MLlab12.py

#RNN Hi Hello Training
import tensorflow as tf
import numpy as np

hidden_size = 5
input_dim = 5
batch_size = 1
sequence_length=6

idx2char = ['h', 'i', 'e', 'l', 'o']
# Teach hello: hihell -> ihello
x_data = [[0, 1, 0, 2, 3, 3]]   # hihell
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

y_data = [[1, 0, 2, 3, 3, 4]]    # ihello
X = tf.placeholder(tf.float32, shape=[None, sequence_length, input_dim])
Y = tf.placeholder(tf.int32, shape=[None, sequence_length])

cell = tf.contrib.rnn.BasicLSTMCell(num_units = hidden_size, state_is_tuple = True)
initial_state = cell.zero_state(batch_size, tf.float32)
outputs, _states = tf.nn.dynamic_rnn(cell, X, initial_state= initial_state)
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.AdamOptimizer(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(2000):
        l,_= sess.run([loss, train], feed_dict={X:x_one_hot, Y:y_data})
        result = sess.run(prediction, feed_dict={X:x_one_hot})
        print(i, "loss:", l, "prediction: ", result, "true Y: ", y_data)

        result_str = [idx2char[c] for c in np.squeeze(result)]
        print("\tPrediction str: ", ''.join(result_str))