d0334962f6602fd835b0ed7219a972ebc36bb0fd,hypergan/discriminators/pyramid_discriminator.py,,discriminator,#Any#Any#Any#Any#Any#Any#Any#,47

Before Change

        net += tf.random_normal(net.get_shape(), mean=0, stddev=config["noise"], dtype=gan.config.dtype)
        

    if strided:
        net = conv2d(net, config.first_strided_conv_size, name=prefix+"_expand", k_w=3, k_h=3, d_h=2, d_w=2,regularizer=None)
    else:
        net = conv2d(net, config.first_conv_size, name=prefix+"_expand", k_w=3, k_h=3, d_h=1, d_w=1,regularizer=None)

    for i in range(depth):
      //TODO better name for `batch_norm`?
      if batch_norm is not None:
          net = batch_norm(batch_size*2, name=prefix+"_expand_bn_"+str(i))(net)

After Change

    for i in range(config.extra_layers):
        output_features = int(int(net.get_shape()[3]))
        net = activation(net)
        net = conv2d(net, output_features//4, name=prefix+"_extra_layer"+str(i), k_w=3, k_h=3, d_h=1, d_w=1, regularizer=None,gain=config.orthogonal_initializer_gain)
        print("[extra discriminator] layer", net)
    k=-1

    net = tf.reshape(net, [batch_size*2, -1])

Instances