(name, nout, activation=relu, batch_norm=False, bias=None)
return layers
Layer configuration for MLP generator/discriminator

    Arguments:
        name (string): Layer name
        nout (int): Number of output feature maps
        activation (object): Activation function (defaults to ReLu)
        batch_norm(bool): Enable batch normalization (defaults to False)
Layer configuration for MLP generator/discriminator
Layer configuration for MLP generator / discriminator
def mlp_layer(name, nout, activation=relu, batch_norm=False, bias=None):
    """
    Layer configuration for MLP generator/discriminator

    Arguments:
        name (string): Layer name
        nout (int): Number of output feature maps
        activation (object): Activation function (defaults to ReLu)
        batch_norm(bool): Enable batch normalization (defaults to False)
    """
    layers = []
    layers.append(Linear(nout=nout, init=init_w, bsum=batch_norm, name=name))
    if batch_norm:
        layers.append(BatchNorm(name=name + '_bnorm', **bn_prm))
    if bias is not None:
        layers.append(Bias(init=None, name=name + '_bias'))
    layers.append(Activation(transform=activation, name=name + '_rectlin'))
    return layers
def mlp_layer ( name , nout , activation = relu , batch_norm = False , bias = None ) : layers = [ ] layers . append ( Linear ( nout = nout , init = init_w , bsum = batch_norm , name = name ) ) if batch_norm : layers . append ( BatchNorm ( name = name + '_bnorm' , * * bn_prm ) ) if bias is not None : layers . append ( Bias ( init = None , name = name + '_bias' ) ) layers . append ( Activation ( transform = activation , name = name + '_rectlin' ) ) return layers