Simple Running Example
=======================
.. highlight:: sh

::

	# start from laoding data
	from boml import utils
	from test_script.script_helper import *

	dataset = boml.load_data.meta_omniglot(
		std_num_classes=args.classes,
		examples_train=args.examples_train,
		examples_test=args.examples_test,
	)
	ex = boml.BOMLExperiment(dataset)
	print("experiment built!")
	# build network structure and define hyperparameters
	boml_ho = boml.BOMLOptimizer(
		method="MetaInit", inner_method="Simple", outer_method="Simple"
	)
	meta_learner = boml_ho.meta_learner(_input=ex.x, dataset=dataset, meta_model="V1")
	print("meta learner built!")
	ex.model = boml_ho.base_learner(_input=ex.x, meta_learner=meta_learner)
	# define LL objectives and LL calculation process
	print("base learner built!")
	loss_inner = utils.cross_entropy(pred=ex.model.out, label=ex.y)
	accuracy = utils.classification_acc(pred=ex.model.out, label=ex.y)
	inner_grad = boml_ho.ll_problem(
		inner_objective=loss_inner,
		learning_rate=args.lr,
		T=args.T,
		experiment=ex,
		var_list=ex.model.var_list,
	)
	# define UL objectives and UL calculation process
	loss_outer = utils.cross_entropy(pred=ex.model.re_forward(ex.x_).out, label=ex.y_)
	boml_ho.ul_problem(
		outer_objective=loss_outer,
		meta_learning_rate=args.meta_lr,
		inner_grad=inner_grad,
		meta_param=tf.get_collection(boml.extension.GraphKeys.METAPARAMETERS),
	)
	# aggregate all the defined operations
	boml_ho.aggregate_all()
	# Meta training step
	with tf.Session() as sess:
		tf.global_variables_initializer().run(session=sess)
		for itr in range(args.meta_train_iterations):
			# generate the feed_dict for calling run() everytime
			train_batch = BatchQueueMock(
				dataset.train, 1, args.meta_batch_size, utils.get_rand_state(1)
			)
			tr_fd, v_fd = utils.feed_dict(train_batch.get_single_batch(), ex)
			boml_ho.run(tr_fd, v_fd)
			print("finish one meta-iteration ")
			if itr % 100 == 0:
				print(sess.run(loss_inner, utils.merge_dicts(tr_fd, v_fd)))