061_DLByABr_05b-LSTM-Language(Python)

Import Notebook

ScaDaMaLe Course site and book

This is a 2019-2021 augmentation and update of Adam Breindel's initial notebooks.

Thanks to Christian von Koch and William Anzén for their contributions towards making these materials Spark 3.0.1 and Python 3+ compliant.

'''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 8192/600901 [..............................] - ETA: 5s 57344/600901 [=>............................] - ETA: 1s 90112/600901 [===>..........................] - ETA: 1s 131072/600901 [=====>........................] - ETA: 1s 180224/600901 [=======>......................] - ETA: 0s 212992/600901 [=========>....................] - ETA: 0s 245760/600901 [===========>..................] - ETA: 0s 319488/600901 [==============>...............] - ETA: 0s 475136/600901 [======================>.......] - ETA: 0s 606208/600901 [==============================] - 1s 1us/step corpus length: 600893 total chars: 57 nb sequences: 200285 Vectorization... Build model... WARNING:tensorflow:From /databricks/python/lib/python3.7/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/lib/python3.7/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: 1.2902 200192/200285 [============================>.] - ETA: 0s - loss: 1.2900 200285/200285 [==============================] - 161s 804us/step - loss: 1.2899 ----- diversity: 0.2 ----- Generating with seed: "re fostered; the species needs itself as" re fostered; the species needs itself as a man is a strong for the strength of the subjection of the strength of the still finally and all that is a present and the strength of the power of the strive of the sense of the probably and the state of the subjection of the spiritual and stands and the strength of the strength of the subjection of the strange, and all that is the strong stands to the former and the strength of the strength of ----- diversity: 0.5 ----- Generating with seed: "re fostered; the species needs itself as" re fostered; the species needs itself as a delight of god, the far to mankind responsibility, and all the head and the problem of the same type--that is the content, to us. the instinct it is sociatisment and serrection of a present and repultion of the will and delight and complancess of the strong that is the secret and ancient devours, a strong strength and habits of the science and and laten his state to the concerned to the stread ----- diversity: 1.0 ----- Generating with seed: "re fostered; the species needs itself as" re fostered; the species needs itself as' by a child. 1 9r =whet for from a altrre as well conscience that there are that is always bemn) pernewlies, and that sthe or unines"--but in his consciven to us? but as attaine and internil, exception id strength of frenchmen spects.--the most bodyy and came to a nemensy in the glooly, reun in every metaphorion; or as srigor appreciates, have path plant, the bestow daesersy; this,--according to ----- diversity: 1.2 ----- Generating with seed: "re fostered; the species needs itself as" re fostered; the species needs itself as side, such belief. 25xaxence; the most fical tumm his not glan apttignd burding, the longly?incitians, toripaped seems ones"--a natural libering, free fortune, suskinatic wisre possessing couragematation, exulation: with a long especient, and they which has been regear who have physeem being i him iting were-do againstives a cases of a cries: "his fact agr ity gup. 6hxthsb these, io do alvated