def __init__(self, dtype):
x = theano.scalar.Scalar(dtype=dtype).make_variable()
mean = theano.scalar.Scalar(dtype=dtype).make_variable()
std = theano.scalar.Scalar(dtype=dtype).make_variable()
gamma = theano.scalar.Scalar(dtype=dtype).make_variable()
beta = theano.scalar.Scalar(dtype=dtype).make_variable()
o = add(mul(true_div(sub(x, mean), std), gamma), beta)
inputs = [x, mean, std, gamma, beta]
outputs = [o]
super(BNComposite, self).__init__(inputs, outputs)
def __init__(self, dtype):
self.dtype = dtype
x = theano.scalar.Scalar(dtype=dtype).make_variable()
mean = theano.scalar.Scalar(dtype=dtype).make_variable()
std = theano.scalar.Scalar(dtype=dtype).make_variable()
gamma = theano.scalar.Scalar(dtype=dtype).make_variable()
beta = theano.scalar.Scalar(dtype=dtype).make_variable()
o = add(mul(true_div(sub(x, mean), std), gamma), beta)
inputs = [x, mean, std, gamma, beta]
outputs = [o]
super().__init__(inputs, outputs)
def fn():
x,s = T.scalars('xs')
fn = function([In(x, update=mul(s,s)+x)], x)