diff options
Diffstat (limited to 'tensorflow/contrib/layers/python/layers/layers.py')
| -rw-r--r-- | tensorflow/contrib/layers/python/layers/layers.py | 12 |
1 files changed, 6 insertions, 6 deletions
diff --git a/tensorflow/contrib/layers/python/layers/layers.py b/tensorflow/contrib/layers/python/layers/layers.py index 32ca0c38d9..7a429f75bb 100644 --- a/tensorflow/contrib/layers/python/layers/layers.py +++ b/tensorflow/contrib/layers/python/layers/layers.py @@ -278,7 +278,7 @@ def _fused_batch_norm( trainable=trainable_gamma) # Create moving_mean and moving_variance variables and add them to the - # appropiate collections. + # appropriate collections. moving_mean_collections = utils.get_variable_collections( variables_collections, 'moving_mean') moving_mean_initializer = param_initializers.get( @@ -632,7 +632,7 @@ def batch_norm(inputs, trainable=trainable) # Create moving_mean and moving_variance variables and add them to the - # appropiate collections. We disable variable partitioning while creating + # appropriate collections. We disable variable partitioning while creating # them, because assign_moving_average is not yet supported for partitioned # variables. partitioner = variable_scope.get_variable_scope().partitioner @@ -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: |