Ejemplo n.º 1
0
if args.resume:
    model.optimizer = opt
model.optimizer.optimize(model.layers_to_optimize, epoch=model.epoch_index)

# run fprop again as a measure of the model state
out_fprop = model.fprop(im)
out_fprop_save2 = [x.get() for x in out_fprop]

if not args.resume:
    save_obj([out_fprop_save, out_fprop_save2], 'serial_test_out1.pkl')
else:
    # load up the saved file and compare
    run1 = load_obj('serial_test_out1.pkl')

    # compare the initial fprops
    for x, y in zip(run1[0], out_fprop_save):
        assert np.max(np.abs(
            x - y)) == 0.0, 'Deserialized model not matching serialized model'

    # and post extra training fprops
    for x, y in zip(run1[1], out_fprop_save2):
        if np.max(np.abs(x - y)) != 0.0:
            neon_logger.error('Max Diff: {}'.format(np.max(np.abs(x - y))))
            raise ValueError(
                'Deserialized training not matching serialized training')

    # see if the single epoch of optimization had any real effect
    for x, y in zip(out_fprop_save, out_fprop_save2):
        assert np.max(np.abs(x - y)) > 0.0, 'Training had no effect on model'
    neon_logger.display('passed')
Ejemplo n.º 2
0
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ----------------------------------------------------------------------------

import sys
from neon import logger as neon_logger
from neon.data.dataloaderadapter import DataLoaderAdapter


try:
    from aeon import DataLoader as AeonLoader
except ImportError:
    neon_logger.error('Unable to load Aeon data loading module.')
    neon_logger.error('Please follow installation instructions at:')
    neon_logger.error('https://github.com/NervanaSystems/aeon')
    sys.exit(1)


def AeonDataLoader(config, adapter=True):
    if adapter:
        return DataLoaderAdapter(AeonLoader(config))
    else:
        return AeonLoader(config)
Ejemplo n.º 3
0
    """
    Helper function to compare two serialized model files

    This is only comparing the model weights and states and layer
    config parameters

    Returns:
        bool: True if the two file match
    """
    models = []
    for fn in [file1, file2]:
        assert os.path.exists(fn), 'Could not find file %s' % fn

        with open(fn, 'r') as fid:
            models.append(ModelDescription(pickle.load(fid)))

    return models[0] == models[1]


if __name__ == '__main__':
    parser = argparse.ArgumentParser(description='Compare two serialized model files.')
    parser.add_argument('file1')
    parser.add_argument('file2')
    args = parser.parse_args()

    if not compare_files(args.file1, args.file2):
        neon_logger.error('Models do not match!')
        sys.exit(1)
    else:
        neon_logger.display('Models match')
Ejemplo n.º 4
0
delta = model.cost.get_errors(im, l)
model.bprop(delta)
if args.resume:
    model.optimizer = opt
model.optimizer.optimize(model.layers_to_optimize, epoch=model.epoch_index)

# run fprop again as a measure of the model state
out_fprop = model.fprop(im)
out_fprop_save2 = [x.get() for x in out_fprop]

if not args.resume:
    save_obj([out_fprop_save, out_fprop_save2], 'serial_test_out1.pkl')
else:
    # load up the saved file and compare
    run1 = load_obj('serial_test_out1.pkl')

    # compare the initial fprops
    for x, y in zip(run1[0], out_fprop_save):
        assert np.max(np.abs(x - y)) == 0.0, 'Deserialized model not matching serialized model'

    # and post extra training fprops
    for x, y in zip(run1[1], out_fprop_save2):
        if np.max(np.abs(x - y)) != 0.0:
            neon_logger.error('Max Diff: {}'.format(np.max(np.abs(x - y))))
            raise ValueError('Deserialized training not matching serialized training')

    # see if the single epoch of optimization had any real effect
    for x, y in zip(out_fprop_save, out_fprop_save2):
        assert np.max(np.abs(x - y)) > 0.0, 'Training had no effect on model'
    neon_logger.display('passed')
Ejemplo n.º 5
0
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ----------------------------------------------------------------------------

import sys
from neon import logger as neon_logger
from neon.data.dataloaderadapter import DataLoaderAdapter

try:
    from aeon import DataLoader as AeonLoader
except ImportError:
    neon_logger.error('Unable to load Aeon data loading module.')
    neon_logger.error('Please follow installation instructions at:')
    neon_logger.error('https://github.com/NervanaSystems/aeon')
    sys.exit(1)


def AeonDataLoader(config, adapter=True):
    if adapter:
        return DataLoaderAdapter(AeonLoader(config))
    else:
        return AeonLoader(config)
Ejemplo n.º 6
0
 def __init__(self, *args, **kwargs):
     logger.error('DataIterator class has been deprecated and renamed'
                  '"ArrayIterator" please use that name.')
     super(DataIterator, self).__init__(*args, **kwargs)
Ejemplo n.º 7
0
 def __init__(self, *args, **kwargs):
     logger.error('DataIterator class has been deprecated and renamed'
                  '"ArrayIterator" please use that name.')
     super(DataIterator, self).__init__(*args, **kwargs)