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

sample = " if you want you"
idx2char = list(set(sample))  # index -> char
char2idx = {c: i for i, c in enumerate(idx2char)}  # char -> idex

# hyper parameters
dic_size = len(char2idx)  # RNN input size (one hot size)
rnn_hidden_size = len(char2idx)  # RNN output size
num_classes = len(char2idx)  # final output size (RNN or softmax, etc.)
batch_size = 1  # one sample data, one batch
sequence_length = len(sample) - 1  # number of lstm rollings (unit #)

sample_idx = [char2idx[c] for c in sample]  # char to index
x_data = [sample_idx[:-1]]  # X data sample (0 ~ n-1) hello: hell
y_data = [sample_idx[1:]]   # Y label sample (1 ~ n) hello: ello

X = tf.placeholder(tf.int32, [None, sequence_length])  # X data
Y = tf.placeholder(tf.int32, [None, sequence_length])  # Y label

x_one_hot = tf.one_hot(X, num_classes)  # one hot: 1 -> 0 1 0 0 0 0 0 0 0 0
cell = tf.contrib.rnn.BasicLSTMCell(
    num_units=rnn_hidden_size, state_is_tuple=True)
initial_state = cell.zero_state(batch_size, tf.float32)
outputs, _states = tf.nn.dynamic_rnn(
    cell, x_one_hot, initial_state=initial_state, dtype=tf.float32)

weights = tf.ones([batch_size, sequence_length])
sequence_loss = tf.contrib.seq2seq.sequence_loss(
    logits=outputs, targets=Y, weights=weights)
loss = tf.reduce_mean(sequence_loss)
train = tf.train.GradientDescentOptimizer(learning_rate=0.1).minimize(loss)

prediction = tf.argmax(outputs, axis=2)

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    for i in range(3000):
        l, _ = sess.run([loss, train], feed_dict={X: x_data, Y: y_data})
        result = sess.run(prediction, feed_dict={X: x_data})

        # print char using dic
        result_str = [idx2char[c] for c in np.squeeze(result)]

        print(i, "loss:", l, "Prediction:", ''.join(result_str))


'''
0 loss: 2.29895 Prediction: nnuffuunnuuuyuy
1 loss: 2.29675 Prediction: nnuffuunnuuuyuy
2 loss: 2.29459 Prediction: nnuffuunnuuuyuy
3 loss: 2.29247 Prediction: nnuffuunnuuuyuy

...

1413 loss: 1.3745 Prediction: if you want you
1414 loss: 1.3743 Prediction: if you want you
1415 loss: 1.3741 Prediction: if you want you
1416 loss: 1.3739 Prediction: if you want you
1417 loss: 1.3737 Prediction: if you want you
1418 loss: 1.37351 Prediction: if you want you
1419 loss: 1.37331 Prediction: if you want you
'''

tensorflow.set_random_seed tensorflow.argmax tensorflow.contrib.rnn.BasicLSTMCell tensorflow.ones tensorflow.Session tensorflow.placeholder tensorflow.train.GradientDescentOptimizer tensorflow.nn.dynamic_rnn tensorflow.one_hot tensorflow.contrib.seq2seq.sequence_loss tensorflow.global_variables_initializer tensorflow.reduce_mean