107 lines
3.2 KiB
Python
107 lines
3.2 KiB
Python
'''Example script to generate text from Nietzsche's writings.
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At least 20 epochs are required before the generated text
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starts sounding coherent.
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It is recommended to run this script on GPU, as recurrent
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networks are quite computationally intensive.
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If you try this script on new data, make sure your corpus
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has at least ~100k characters. ~1M is better.
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'''
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from __future__ import print_function
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from keras.models import Sequential
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from keras.layers import Dense, Activation
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from keras.layers import LSTM
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from keras.optimizers import RMSprop
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from keras.utils.data_utils import get_file
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import numpy as np
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import random
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import sys
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path = get_file('nietzsche.txt', origin='https://s3.amazonaws.com/text-datasets/nietzsche.txt')
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text = open(path).read().lower()
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print('corpus length:', len(text))
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chars = sorted(list(set(text)))
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print('total chars:', len(chars))
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char_indices = dict((c, i) for i, c in enumerate(chars))
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indices_char = dict((i, c) for i, c in enumerate(chars))
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# cut the text in semi-redundant sequences of maxlen characters
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maxlen = 40
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step = 3
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sentences = []
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next_chars = []
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for i in range(0, len(text) - maxlen, step):
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sentences.append(text[i: i + maxlen])
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next_chars.append(text[i + maxlen])
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print('nb sequences:', len(sentences))
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print('Vectorization...')
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X = np.zeros((len(sentences), maxlen, len(chars)), dtype=np.bool)
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y = np.zeros((len(sentences), len(chars)), dtype=np.bool)
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for i, sentence in enumerate(sentences):
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for t, char in enumerate(sentence):
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X[i, t, char_indices[char]] = 1
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y[i, char_indices[next_chars[i]]] = 1
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# build the model: a single LSTM
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print('Build model...')
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model = Sequential()
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model.add(LSTM(128, input_shape=(maxlen, len(chars))))
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model.add(Dense(len(chars)))
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model.add(Activation('softmax'))
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optimizer = RMSprop(lr=0.01)
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model.compile(loss='categorical_crossentropy', optimizer=optimizer)
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def sample(preds, temperature=1.0):
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# helper function to sample an index from a probability array
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preds = np.asarray(preds).astype('float64')
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preds = np.log(preds) / temperature
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exp_preds = np.exp(preds)
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preds = exp_preds / np.sum(exp_preds)
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probas = np.random.multinomial(1, preds, 1)
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return np.argmax(probas)
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# train the model, output generated text after each iteration
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for iteration in range(1, 60):
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print()
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print('-' * 50)
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print('Iteration', iteration)
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model.fit(X, y,
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batch_size=128,
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epochs=1)
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start_index = random.randint(0, len(text) - maxlen - 1)
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for diversity in [0.2, 0.5, 1.0, 1.2]:
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print()
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print('----- diversity:', diversity)
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generated = ''
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sentence = text[start_index: start_index + maxlen]
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generated += sentence
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print('----- Generating with seed: "' + sentence + '"')
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sys.stdout.write(generated)
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for i in range(400):
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x = np.zeros((1, maxlen, len(chars)))
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for t, char in enumerate(sentence):
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x[0, t, char_indices[char]] = 1.
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preds = model.predict(x, verbose=0)[0]
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next_index = sample(preds, diversity)
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next_char = indices_char[next_index]
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generated += next_char
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sentence = sentence[1:] + next_char
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sys.stdout.write(next_char)
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sys.stdout.flush()
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print()
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