# # for a, p, l in zip(batch[0].tolist(), batch[1].tolist(), batch[2].tolist()):
# # print(voc.id2word[a], voc.id2word[p], l)
# print(time.asctime( time.localtime(time.time()) ))
#
# sys.exit()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
summary_writer = tf.summary.FileWriter(graph_saving_path, graph=sess.graph)
# Restore from checkpoint
# saver.restore(sess, ckpt_path)
# sess.graph.as_default()
for e in range(epochs):
batch = reader.next_batch()
first_batch = batch
while batch is not None:
in_words, out_words, labels = batch
_, batch_count = sess.run([train_, adder_], {
in_words_: in_words,
out_words_: out_words,
labels_: labels
})
if batch_count % 1000 == 0:
# in_words, out_words, labels = first_batch
loss_val, summary, _ = sess.run([loss_, saveloss_, final_], {
