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06_Ilayaraja_book_Modelbuilding.py
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import numpy as np | |
import tensorflow as tf | |
from tensorflow.keras.preprocessing.text import Tokenizer | |
from tensorflow.keras.models import Sequential | |
from tensorflow.keras.layers import Embedding, LSTM, Dense | |
from tensorflow.keras.preprocessing.sequence import pad_sequences | |
import pickle | |
x = open(r'/content/இசை_ஜீனியஸ்_ராஜா_ரவி_நடராஜன்.txt', 'rb').read().decode(encoding='utf-8') | |
x = x.replace('\n', '').replace('\r', '').replace('\ufeff', '').replace('“','').replace('”','') | |
tokens = Tokenizer() | |
tokens.fit_on_texts([x]) | |
pickle.dump(tokens, open('Ilayaraja_book_tokens.pkl', 'wb')) | |
dictionary = tokens.word_index | |
x_n_grams = [] | |
for line in x.split('.'): | |
line_tokens = tokens.texts_to_sequences([line])[0] | |
for i in range(1, len(line_tokens)): | |
n_grams = line_tokens[:i+1] | |
x_n_grams.append(n_grams) | |
max_line_len = max([len(i) for i in x_n_grams]) | |
training_data = np.array(pad_sequences(x_n_grams, maxlen=max_line_len, padding='pre')) | |
train_X = training_data[:, :-1] | |
train_y = training_data[:, -1] | |
total_words = len(dictionary) + 1 | |
y = np.array(tf.keras.utils.to_categorical(train_y, num_classes=total_words)) | |
model = Sequential() | |
model.add(Embedding(total_words, 100, input_length=max_line_len-1)) | |
model.add(LSTM(150)) | |
model.add(Dense(total_words, activation='softmax')) | |
model.build(input_shape=(None, max_line_len-1)) | |
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) | |
model.fit(train_X, y, epochs=40, verbose=1) | |
Ilayaraja_book_model = {'model_json': model.to_json(),'model_weights': model.get_weights()} | |
pickle.dump(Ilayaraja_book_model, open('Ilayaraja_book_model.pkl', 'wb')) |
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