tf.contrib.layers.bias_add(
inputs,
activation_fn=None,
initializer=tf.zeros_initializer(),
regularizer=None,
reuse=None,
variables_collections=None,
outputs_collections=None,
trainable=True,
data_format=DATA_FORMAT_NHWC,
scope=None
)
Defined in tensorflow/contrib/layers/python/layers/layers.py
.
Adds a bias to the inputs.
Can be used as a normalizer function for conv2d and fully_connected.
Args:
inputs
: A tensor of with at least rank 2 and value for the last dimension, e.g.[batch_size, depth]
,[None, None, None, depth]
.activation_fn
: Activation function, default set to None to skip it and maintain a linear activation.initializer
: An initializer for the bias, defaults to 0.regularizer
: A regularizer like the result ofl1_regularizer
orl2_regularizer
.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
: IfTrue
also add variables to the graph collectionGraphKeys.TRAINABLE_VARIABLES
(see tf.Variable).data_format
: A string. 'NHWC' and 'NCHW' are supported.scope
: Optional scope for variable_scope.
Returns:
A tensor representing the result of adding biases to the inputs.
Raises:
ValueError
: Ifdata_format
is neitherNHWC
norNCHW
.ValueError
: Ifdata_format
isNCHW
and rank ofinputs
is not 4.ValueError
: If the rank ofinputs
is undefined.ValueError
: If rank orC
dimension ofinputs
is undefined.