def test():
# build graph
inputs, labels, dropout_keep_prob, learning_rate = model.input_placeholder(FLAGS.image_size, FLAGS.image_channel,
FLAGS.label_cnt)
logits = model.inference(inputs, dropout_keep_prob)
predict = tf.argmax(logits, 1)
# session
init = tf.initialize_all_variables()
sess = tf.Session()
sess.run(init)
# tf saver
saver = tf.train.Saver()
if os.path.isfile(FLAGS.save_name):
saver.restore(sess, FLAGS.save_name)
i = 1
# load test data
test_images, test_ranges = loader.load_mnist_test(FLAGS.batch_size)
# ready for result file
test_result_file = open(FLAGS.test_result, 'wb')
csv_writer = csv.writer(test_result_file)
csv_writer.writerow(['ImageId', 'Label'])
total_start_time = time.time()
for file_start, file_end in test_ranges:
test_x = test_images[file_start:file_end]
predict_label = sess.run(predict, feed_dict={inputs: test_x, dropout_keep_prob: 1.0})
for cur_predict in predict_label:
csv_writer.writerow([i, cur_predict])
print('[Result %s: %s]' % (i, cur_predict))
i += 1
print("[%s][total exec %s seconds" % (time.strftime("%Y-%m-%d %H:%M:%S"), (time.time() - total_start_time)))
def train():
# build graph
inputs, labels, dropout_keep_prob, learning_rate = model.input_placeholder(image_size, image_channel, label_cnt)
logits = model.inference(inputs, dropout_keep_prob, label_cnt)
accuracy = model.accuracy(logits, labels)
loss = model.loss(logits, labels)
train = tf.train.RMSPropOptimizer(learning_rate, FLAGS.rms_decay).minimize(loss)
# session
init = tf.initialize_all_variables()
sess = tf.Session()
sess.run(init)
# ready for summary
merged = tf.merge_all_summaries()
train_writer = tf.train.SummaryWriter('./summary/train', sess.graph)
validation_writer = tf.train.SummaryWriter('./summary/validation')
# tf saver
saver = tf.train.Saver()
if os.path.isfile(FLAGS.save_name):
saver.restore(sess, FLAGS.save_name)
total_start_time = time.time()
# load mnist data
train_images, train_labels, train_range, validation_images, validation_labels, validation_indices = loader.load_mnist_train(
FLAGS.validation_size, FLAGS.batch_size)
total_train_len = len(train_images)
i = 0
cur_learning_rate = FLAGS.learning_rate
for epoch in range(FLAGS.training_epoch):
if epoch % 10 == 0 and epoch > 0:
cur_learning_rate /= 10
epoch_start_time = time.time()
for start, end in train_range:
batch_start_time = time.time()
train_x = train_images[start:end]
train_y = train_labels[start:end]
if i % 20 == 0:
summary, _, loss_result = sess.run([merged, train, loss], feed_dict={inputs: train_x, labels: train_y,
dropout_keep_prob: FLAGS.dropout_keep_prob,
learning_rate: cur_learning_rate})
train_writer.add_summary(summary, i)
else:
_, loss_result = sess.run([train, loss], feed_dict={inputs: train_x, labels: train_y,
dropout_keep_prob: FLAGS.dropout_keep_prob,
learning_rate: cur_learning_rate})
print('[%s][training][epoch %d, step %d exec %.2f seconds] [file: %5d ~ %5d / %5d] loss : %3.10f' % (
time.strftime("%Y-%m-%d %H:%M:%S"), epoch, i, (time.time() - batch_start_time), start, end,
total_train_len, loss_result))
if i % FLAGS.validation_interval == 0 and i > 0:
validation_start_time = time.time()
shuffle_indices = loader.shuffle_validation(validation_indices, FLAGS.batch_size)
validation_x = validation_images[shuffle_indices]
validation_y = validation_labels[shuffle_indices]
summary, accuracy_result, loss_result = sess.run([merged, accuracy, loss],
feed_dict={inputs: validation_x, labels: validation_y,
dropout_keep_prob: 1.0})
validation_writer.add_summary(summary, i)
print('[%s][validation][epoch %d, step %d exec %.2f seconds] accuracy : %1.3f, loss : %3.10f' % (
time.strftime("%Y-%m-%d %H:%M:%S"), epoch, i, (time.time() - validation_start_time),
accuracy_result, loss_result))
i += 1
print("[%s][epoch exec %s seconds] epoch : %d" % (
time.strftime("%Y-%m-%d %H:%M:%S"), (time.time() - epoch_start_time), epoch))
saver.save(sess, FLAGS.save_name)
print("[%s][total exec %s seconds" % (time.strftime("%Y-%m-%d %H:%M:%S"), (time.time() - total_start_time)))
train_writer.close()
validation_writer.close()
min_after_dequeue=80)
#img_batch, label_batch, img_class_batch = tf.train.shuffle_batch([img, label, img_class],
# batch_size=40, capacity=1000 + 3 * 40,
# min_after_dequeue=1000)
#img_batch, label_batch, img_class_batch = tf.train.shuffle_batch([img, label, img_class],
# batch_size=4, capacity= 70,
# min_after_dequeue=10)
### OK with v2
#img_batch, label_batch, img_class_batch = tf.train.shuffle_batch([img, label, img_class],
# batch_size=5, capacity=40,
# min_after_dequeue=30)
inputs, labels, dropout_keep_prob, learning_rate = model.input_placeholder(
image_size, image_channel, label_cnt)
logits = model.inference(inputs, dropout_keep_prob, label_cnt)
accuracy = model.accuracy(logits, labels)
loss = model.loss(logits, labels)
train = tf.train.RMSPropOptimizer(learning_rate, 0.9).minimize(loss)
#初始化所有的op
init = tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
#启动队列
try: