Untitled

import random
import numpy as np
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense, Activation
from tensorflow.keras.optimizers import RMSprop

filepath = tf.keras.utils.get_file('shakespeare.txt', 'https://storage.googleapis.com/download.tensorflow.org/data/shakespeare.txt')

text = open(filepath, 'rb').read().decode(encoding='utf-8').lower()

text = text[100000:1000000]

characters = sorted(set(text))

char_to_index = dict((c,i) for i ,c in enumerate(characters))
index_to_char = dict((i,c) for i ,c in enumerate(characters))

SEQ_LENGTH = 50
STEP_SIZE = 4
sentences = []
next_characters = []
'''
for i in range(0, len(text) - SEQ_LENGTH, STEP_SIZE ):
    sentences.append(text[i: i+SEQ_LENGTH])
    next_characters.append(text[i+SEQ_LENGTH])

x = np.zeros((len(sentences), SEQ_LENGTH, len(characters)), dtype=np.bool)
y = np.zeros((len(sentences), len(characters)), dtype=np.bool)

for i, sentence in enumerate(sentences):
    for t, character in enumerate(sentence):
        x[i, t, char_to_index[character]] = 1
    y[i, char_to_index[next_characters[i]]] = 1

model = Sequential()
model.add(LSTM(128, input_shape=(SEQ_LENGTH, len(characters))))
model.add(Dense(len(characters)))
model.add(Activation('softmax'))

model.compile(loss='categorical_crossentropy', optimizer=RMSprop(lr=0.01))

model.fit(x, y, batch_size=256, epochs=6)
model.save('text.model')
'''
model = tf.keras.models.load_model('text.model')


def sample(preds, temperature=1.0):
    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)

def generate_text(length, temperature):
    start_index = random.randint(0, len(text) - SEQ_LENGTH - 1)
    generated = ''
    sentence = text[start_index: start_index + SEQ_LENGTH]
    generated += sentence
    for i in range(length):
        x = np.zeros((1, SEQ_LENGTH, len(characters)))
        for t, character in enumerate(sentence):
            x[0, t, char_to_index[character]] = 1

        predictions = model.predict(x, verbose=0)[0]
        next_index = sample(predictions, temperature)
        next_character = index_to_char[next_index]

        generated += next_character
        sentence = sentence[1:] + next_character
    return generated
print('-------------0.2------------')
print(generate_text(200,0.2))
print('-------------0.4------------')
print(generate_text(200,0.4))
print('-------------0.6------------')
print(generate_text(200,0.6))
print('-------------0.8------------')
print(generate_text(200,0.8))
print('-------------1------------')
print(generate_text(200,1))