character_level_rnn.py

from io import open
import glob
import os

# Preparing the training data -----

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)

print(all_letters)

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('Betül'))

category_lines = {}
all_categories = []

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

print(category_lines['Korean'])
print(all_letters)
n_categories = len(all_categories)

# abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ .,;'
# 000000000000000000000000000000000000100000000000000000000

import torch

def letterToIndex(letter):
	return all_letters.find(letter)

def letterToTensor(letter):
	tensor = torch.zeros(1, n_letters)
	tensor[0][letterToIndex(letter)] = 1
	return tensor

print(letterToTensor('b'))

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(lineToTensor('Choi').size())
print(lineToTensor('Choi'))

# Build the neural network architecture -----

import torch.nn as nn
import torch.nn.functional as F

print(n_letters)
print(n_categories)

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)
		self.h2h = nn.Linear(hidden_size, hidden_size)
		self.h2o = nn.Linear(hidden_size, output_size)
		self.softmax = nn.LogSoftmax(dim=1)

	def forward(self, input, hidden):
		hidden = F.tanh(self.i2h(input) + self.h2h(hidden))
		output = self.h2o(hidden)
		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)

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

output, next_hidden = rnn(input, hidden)

print(output)

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

output, next_hidden = rnn(input, hidden)
print(output)

# Train the model -----

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))

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)


criterion = nn.NLLLoss()

learning_rate = 0.005

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()

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

	return output, loss.item()

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

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

plt.figure()
plt.plot(all_losses)


######################################################################
# 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()


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('Jackson')
predict('Satoshi')