BOML is a modularized optimization library that unifies several ML algorithms into a common bilevel optimization framework. It provides interfaces to implement popular bilevel optimization algorithms, so that you could quickly build your own meta learning neural network and test its performance.
ReadMe.md contains brief introduction to implement meta-initialization-based and meta-feature-based methods in few-shot classification field. Except for algorithms which have been proposed, various combinations of lower level and upper level strategies are available.
Meta learning works fairly well when facing incoming new tasks by learning an initialization with favorable generalization capability. And it also has good performance even provided with a small amount of training data available, which gives birth to various solutions for different application such as few-shot learning problem.
We present a general bilevel optimization paradigm to unify different types of meta learning approaches, and the mathematical form could be summarized as below:
Here we illustrate the generic optimization process and hierarchically built strategies in the figure, which could be quikcly implemented in the following example.
For more detailed information of basic function and construction process, please refer to our Documentation orProject Page. Scripts in the directory named test_script are useful for constructing general training process.
Here we give recommended settings for specific hyper paremeters to quickly test performance of popular algorithms.
import boml 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, ) # create instance of BOMLExperiment for ong single task ex = boml.BOMLExperiment(dataset)
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") ex.model = boml_ho.base_learner(_input=ex.x, meta_learner=meta_learner)
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, )
loss_outer = utils.cross_entropy(pred=ex.model.re_forward(ex.x_).out, label=ex.y_) # loss function 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), )
# Only need to be called once after all the tasks are ready boml_ho.aggregate_all()
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) # Meta training step boml_ho.run(tr_fd, v_fd) if itr % 100 == 0: print(sess.run(loss_inner, utils.merge_dicts(tr_fd, v_fd)))
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