tf.nn.l2_normalize( x, axis=None, epsilon=1e-12, name=None, dim=None )
Defined in tensorflow/python/ops/nn_impl.py
.
See the guide: Neural Network > Normalization
Normalizes along dimension axis
using an L2 norm. (deprecated arguments)
SOME ARGUMENTS ARE DEPRECATED. They will be removed in a future version. Instructions for updating: dim is deprecated, use axis instead
For a 1-D tensor with axis = 0
, computes
output = x / sqrt(max(sum(x**2), epsilon))
For x
with more dimensions, independently normalizes each 1-D slice along dimension axis
.
x
: A Tensor
.axis
: Dimension along which to normalize. A scalar or a vector of integers.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).dim
: Deprecated alias for axis.A Tensor
with the same shape as x
.
© 2018 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/nn/l2_normalize