Name Language Prediction using Recurrent Neural Network in PyTorch
Recurrent Neural Networks have been applied very successfully as the deep learning models in the tasks that deal with the sequential data especially the Natural Language Processing. The traditional feed-forward networks operate with the entire fixed training batch at once and produce a fixed amount of output. On the other hand, the recurrent neural networks process the same in sequence. This feature makes them outperforming in many NLP applications. With these capabilities, RNN models are popularly applied in the text classification problems. In this article, we will demonstrate the implementation of a Recurrent Neural Network (RNN) using PyTorch in the task of multi-class text classification. This RNN model will be trained on the names of the person belonging to 18 language classes. After successful training, the model will predict the language category for a given name that it is most likely to belong. Implementation of RNN in PyTorch This implementation was done in the Google Colab and the data set was read from the Google Drive. The below line of codes will mount the Google Drive to the Colab notebook and print the text files in the data set. from google.colab import drive drive.mount('/content/gdrive') from __future__ import unicode_literals, print_function, division from io import open import glob import os def printFiles(path): return glob.glob(path) printFiles('gdrive/My Drive/Dataset/data/data/names/*.txt') The below lines of codes define function modules to convert Unicode text to equivalent ASCII value. import unicodedata import string all_let = string.ascii_letters + " .,;'" n_let = len(all_let) def unicodeToAscii(s): return ''.join( c for c in unicodedata.normalize('NFD', s) if unicodedata.category(c) != 'Mn' and c in all_let ) Using the below code snippet, a function will be defined to build the dictionary of categories and a list of names in every language. cat_line = {} all_cats = [] # 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 printFiles('gdrive/My Drive/Dataset/data/data/names/*.txt'): category = os.path.splitext(os.path.basename(filename))[0] all_cats.append(category) lines = readLines(filename) cat_line[category] = lines n_categories = len(all_cats) We will check the above function for 4 Japanese names. #Check names in a category print(cat_line['Japanese'][:4]) In the next step, the function modules will be defined to turn the names into tensors to make them compatible with the RNN model. import torch # Find letter index from all_let, e.g. "a" = 0 def letterToIndex(letter): return all_let.find(letter) # Turn a letter into a <1 x n_let> Tensor def letterToTensor(letter): tensor = torch.zeros(1, n_let) tensor[0][letterToIndex(letter)] = 1 return tensor # Turn a line into a <line_length x 1 x n_let>, # or an array of one-hot letter vectors def lineToTensor(line): tensor = torch.zeros(len(line), 1, n_let) for li, letter in enumerate(line): tensor[li][0][letterToIndex(letter)] = 1 return tensor We will check the above module by converting a letter to tensor and a line to tensor. print(letterToTensor('K')) print(lineToTensor('Kakinomoto').size()) In the next step, we will define the Recurrent Neural Network model. 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 #Binding model rnn = RNN(n_let, n_hidden, n_categories) This model will be checked on generating tensor output for a name. input = lineToTensor('Aalsburg') hidden = torch.zeros(1, n_hidden) output, next_hidden = rnn(input[0], hidden) print(output) This untrained model has generated the likelihoods of all the categories the given input name belongs to. Now, we will define functions for providing random training examples to the network during training and generating categories for the network outputs. import random def randomChoice(l): return l[random.randint(0, len(l) - 1)] def randomTrainingExample(): category = randomChoice(all_cats) line = randomChoice(cat_line[category]) category_tensor = torch.tensor([all_cats.index(category)], dtype=torch.long) line_tensor = lineToTensor(line) return category, line, category_tensor, line_tensor #Check on a random sample for i in range(10): category, line, category_tensor, line_tensor = randomTrainingExample() print('category =', category, '/ line =', line) def categoryFromOutput(output): top_n, top_i = output.topk(1) category_i = top_i[0].item() return all_cats[category_i], category_i #Check category for an output print(categoryFromOutput(output)) In the next step, the hyperparameters and the training function will be defined and the RNN model will be trained in 100 epochs. 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() # Add parameters' gradients to their values, multiplied by learning rate 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 After training, we will visualize the loss to see the performance. import matplotlib.pyplot as plt import matplotlib.ticker as ticker plt.figure() plt.plot(all_losses) The below code snippet will test the on the unseen texts and plot the confusion matrix. # 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_cats.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 figsize = (10, 10) fig = plt.figure(figsize=figsize) ax = fig.add_subplot(111) cax = ax.matshow(confusion.numpy()) fig.colorbar(cax) # Set up axes ax.set_xticklabels([''] + all_cats, rotation=90) ax.set_yticklabels([''] + all_cats) # 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() The below function will print the likelihood of belonging to a language category for the given names. 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_cats[category_index])) predictions.append([value, all_cats[category_index]]) Finally, we will check the predicted likelihoods for the given three names. predict('Aggelen') predict('Accardo') predict('Ferreiro') So, as we can see above, the RNN model has given the likelihoods for the given names which of the language categories they belong to. For example, for the name ‘Aggelen’, it has given the top 3 likelihoods in which ‘French’ has the highest value. All three predictions are correct. That means, according to the trained RNN model, the name ‘Aggelen’ has the highest chances of belonging to the ‘French’ language. We could apply the argmax to print only the language category with the highest likelihood, but to make it more clear, the top 3 predictions are given in the result. You can check this model on more numbers of predictions and tune the parameters to improve the accuracy. References:- Gabriel Loye, ‘A Beginner’s Guide on Recurrent Neural Networks with PyTorch’ ‘NLP from Scratch: Classifying Names with a Character-Level RNN’, PyTorch Tutorial.
In this article, we will demonstrate the implementation of a Recurrent Neural Network (RNN) using PyTorch in the task of multi-class text classification. This RNN model will be trained on the names of the person belonging to 18 language classes. After successful training, the model will predict the language category for a given name that it is most likely to belong.
Name Language Prediction using Recurrent Neural Network in PyTorch - RNN in Natural Language Processing - Text Classification
["cnn neural network", "Deep Learning", "NLP", "Pytorch", "Recurrent Neural Network", "simple neural network"]
["Recurrent Neural Network", "Pytorch", "NumPy", "text classification", "AI", "neural network", "PyTorch", "cnn neural network", "NLP", "Ray", "Colab", "deep learning", "Deep Learning", "Matplotlib", "simple neural network"]
["AI", "deep learning", "neural network", "NLP", "Ray", "PyTorch", "Colab", "NumPy", "Matplotlib", "text classification"]
Dr. Vaibhav Kumar is a seasoned data science professional with great exposure to machine learning and deep learning. He has good exposure to research, where he has published several research papers in reputed international journals and presented papers at reputed international conferences. He has worked across industry and academia and has led many research and development projects in AI and machine learning. Along with his current role, he has also been associated with many reputed research labs and universities where he contributes as visiting researcher and professor.