I don't understand the code related to RNN - tensorflow

Question

from __future__ import print_function
import tensorflow as tf
import numpy as np
from tensorflow.contrib import rnn
tf.set_random_seed(777) # reproducibility
sentence = ("if you want to build a ship, don't drum up people together to "
"collect wood and don't assign them tasks and work, but rather "
"teach them to long for the endless immensity of the sea.")
char_set = list(set(sentence))
char_dic = {w: i for i, w in enumerate(char_set)}
data_dim = len(char_set)
hidden_size = len(char_set)
num_classes = len(char_set)
sequence_length = 10 # Any arbitrary number
learning_rate = 0.1
dataX = []
dataY = []
for i in range(0, len(sentence) - sequence_length):
x_str = sentence[i:i + sequence_length]
y_str = sentence[i + 1: i + sequence_length + 1]
print(i, x_str, '->', y_str)
x = [char_dic[c] for c in x_str] # x str to index
y = [char_dic[c] for c in y_str] # y str to index
dataX.append(x)
dataY.append(y)
batch_size = len(dataX)
X = tf.placeholder(tf.int32, [None, sequence_length])
Y = tf.placeholder(tf.int32, [None, sequence_length])
# One-hot encoding
X_one_hot = tf.one_hot(X, num_classes)
print(X_one_hot) # check out the shape
def lstm_cell():
cell = rnn.BasicLSTMCell(hidden_size, state_is_tuple=True)
return cell
multi_cells = rnn.MultiRNNCell([lstm_cell() for _ in range(2)], state_is_tuple=True)
# outputs: unfolding size x hidden size, state = hidden size
outputs, _states = tf.nn.dynamic_rnn(multi_cells, X_one_hot, dtype=tf.float32)
# FC layer
X_for_fc = tf.reshape(outputs, [-1, hidden_size])
outputs = tf.contrib.layers.fully_connected(X_for_fc, num_classes, activation_fn=None)
# reshape out for sequence_loss
outputs = tf.reshape(outputs, [batch_size, sequence_length, num_classes])
# All weights are 1 (equal weights)
weights = tf.ones([batch_size, sequence_length])
sequence_loss = tf.contrib.seq2seq.sequence_loss(
logits=outputs, targets=Y, weights=weights)
mean_loss = tf.reduce_mean(sequence_loss)
train_op = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(mean_loss)
sess = tf.Session()
sess.run(tf.global_variables_initializer())
for i in range(500):
_, l, results = sess.run(
[train_op, mean_loss, outputs], feed_dict={X: dataX, Y: dataY})
for j, result in enumerate(results):
index = np.argmax(result, axis=1)
print(i, j, ''.join([char_set[t] for t in index]), l)
# Let's print the last char of each result to check it works
results = sess.run(outputs, feed_dict={X: dataX})
for j, result in enumerate(results):
index = np.argmax(result, axis=1)
if j is 0: # print all for the first result to make a sentence
print(''.join([char_set[t] for t in index]), end='')
else:
print(char_set[index[-1]], end='')
'''
0 167 tttttttttt 3.23111
0 168 tttttttttt 3.23111
0 169 tttttttttt 3.23111
…
499 167 of the se 0.229616
499 168 tf the sea 0.229616
499 169 the sea. 0.229616
g you want to build a ship, don't drum up people together to collect wood and don't assign them tasks and work, but rather teach them to long for the endless immensity of the sea.
'''
(Please understand that English is not my native language)
I don't understand the last 'if, else' part of the code above, can anyone explain?
Why do print(''. Join ([char_set [t] for t in index]), end = '') only when j is 0,
In the case of else, why do print(char_set [index [-1]], end = '')?
Please explain how the code works

Answer 1

That last bit is just checking whether the network works or not. It generates multiple results first, and then iterates through those results. I guess the creator of this code snippet wanted to check the whole sentence in the first result, and then only the last characters for the rest. That is up to you entirely, if you want to change it.