def setUp(self):
try:
import pydybm.arraymath.dycupy as dycupy
amath.setup(dycupy)
print('\ncupy test')
setup = getattr(super(CupyTestMixin, self), 'setUp', None)
if setup is not None:
setup()
except ImportError:
print('cupy test skipped')
self.skipTest(
'cupy is not installed and tests with cupy are passed')
def setUp(self):
amath.setup(dynumpy)
print('\nnumpy test')
setup = getattr(super(NumpyTestMixin, self), 'setUp', None)
if setup is not None:
setup()
delta = SGD.get_delta()
SGD.update_with_L1_regularization(variables, delta, L1)
t += 1
softerrors = dict()
prederrors = dict()
softerrors["lay1"] = softerr1 / (tm1)
softerrors["lay2"] = softerr2 / (tm1)
softerrors["laym"] = softerrm / (tm1)
# prederrors["lay1"] = err1 * 100.0 / (tm1)
# prederrors["lay2"] = err2 * 100.0 / (tm1)
prederrors["laym"] = errm * 100.0 / (tm1)
return prederrors, softerrors, variables
amath.setup(dycupy)
chunkfile = '/home/user01/dev/language-model/chunks256.p'
train1280 = '/home/user01/dev/language-model/train1280.p'
test128 = '/home/user01/dev/language-model/test128.p'
chunklist = pickle.load(open(chunkfile, "rb"))
layerscales = dict()
variables = dict()
inweights = dict()
L2 = dict()
L1 = dict()
steps = dict()
trainchunks = []
testchunks = []
cp.random.seed(481639)