Text Generation using Recurrent Long Short Term Memory Network

This article will demonstrate how to build a Text Generator by building a Recurrent Long Short Term Memory 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 was downloaded from Kaggle. This dataset contains the articles published in the New York Times from April 2017 to April 2018. separated according to the month of publication. The dataset is in the form of .csv file which contains the url of the published article along with other details. Any one random url was chosen for the training process and then on visiting this url, the text was copied into a text file and this text file was used for the training process.
Step 1: Importing the required libraries

Python3

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

Python3

# Changing the working location to the location of the text file
cd C:\Users\Dev\Desktop\Kaggle\New York Times

# Reading the text file into a string
with open('article1.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

Python3

# 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

Python3

# 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
X = np.zeros((len(sentences), max_length, len(vocabulary)), dtype = np.bool)
y = np.zeros((len(sentences), len(vocabulary)), dtype = np.bool)
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 LSTM network

Python3

# Building the LSTM network for the task
model = Sequential()
model.add(LSTM(128, input_shape =(max_length, len(vocabulary))))
model.add(Dense(len(vocabulary)))
model.add(Activation('softmax'))
optimizer = RMSprop(lr = 0.01)
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:

Python3

# Helper function to sample an index from a probability array
def sample_index(preds, temperature = 1.0):
    preds = np.asarray(preds).astype('float64')
    preds = np.log(preds) / temperature
    exp_preds = np.exp(preds)
    preds = exp_preds / np.sum(exp_preds)
    probas = np.random.multinomial(1, preds, 1)
    return np.argmax(probas)

b) Helper function to generate text after each epoch

Python3

# 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)

    start_index = random.randint(0, len(text) - max_length - 1)
    for diversity in [0.2, 0.5, 1.0, 1.2]:
        print('----- diversity:', diversity)

        generated = ''
        sentence = text[start_index: start_index + max_length]
        generated += sentence
        print('----- Generating with seed: "' + sentence + '"')
        sys.stdout.write(generated)

        for i in range(400):
            x_pred = np.zeros((1, max_length, len(vocabulary)))
            for t, char in enumerate(sentence):
                x_pred[0, t, char_to_indices[char]] = 1.

            preds = model.predict(x_pred, verbose = 0)[0]
            next_index = sample_index(preds, diversity)
            next_char = indices_to_char[next_index]

            generated += next_char
            sentence = sentence[1:] + next_char

            sys.stdout.write(next_char)
            sys.stdout.flush()
        print()
print_callback = LambdaCallback(on_epoch_end = on_epoch_end)

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

Python3

# 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

Python3

# 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 LSTM model

Python3

# Training the LSTM model
model.fit(X, y, batch_size = 128, epochs = 500, callbacks = callbacks)

Step 8: Generating new and random text

Python3

# Defining a utility function to generate new and random text based on the
# network's learnings
def generate_text(length, diversity):
    # Get random starting text
    start_index = random.randint(0, len(text) - max_length - 1)
    generated = ''
    sentence = text[start_index: start_index + max_length]
    generated += sentence
    for i in range(length):
            x_pred = np.zeros((1, max_length, len(vocabulary)))
            for t, char in enumerate(sentence):
                x_pred[0, t, char_to_indices[char]] = 1.

            preds = model.predict(x_pred, verbose = 0)[0]
            next_index = sample_index(preds, diversity)
            next_char = indices_to_char[next_index]

            generated += next_char
            sentence = sentence[1:] + next_char
    return generated

print(generate_text(500, 0.2))