tf.math.lbeta

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Computes \(ln(|Beta(x)|)\), reducing along the last dimension.

Aliases:

  • tf.compat.v1.lbeta
  • tf.compat.v1.math.lbeta
  • tf.compat.v2.math.lbeta
tf.math.lbeta(
    x,
    name=None
)

Given one-dimensional z = [z_0,...,z_{K-1}], we define

免费成长做爱直播有哪些$$Beta(z) = \prod_j Gamma(z_j) / Gamma(\sum_j z_j)$$

And for n + 1 dimensional x with shape [N1, ..., Nn, K], we define

免费成长做爱直播有哪些$$lbeta(x)[i1, ..., in] = Log(|Beta(x[i1, ..., in, :])|)$$.

In other words, the last dimension is treated as the z vector.

Note that if z = [u, v]免费成长做爱直播有哪些, then \(Beta(z) = int_0^1 t^{u-1} (1 - t)^{v-1} dt\), which defines the traditional bivariate beta function.

免费成长做爱直播有哪些If the last dimension is empty, we follow the convention that the sum over the empty set is zero, and the product is one.

Args:

  • x: A rank n + 1 Tensor, n >= 0 with type float, or double.
  • name: A name for the operation (optional).

Returns:

免费成长做爱直播有哪些The logarithm of \(|Beta(x)|\) reducing along the last dimension.

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