def __init__(self): self.net = pf.Sequential([ pf.Dense(5, 128), tf.nn.relu, pf.Dense(128, 64), tf.nn.relu, pf.Dense(64, 1), ]) self.s = pf.ScaleParameter()
def __init__(self, d, k): self.m = pf.Parameter([d, k]) self.s = pf.ScaleParameter([d, k]) self.w = pf.DirichletParameter(k)
def __init__(self): self.mu = pf.Parameter(name='mu') self.sig = pf.ScaleParameter(name='sig')
def __init__(self, dims): self.net = DenseNetwork(dims) self.s = pf.ScaleParameter([1, 1])
def __init__(self): self.mu = pf.Parameter(name="mu") self.sig = pf.ScaleParameter(name="sig")
def __init__(self): self.weight = pf.Parameter(name="Weight") self.bias = pf.Parameter(name="Bias") self.std = pf.ScaleParameter(name="Noise Std Dev", prior=pf.Gamma(1.0, 1.0))
def __init__(self): self.weight = pf.Parameter(name="Weight") self.bias = pf.Parameter(name="Bias") self.std = pf.ScaleParameter(name="Noise Std Dev")
def __init__(self): self.weight = pf.Parameter(name='Weight') self.bias = pf.Parameter(name='Bias') self.std = pf.ScaleParameter(name='Noise Std Dev')
def __init__(self, d, q): self.W = pf.Parameter(shape=[d, q]) self.sigma = pf.ScaleParameter()
def __init__(self, dims): self.w = pf.Parameter([dims, 1]) self.b = pf.Parameter() self.s = pf.ScaleParameter()
def __init__(self): self.w = pf.Parameter() self.b = pf.Parameter() self.s = pf.ScaleParameter()
def __init__(self): self.weight = pf.Parameter(name='Weight') self.bias = pf.Parameter(name='Bias') self.std = pf.ScaleParameter(name='Noise Std Dev', prior=pf.Gamma(1., 1.))