt', 'data/names/Irish.txt', 'data/names/Japanese.txt', 'data/names/Dutch.txt', 'data/names/Vietnamese.txt']
Slusarski

Now we have category_lines, a dictionary mapping each category (language) to a list of lines (names). We also kept track of all_categories (just a list of languages) and n_categories for later reference.

print(category_lines['Italian'][:5])

Out:

['Abandonato', 'Abatangelo', 'Abatantuono', 'Abate', 'Abategiovanni']

Turning Names into Tensors¶

Now that we have all the names organized, we need to turn them into Tensors to make any use of them.

To represent a single letter, we use a “one-hot vector” of size <1 x n_letters>. A one-hot vector is filled with 0s except for a 1 at index of the current letter, e.g. "b" = <0 1 0 0 0 ...>.

To make a word we join a bunch of those into a 2D matrix <line_length x 1 x n_letters>.

That extra 1 dimension is because PyTorch assumes everything is in batches - we’re just using a batch size of 1 here.

import torch

# Find letter index from all_letters, e.g. "a" = 0
def letterToIndex(letter):
    return all_letters.find(letter)

# Just for demonstration, turn a letter into a <1 x n_letters> Tensor
def letterToTensor(letter):
    tensor = torch.zeros(1, n_letters)
    tensor[0][letterToIndex(letter)] = 1
    return tensor

# Turn a line into a <line_length x 1 x n_letters>,
# or an array of one-hot letter vectors
def lineToTensor(line):
    tensor = torch.zeros(len(line), 1, n_letters)
    for li, letter in enumerate(line):
        tensor[li][0][letterToIndex(letter)] = 1
    return tensor

print(letterToTensor('J'))

print(lineToTensor('Jones').size())

Out:

tensor([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
         0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1.,
         0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
         0., 0., 0.]])
torch.Size([5, 1, 57])

Creating the Network¶

Before autograd, creating a recurrent neural network in Torch involved cloning the parameters of a layer over several timesteps. The layers held hidden state and gradients which are now entirely handled by the graph itself. This means you can implement a RNN in a very “pure” way, as regular feed-forward layers.

This RNN module (mostly copied from the PyTorch for Torch users tutorial) is just 2 linear layers which operate on an input and hidden state, with a LogSoftmax layer after the output.

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(input_size + hidden_size, hidden_size)
        self.i2o = nn.Linear(input_size + hidden_size, output_size)
        self.softmax = nn.LogSoftmax(dim=1)

    def forward(self, input, hidden):
        combined = torch.cat((input, hidden), 1)
        hidden = self.i2h(combined)
        output = self.i2o(combined)
        output = self.softmax(output)
        return output, hidden

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

n_hidden = 128
rnn = RNN(n_letters, n_hidden, n_categories)

To run a step of this network we need to pass an input (in our case, the Tensor for the current letter) and a previous hidden state (which we initialize as zeros at first). We’ll get back the output (probability of each language) and a next hidden state (which we keep for the next step).

input = letterToTensor('A')
hidden =torch.zeros(1, n_hidden)

output, next_hidden = rnn(input, hidden)

For the sake of efficiency we don’t want to be creating a new Tensor for every step, so we will use lineToTensor instead of letterToTensor and use slices. This could be further optimized by pre-computing batches of Tensors.

input = lineToTensor('Albert')
hidden = torch.zeros(1, n_hidden)

output, next_hidden = rnn(input[0], hidden)
print(output)

Out:

tensor([[-2.9362, -2.9114, -2.9074, -2.8735, -2.8507, -2.7882, -2.7943, -2.9810,
         -2.7996, -2.8723, -3.0054, -2.9633, -2.9095, -2.8729, -3.0085, -2.8379,
         -2.8789, -2.8742]], grad_fn=<LogSoftmaxBackward>)

As you can see the output is a <1 x n_categories> Tensor, where every item is the likelihood of that category (higher is more likely).

