Study_TextGen - GitPress.io

Example Python program Study_TextGen.py Python version 3.x or newer. To check the Python version use:

python --version

Modules

from future import print_function
import numpy as np
import random
import sys
from keras.models import Sequential
from keras.layers import Dense, LSTM, Activation, Dropout
from keras.optimizers import RMSprop

Methods

def pred_indices(preds, metric=1.0):

Code

Python example

from __future__ import print_function
import numpy as np
import random
import sys
from keras.models import Sequential
from keras.layers import Dense, LSTM, Activation, Dropout
from keras.optimizers import RMSprop


path = 'RomeoAndJuliet.txt'
text = open(path, encoding='utf-8').read().lower()
characters = sorted(list(set(text)))
print('corpus length:', len(text))
print('total chars:', len(characters))

char2indices = dict((c, i) for i, c in enumerate(characters))
indices2char = dict((i, c) for i, c in enumerate(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))

X = np.zeros((len(sentences), maxlen, len(characters)), dtype=np.bool)
y = np.zeros((len(sentences), len(characters)), dtype=np.bool)
for i, sentence in enumerate(sentences):
    for t, char in enumerate(sentence):
        X[i, t, char2indices[char]] = 1
        y[i, char2indices[next_chars[i]]] = 1

model = Sequential()
model.add(LSTM(128, input_shape=(maxlen, len(characters))))
model.add(Dense(len(characters)))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy', optimizer=RMSprop(lr=0.01))

print(model.summary())

def pred_indices(preds, metric=1.0):
    preds = np.asarray(preds).astype('float64')
    preds = np.log(preds) / metric
    exp_preds = np.exp(preds)
    preds = exp_preds / np.sum(exp_preds)
    probs = np.random.multinomial(1, preds, 1)
    return np.argmax(probs)

for iteration in range(1,100):
    print('-' * 40)
    print('Iteration', iteration)
    model.fit(X, y, batch_size=128, epochs=1)
    start_index = random.randint(0, len(text) - maxlen - 1)
    for diversity in [1.5]:
        print('\n----- 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(characters)))
            for t, char in enumerate(sentence):
                x[0, t, char2indices[char]] = 1.
            preds = model.predict(x, verbose=0)[0]
            next_index = pred_indices(preds, diversity)
            pred_char = indices2char[next_index]
            generated += pred_char
            sentence = sentence[1:] + pred_char
            sys.stdout.write(pred_char)
            sys.stdout.flush()
        print("\nOne combination completed \n")

Useful Links

Articles: https://python-commandments.org/
Python shell: https://bsdnerds.org/learn-python/
Tutorial: https://pythonprogramminglanguage.com/