43 lines
1.2 KiB
Python
43 lines
1.2 KiB
Python
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from pickle import load
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from keras.models import load_model
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from keras.utils import to_categorical
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from keras.preprocessing.sequence import pad_sequences
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# generate a sequence of characters with a language model
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def generate_seq(model, mapping, seq_length, seed_text, n_chars):
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in_text = seed_text
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# generate a fixed number of characters
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for _ in range(n_chars):
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# encode the characters as integers
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encoded = [mapping[char] for char in in_text]
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# truncate sequences to a fixed length
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encoded = pad_sequences([encoded], maxlen=seq_length, truncating='pre')
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# one hot encode
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encoded = to_categorical(encoded, num_classes=len(mapping))
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# predict character
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yhat = model.predict_classes(encoded, verbose=0)
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# reverse map integer to character
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out_char = ''
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for char, index in mapping.items():
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if index == yhat:
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out_char = char
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break
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# append to input
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in_text += char
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return in_text
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# load the model
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model = load_model('model.h5')
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# load the mapping
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mapping = load(open('mapping.pkl', 'rb'))
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print(generate_seq(model, mapping, 10, 'Mar', 7))
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