def MpcRandom_gamma(shape, alpha, beta=None, dtype=dtypes.float32, seed=None, name=None): dtype = dtype_check_and_set(dtype) return random_ops.random_gamma(shape, alpha, beta, dtype, seed, name)
def MpcTruncated_normal(shape, mean=0.0, stddev=1.0, dtype=dtypes.float32, seed=None, name=None): dtype = dtype_check_and_set(dtype) return random_ops.truncated_normal(shape, mean, stddev, dtype, seed, name)
def MpcRandom_uniform(shape, minval=0, maxval=None, dtype=dtypes.float32, seed=None, name=None): dtype = dtype_check_and_set(dtype) return random_ops.random_uniform(shape, minval, maxval, dtype, seed, name)
def MpcRandom_normal(shape, mean=0.0, stddev=1.0, dtype=dtypes.float64, seed=None, name=None): dtype = dtype_check_and_set(dtype) return random_ops.random_normal(shape, mean, stddev, dtype, seed, name)
def MpcOnes_like(tensor, dtype=None, name=None, optimize=True): dtype = dtype_check_and_set(dtype) return array_ops.ones_like(tensor, dtype, name)
def MpcOnes(shape, dtype=dtypes.float64, name=None): dtype = dtype_check_and_set(dtype) return array_ops.ones(shape, dtype, name)
def MpcPlaceholder(dtype, shape=None, name=None): dtype = dtype_check_and_set(dtype) return array_ops.placeholder(dtype, shape, name)
def MpcConstant(value, dtype=None, shape=None, name="Const", verify_shape=False): dtype = dtype_check_and_set(dtype) return constant_op.constant_v1(value, dtype, shape, name, verify_shape)
def MpcRandom_poisson(lam, shape, dtype=dtypes.float32, seed=None, name=None): dtype = dtype_check_and_set(dtype) #return random_ops.random_poisson(shape, mean, stddev, dtype, seed, name) return random_ops.random_poisson(lam, shape, dtype, seed, name)
def __init__(self, *args, **kwargs): kwargs['dtype'] = dtype_check_and_set(kwargs.get('dtype', None)) super(MPCRefVariable, self).__init__(*args, **kwargs)