061_DLByABr_05b-LSTM-Language(Python)
Import Notebook

SDS-2.x, Scalable Data Engineering Science

This is a 2019 augmentation and update of Adam Breindel's initial notebooks.

'''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 139264/600901 [=====>........................] - ETA: 0s 303104/600901 [==============>...............] - ETA: 0s 606208/600901 [==============================] - 0s 0us/step 614400/600901 [==============================] - 0s 0us/step corpus length: 600901 total chars: 59 nb sequences: 200287 Vectorization... Build model... WARNING:tensorflow:From /databricks/python/local/lib/python2.7/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version. Instructions for updating: Colocations handled automatically by placer. -------------------------------------------------- Iteration 1 WARNING:tensorflow:From /databricks/python/local/lib/python2.7/site-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use tf.cast instead. 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ETA: 0s - loss: 1.2955 199680/200287 [============================>.] - ETA: 0s - loss: 1.2956 199808/200287 [============================>.] - ETA: 0s - loss: 1.2955 199936/200287 [============================>.] - ETA: 0s - loss: 1.2957 200064/200287 [============================>.] - ETA: 0s - loss: 1.2958 200192/200287 [============================>.] - ETA: 0s - loss: 1.2958 200287/200287 [==============================] - 141s 704us/step - loss: 1.2958 ----- diversity: 0.2 ----- Generating with seed: "e things exist (although in point of fac" e things exist (although in point of fact that the last in the same reamity of the same philosophy and the streen and the science of the most sense and actions and the primitive the soul of the problem of the protection of the spirit of the process of the soul of the same mankind, and the strange of the process to the strange, and with the strength of the soul and the streen and all the same reaming the strength of the contemplation of ----- diversity: 0.5 ----- Generating with seed: "e things exist (although in point of fac" e things exist (although in point of fact against the fundamental manking and the lapses in a thought to see through the same rumpotive to an endeaves and the propestible, and a soul of the most case of superiors, and a constitution of the same and strength and are necessary and that every schopenhauerd of the books and deceived to the designative, process always body and sympathy of the nature with another be in the strange and into th ----- diversity: 1.0 ----- Generating with seed: "e things exist (although in point of fac" e things exist (although in point of faceoric there under tyrannian to its ardt hearts anying one of soul yet it is affinie, we might are scheepjessed and if to begin has enwalled number certain se subtlest neighbory much is inligios of ; scloymis of deceion nederate and plency of the feeling of the historily causes: one must suscreds, to weary on masture is circumstance of the race simply andumanily, fly was anything about to prion ----- diversity: 1.2 ----- Generating with seed: "e things exist (although in point of fac" e things exist (although in point of fact all powers--rule about to dejess set and ibbreond of the chiralitical and tapabfecician. 1w him the time one may have togal tpil againist mankind. even of him; at the prirfom presenth by german that the crual blice of bort to able that nowadays iheat, with otherso indo if theh. perhaps an power. o
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