def main(model, training_cnf, data_dir, start_epoch, resume_lr, weights_from, clean, visuals): util.check_required_program_args([model, training_cnf, data_dir]) model_def = util.load_module(model) model = model_def.model cnf = util.load_module(training_cnf).cnf util.init_logging('train.log', file_log_level=logging.INFO, console_log_level=logging.INFO, clean=clean) if weights_from: weights_from = str(weights_from) data_set = DataSet(data_dir, model_def.image_size[0]) training_iter, validation_iter = create_training_iters( cnf, data_set, model_def.crop_size, start_epoch, cnf.get('iterator_type', 'queued') == 'parallel') trainer = SupervisedTrainer(model, cnf, training_iter, validation_iter, classification=cnf['classification']) trainer.fit(data_set, weights_from, start_epoch, resume_lr, verbose=1, summary_every=cnf.get('summary_every', 10), clean=clean, visuals=visuals)
def main(model, training_cnf, data_dir, parallel, start_epoch, task_id, job_name, ps_hosts, worker_hosts, weights_from, resume_lr, gpu_memory_fraction, is_summary, loss_type): model_def = util.load_module(model) model = model_def.model cnf = util.load_module(training_cnf).cnf ps_hosts = ps_hosts.split(',') worker_hosts = worker_hosts.split(',') cluster_spec = tf.train.ClusterSpec({'ps': ps_hosts, 'worker': worker_hosts}) server = tf.train.Server( {'ps': ps_hosts, 'worker': worker_hosts}, job_name=job_name, task_index=task_id) util.init_logging('train.log', file_log_level=logging.INFO, console_log_level=logging.INFO) if weights_from: weights_from = str(weights_from) if job_name == 'ps': server.join() else: learner = DistSupervisedLearner(model, cnf, resume_lr=resume_lr, classification=cnf[ 'classification'], gpu_memory_fraction=gpu_memory_fraction, is_summary=is_summary, loss_type=loss_type, verbosity=1) data_dir_train = os.path.join(data_dir, 'train') data_dir_val = os.path.join(data_dir, 'val') learner.fit(task_id, server, cluster_spec, data_dir_train, data_dir_val, weights_from=weights_from, start_epoch=start_epoch, training_set_size=50000, val_set_size=10000, summary_every=399, keep_moving_averages=True)
def main(): train, test, _ = imdb.load_data(path='imdb.pkl', n_words=10000, valid_portion=0.1) trainX, trainY = train testX, testY = test trainX = pad_sequences(trainX, maxlen=100, value=0.) testX = pad_sequences(testX, maxlen=100, value=0.) trainY = np.asarray(trainY) testY = np.asarray(testY) data_set = DataSet(trainX, trainY, testX, testY) training_cnf = { 'classification': True, 'validation_scores': [('validation accuracy', util.accuracy_tf)], 'num_epochs': 50, 'input_size': (100, ), 'lr_policy': StepDecayPolicy(schedule={ 0: 0.01, 30: 0.001, }) } util.init_logging('train.log', file_log_level=logging.INFO, console_log_level=logging.INFO) learner = SupervisedLearner(model, training_cnf, classification=training_cnf['classification'], is_summary=False) learner.fit(data_set, weights_from=None, start_epoch=1)
def main(model, training_cnf, data_dir, parallel, start_epoch, weights_from, resume_lr, gpu_memory_fraction, is_summary): model_def = util.load_module(model) model = model_def.model cnf = util.load_module(training_cnf).cnf util.init_logging('train.log', file_log_level=logging.INFO, console_log_level=logging.INFO) if weights_from: weights_from = str(weights_from) data_set = DataSet(data_dir, model_def.image_size[0]) standardizer = cnf.get('standardizer', NoOpStandardizer()) training_iter, validation_iter = create_training_iters(cnf, data_set, standardizer, model_def.crop_size, start_epoch, parallel=parallel) trainer = SupervisedTrainer(model, cnf, training_iter, validation_iter, resume_lr=resume_lr, classification=cnf['classification'], gpu_memory_fraction=gpu_memory_fraction, is_summary=is_summary, loss_type='kappa_log') trainer.fit(data_set, weights_from, start_epoch, verbose=1, summary_every=399)
def main(model, training_cnf, data_dir, parallel, start_epoch, weights_from, resume_lr, gpu_memory_fraction, is_summary, num_classes): model_def = util.load_module(model) model = model_def cnf = util.load_module(training_cnf).cnf util.init_logging('train_ss.log', file_log_level=logging.INFO, console_log_level=logging.INFO) if weights_from: weights_from = str(weights_from) data_set = DataSet(data_dir, model_def.image_size[0]) standardizer = cnf.get('standardizer', NoOpStandardizer()) training_iter, validation_iter = create_training_iters(cnf, data_set, standardizer, model_def.crop_size, start_epoch, parallel=parallel) trainer = GenerativeLearner(model, cnf, training_iterator=training_iter, validation_iterator=validation_iter, resume_lr=resume_lr, classification=cnf['classification'], gpu_memory_fraction=gpu_memory_fraction, is_summary=is_summary, verbosity=2) trainer.fit(data_set, num_classes, weights_from, start_epoch, summary_every=399)
