LSTM_summary.py

import numpy as np
import sys
import time
from nltk.tokenize import RegexpTokenizer
from nltk.corpus import stopwords
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, LSTM
from keras.utils import np_utils
from tensorflow.keras.callbacks import ModelCheckpoint

def tokenize_words(input):
    tokenizer = RegexpTokenizer(r'\w+')
    tokens = tokenizer.tokenize(input)
    filtered = filter(lambda token: token not in stopwords.words('english'), tokens)
    return " ".join(filtered)

with open("summary.txt", "r") as file:
    text = file.read().decode(encoding='utf-8')

processed_inputs = tokenize_words(text)
chars = sorted(list(set(processed_inputs)))
char_to_num = dict((c, i) for i, c in enumerate(chars))

input_len = len(processed_inputs)
vocab_len = len(chars)
print ("Total number of characters:", input_len)
print ("Total vocab:", vocab_len)

seq_length = 100 # CHANGE SEQUENCE LENGTH
x_data = []
y_data = []

for i in range(0, input_len - seq_length, 1):
    in_seq = processed_inputs[i:i + seq_length]

    out_seq = processed_inputs[i + seq_length]

    x_data.append([char_to_num[char] for char in in_seq])
    y_data.append(char_to_num[out_seq])

n_patterns = len(x_data)
print ("Total Patterns:", n_patterns)

X = np.reshape(x_data, (n_patterns, seq_length, 1))
X = X/float(vocab_len)
y = np_utils.to_categorical(y_data)

model = Sequential()
model.add(LSTM(256, input_shape=(X.shape[1], X.shape[2]), return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(256, return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(128))
model.add(Dropout(0.2))
model.add(Dense(y.shape[1], activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam')

filepath = "model_weights_saved.hdf5"
checkpoint = ModelCheckpoint(filepath, monitor='loss', verbose=1, save_best_only=True, mode='min')
desired_callbacks = [checkpoint]

model.fit(X, y, epochs=20, batch_size=256, callbacks=desired_callbacks)

filename = "model_weights_saved.hdf5"
model.load_weights(filename)
model.compile(loss='categorical_crossentropy', optimizer='adam')
num_to_char = dict((i, c) for i, c in enumerate(chars))
start = numpy.random.randint(0, len(x_data) - 1)
pattern = x_data[start]
print("Random Seed:")
print("\"", ''.join([num_to_char[value] for value in pattern]), "\"")

for i in range(1000):
    x = numpy.reshape(pattern, (1, len(pattern), 1))
    x = x / float(vocab_len)
    prediction = model.predict(x, verbose=0)
    index = numpy.argmax(prediction)
    result = num_to_char[index]
    seq_in = [num_to_char[value] for value in pattern]

    print(result)

    pattern.append(index)
    pattern = pattern[1:len(pattern)]