def main(_):
print("Model Architecture: {}".format(FLAGS.model_architecture))
# Adjust some parameters
if FLAGS.debug:
FLAGS.small_label_set = False
print("RUNNING IN DEBUG MODE")
FLAGS.num_classes = utils.get_num_classes(FLAGS)
X_train, y_train = data_utils.load_dataset_tf(FLAGS, mode="train")
X_val, y_val = data_utils.load_dataset_tf(FLAGS, mode="val")
# comet_ml experiment logging (https://www.comet.ml/)
experiment = Experiment(api_key="J55UNlgtffTDmziKUlszSMW2w",
log_code=False)
experiment.log_multiple_params(utils.gather_params(FLAGS))
experiment.set_num_of_epocs(FLAGS.epochs)
experiment.log_dataset_hash(X_train)
tf.logging.set_verbosity(tf.logging.INFO)
# Start a new, DEFAULT TensorFlow session.
sess = tf.InteractiveSession()
utils.set_seeds() # Get deterministic behavior?
model = models.create_model(FLAGS)
fw = framework.Framework(sess, model, experiment, FLAGS)
num_params = int(utils.get_number_of_params())
model_size = num_params * 4
print("\nNumber of trainable parameters: {}".format(num_params))
print("Model is ~ {} bytes out of max 5000000 bytes\n".format(model_size))
experiment.log_parameter("num_params", num_params)
experiment.log_parameter("approx_model_size", model_size)
fw.optimize(X_train, y_train, X_val, y_val)