def main(model, training_cnf, data_dir, parallel, start_epoch, weights_from, weights_dir, resume_lr, gpu_memory_fraction, is_summary, loss_type): with tf.Graph().as_default(): model_def = util.load_module(model) model = model_def.model cnf = util.load_module(training_cnf).cnf util.init_logging('train.log', file_log_level=logging.INFO, console_log_level=logging.INFO) if weights_from: weights_from = str(weights_from) trainer = SupervisedLearner(model, cnf, log_file_name='train_seg.log', resume_lr=resume_lr, classification=cnf['classification'], gpu_memory_fraction=gpu_memory_fraction, num_classes=15, is_summary=is_summary, loss_type=loss_type, verbosity=1) trainer.fit(data_dir, weights_from=weights_from, weights_dir=weights_dir, start_epoch=start_epoch, summary_every=399, keep_moving_averages=True)
def train(): mnist = input_data.read_data_sets("MNIST_data/", one_hot=False) width = 28 height = 28 train_images = mnist[0].images.reshape(-1, height, width, 1) train_labels = mnist[0].labels validation_images = mnist[1].images.reshape(-1, height, width, 1) validation_labels = mnist[1].labels data_set = DataSet(train_images, train_labels, validation_images, validation_labels) training_cnf = { 'classification': True, 'validation_scores': [('validation accuracy', util.accuracy_wrapper), ('validation kappa', util.kappa_wrapper)], 'num_epochs': 50, 'lr_policy': StepDecayPolicy( schedule={ 0: 0.01, 30: 0.001, } ) } util.init_logging('train.log', file_log_level=logging.INFO, console_log_level=logging.INFO) trainer = SupervisedTrainer(model, training_cnf, classification=training_cnf[ 'classification'], is_summary=True) trainer.fit(data_set, weights_from=None, start_epoch=1, verbose=1, summary_every=10)
def main(model, training_cnf, data_dir, start_epoch, resume_lr, weights_from, clean): util.check_required_program_args([model, training_cnf, data_dir]) model_def = util.load_module(model) model = model_def.model cnf = util.load_module(training_cnf).cnf util.init_logging('train.log', file_log_level=logging.INFO, console_log_level=logging.INFO, clean=clean) if weights_from: weights_from = str(weights_from) data_set = DataSet(data_dir, model_def.image_size[0]) training_iter = BatchIterator(cnf['batch_size_train'], True) validation_iter = BatchIterator(cnf['batch_size_test'], True) trainer = SupervisedTrainer(model, cnf, training_iter, validation_iter, classification=cnf['classification']) trainer.fit(data_set, weights_from, start_epoch, resume_lr, verbose=1, summary_every=cnf.get('summary_every', 10), clean=clean)
def main(model, training_cnf, data_dir, start_epoch, resume_lr, weights_from, weights_exclude_scopes, trainable_scopes, clean, visuals): util.check_required_program_args([model, training_cnf, data_dir]) sys.path.insert(0, '.') model_def = util.load_module(model) model = model_def.model cnf = util.load_module(training_cnf).cnf util.init_logging('train.log', file_log_level=logging.INFO, console_log_level=logging.INFO, clean=clean) if weights_from: weights_from = str(weights_from) data_set = DataSet(data_dir, model_def.image_size[0]) training_iter, validation_iter = create_training_iters( cnf, data_set, model_def.crop_size, start_epoch, cnf.get('iterator_type', 'parallel') == 'parallel') try: input_shape = (-1, model_def.crop_size[1], model_def.crop_size[0], model_def.num_channels) except AttributeError: input_shape = (-1, model_def.crop_size[1], model_def.crop_size[0], 3) trainer = SupervisedTrainerQ(model, cnf, input_shape, trainable_scopes, training_iter, validation_iter, classification=cnf['classification']) trainer.fit(data_set, weights_from, weights_exclude_scopes, start_epoch, resume_lr, verbose=1, summary_every=cnf.get('summary_every', 10), clean=clean, visuals=visuals)
return end_points(is_training) training_cnf = { 'classification': True, 'validation_scores': [('validation accuracy', util.accuracy_wrapper), ('validation kappa', util.kappa_wrapper)], 'num_epochs': 30, 'lr_policy': StepDecayPolicy(schedule={ 0: 0.001, 15: 0.0001, }), 'optimizer': tf.train.AdamOptimizer() } util.init_logging('train.log', file_log_level=logging.INFO, console_log_level=logging.INFO) trainer = SupervisedTrainer(model, training_cnf, classification=training_cnf['classification']) trainer.fit(data_set, weights_from=None, start_epoch=1, verbose=1, summary_every=10)