def run():
tfrecord_dataset = tf_record_dataset.TFRecordDataset(
tfrecord_dir=FLAGS.record_dir,
dataset_name=FLAGS.dataset_name,
num_classes=FLAGS.num_classes)
dataset = tfrecord_dataset.get_split(split_name='validation')
# Choice Model . vgg, inception.V1~3 , alexnet , resnet .. etc...
inception = nets.inception
X_image = tf.placeholder(tf.float32,
shape=[
None,
inception.inception_v3.default_image_size,
inception.inception_v3.default_image_size, 3
])
images, labels, _ = load_batch(
dataset,
height=inception.inception_v3.default_image_size,
width=inception.inception_v3.default_image_size,
num_classes=FLAGS.num_classes)
with slim.arg_scope(inception.inception_v3_arg_scope()):
logits, end_points = inception.inception_v3(
inputs=X_image, num_classes=FLAGS.num_classes)
predictions = tf.argmax(logits, 1)
Y_label = tf.placeholder(tf.float32, shape=[None, 5])
targets = tf.argmax(Y_label, 1)
provider = slim.dataset_data_provider.DatasetDataProvider(dataset)
log_dir = FLAGS.log
eval_dir = FLAGS.log
if not tf.gfile.Exists(eval_dir):
tf.gfile.MakeDirs(eval_dir)
if not tf.gfile.Exists(log_dir):
raise Exception("trained check point does not exist at %s " % log_dir)
else:
checkpoint_path = tf.train.latest_checkpoint(log_dir)
import matplotlib.pyplot as plt
with tf.Session() as sess:
saver = tf.train.Saver()
saver.restore(sess, checkpoint_path)
with slim.queues.QueueRunners(sess):
for i in range(100):
np_image, np_label = sess.run([images, labels])
tempimage, tflabel = sess.run([predictions, targets],
feed_dict={
X_image: np_image,
Y_label: np_label
})
print("Predict : ", tempimage)
print("Answer : ", tflabel)
print('enter')
def run():
tfrecord_dataset = tf_record_dataset.TFRecordDataset(
tfrecord_dir=FLAGS.record_dir,
dataset_name=FLAGS.dataset_name,
num_classes=FLAGS.num_classes)
dataset = tfrecord_dataset.get_split(split_name='train')
# Choice Model . vgg, inception.V1~3 , alexnet , resnet .. etc...
inception = nets.inception
images, labels, _ = load_batch(
dataset,
height=inception.inception_v3.default_image_size,
width=inception.inception_v3.default_image_size,
num_classes=FLAGS.num_classes)
with slim.arg_scope(inception.inception_v3_arg_scope()):
logits, end_points = inception.inception_v3(
inputs=images, num_classes=FLAGS.num_classes)
#loss Function
loss = slim.losses.softmax_cross_entropy(logits, labels)
total_loss = slim.losses.get_total_loss()
#optimizer
optimizer = tf.train.AdamOptimizer(learning_rate=FLAGS.learning_rate)
predictions = tf.argmax(logits, 1)
targets = tf.argmax(labels, 1)
correct_prediction = tf.equal(predictions, targets)
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
tf.summary.scalar('losses/Total', total_loss)
tf.summary.scalar('accuracy', accuracy)
summary_op = tf.summary.merge_all()
#
log_dir = FLAGS.log
if not tf.gfile.Exists(log_dir):
tf.gfile.MakeDirs(log_dir)
# 훈련 오퍼레이션 정의
train_op = slim.learning.create_train_op(total_loss, optimizer)
final_loss = slim.learning.train(train_op,
log_dir,
number_of_steps=FLAGS.step,
summary_op=summary_op,
save_summaries_secs=30,
save_interval_secs=30)
# train 데이터셋 생성
dataset = mnist_tfrecord_dataset.get_split(split_name='train')
"""
# slim.dataset_data_provider.DatasetDataProvider를 생성
"""
provider = slim.dataset_data_provider.DatasetDataProvider(dataset)
[image, label] = provider.get(['image', 'label'])
# 테스트
import matplotlib.pyplot as plt
with tf.Session() as sess:
with slim.queues.QueueRunners(sess):
plt.figure()
for i in range(4):
np_image, np_label = sess.run([image, label])
height, width, _ = np_image.shape
class_name = name = dataset.labels_to_names[np_label]
plt.subplot(2, 2, i+1)
plt.imshow(np_image)
plt.title('%s, %d x %d' % (name, height, width))
plt.axis('off')
plt.show()
'''
tf.train.batch를 생성
'''
images, labels, _ = load_batch(dataset)
from utils.dataset_utils import load_batch
from datasets import tf_record_dataset
tf.logging.set_verbosity(tf.logging.INFO)
'''
# 평가 데이터 로드
'''
batch_size = 16
tfrecord_dataset = tf_record_dataset.TFRecordDataset(
tfrecord_dir='/home/itrocks/Git/Tensorflow/dog-breed-classification.tf/raw_data/dog/tfrecord',
dataset_name='dog',
num_classes=120)
# Selects the 'train' dataset.
dataset = tfrecord_dataset.get_split(split_name='validation')
images, labels, num_samples = load_batch(dataset, batch_size=batch_size, height=224, width=224)
'''
# 네트워크 모델 로드: VGG-16
'''
vgg = tf.contrib.slim.nets.vgg
with slim.arg_scope(vgg.vgg_arg_scope()):
logits, end_points = vgg.vgg_16(inputs=images, num_classes=120, is_training=True)
'''
# 메트릭 정의
'''
logits = tf.argmax(logits, 1)
labels = tf.argmax(labels, 1)