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)
