Functional interface for the instance normalization layer.
tf.contrib.layers.instance_norm(
inputs, center=True, scale=True, epsilon=1e-06, activation_fn=None,
param_initializers=None, reuse=None, variables_collections=None,
outputs_collections=None, trainable=True, data_format=DATA_FORMAT_NHWC,
scope=None
)
Reference: https://arxiv.org/abs/1607.08022
"Instance Normalization: The Missing Ingredient for Fast Stylization"
Dmitry Ulyanov, Andrea Vedaldi, Victor Lempitsky
Args |
inputs
|
A tensor with 2 or more dimensions, where the first dimension has
batch_size . The normalization is over all but the last dimension if
data_format is NHWC and the second dimension if data_format is
NCHW .
|
center
|
If True, add offset of beta to normalized tensor. If False, beta
is ignored.
|
scale
|
If True, multiply by gamma . If False, gamma is
not used. When the next layer is linear (also e.g. nn.relu ), this can be
disabled since the scaling can be done by the next layer.
|
epsilon
|
Small float added to variance to avoid dividing by zero.
|
activation_fn
|
Activation function, default set to None to skip it and
maintain a linear activation.
|
param_initializers
|
Optional initializers for beta, gamma, moving mean and
moving variance.
|
reuse
|
Whether or not the layer and its variables should be reused. To be
able to reuse the layer scope must be given.
|
variables_collections
|
Optional collections for the variables.
|
outputs_collections
|
Collections to add the outputs.
|
trainable
|
If True also add variables to the graph collection
GraphKeys.TRAINABLE_VARIABLES (see tf.Variable ).
|
data_format
|
A string. NHWC (default) and NCHW are supported.
|
scope
|
Optional scope for variable_scope .
|
Returns |
A Tensor representing the output of the operation.
|
Raises |
ValueError
|
If data_format is neither NHWC nor NCHW .
|
ValueError
|
If the rank of inputs is undefined.
|
ValueError
|
If rank or channels dimension of inputs is undefined.
|