def main(argv=None):
model = DNN(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="DNN",
model='DNN')
model.create_network()
model.initialize_network()
if False:
model.train_model(1, 1, 1, 4)
else:
model.test_model()
def main(argv=None):
model = DNN(
train_images_dir=FLAGS.train_images_dir,
val_images_dir=FLAGS.val_images_dir,
test_images_dir=FLAGS.test_images_dir,
num_epochs=FLAGS.num_epochs,
train_batch_size=FLAGS.train_batch_size,
val_batch_size=FLAGS.val_batch_size,
test_batch_size=FLAGS.test_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,
)
if FLAGS.train:
model.create_network(model_type="train")
model.initialize_network()
model.train_model(FLAGS.display_step, FLAGS.validation_step,
FLAGS.checkpoint_step, FLAGS.summary_step)
else:
model.create_network(model_type="test")
model.initialize_network()
model.test_model()
def main(argv=None):
model = DNN(
train_spec_dir=FLAGS.train_spec_dir,
val_spec_dir=FLAGS.val_spec_dir,
test_spec_dir=FLAGS.test_spec_dir,
num_epochs=FLAGS.num_epochs,
train_batch_size=FLAGS.train_batch_size,
val_batch_size=FLAGS.val_batch_size,
test_batch_size=FLAGS.test_batch_size,
sequence_length=FLAGS.sequence_length,
fft_length=FLAGS.fft_length,
learning_rate=FLAGS.learning_rate,
base_dir=FLAGS.base_dir,
max_to_keep=FLAGS.max_to_keep,
model_name=FLAGS.model_name,
)
model.create_network()
print('[*] Network created')
model.initialize_network()
print('[*] Network initialized')
if FLAGS.train:
model.train_model(FLAGS.display_step, FLAGS.validation_step, FLAGS.checkpoint_step, FLAGS.summary_step)
print('[*] Model trained')
else:
model.test_model()
print('[*] Model tested')