In [1]:

    

import tensorflow as tf
import numpy as np
tf.set_random_seed(777) # reproducibility


    

In [2]:

    

idx2char = ['h', 'i', 'e', 'l', 'o']


    

In [3]:

    

idx2char


    

    
Out[3]:





['h', 'i', 'e', 'l', 'o']

In [4]:

    

x_data = [[0, 1, 0, 2, 3, 3]]  # hihell


    

In [5]:

    

x_one_hot = [[[1, 0, 0, 0, 0],   # h 0
              [0, 1, 0, 0, 0],   # i 1
              [1, 0, 0, 0, 0],   # h 0
              [0, 0, 1, 0, 0],   # e 2
              [0, 0, 0, 1, 0],   # l 3
              [0, 0, 0, 1, 0]]]  # l 3


    

In [6]:

    

y_data = [[1, 0, 2, 3, 3, 4]] # ihello


    

In [7]:

    

num_classes = 5
input_dim = 5  # one-hot size
hidden_size = 5  # output from the LSTM. 5 to directly predict one-hot
batch_size = 1   # one sentence
sequence_length = 6  # |ihello| == 6
learning_rate = 0.1


    

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