univariate.py 文件源码

def __init__(self,
                 logits,
                 n_experiments,
                 dtype=None,
                 group_ndims=0,
                 check_numerics=False,
                 **kwargs):
        self._logits = tf.convert_to_tensor(logits)
        param_dtype = assert_same_float_dtype(
            [(self._logits, 'Binomial.logits')])

        if dtype is None:
            dtype = tf.int32
        assert_same_float_and_int_dtype([], dtype)

        sign_err_msg = "n_experiments must be positive"
        if isinstance(n_experiments, int):
            if n_experiments <= 0:
                raise ValueError(sign_err_msg)
            self._n_experiments = n_experiments
        else:
            try:
                n_experiments = tf.convert_to_tensor(n_experiments, tf.int32)
            except ValueError:
                raise TypeError('n_experiments must be int32')
            _assert_rank_op = tf.assert_rank(
                n_experiments, 0,
                message="n_experiments should be a scalar (0-D Tensor).")
            _assert_positive_op = tf.assert_greater(
                n_experiments, 0, message=sign_err_msg)
            with tf.control_dependencies([_assert_rank_op,
                                          _assert_positive_op]):
                self._n_experiments = tf.identity(n_experiments)

        self._check_numerics = check_numerics
        super(Binomial, self).__init__(
            dtype=dtype,
            param_dtype=param_dtype,
            is_continuous=False,
            is_reparameterized=False,
            group_ndims=group_ndims,
            **kwargs)