65 lines
1.7 KiB
Python
65 lines
1.7 KiB
Python
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from numpy import array
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from pickle import dump
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from keras.utils import to_categorical
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from keras.models import Sequential
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from keras.layers import Dense
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from keras.layers import LSTM
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from keras.callbacks import CSVLogger
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# load doc into memory
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def load_doc(filename):
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# open the file as read only
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file = open(filename, 'r')
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# read all text
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text = file.read()
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# close the file
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file.close()
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return text
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# load
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in_filename = 'char_sequences.txt'
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raw_text = load_doc(in_filename)
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lines = raw_text.split('\n')
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# integer encode sequences of characters
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chars = sorted(list(set(raw_text)))
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mapping = dict((c, i) for i, c in enumerate(chars))
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sequences = list()
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for line in lines:
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# integer encode line
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encoded_seq = [mapping[char] for char in line]
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# store
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sequences.append(encoded_seq)
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# vocabulary size
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vocab_size = len(mapping)
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print('Vocabulary Size: %d' % vocab_size)
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# separate into input and output
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sequences = array(sequences)
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X, y = sequences[:, :-1], sequences[:, -1]
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sequences = [to_categorical(x, num_classes=vocab_size) for x in X]
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X = array(sequences)
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y = to_categorical(y, num_classes=vocab_size)
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# define model
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model = Sequential()
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model.add(LSTM(250, input_shape=(X.shape[1], X.shape[2]), return_sequences=True))
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model.add(LSTM(250, return_sequences=True))
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model.add((LSTM(250)))
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model.add(Dense(vocab_size, activation='softmax'))
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# compile model
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model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
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# print(model.summary())
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# fit model
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csv_logger = CSVLogger('log.csv', append=True, separator=';')
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model.fit(X, y, epochs=30, verbose=2, callbacks=[csv_logger])
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# save the model to file
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model.save('model.h5')
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# save the mapping
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dump(mapping, open('mapping.pkl', 'wb'))
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