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, 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 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)
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)