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Including batch_norm as the normalizer function by default, as mentioned in function description (#9652)

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* fixed separable_conv2d description error and included batch_norm by default as mentioned in description

* Update layers.py

* Update layers.py
This commit is contained in:
Kwotsin 2017-05-07 01:50:35 +08:00 committed by drpngx
parent 50d816cbc5
commit 91c9366589
1 changed files with 4 additions and 4 deletions
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8
tensorflow/contrib/layers/python/layers/layers.py
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@ -1087,7 +1087,7 @@ def convolution2d_transpose(
"""Adds a convolution2d_transpose with an optional batch normalization layer.
The function creates a variable called `weights`, representing the
kernel, that is convolved with the input. If `batch_norm_params` is `None`, a
kernel, that is convolved with the input. If `normalizer_fn` is `None`, a
second variable called 'biases' is added to the result of the operation.
Args:
@ -1847,9 +1847,9 @@ def separable_convolution2d(
This op first performs a depthwise convolution that acts separately on
channels, creating a variable called `depthwise_weights`. If `num_outputs`
is not None, it adds a pointwise convolution that mixes channels, creating a
variable called `pointwise_weights`. Then, if `batch_norm_params` is None,
it adds bias to the result, creating a variable called 'biases', otherwise
it adds a batch normalization layer. It finally applies an activation function
variable called `pointwise_weights`. Then, if `normalizer_fn` is None,
it adds bias to the result, creating a variable called 'biases', otherwise,
the `normalizer_fn` is applied. It finally applies an activation function
to produce the end result.
Args: