def run_training(data):
with tf.Graph().as_default():
global_step = tf.Variable(0, name='global_step', trainable=False)
images_pl = tf.placeholder(tf.float32, shape=[BATCH_SIZE, 32, 32, 3])
labels_pl = tf.placeholder(tf.int32, shape=[BATCH_SIZE])
logits = graph.inference(images_pl)
loss = graph.loss(logits, labels_pl)
train_op = graph.train(loss, global_step)
eval_correct = graph.evaluate(logits, labels_pl)
summary_op = tf.merge_all_summaries()
saver = tf.train.Saver(tf.all_variables())
init = tf.initialize_all_variables()
sess = tf.Session()
sess.run(init)
summary_writer = tf.train.SummaryWriter(SUMMARY_DIR, sess.graph)
for step in range(N_EPOCH * (DS_SIZE // BATCH_SIZE)):
start_time = time.time()
feed_dict = fill_feed_dict(data.train, images_pl, labels_pl)
_, loss_val = sess.run([train_op, loss], feed_dict=feed_dict)
duration = time.time() - start_time
assert not np.isnan(loss_val), 'Model diverged with loss = NaN'
if step % 10 == 0 or step == N_EPOCH * (DS_SIZE // BATCH_SIZE) - 1:
print('Step %d: loss = %.2f (%.3f sec)' % (step, loss_val, duration))
if step > 0:
summary_str = sess.run(summary_op, feed_dict)
summary_writer.add_summary(summary_str, step)
summary_writer.flush()
if step > 0:
if step < 1000 and step % 200 == 0:
print('Training Data Eval:')
do_eval(sess, eval_correct, images_pl, labels_pl, data.train)
print('Validation Data Eval:')
do_eval(sess, eval_correct, images_pl, labels_pl, data.validation)
if step % 1000 == 0 or step == N_EPOCH * (DS_SIZE // BATCH_SIZE) - 1:
print('Training Data Eval:')
do_eval(sess, eval_correct, images_pl, labels_pl, data.train)
print('Validation Data Eval:')
do_eval(sess, eval_correct, images_pl, labels_pl, data.validation)
if step == N_EPOCH * (DS_SIZE // BATCH_SIZE) - 1:
print('Test Data Eval:')
do_eval(sess, eval_correct, images_pl, labels_pl, data.test)
# Save the model checkpoint periodically.
if step % 1000 == 0 or step == N_EPOCH * (DS_SIZE // BATCH_SIZE) - 1:
checkpoint_path = CHECKPOINT_DIR
saver.save(sess, checkpoint_path, global_step=step)
def run_training(data):
with tf.Graph().as_default():
images_pl = tf.placeholder(tf.float32, shape=[BATCH_SIZE, 512, 512, 3])
labels_pl = tf.placeholder(tf.int32, shape=[BATCH_SIZE])
logits = graph.inference(images_pl)
loss = graph.loss(logits, labels_pl)
train_op = graph.train(loss, 0.0001)
eval_correct = graph.evaluate(logits, labels_pl)
saver = tf.train.Saver(tf.trainable_variables())
summary_op = tf.merge_all_summaries()
init = tf.initialize_all_variables()
sess = tf.Session()
sess.run(init)
# c,d = data.train.next_batch(BATCH_SIZE)
# a = sess.run(logits,feed_dict={images_pl: c})
# print a
summary_writer = tf.train.SummaryWriter("summary", sess.graph)
for step in range(N_EPOCH * (DS_SIZE // BATCH_SIZE)):
start_time = time.time()
feed_dict = fill_feed_dict(data.train, images_pl, labels_pl)
_, loss_val = sess.run([train_op, loss], feed_dict=feed_dict)
duration = time.time() - start_time
assert not np.isnan(loss_val), 'Model diverged with loss = NaN'
if step % 10 == 0 or step == N_EPOCH * (DS_SIZE // BATCH_SIZE) - 1:
print('Step %d: loss = %.2f (%.3f sec)' %
(step, loss_val, duration))
if step > 0:
summary_str = sess.run(summary_op, feed_dict)
summary_writer.add_summary(summary_str, step)
summary_writer.flush()
if step % 100 == 0 or step == N_EPOCH * (DS_SIZE //
BATCH_SIZE) - 1:
save_path = saver.save(sess, "model.ckpt")
print("Model saved in file: %s" % save_path)
print('Training Data Eval:')
do_eval(sess, eval_correct, images_pl, labels_pl, data.train)
print('Validation Data Eval:')
do_eval(sess, eval_correct, images_pl, labels_pl,
data.validation)
def run_evaluation(data):
with tf.Graph().as_default():
images_pl = tf.placeholder(tf.float32, shape=[BATCH_SIZE, 32, 32, 3])
labels_pl = tf.placeholder(tf.int32, shape=[BATCH_SIZE])
logits = graph.inference(images_pl)
eval_correct = graph.evaluate(logits, labels_pl)
saver = tf.train.Saver(tf.all_variables())
# init = tf.initialize_all_variables()
sess = tf.Session()
saver.restore(sess, "checkpoints/-4500")
print("Model restored.")
# sess.run(init)
do_eval(sess, eval_correct, images_pl, labels_pl, data.test)