def main(argv=None):
model = CNN(
train_images_dir='data/train/',
val_images_dir='data/val/',
test_images_dir='data/test/',
num_epochs=40,
train_batch_size=1000,
val_batch_size=1000,
test_batch_size=10000,
height_of_image=28,
width_of_image=28,
num_channels=1,
num_classes=10,
learning_rate=0.001,
base_dir='results',
max_to_keep=2,
model_name="CNN",
model='CNN'
)
model.create_network()
model.initialize_network()
if True:
model.train_model(1, 1, 1, 4)
else:
model.test_model()
def main(argv=None):
model = CNN(train_images_dir=FLAGS.train_images_dir,
val_images_dir=FLAGS.val_images_dir,
test_images_dir=FLAGS.test_images_dir,
inference_dir=FLAGS.inference_dir,
num_epochs=FLAGS.num_epochs,
train_batch_size=FLAGS.train_batch_size,
val_batch_size=FLAGS.val_batch_size,
height_of_image=FLAGS.height_of_image,
width_of_image=FLAGS.width_of_image,
num_channels=FLAGS.num_channels,
num_classes=FLAGS.num_classes,
learning_rate=FLAGS.learning_rate,
base_dir=FLAGS.base_dir,
max_to_keep=FLAGS.max_to_keep,
model_name=FLAGS.model_name,
keep_prob=FLAGS.keep_prob)
model.create_network()
model.initialize_network()
if FLAGS.train:
model.train_model(FLAGS.display_step, FLAGS.validation_step,
FLAGS.checkpoint_step, FLAGS.summary_step)
else:
model.test_model()
