def __call__(self, shape, name=None):
flat_shape = (shape[0], np.prod(shape[1:]))
a = self.rng.normal(0.0, 1.0, flat_shape)
u, _, v = np.linalg.svd(a, full_matrices=False)
# pick the one with the correct shape
q = u if u.shape == flat_shape else v
q = q.reshape(shape)
return sharedX(self.scale * q[:shape[0], :shape[1]], name=name)
def __call__(self, shape, name=None):
return sharedX(self.rng.randn(*shape) * self.scale, name=name)
def __call__(self, shape, name=None):
return sharedX(self.rng.uniform(low=-self.scale, high=self.scale, size=shape), name=name)
def __call__(self, shape, name=None):
if len(shape) != 2 or shape[0] != shape[1]:
raise Exception("Identity matrix initialization can only be used for 2D square matrices")
else:
return sharedX(self.scale * np.identity(shape[0]), name=name)
def __call__(self, shape, name=None):
return sharedX(self.scale * np.ones(shape), dtype=theano.config.floatX, name=name)
