tf.linspace(start, stop, num, name=None) Generates values in an interval. A sequence of num evenly-spaced values are generated beginning at start. If num > 1, the values in the sequence increase by stop - start / num - 1, so that the last one is exactly stop. For example: tf.linspace(10.0, 12.0, 3, name="linspace") => [ 10.0 11.0 12.0] Args: start: A Tensor. Must be one of the following types: float32, float64. First entry in the range. stop: A Tensor. Must have the same type as sta

tf.log(x, name=None) Computes natural logarithm of x element-wise. I.e., \(y = \log_e x\). Args: x: A Tensor. Must be one of the following types: half, float32, float64, complex64, complex128. name: A name for the operation (optional). Returns: A Tensor. Has the same type as x.

tf.listdiff(x, y, out_idx=None, name=None) Computes the difference between two lists of numbers or strings. Given a list x and a list y, this operation returns a list out that represents all values that are in x but not in y. The returned list out is sorted in the same order that the numbers appear in x (duplicates are preserved). This operation also returns a list idx that represents the position of each out element in x. In other words: out[i] = x[idx[i]] for i in [0, 1, ..., len(out) - 1] F

tf.lbeta(x, name='lbeta') Computes ln(|Beta(x)|), reducing along the last dimension. Given one-dimensional z = [z_0,...,z_{K-1}], we define 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 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], then Beta(z) = int_0^1 t^{u-1} (1 - t)^{v-1} dt, which defines the traditional bivariate beta func

tf.lgamma(x, name=None) Computes the log of the absolute value of Gamma(x) element-wise. Args: x: A Tensor. Must be one of the following types: half, float32, float64. name: A name for the operation (optional). Returns: A Tensor. Has the same type as x.

tf.is_strictly_increasing(x, name=None) Returns True if x is strictly increasing. Elements of x are compared in row-major order. The tensor [x[0],...] is strictly increasing if for every adjacent pair we have x[i] < x[i+1]. If x has less than two elements, it is trivially strictly increasing. See also: is_non_decreasing Args: x: Numeric Tensor. name: A name for this operation (optional). Defaults to "is_strictly_increasing" Returns: Boolean Tensor, equal to True iff x is strictly increa

tf.is_numeric_tensor(tensor)

tf.is_numeric_tensor(tensor)

tf.is_non_decreasing(x, name=None) Returns True if x is non-decreasing. Elements of x are compared in row-major order. The tensor [x[0],...] is non-decreasing if for every adjacent pair we have x[i] <= x[i+1]. If x has less than two elements, it is trivially non-decreasing. See also: is_strictly_increasing Args: x: Numeric Tensor. name: A name for this operation (optional). Defaults to "is_non_decreasing" Returns: Boolean Tensor, equal to True iff x is non-decreasing. Raises: TypeError

tf.is_strictly_increasing(x, name=None) Returns True if x is strictly increasing. Elements of x are compared in row-major order. The tensor [x[0],...] is strictly increasing if for every adjacent pair we have x[i] < x[i+1]. If x has less than two elements, it is trivially strictly increasing. See also: is_non_decreasing Args: x: Numeric Tensor. name: A name for this operation (optional). Defaults to "is_strictly_increasing" Returns: Boolean Tensor, equal to True iff x is strictly increa