def square(a): """Calculate the square of input. Parameters ---------- a : Tensor The input tensor. Returns ------- Tensor The square result. """ return ops.Square(a)
def square(x, name=None): """ Computes square of x element-wise. I.e., \\(y = x * x = x^2\\). Args: x: A `Tensor`. name: A name for the operation (optional). Returns: A `Tensor`. Has the same type as `x`. """ return ops.Square(x, name=name)
def l2_loss(t, name=None): """ Computes half the L2 norm of a tensor without the sqrt: output = sum(t ** 2) / 2 Args: t: A Tensor. Typically 2-D, but may have any dimensions. name: Optional name for the operation. Returns: A Tensor. Has the same type as t. 0-D. """ return (ops.Reduce(ops.Square(t), operation='SUM') * 0.5)
def square(x, name=None): return ops.Square(x, name=name)
def l2_loss(t, name=None): return (ops.Reduce(ops.Square(t), operation='SUM') * 0.5)