Training¶

Preparing for Training¶

Before going into training we should make a few helper functions. The first is to interpret the output of the network, which we know to be a likelihood of each category. We can use Tensor.topk to get the index of the greatest value:

def categoryFromOutput(output):
    top_n, top_i = output.topk(1)
    category_i = top_i[0].item()
    return all_categories[category_i], category_i

print(categoryFromOutput(output))

Out:

('Spanish', 5)

We will also want a quick way to get a training example (a name and its language):

import random

def randomChoice(l):
    return l[random.randint(0, len(l) - 1)]

def randomTrainingExample():
    category = randomChoice(all_categories)
    line = randomChoice(category_lines[category])
    category_tensor = torch.tensor([all_categories.index(category)], dtype=torch.long)
    line_tensor = lineToTensor(line)
    return category, line, category_tensor, line_tensor

for i in range(10):
    category, line, category_tensor, line_tensor = randomTrainingExample()
    print('category =', category, '/ line =', line)

Out:

category = Polish / line = Bartosz
category = English / line = Goldsmith
category = Scottish / line = Thomson
category = Scottish / line = Maclean
category = Japanese / line = Tojo
category = Vietnamese / line = Pham
category = Portuguese / line = Melo
category = Portuguese / line = Lobo
category = Polish / line = Mikolajczak
category = Chinese / line = Zhui

Training the Network¶

Now all it takes to train this network is show it a bunch of examples, have it make guesses, and tell it if it’s wrong.

For the loss function nn.NLLLoss is appropriate, since the last layer of the RNN is nn.LogSoftmax.

criterion = nn.NLLLoss()

Each loop of training will:

Create input and target tensors
Create a zeroed initial hidden state
Read each letter in and
- Keep hidden state for next letter
Compare final output to target
Back-propagate
Return the output and loss

learning_rate = 0.005 # If you set this too high, it might explode. If too low, it might not learn

def train(category_tensor, line_tensor):
    hidden = rnn.initHidden()

    rnn.zero_grad()

    for i in range(line_tensor.size()[0]):
        output, hidden = rnn(line_tensor[i], hidden)

    loss = criterion(output, category_tensor)
    loss.backward()

    # Add parameters' gradients to their values, multiplied by learning rate
    for p in rnn.parameters():
        p.data.add_(-learning_rate, p.grad.data)

    return output, loss.item()

Now we just have to run that with a bunch of examples. Since the train function returns both the output and loss we can print its guesses and also keep track of loss for plotting. Since there are 1000s of examples we print only every print_every examples, and take an average of the loss.

import time
import math

n_iters = 100000
print_every = 5000
plot_every = 1000



# Keep track of losses for plotting
current_loss = 0
all_losses = []

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

start = time.time()

for iter in range(1, n_iters + 1):
    category, line, category_tensor, line_tensor = randomTrainingExample()
    output, loss = train(category_tensor, line_tensor)
    current_loss += loss

    # Print iter number, loss, name and guess
    if iter % print_every == 0:
        guess, guess_i = categoryFromOutput(output)
        correct = '✓' if guess == category else '✗ (%s)' % category
        print('%d %d%% (%s) %.4f %s / %s %s' % (iter, iter / n_iters * 100, timeSince(start), loss, line, guess, correct))

    # Add current loss avg to list of losses
    if iter % plot_every == 0:
        all_losses.append(current_loss / plot_every)
        current_loss = 0

Out:

5% (0m 7s) 2.7870 Furlan / Scottish ✗ (Italian)
10% (0m 14s) 2.8242 Best / Chinese ✗ (English)
15% (0m 21s) 2.5783 Laurie / French ✗ (English)
20% (0m 28s) 1.6628 Murchadh / Irish ✓
25% (0m 35s) 1.2500 Marquez / Portuguese ✗ (Spanish)
30% (0m 42s) 0.6578 Shamoun / Arabic ✓
35% (0m 49s) 1.4123 Cucinotta / Spanish ✗ (Italian)
40% (0m 56s) 0.4124 Lai / Chinese ✓
45% (1m 3s) 1.5521 Fabbro / Portuguese ✗ (Italian)
50% (1m 11s) 0.8191 Zhagalin / Russian ✓
55% (1m 18s) 1.0131 Tai / Vietnamese ✗ (Chinese)
60% (1m 25s) 1.0099 Ri / Korean ✓
65% (1m 32s) 0.3811 Whyte / Scottish ✓
70% (1m 39s) 0.3431 Khoury / Arabic ✓
75% (1m 46s) 0.8095 Ma / Korean ✓
80% (1m 53s) 1.3727 Sokoloff / Polish ✓
85% (2m 0s) 2.0966 Ojeda / Japanese ✗ (Spanish)
90% (2m 7s) 1.0790 Rosa / Italian ✓
95% (2m 14s) 1.0394 Yarnold / English ✓
100000 100% (2m 21s) 0.2296 Sarraf / Arabic ✓

Plotting the Results¶

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_classification_tutorial_001.png

Evaluating the Results¶

To see how well the network performs on different categories, we will create a confusion matrix, indicating for every actual language (rows) which language the network guesses (columns). To calculate the confusion matrix a bunch of samples are run through the network with evaluate(), which is the same as train() minus the backprop.

