SDS-2.2, Scalable Data Science
This is used in a non-profit educational setting with kind permission of Adam Breindel.
This is not licensed by Adam for use in a for-profit setting. Please contact Adam directly at [email protected] to request or report such use cases or abuses.
A few minor modifications and additional mathematical statistical pointers have been added by Raazesh Sainudiin when teaching PhD students in Uppsala University.
'''Example script to generate text from Nietzsche's writings.
At least 20 epochs are required before the generated text
starts sounding coherent.
It is recommended to run this script on GPU, as recurrent
networks are quite computationally intensive.
If you try this script on new data, make sure your corpus
has at least ~100k characters. ~1M is better.
'''
from __future__ import print_function
from keras.models import Sequential
from keras.layers import Dense, Activation
from keras.layers import LSTM
from keras.optimizers import RMSprop
from keras.utils.data_utils import get_file
import numpy as np
import random
import sys
path = get_file('nietzsche.txt', origin='https://s3.amazonaws.com/text-datasets/nietzsche.txt')
text = open(path).read().lower()
print('corpus length:', len(text))
chars = sorted(list(set(text)))
print('total chars:', len(chars))
char_indices = dict((c, i) for i, c in enumerate(chars))
indices_char = dict((i, c) for i, c in enumerate(chars))
# cut the text in semi-redundant sequences of maxlen characters
maxlen = 40
step = 3
sentences = []
next_chars = []
for i in range(0, len(text) - maxlen, step):
sentences.append(text[i: i + maxlen])
next_chars.append(text[i + maxlen])
print('nb sequences:', len(sentences))
print('Vectorization...')
X = np.zeros((len(sentences), maxlen, len(chars)), dtype=np.bool)
y = np.zeros((len(sentences), len(chars)), dtype=np.bool)
for i, sentence in enumerate(sentences):
for t, char in enumerate(sentence):
X[i, t, char_indices[char]] = 1
y[i, char_indices[next_chars[i]]] = 1
# build the model: a single LSTM
print('Build model...')
model = Sequential()
model.add(LSTM(128, input_shape=(maxlen, len(chars))))
model.add(Dense(len(chars)))
model.add(Activation('softmax'))
optimizer = RMSprop(lr=0.01)
model.compile(loss='categorical_crossentropy', optimizer=optimizer)
def sample(preds, temperature=1.0):
# helper function to sample an index from a probability array
preds = np.asarray(preds).astype('float64')
preds = np.log(preds) / temperature
exp_preds = np.exp(preds)
preds = exp_preds / np.sum(exp_preds)
probas = np.random.multinomial(1, preds, 1)
return np.argmax(probas)
# train the model, output generated text after each iteration
for iteration in range(1, 60):
print()
print('-' * 50)
print('Iteration', iteration)
model.fit(X, y,
batch_size=128,
epochs=1)
start_index = random.randint(0, len(text) - maxlen - 1)
for diversity in [0.2, 0.5, 1.0, 1.2]:
print()
print('----- diversity:', diversity)
generated = ''
sentence = text[start_index: start_index + maxlen]
generated += sentence
print('----- Generating with seed: "' + sentence + '"')
sys.stdout.write(generated)
for i in range(400):
x = np.zeros((1, maxlen, len(chars)))
for t, char in enumerate(sentence):
x[0, t, char_indices[char]] = 1.
preds = model.predict(x, verbose=0)[0]
next_index = sample(preds, diversity)
next_char = indices_char[next_index]
generated += next_char
sentence = sentence[1:] + next_char
sys.stdout.write(next_char)
sys.stdout.flush()
print()
Using TensorFlow backend. Downloading data from https://s3.amazonaws.com/text-datasets/nietzsche.txt 16384/600901 [..............................] - ETA: 0s 24576/600901 [>.............................] - ETA: 1s 57344/600901 [=>............................] - ETA: 1s 73728/600901 [==>...........................] - ETA: 1s122880/600901 [=====>........................] - ETA: 1s180224/600901 [=======>......................] - ETA: 0s229376/600901 [==========>...................] - ETA: 0s303104/600901 [==============>...............] - ETA: 0s385024/600901 [==================>...........] - ETA: 0s491520/600901 [=======================>......] - ETA: 0scorpus length: 600901 total chars: 59 nb sequences: 200287 Vectorization... Build model... -------------------------------------------------- Iteration 1 Epoch 1/1 128/200287 [..............................] - ETA: 542s - loss: 4.0798 256/200287 [..............................] - ETA: 398s - loss: 3.9546 384/200287 [..............................] - ETA: 350s - loss: 4.1873 512/200287 [..............................] - ETA: 326s - loss: 4.1219 640/200287 [..............................] - ETA: 311s - loss: 3.9707 768/200287 [..............................] - ETA: 303s - loss: 3.8342 896/200287 [..............................] - ETA: 297s - loss: 3.7312 1024/200287 [..............................] - ETA: 293s - loss: 3.6536 1152/200287 [..............................] - ETA: 290s - loss: 3.5834 1280/200287 [..............................] - ETA: 286s - loss: 3.5248 1408/200287 [..............................] - ETA: 287s - loss: 3.4880 1536/200287 [..............................] - ETA: 285s - loss: 3.4674 1664/200287 [..............................] - ETA: 282s - 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loss: 1.3307199040/200287 [============================>.] - ETA: 1s - loss: 1.3308199168/200287 [============================>.] - ETA: 1s - loss: 1.3308199296/200287 [============================>.] - ETA: 1s - loss: 1.3309199424/200287 [============================>.] - ETA: 1s - loss: 1.3309199552/200287 [============================>.] - ETA: 1s - loss: 1.3309199680/200287 [============================>.] - ETA: 0s - loss: 1.3310199808/200287 [============================>.] - ETA: 0s - loss: 1.3309199936/200287 [============================>.] - ETA: 0s - loss: 1.3310200064/200287 [============================>.] - ETA: 0s - loss: 1.3310200192/200287 [============================>.] - ETA: 0s - loss: 1.3310200287/200287 [==============================] - 286s - loss: 1.3311 ----- diversity: 0.2 ----- Generating with seed: "xture of stiffness and elegance, is no e" xture of stiffness and elegance, is no end the power, and in the same the highest and the subject of the subject of the subject of the sense the more superstition of the strongest the power, and in the sacrifice in the sacrifice and every so the fact that the sense of the soul of the subject of the soul of the sense which is a religious of the subject of the state of the subject of the sense of the sense and morality in the same and acc ----- diversity: 0.5 ----- Generating with seed: "xture of stiffness and elegance, is no e" xture of stiffness and elegance, is no extent of the proposion of principal and presenting and account the moment of all the interprete with a result to put them to good heart to a laws with the placient consequence and souls of man of the all instinct which, will admiration so far as it is the morality and most philosophy and every such experiences of the suffer surpoctively with its own super, and myself and in the inverting a sther t ----- diversity: 1.0 ----- Generating with seed: "xture of stiffness and elegance, is no e" xture of stiffness and elegance, is no every right hall and more derint, skeptic which is at lookition to some most gract which is only a good and one cause of sugetifickish against itself! 1al-what be more schopenhaue, here scholar--in the novel importals-wracking, realive ways satite belietive of retain them--i were betasts or a them auses a honest the world in the souled. 11 " as irrivally, them. in the sever