Poetry_AI.py

import tensorflow as tf
import os
from tensorflow.keras.preprocessing.sequence import pad_sequences       #type: ignore
from tensorflow.keras.layers import Embedding, LSTM, Dense, Bidirectional       #type: ignore
from tensorflow.keras.preprocessing.text import Tokenizer       #type: ignore
from tensorflow.keras.models import Sequential      #type: ignore
from tensorflow.keras.optimizers import Adam        #type: ignore
import numpy as np 

os.system('wget --no-check-certificate https://storage.googleapis.com/learning-datasets/irish-lyrics-eof.txt -O /tmp/irish-lyrics-eof.txt')

data = open('irish-lyrics-eof.txt').read()

corpus = data.lower().split("\n")

tokenizer = Tokenizer()
tokenizer.fit_on_texts(corpus)
total_words = len(tokenizer.word_index) + 1

print(tokenizer.word_index)
print(total_words)

input_sequences = []
for line in corpus:
	token_list = tokenizer.texts_to_sequences([line])[0]
	for i in range(1, len(token_list)):
		n_gram_sequence = token_list[:i+1]
		input_sequences.append(n_gram_sequence)

# pad sequences 
max_sequence_len = max([len(x) for x in input_sequences])
input_sequences = np.array(pad_sequences(input_sequences, maxlen=max_sequence_len, padding='pre'))

# create predictors and label
xs, labels = input_sequences[:,:-1],input_sequences[:,-1]

ys = tf.keras.utils.to_categorical(labels, num_classes=total_words)

print(tokenizer.word_index['in'])
print(tokenizer.word_index['the'])
print(tokenizer.word_index['town'])
print(tokenizer.word_index['of'])
print(tokenizer.word_index['athy'])
print(tokenizer.word_index['one'])
print(tokenizer.word_index['jeremy'])
print(tokenizer.word_index['lanigan'])

print(tokenizer.word_index)

model = Sequential()
model.add(Embedding(total_words, 100, input_length=max_sequence_len-1))
model.add(Bidirectional(LSTM(150)))
model.add(Dense(total_words, activation='softmax'))
adam = Adam(learning_rate=0.01)
model.compile(loss='categorical_crossentropy', optimizer=adam, metrics=['accuracy'])
#earlystop = EarlyStopping(monitor='val_loss', min_delta=0, patience=5, verbose=0, mode='auto')
history = model.fit(xs, ys, epochs=100, verbose=1)
#print model.summary()
print(model)


import matplotlib.pyplot as plt


def plot_graphs(history, string):
  plt.plot(history.history[string])
  plt.xlabel("Epochs")
  plt.ylabel(string)
  plt.show()
  
  
seed_text = "I've got a bad feeling about this"
next_words = 100
  
for _ in range(next_words):
	token_list = tokenizer.texts_to_sequences([seed_text])[0]
	token_list = pad_sequences([token_list], maxlen=max_sequence_len-1, padding='pre')
	predicted = np.argmax(model.predict(token_list), axis=-1)
	output_word = ""
	for word, index in tokenizer.word_index.items():
		if index == predicted:
			output_word = word
			break
	seed_text += " " + output_word
print(seed_text)