query_model.py

from __future__ import print_function
from keras.models import Sequential
from keras.layers.core import Dense, Activation, Dropout
from keras.layers.recurrent import LSTM
from keras.datasets.data_utils import get_file
import numpy as np
import random
import sys
import operator

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 = 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))

maxlen = 20

try:
    print('Loading model...')
    fh = open('keras-nietzsche.model.json', 'rb')
    model = model_from_json(fh.read())
    fh.close()
except Exception as error:
    print('Build model...')
    model = Sequential()
    model.add(LSTM(512, return_sequences=True, input_shape=(maxlen, len(chars))))
    model.add(Dropout(0.2))
    model.add(LSTM(512, return_sequences=False))
    model.add(Dropout(0.2))
    model.add(Dense(len(chars)))
    model.add(Activation('softmax'))

    model.compile(loss='categorical_crossentropy', optimizer='rmsprop')
    fh = open('keras-nietzsche.model.json', 'wb')
    fh.write(model.to_json())
    fh.close()
    
model.load_weights('nietzsche.model.weights.h5')

def sample(a, temperature=1.0):
    # helper function to sample an index from a probability array
    a = np.log(a) / temperature
    a = np.exp(a) / np.sum(np.exp(a))
    return np.argmax(np.random.multinomial(1, a, 1))

while True:
    sentence = raw_input('Text to spell check: ')
    for iteration in range(len(sentence)):
        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, 0.1)
        next_char = indices_char[next_index]

        sentence = sentence[1:] + next_char

        sys.stdout.write(next_char)
        sys.stdout.flush()