Generating / Predicting Text

Introduction:

• Techniques for preparing data and building models for text generation.
• Objective: Train a neural network with lyrics from an Irish song to generate a new song.

1. Data Preparation:
   • Obtain lyrics from an Irish song as the training dataset.
   • Preprocess the data: Tokenization, lowercasing, and removing special characters.
   • Split the data into training and validation sets.
2. Neural Network Architecture:
   • Use a sequence-to-sequence model for text generation.
   • Input layer: Convert text data into numerical representations (e.g., word embeddings).
   • Recurrent Neural Network (RNN) or Transformer: Captures sequential patterns in the text.
   • Output layer: Generate text one word at a time.
3. Training the Neural Network:
   • Define loss function: Cross-entropy for text generation tasks.
   • Configure the optimizer: Adam, SGD, etc.
   • Train the model on the training data.
   • Monitor the validation loss to avoid overfitting.
4. Text Generation Process:
   • Provide a starting seed or prompt for the new song.
   • Convert the seed into numerical form using the same preprocessing as the training data.
   • Initialize the model with the trained weights.
   • Generate new text word-by-word using the model's predictions.
5. Similarity to Previous Techniques:
   • Despite being a complex application, the process is similar to previous weeks' tasks.
   • Minor modifications are required to adapt the existing techniques for text generation.

import tensorflow as tf
import numpy as np 
from tensorflow.keras.layers import Embedding, LSTM, Dense, Bidirectional
from tensorflow.keras.models import Sequential
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences

# Define the lyrics of the song
data="In the town of Athy one Jeremy Lanigan \\n Battered away til he hadnt a pound. \\nHis father died and made him a man again \\n Left him a farm and ten acres of ground. \\nHe gave a grand party for friends and relations \\nWho didnt forget him when come to the wall, \\nAnd if youll but listen Ill make your eyes glisten \\nOf the rows and the ructions of Lanigans Ball. \\nMyself to be sure got free invitation, \\nFor all the nice girls and boys I might ask, \\nAnd just in a minute both friends and relations \\nWere dancing round merry as bees round a cask. \\nJudy ODaly, that nice little milliner, \\nShe tipped me a wink for to give her a call, \\nAnd I soon arrived with Peggy McGilligan \\nJust in time for Lanigans Ball. \\nThere were lashings of punch and wine for the ladies, \\nPotatoes and cakes; there was bacon and tea, \\nThere were the Nolans, Dolans, OGradys \\nCourting the girls and dancing away. \\nSongs they went round as plenty as water, \\nThe harp that once sounded in Taras old hall,\\nSweet Nelly Gray and The Rat Catchers Daughter,\\nAll singing together at Lanigans Ball. \\nThey were doing all kinds of nonsensical polkas \\nAll round the room in a whirligig. \\nJulia and I, we banished their nonsense \\nAnd tipped them the twist of a reel and a jig. \\nAch mavrone, how the girls got all mad at me \\nDanced til youd think the ceiling would fall. \\nFor I spent three weeks at Brooks Academy \\nLearning new steps for Lanigans Ball. \\nThree long weeks I spent up in Dublin, \\nThree long weeks to learn nothing at all,\\n Three long weeks I spent up in Dublin, \\nLearning new steps for Lanigans Ball. \\nShe stepped out and I stepped in again, \\nI stepped out and she stepped in again, \\nShe stepped out and I stepped in again, \\nLearning new steps for Lanigans Ball. \\nBoys were all merry and the girls they were hearty \\nAnd danced all around in couples and groups, \\nTil an accident happened, young Terrance McCarthy \\nPut his right leg through miss Finnertys hoops. \\nPoor creature fainted and cried Meelia murther, \\nCalled for her brothers and gathered them all. \\nCarmody swore that hed go no further \\nTil he had satisfaction at Lanigans Ball. \\nIn the midst of the row miss Kerrigan fainted, \\nHer cheeks at the same time as red as a rose. \\nSome of the lads declared she was painted, \\nShe took a small drop too much, I suppose. \\nHer sweetheart, Ned Morgan, so powerful and able, \\nWhen he saw his fair colleen stretched out by the wall, \\nTore the left leg from under the table \\nAnd smashed all the Chaneys at Lanigans Ball. \\nBoys, oh boys, twas then there were runctions. \\nMyself got a lick from big Phelim McHugh. \\nI soon replied to his introduction \\nAnd kicked up a terrible hullabaloo. \\nOld Casey, the piper, was near being strangled. \\nThey squeezed up his pipes, bellows, chanters and all. \\nThe girls, in their ribbons, they got all entangled \\nAnd that put an end to Lanigans Ball."

# Split the long string per line and put in a list
corpus = data.lower().split("\\n")

# Preview the result
print(corpus)

# Initialize the Tokenizer class
tokenizer = Tokenizer()

# Generate the word index dictionary
tokenizer.fit_on_texts(corpus)

# Define the total words. You add 1 for the index `0` which is just the padding token.
total_words = len(tokenizer.word_index) + 1

print(f'word index dictionary: {tokenizer.word_index}')
print(f'total words: {total_words}')

# Initialize the sequences list
input_sequences = []

# Loop over every line
for line in corpus:

	# Tokenize the current line
	token_list = tokenizer.texts_to_sequences([line])[0]

	# Loop over the line several times to generate the subphrases
	for i in range(1, len(token_list)):
		
		# Generate the subphrase
		n_gram_sequence = token_list[:i+1]

		# Append the subphrase to the sequences list
		input_sequences.append(n_gram_sequence)

# Get the length of the longest line
max_sequence_len = max([len(x) for x in input_sequences])

# Pad all sequences
input_sequences = np.array(pad_sequences(input_sequences, maxlen=max_sequence_len, padding='pre'))

# Create inputs and label by splitting the last token in the subphrases
xs, labels = input_sequences[:,:-1],input_sequences[:,-1]

# Convert the label into one-hot arrays
ys = tf.keras.utils.to_categorical(labels, num_classes=total_words)

# Get sample sentence
sentence = corpus[0].split()
print(f'sample sentence: {sentence}')

# Initialize token list
token_list = []

# Look up the indices of each word and append to the list
for word in sentence: 
  token_list.append(tokenizer.word_index[word])

# Print the token list
print(token_list)

# Pick element
elem_number = 6

# Print token list and phrase
print(f'token list: {xs[elem_number]}')
print(f'decoded to text: {tokenizer.sequences_to_texts([xs[elem_number]])}')

# Print label
print(f'one-hot label: {ys[elem_number]}')
print(f'index of label: {np.argmax(ys[elem_number])}')

# Pick element
elem_number = 5

# Print token list and phrase
print(f'token list: {xs[elem_number]}')
print(f'decoded to text: {tokenizer.sequences_to_texts([xs[elem_number]])}')

# Print label
print(f'one-hot label: {ys[elem_number]}')
print(f'index of label: {np.argmax(ys[elem_number])}')

# Build the model
model = Sequential([
          Embedding(total_words, 64, input_length=max_sequence_len-1),
          Bidirectional(LSTM(20)),
          Dense(total_words, activation='softmax')
])

# Use categorical crossentropy because this is a multi-class problem
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

# Print the model summary
model.summary()

# Train the model
history = model.fit(xs, ys, epochs=500)

import matplotlib.pyplot as plt

# Plot utility
def plot_graphs(history, string):
  plt.plot(history.history[string])
  plt.xlabel("Epochs")
  plt.ylabel(string)
  plt.show()

# Visualize the accuracy
plot_graphs(history, 'accuracy')