def __init__(
        self,
        size,
        weights=None,
        bias=True,
        l2_regularization=0.0,
        l1_regularization=0.0,
        trainable=True,
        named_tensors=None,
        scope='linear',
        summary_labels=()
    ):
        """
        Linear layer.

        Args:
            size: Layer size.
            weights: Weight initialization, random if None.
            bias: Bias initialization, random if True, no bias added if False.
            l2_regularization: L2 regularization weight.
            l1_regularization: L1 regularization weight.
        """
        self.size = size
        self.weights_init = weights
        self.bias_init = bias
        self.l2_regularization = l2_regularization
        self.l1_regularization = l1_regularization
        self.trainable = trainable
        super(Linear, self).__init__(named_tensors=named_tensors, scope=scope, summary_labels=summary_labels)
def __init__ ( self , size , weights = None , bias = True , l2_regularization = 0.0 , l1_regularization = 0.0 , trainable = True , named_tensors = None , scope = 'linear' , summary_labels = ( ) ) : self . size = size self . weights_init = weights self . bias_init = bias self . l2_regularization = l2_regularization self . l1_regularization = l1_regularization self . trainable = trainable super ( Linear , self ) . __init__ ( named_tensors = named_tensors , scope = scope , summary_labels = summary_labels )
Linear layer.

        Args:
            size: Layer size.
            weights: Weight initialization, random if None.
            bias: Bias initialization, random if True, no bias added if False.
            l2_regularization: L2 regularization weight.
            l1_regularization: L1 regularization weight.