layers.py 文件源码

def unit_norm(inputs, dim, epsilon=1e-7, scope=None):
    """Normalizes the given input across the specified dimension to unit length.
    Note that the rank of `input` must be known.

    Args:
        inputs: A `Tensor` of arbitrary size.
        dim: The dimension along which the input is normalized.
        epsilon: A small value to add to the inputs to avoid dividing by zero.
        scope: Optional scope for variable_scope.

    Returns:
        The normalized `Tensor`.

    Raises:
        ValueError: If dim is larger than the number of dimensions in 'inputs'.
    """
    with tf.variable_scope(scope, 'UnitNorm', [inputs]):
        if not inputs.get_shape():
            raise ValueError('The input rank must be known.')
        input_rank = len(inputs.get_shape().as_list())
        if dim < 0 or dim >= input_rank:
            raise ValueError(
                'dim must be positive but smaller than the input rank.')

        lengths = tf.sqrt(
            epsilon + tf.reduce_sum(tf.square(inputs), dim, True))
        multiples = []
        if dim > 0:
            multiples.append(tf.ones([dim], tf.int32))
        multiples.append(tf.strided_slice(
            tf.shape(inputs), [dim], [dim + 1], [1]))
        if dim < (input_rank - 1):
            multiples.append(tf.ones([input_rank - 1 - dim], tf.int32))
        multiples = tf.concat(multiples, 0)
        return tf.div(inputs, tf.tile(lengths, multiples))