class CustomLSTMWeights(NdarrayInitialization):
# Identity in the diagonal and IsotropicGaussian everywhere else
def __init__(self, std=1, mean=0):
self.gaussian_init = IsotropicGaussian(std = std, mean = mean)
self.identity = Identity()
def generate(self, rng, shape):
if len(shape) != 2:
raise ValueError
assert shape[0] == shape[1]
size = shape/4
assert size*4 == shape[0]
result = numpy.array([])
for i in range(4):
row = numpy.array([])
for j in range(4):
if i == j:
square = self.gaussian_init.generate(rng, (size,size))
else:
square = self.identity.generate(rng, (size,size))
row = numpy.hstack(row,square)
result.vstack(row)
return result.astype(theano.config.floatX)
class CustomLSTMWeights(NdarrayInitialization):
# Identity in the diagonal and IsotropicGaussian everywhere else
def __init__(self, std=1, mean=0):
self.gaussian_init = IsotropicGaussian(std=std, mean=mean)
self.identity = Identity()
def generate(self, rng, shape):
if len(shape) != 2:
raise ValueError
assert shape[0] == shape[1]
size = shape / 4
assert size * 4 == shape[0]
result = numpy.array([])
for i in range(4):
row = numpy.array([])
for j in range(4):
if i == j:
square = self.gaussian_init.generate(rng, (size, size))
else:
square = self.identity.generate(rng, (size, size))
row = numpy.hstack(row, square)
result.vstack(row)
return result.astype(theano.config.floatX)
