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ML | Text Generation using Gated Recurrent Unit Networks

  • Last Updated : 18 Aug, 2021

This article will demonstrate how to build a Text Generator by building a Gated Recurrent Unit Network. The conceptual procedure of training the network is to first feed the network a mapping of each character present in the text on which the network is training to a unique number. Each character is then hot-encoded into a vector which is the required format for the network.
The data for the described procedure is a collection of short and famous poems by famous poets and is in a .txt format. It can be downloaded from here.
Step 1: Importing the required libraries
 

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from __future__ import absolute_import, division,
                       print_function, unicode_literals
 
import numpy as np
import tensorflow as tf
 
from keras.models import Sequential
from keras.layers import Dense, Activation
from keras.layers import LSTM
 
from keras.optimizers import RMSprop
 
from keras.callbacks import LambdaCallback
from keras.callbacks import ModelCheckpoint
from keras.callbacks import ReduceLROnPlateau
import random
import sys

Step 2: Loading the data into a string
 

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# Changing the working location to the location of the text file
cd C:\Users\Dev\Desktop\Kaggle\Poems
 
# Reading the text file into a string
with open('poems.txt', 'r') as file:
    text = file.read()
 
# A preview of the text file   
print(text)



Step 3: Creating a mapping from each unique character in the text to a unique number
 

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# Storing all the unique characters present in the text
vocabulary = sorted(list(set(text)))
 
# Creating dictionaries to map each character to an index
char_to_indices = dict((c, i) for i, c in enumerate(vocabulary))
indices_to_char = dict((i, c) for i, c in enumerate(vocabulary))
 
print(vocabulary)

Step 4: Pre-processing the data
 

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# Dividing the text into subsequences of length max_length
# So that at each time step the next max_length characters
# are fed into the network
max_length = 100
steps = 5
sentences = []
next_chars = []
for i in range(0, len(text) - max_length, steps):
    sentences.append(text[i: i + max_length])
    next_chars.append(text[i + max_length])
     
# Hot encoding each character into a boolean vector
 
# Initializing a matrix of boolean vectors with each column representing
# the hot encoded representation of the character
X = np.zeros((len(sentences), max_length, len(vocabulary)), dtype = np.bool)
y = np.zeros((len(sentences), len(vocabulary)), dtype = np.bool)
 
# Placing the value 1 at the appropriate position for each vector
# to complete the hot-encoding process
for i, sentence in enumerate(sentences):
    for t, char in enumerate(sentence):
        X[i, t, char_to_indices[char]] = 1
    y[i, char_to_indices[next_chars[i]]] = 1

Step 5: Building the GRU network
 

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# Initializing the LSTM network
model = Sequential()
 
# Defining the cell type
model.add(GRU(128, input_shape =(max_length, len(vocabulary))))
 
# Defining the densely connected Neural Network layer
model.add(Dense(len(vocabulary)))
 
# Defining the activation function for the cell
model.add(Activation('softmax'))
 
# Defining the optimizing function
optimizer = RMSprop(lr = 0.01)
 
# Configuring the model for training
model.compile(loss ='categorical_crossentropy', optimizer = optimizer)

Step 6: Defining some helper functions which will be used during the training of the network
Note that the first two functions given below have been referred from the documentation of the official text generation example from the Keras team.
a) Helper function to sample the next character: 
 



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# Helper function to sample an index from a probability array
def sample_index(preds, temperature = 1.0):
# temperature determines the freedom the function has when generating text
 
    # Converting the predictions vector into a numpy array
    preds = np.asarray(preds).astype('float64')
 
    # Normalizing the predictions array
    preds = np.log(preds) / temperature
    exp_preds = np.exp(preds)
    preds = exp_preds / np.sum(exp_preds)
 
    # The main sampling step. Creates an array of probablities signifying
    # the probability of each character to be the next character in the
    # generated text
    probas = np.random.multinomial(1, preds, 1)
 
    # Returning the character with maximum probability to be the next character
    # in the generated text
    return np.argmax(probas)

b) Helper function to generate text after each epoch
 

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# Helper function to generate text after the end of each epoch
def on_epoch_end(epoch, logs):
    print()
    print('----- Generating text after Epoch: % d' % epoch)
 
    # Choosing a random starting index for the text generation
    start_index = random.randint(0, len(text) - max_length - 1)
 
    # Sampling for different values of diversity
    for diversity in [0.2, 0.5, 1.0, 1.2]:
        print('----- diversity:', diversity)
 
        generated = ''
 
        # Seed sentence
        sentence = text[start_index: start_index + max_length]
 
        generated += sentence
        print('----- Generating with seed: "' + sentence + '"')
        sys.stdout.write(generated)
 
        for i in range(400):
            # Initializing the predictions vector
            x_pred = np.zeros((1, max_length, len(vocabulary)))
 
            for t, char in enumerate(sentence):
                x_pred[0, t, char_to_indices[char]] = 1.
 
            # Making the predictions for the next character
            preds = model.predict(x_pred, verbose = 0)[0]
 
            # Getting the index of the most probable next character
            next_index = sample_index(preds, diversity)
 
            # Getting the most probable next character using the mapping built
            next_char = indices_to_char[next_index]
 
            # Building the generated text
            generated += next_char
            sentence = sentence[1:] + next_char
 
            sys.stdout.write(next_char)
            sys.stdout.flush()
        print()
 
# Defining a custom callback function to
# describe the internal states of the network
print_callback = LambdaCallback(on_epoch_end = on_epoch_end)

c) Helper function to save the model after each epoch in which loss decreases
 

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# Defining a helper function to save the model after each epoch
# in which the loss decreases
filepath = "weights.hdf5"
checkpoint = ModelCheckpoint(filepath, monitor ='loss',
                             verbose = 1, save_best_only = True,
                             mode ='min')

d) Helper function to reduce the learning rate each time the learning plateaus
 

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# Defining a helper function to reduce the learning rate each time
# the learning plateaus
reduce_alpha = ReduceLROnPlateau(monitor ='loss', factor = 0.2,
                              patience = 1, min_lr = 0.001)
callbacks = [print_callback, checkpoint, reduce_alpha]

Step 7: Training the GRU model
 

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# Training the GRU model
model.fit(X, y, batch_size = 128, epochs = 30, callbacks = callbacks)

Step 8: Generating new and random text
 

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def generate_text(length, diversity):
    # Get random starting text
    start_index = random.randint(0, len(text) - max_length - 1)
 
    # Defining the generated text
    generated = ''
    sentence = text[start_index: start_index + max_length]
    generated += sentence
 
    # Generating new text of given length
    for i in range(length):
 
            # Initializing the predicition vector
            x_pred = np.zeros((1, max_length, len(vocabulary)))
            for t, char in enumerate(sentence):
                x_pred[0, t, char_to_indices[char]] = 1.
 
            # Making the predictions
            preds = model.predict(x_pred, verbose = 0)[0]
 
            # Getting the index of the next most probable index
            next_index = sample_index(preds, diversity)
 
            # Getting the most probable next character using the mapping built
            next_char = indices_to_char[next_index]
 
            # Generating new text
            generated += next_char
            sentence = sentence[1:] + next_char
    return generated
 
print(generate_text(500, 0.2))

Note: Although the output does not make much sense now, the output can be significantly improved by training the model for more epochs.
 

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