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
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
# 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
# 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
# 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
# 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:
# 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
# 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
# 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
# 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
# Training the LSTM model model.fit(X, y, batch_size = 128 , epochs = 500 , callbacks = callbacks)
|
Step 8: Generating new and random text
# 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 ))
|