create dir
:param path:
:return:
"""
a = os.path.exists(path)
if os.path.exists(path) is False:
try:
os.makedirs(path)
print('{0} has been created'.format(path))
except Exception as e:
print(e)
if __name__ == "__main__":
num_train_samples = get_num_samples(record_dir=FLAGS.train_data_dir)
num_val_samples = get_num_samples(record_dir=FLAGS.test_data_dir)
# get total step of the number train epoch
step_per_epoch = num_train_samples // FLAGS.batch_size # get num step of per epoch
max_step = FLAGS.epoch * step_per_epoch # get total step of several epoch
vgg = VGG16(input_shape=[FLAGS.height, FLAGS.width, FLAGS.depth],
num_classes=FLAGS.num_classes,
batch_size=FLAGS.batch_size,
learning_rate=FLAGS.learning_rate,
decay_rate=FLAGS.decay_rate,
num_samples_per_epoch=num_train_samples,
num_epoch_per_decay=FLAGS.num_epoch_per_decay,
keep_prob=FLAGS.keep_prop,
weight_decay=FLAGS.weight_decay)
tf.global_variables_initializer(),
tf.local_variables_initializer()
)
sess.run(init_op)
inputs, labels = sess.run([inputs, labels])
feed_dict = inception_v3.fill_feed_dict(image_feed=inputs, label_feed=labels, is_training=True)
logits = sess.run(fetches=[inception_v3.logits], feed_dict=feed_dict)
assert list(logits[0].shape) == [batch_size, FLAGS.num_classes]
#
#
if __name__ == "__main__":
num_samples = get_num_samples(record_file=FLAGS.train_dir)
batch_size = num_samples // FLAGS.step_per_epoch
inception_v3 = InceptionV3(input_shape=[FLAGS.height, FLAGS.width, FLAGS.depth],
num_classes=FLAGS.num_classes,
batch_size=batch_size,
decay_rate=FLAGS.decay_rate,
learning_rate=FLAGS.learning_rate,
num_samples_per_epoch=num_samples,
num_epoch_per_decay=FLAGS.num_epoch_per_decay,
keep_prob=FLAGS.keep_prob,
regular_weight_decay=FLAGS.regular_weight_decay,
batch_norm_decay=FLAGS.batch_norm_decay,
batch_norm_epsilon=FLAGS.batch_norm_epsilon,
batch_norm_fused=FLAGS.batch_norm_fused,
is_pretrain = FLAGS.is_pretrain
'Number of probability that each element is kept.')
flags.DEFINE_float('weight_decay', 0.0005, 'Number of regular scale size')
flags.DEFINE_bool('is_pretrain', True, 'if True, use pretrain model.')
flags.DEFINE_string('pretrain_model_dir', pretrain_model_dir,
'Directory to restore pretrain model dir.')
flags.DEFINE_string('train_data', train_data,
'Directory to put the training data.')
flags.DEFINE_string('val_data', val_data,
'Directory to put the validation data.')
flags.DEFINE_string('model_dir', model_dir, 'Directory to save model.')
flags.DEFINE_string('logs_dir', logs_dir, 'direct of summary logs.')
flags.DEFINE_integer('save_step_period', 2000, 'save model step period')
if __name__ == "__main__":
num_train_samples = get_num_samples(record_dir=FLAGS.train_data)
num_val_samples = get_num_samples(record_dir=FLAGS.val_data)
# approximate samples per epoch
# get total step of the number train epoch
train_step_per_epoch = num_train_samples // FLAGS.batch_size # get num step of per epoch
# max_step = FLAGS.epoch * step_per_epoch # get total step of several epoch
# get the number step of one validation epoch
val_step_per_epoch = num_val_samples // FLAGS.batch_size
alex_net = AlexNet(input_shape=[FLAGS.height, FLAGS.width, FLAGS.depth],
num_classes=FLAGS.num_classes,
batch_size=FLAGS.batch_size,
learning_rate=FLAGS.learning_rate,
keep_prob=FLAGS.keep_prop,