37 lines
951 B
Python
37 lines
951 B
Python
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from tensorflow.python.keras.utils.np_utils import to_categorical
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from utils import SEQ_LEN, N_EPOCHS, BATCH_SIZE, PREDICTION_LEN
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from data import prepare_text, generate_text, read_text, peek_random_seed
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from model import Model
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import numpy as np
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if __name__ == '__main__':
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text = read_text('en.txt')
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vocab = sorted(set(text))
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n_vocab = len(vocab)
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ids = dict((c, i) for i, c in enumerate(vocab))
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chars = dict((i, c) for i, c in enumerate(vocab))
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dataX, dataY = prepare_text(text=text, ids=ids, seq_length=SEQ_LEN)
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n_patterns = len(dataX)
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X = np.reshape(dataX, (n_patterns, SEQ_LEN, 1))
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X = X / float(n_vocab)
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y = to_categorical(dataY)
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model = Model(X, y)
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model.compile_model()
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model.fit_model(X, y, BATCH_SIZE, N_EPOCHS)
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pattern = dataX[peek_random_seed(dataX)]
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prediction = generate_text(model, pattern, chars, n_vocab, PREDICTION_LEN)
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print("\nPREDICTION: ", prediction)
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