Greeting_Generation.py

from keras.models import Sequential
from keras.layers import Dense, Activation
from keras.layers import LSTM
from keras.layers import Dropout
from keras.optimizers import RMSprop
#from keras.utils.data_utils import get_file
import numpy as np
import random
import sys

path = "/home/harsha/Downloads/Halloween_Data.txt"

text = open(path).read().lower()

print("corpus length:", len(text))

words = set(open(path).read().lower().split())
#print(words)
print("words:", type(words))
#print(words)

word_indices = dict((c, i) for i, c in enumerate(words))
reverse_indices = dict((i, c) for i, c in enumerate(words))

sequence_len = 30
step = 3

print("sequence length:", sequence_len, "step:", step)

sentences = []
next_words = []
sentences_1 = []

list_words = []

list_words = text.lower().split()

for i in range(0, len(list_words)-sequence_len, step):
    sentences_1 = ' '.join(list_words[i: i + sequence_len])
    sentences.append(sentences_1)
    next_words.append((list_words[i + sequence_len]))
print("Sequences {Length of the sentence}", len(sentences))
print("Length of next word:", len(next_words))

X = np.zeros((len(sentences), sequence_len, len(words)), dtype=np.bool)
Y = np.zeros((len(sentences), len(words)), dtype=np.bool)

for i, sentence in enumerate(sentences):
    #print(sentence)
    for t, word in enumerate(sentence.split()):
        #print(i,t,word)
        X[i, t, word_indices[word]] = 1
    Y[i, word_indices[next_words[i]]] = 1

print("Model Initialization Begin")
model = Sequential()

model.add(LSTM(512, return_sequences=True, input_shape=(sequence_len, len(words))))
model.add(Dropout(0.2))
model.add(LSTM(512, return_sequences=False))
model.add(Dropout(0.2))
model.add(Dense(len(words)))
model.add(Activation("softmax"))
model.compile(loss='categorical_crossentropy', optimizer='rmsprop')

def sample(a, temperature = 1.0):
    a = np.log(a) / temperature
    dist = np.exp(a) / np.sum(np.exp(a))
    choices = range(len(a))
    return np.random.choice(choices, p=dist)

for iteration in range(1, 1000):
    print()
    print('-' * 50)
    print("Iteration, ", iteration)
    model.fit(X, Y, batch_size=128, epochs=1)
    model.save_weights("./weightfile.h5", overwrite=True)

    start_index = random.randint(0, len(list_words) - sequence_len - 1)

    for diversity in [0.2, 0.5, 1.0, 1.2]:
        print()
        print("Diversity", diversity)
        generated = ''
        sentence = list_words[start_index: start_index + sequence_len]
        generated += ' '.join(sentence)
        print("Generating", sentence)
        sys.stdout.write(generated)
        print()

        for i in range(1024):
            x = np.zeros((1, sequence_len, len(words)))
            for t, word in enumerate(sentence):
                #print(t)
                x[0, t, word_indices[word]] = 1

            prediction = model.predict(x, verbose=0)[0]
            next_index = sample(prediction, diversity)
            next_word = reverse_indices[next_index]
            generated += next_word
            del sentence[0]
            sentence.append(next_word)
            sys.stdout.write(' ')
            sys.stdout.write(next_word)
            sys.stdout.flush()
        print()