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)