# Keep track of correct guesses in a confusion matrix
confusion = torch.zeros(n_categories, n_categories)
n_confusion = 10000

# Just return an output given a line
def evaluate(line_tensor):
    hidden = rnn.initHidden()

    for i in range(line_tensor.size()[0]):
        output, hidden = rnn(line_tensor[i], hidden)

    return output

# Go through a bunch of examples and record which are correctly guessed
for i in range(n_confusion):
    category, line, category_tensor, line_tensor = randomTrainingExample()
    output = evaluate(line_tensor)
    guess, guess_i = categoryFromOutput(output)
    category_i = all_categories.index(category)
    confusion[category_i][guess_i] += 1

# Normalize by dividing every row by its sum
for i in range(n_categories):
    confusion[i] = confusion[i] / confusion[i].sum()

# Set up plot
fig = plt.figure()
ax = fig.add_subplot(111)
cax = ax.matshow(confusion.numpy())
fig.colorbar(cax)

# Set up axes
ax.set_xticklabels([''] + all_categories, rotation=90)
ax.set_yticklabels([''] + all_categories)

# Force label at every tick
ax.xaxis.set_major_locator(ticker.MultipleLocator(1))
ax.yaxis.set_major_locator(ticker.MultipleLocator(1))

# sphinx_gallery_thumbnail_number = 2
plt.show()

../_images/sphx_glr_char_rnn_classification_tutorial_002.png

You can pick out bright spots off the main axis that show which languages it guesses incorrectly, e.g. Chinese for Korean, and Spanish for Italian. It seems to do very well with Greek, and very poorly with English (perhaps because of overlap with other languages).

Running on User Input¶

def predict(input_line, n_predictions=3):
    print('\n> %s' % input_line)
    with torch.no_grad():
        output = evaluate(lineToTensor(input_line))

        # Get top N categories
        topv, topi = output.topk(n_predictions, 1, True)
        predictions = []

        for i in range(n_predictions):
            value = topv[0][i].item()
            category_index = topi[0][i].item()
            print('(%.2f) %s' % (value, all_categories[category_index]))
            predictions.append([value, all_categories[category_index]])

predict('Dovesky')
predict('Jackson')
predict('Satoshi')

Out:

> Dovesky
(-0.47) Russian
(-1.53) Czech
(-2.39) Polish

> Jackson
(-0.56) Scottish
(-1.67) English
(-2.04) Russian

> Satoshi
(-0.83) Japanese
(-1.32) Arabic
(-2.13) Polish

The final versions of the scripts in the Practical PyTorch repo split the above code into a few files:

data.py (loads files)
model.py (defines the RNN)
train.py (runs training)
predict.py (runs predict() with command line arguments)
server.py (serve prediction as a JSON API with bottle.py)

Run train.py to train and save the network.

Run predict.py with a name to view predictions:

$ python predict.py Hazaki
(-0.42) Japanese
(-1.39) Polish
(-3.51) Czech

Run server.py and visit http://localhost:5533/Yourname to get JSON output of predictions.

Exercises¶

Try with a different dataset of line -> category, for example:
- Any word -> language
- First name -> gender
- Character name -> writer
- Page title -> blog or subreddit
Get better results with a bigger and/or better shaped network
- Add more linear layers
- Try the nn.LSTM and nn.GRU layers
- Combine multiple of these RNNs as a higher level network

Total running time of the script: ( 2 minutes 28.395 seconds)

Gallery generated by Sphinx-Gallery

Classifying Names with a Character-Level RNN¶

Preparing the Data¶

Turning Names into Tensors¶

Creating the Network¶

Training¶

Preparing for Training¶

Training the Network¶

Plotting the Results¶

Evaluating the Results¶

Running on User Input¶

Exercises¶

Docs

Tutorials

Resources