(name, n_feature, ker_size=4, strides=2, padding=1,
                 activation=lrelu, batch_norm=True, bias=None)
return layers
Layer configuration for deep-convolutional (DC) discriminator

    Arguments:
        name (string): Layer name'
        n_feature (int): Number of output feature maps
        ker_size (int): Size of convolutional kernel (defaults to 4)
        strides (int): Stride of convolution (defaults to 2)
        padding (int): Padding of convolution (defaults to 1)
        activation (object): Activation function (defaults to leaky ReLu)
        batch_norm(bool): Enable batch normalization (defaults to True)
Layer configuration for deep-convolutional (DC) discriminator
Layer configuration for deep - convolutional ( DC ) discriminator
def deconv_layer(name, n_feature, ker_size=4, strides=2, padding=1,
                 activation=lrelu, batch_norm=True, bias=None):
    """
    Layer configuration for deep-convolutional (DC) discriminator

    Arguments:
        name (string): Layer name'
        n_feature (int): Number of output feature maps
        ker_size (int): Size of convolutional kernel (defaults to 4)
        strides (int): Stride of convolution (defaults to 2)
        padding (int): Padding of convolution (defaults to 1)
        activation (object): Activation function (defaults to leaky ReLu)
        batch_norm(bool): Enable batch normalization (defaults to True)
    """
    layers = []
    layers.append(Deconvolution(fshape=(ker_size, ker_size, n_feature), strides=strides,
                                padding=padding, dilation={}, 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 deconv_layer ( name , n_feature , ker_size = 4 , strides = 2 , padding = 1 , activation = lrelu , batch_norm = True , bias = None ) : layers = [ ] layers . append ( Deconvolution ( fshape = ( ker_size , ker_size , n_feature ) , strides = strides , padding = padding , dilation = { } , 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