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]
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()
