class Autoencoder:
def __init__(self):
self.config = Config()
self.model = Model(self.config)
self.model_dir = 'Psych209_AE'
self.writer = tf.summary.FileWriter(self.model_dir)
self.saver = tf.train.Saver(max_to_keep=200)
self.global_step = 0
self.sess = tf.Session()
self.num_epochs = 100
self.save_interval = 1000
self.print_interval = 100
def run(self):
train, test = utils.load_data('Data/movie_lines.txt')
embeddings = utils.load_embeddings()
self.sess.run(tf.global_variables_initializer())
self.writer.add_graph(sess.graph)
merged_summaries = tf.summary.merge_all()
print 'Starting training...'
for epoch in range(self.num_epochs):
print '-----Epoch', epoch, '-----'
batches = utils.get_batches(train, self.config.batch_size)
start_time = datetime.datetime.now()
for batch in tqdm(batches):
ops, feed = self.model.train_step(batch, training=True)
_, loss, summary = sess.run(ops + (merged_summaries, ), feed)
self.writer.add_summary(summary, self.global_step)
self.global_step += 1
# training status
if self.global_step % self.print_interval == 0:
perplexity = math.exp(
float(loss)) if loss < 300 else float('inf')
tqdm.write(
"----- Step %d -- Loss %.2f -- Perplexity %.2f" %
(self.global_step, loss, perplexity))
# run test periodically
ops, feed = self.model.train_step(batch, training=False)
_, loss, summary = sess.run(ops + (merged_summaries, ),
feed)
# save checkpoint
if self.global_step % self.save_interval == 0:
self.save_session(sess)
end_time = datetime.datetime.now()
print 'Epoch finish in ', end_time - start_time, 'ms'
def save_session(self, sess):
print 'Saving session at checkpoint', self.global_step
name = self.model_dir + str(self.global_step)
self.saver.save(sess, name)
print 'Save complete with name', name
