Simple Tensorflow RNN LSTM text generator

lstm_text_generator.py

import tensorflow as tf
import numpy as np

#set hyperparameters
max_len = 40
step = 2
num_units = 128
learning_rate = 0.001
batch_size = 200
epoch = 60
temperature = 0.5

def read_data(file_name):
    '''
     open and read text file
    '''
    text = open(file_name, 'r').read()
    return text.lower()

def featurize(text):
    '''
     featurize the text to train and target dataset
    '''
    unique_chars = list(set(text))
    len_unique_chars = len(unique_chars)

    input_chars = []
    output_char = []

    for i in range(0, len(text) - max_len, step):
        input_chars.append(text[i:i+max_len])
        output_char.append(text[i+max_len])

    train_data = np.zeros((len(input_chars), max_len, len_unique_chars))
    target_data = np.zeros((len(input_chars), len_unique_chars))

    for i , each in enumerate(input_chars):
        for j, char in enumerate(each):
            train_data[i, j, unique_chars.index(char)] = 1
        target_data[i, unique_chars.index(output_char[i])] = 1
    return train_data, target_data, unique_chars, len_unique_chars

def rnn(x, weight, bias, len_unique_chars):
    '''
     define rnn cell and prediction
    '''
    x = tf.transpose(x, [1, 0, 2])
    x = tf.reshape(x, [-1, len_unique_chars])
    x = tf.split(x, max_len, 0)

    cell = tf.contrib.rnn.BasicLSTMCell(num_units, forget_bias=1.0)
    outputs, states = tf.contrib.rnn.static_rnn(cell, x, dtype=tf.float32)
    prediction = tf.matmul(outputs[-1], weight) + bias
    return prediction

def sample(predicted):
    '''
     helper function to sample an index from a probability array
    '''
    exp_predicted = np.exp(predicted/temperature)
    predicted = exp_predicted / np.sum(exp_predicted)
    probabilities = np.random.multinomial(1, predicted, 1)
    return probabilities

def run(train_data, target_data, unique_chars, len_unique_chars):
    '''
     main run function
    '''
    x = tf.placeholder("float", [None, max_len, len_unique_chars])
    y = tf.placeholder("float", [None, len_unique_chars])
    weight = tf.Variable(tf.random_normal([num_units, len_unique_chars]))
    bias = tf.Variable(tf.random_normal([len_unique_chars]))

    prediction = rnn(x, weight, bias, len_unique_chars)
    softmax = tf.nn.softmax_cross_entropy_with_logits(logits=prediction, labels=y)
    cost = tf.reduce_mean(softmax)
    optimizer = tf.train.RMSPropOptimizer(learning_rate=learning_rate).minimize(cost)

    init_op = tf.global_variables_initializer()
    sess = tf.Session()
    sess.run(init_op)

    num_batches = int(len(train_data)/batch_size)

    for i in range(epoch):
        print "----------- Epoch {0}/{1} -----------".format(i+1, epoch)
        count = 0
        for _ in range(num_batches):
            train_batch, target_batch = train_data[count:count+batch_size], target_data[count:count+batch_size]
            count += batch_size
            sess.run([optimizer] ,feed_dict={x:train_batch, y:target_batch})

        #get on of training set as seed
        seed = train_batch[:1:]

        #to print the seed 40 characters
        seed_chars = ''
        for each in seed[0]:
                seed_chars += unique_chars[np.where(each == max(each))[0][0]]
        print "Seed:", seed_chars

        #predict next 1000 characters
        for i in range(1000):
            if i > 0:
                remove_fist_char = seed[:,1:,:]
                seed = np.append(remove_fist_char, np.reshape(probabilities, [1, 1, len_unique_chars]), axis=1)
            predicted = sess.run([prediction], feed_dict = {x:seed})
            predicted = np.asarray(predicted[0]).astype('float64')[0]
            probabilities = sample(predicted)
            predicted_chars = unique_chars[np.argmax(probabilities)]
            seed_chars += predicted_chars
        print 'Result:', seed_chars
    sess.close()


if __name__ == "__main__":
    #get data from https://s3.amazonaws.com/text-datasets/nietzsche.txt
    text = read_data('nietzsche.txt')
    train_data, target_data, unique_chars, len_unique_chars = featurize(text)
    run(train_data, target_data, unique_chars, len_unique_chars)

Result:

----------- Epoch 40/60 -----------
Seed: discipline and training of the intellect
Result: discipline and training of the intellectual into the same restrain, or the contempt of the same estimates which one of the need of superiority of the good and man are the fact that the same as the sacrifice of a god and the same time alternatory of the moral form of such protested by the world with the problem of the demonstroup religious feelings, the world as the contempt of the master of the experience of the same moral true spectacle and instinctive men through the same with a the strength of an end with the more devolved as the beneficted when they will protes the same time the sense of the saint of the sacrifice of in all the superstitious and man is to be accepts in the self range and the present spectacle the slave as a peoples of the soul of the same into the same wholly because still misunderstanding the reality in the saints of god and the most strong and man who exercise with the saints of delight that
the subjection of the singular conscience when they have been produces of the same way the saint of the same and

Hi where can i change the seed? thanks. Or ask the user to input the seed.