def train(self):
""" train 10-class MNIST classifier """
# load data
tr_img, tr_lab = read_tfrecord(self.args.datadir, self.args.batch, self.args.epoch)
val_img, val_lab = read_tfrecord(self.args.val_datadir, self.args.batch, self.args.epoch)
# graph
tr_logit = self.build(tr_img)
val_logit = self.build(val_img, True)
step = tf.Variable(0, trainable=False)
increment_step = tf.assign_add(step, 1)
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(labels=tr_lab, logits=tr_logit))
optimizer = tf.train.AdamOptimizer(self.args.lr).minimize(loss, global_step=step)
tr_accuracy = self.accuracy(tr_lab, tr_logit)
val_accuracy = self.accuracy(val_lab, val_logit)
var_list = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES) + tf.get_collection(tf.GraphKeys.LOCAL_VARIABLES)
saver = tf.train.Saver(max_to_keep=2, var_list=var_list)
# session
with tf.Session() as sess:
if self.args.restore:
saver.restore(sess, tf.train.latest_checkpoint(self.args.ckptdir))
else:
sess.run([tf.global_variables_initializer(), tf.local_variables_initializer()])
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
try:
min_val_acc = 10000.
while not coord.should_stop():
global_step = sess.run(step)
batch_loss, batch_acc, _ = sess.run([loss, tr_accuracy, optimizer])
if global_step % 100 == 0:
print('step:: %d, loss= %.3f, accuracy= %.3f' % (global_step, batch_loss, batch_acc))
if global_step % 3000 == 0:
val_acc = sess.run(val_accuracy)
print('val accuracy= %.3f' % val_acc)
if val_acc < min_val_acc:
min_val_acc = val_acc
save_path = saver.save(sess, self.args.ckptdir + '/model_%.3f.ckpt' % val_acc,
global_step=step)
print('model saved in file: %s' % save_path)
sess.run(increment_step)
except KeyboardInterrupt:
print('keyboard interrupted')
coord.request_stop()
except Exception as e:
coord.request_stop(e)
finally:
save_path = saver.save(sess, self.args.ckptdir + '/model.ckpt', global_step=step)
print('model saved in file : %s' % save_path)
coord.request_stop()
coord.join(threads)
def test(self):
# load data
ts_img, ts_lab = read_tfrecord(self.args.datadir, self.args.batch, None)
# graph
ts_logit = self.build(ts_img)
step = tf.Variable(0, trainable=False)
ts_accuracy = self.accuracy(ts_lab, ts_logit)
var_list = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES) + tf.get_collection(tf.GraphKeys.LOCAL_VARIABLES)
saver = tf.train.Saver(var_list=var_list)
# session
with tf.Session() as sess:
saver.restore(sess, tf.train.latest_checkpoint(self.args.ckptdir))
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
total_acc = 0.
steps = 0
while steps < 10000 / self.args.batch:
batch_acc = sess.run(ts_accuracy)
total_acc += batch_acc
steps += 1
total_acc /= steps
print('number: %d, total acc: %.1f' % (steps, total_acc * 100) + '%')
coord.request_stop()
coord.join(threads)
