main.py

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()

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 = 40
STEP_SIZE = 3
"""
# textgenerator.model
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=5)

model.save('textgenerator.model')
"""

model = tf.keras.models.load_model('textgenerator.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(300,0.2))
print('-----0.4-------')
print(generate_text(300,0.4))
print('-----0.6-------')
print(generate_text(300,0.6))
print('-----0.8-------')
print(generate_text(300,0.8))
print('-----1-------')
print(generate_text(300,1.0))