def setUp(self): self.current_dir = os.getcwd() # go into this file's directory abspath = os.path.realpath(__file__) dname = os.path.dirname(abspath) os.chdir(dname) # change sys.stdout to StringIO for the duration of the test to test console output self.saved_stdout = sys.stdout self.out = StringIO() sys.stdout = self.out # configure the root logger config_root_logger() # get a logger for this session self.log = logging.getLogger(__name__) # set the paths for the logs (comes from reading logging_config.json) self.error_path = '../logs/error/errors.log' self.info_path = '../logs/info/info.log' self.paths = [self.error_path, self.info_path]
elif subset is TEST: return self.test_shape else: return None def main(): pass if __name__ == '__main__': # http://www.physionet.org/physiobank/database/apnea-ecg/ # if we want logging config_root_logger() i=0 for f in find_train_files(basedir, label_ext): if i==0: data = numpy.fromfile(f, dtype=numpy.bool) print(data.shape) else: pass i+=1 i=0 for f in find_train_files(basedir, data_ext): if i == 0: data = numpy.fromfile(f, dtype=numpy.float16) print(data.shape)
from __future__ import print_function from opendeep.models.single_layer.basic import SoftmaxLayer # import the dataset and optimizer to use from opendeep.data.standard_datasets.image.mnist import MNIST from opendeep.optimization.adadelta import AdaDelta if __name__ == '__main__': # set up the logging environment to display outputs (optional) # although this is recommended over print statements everywhere import logging from opendeep.log import config_root_logger config_root_logger() log = logging.getLogger(__name__) log.info("Creating softmax!") # grab the MNIST dataset mnist = MNIST() # create the softmax classifier s = SoftmaxLayer(input_size=28 * 28, output_size=10, out_as_probs=False) # make an optimizer to train it (AdaDelta is a good default) optimizer = AdaDelta(model=s, dataset=mnist, epochs=20) # perform training! optimizer.train() # test it on some images! test_data, test_labels = mnist.test_inputs[:25], mnist.test_targets[:25] # use the run function! preds = s.run(test_data) print('-------') print(preds) print(test_labels.astype('int32')) print()