tf.contrib.layers.bias_add

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.