Adds a bias to the inputs.
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
)
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 of l1_regularizer or
l2_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
|
If True also add variables to the graph collection
GraphKeys.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
|
If data_format is neither NHWC nor NCHW .
|
ValueError
|
If data_format is NCHW and rank of inputs is not 4.
|
ValueError
|
If the rank of inputs is undefined.
|
ValueError
|
If rank or C dimension of inputs is undefined.
|