Function fused_l2_normalize

tensorflow/python/ops/nn_impl.py:655–677

Normalizes along last dimension using an L2 norm. For a 1-D tensor, computes output = x / sqrt(max(sum(x**2), epsilon)) For `x` with more dimensions, independently normalizes each 1-D slice along lastdimension. Args: x: A `Tensor`. epsilon: A lower bound value for the norm.

(x, epsilon=1e-12, name=None)

      return math_ops.multiply(x, x_inv_norm, name=name)

def fused_l2_normalize(x, epsilon=1e-12, name=None):
  """Normalizes along last dimension using an L2 norm.

  For a 1-D tensor, computes

      output = x / sqrt(max(sum(x**2), epsilon))

  For `x` with more dimensions, independently normalizes each 1-D slice along
  lastdimension.

  Args:
    x: A `Tensor`.
    epsilon: A lower bound value for the norm. Will use `sqrt(epsilon)` as the
      divisor if `norm < sqrt(epsilon)`.
    name: A name for this operation (optional).

  Returns:
    A `Tensor` with the same shape as `x`.
  """
  with ops.name_scope(name, "fused_l2_normalize", [x]) as name:
    x = ops.convert_to_tensor(x, name="x")
    return gen_fused_l2_normalize_ops.fused_l2_normalize(x, 
              epsilon=epsilon, name=name)

@tf_export("nn.fused_layer_normalize")
def fused_layer_normalize(