ValueError: Dimensions must be equal, but are 8 and 128 for 'Generator.1.Shortcut/Conv2D' (op: 'Conv2D') with input shapes: [?,128,8,8], [1,1,128,128].

[rookiexiao123]

hi, I was using WGAN to run your code with the learned model.but did't success.the error is the title.

`def Generator_Resnet_32(cfg, n_samples, labels, noise=None, is_training=True):

if noise is None: noise = tf.random_normal([n_samples, 128]) add_dim = 0 if cfg.LAYER_COND: y = labels noise = tflib.ops.concat.concat([noise, y], 1) add_dim = cfg.N_LABELS output = lib.ops.linear.Linear('Generator.Input', 128 + add_dim, 4 4 cfg.DIM_G, noise) output = tf.reshape(output, [-1, cfg.DIM_G, 4, 4])`

in conv2d.py, the filters and the inputs had the problem.

def Generator_Resnet_32(cfg, n_samples, labels, noise=None, is_training=True): if noise is None: noise = tf.random_normal([n_samples, 128]) add_dim = 0 if cfg.LAYER_COND: y = labels noise = tflib.ops.concat.concat([noise, y], 1) add_dim = cfg.N_LABELS output = lib.ops.linear.Linear('Generator.Input', 128 + add_dim, 4 4 cfg.DIM_G, noise) output = tf.reshape(output, [-1, cfg.DIM_G, 4, 4]) print(output) output = ResidualBlock(cfg, 'Generator.1', cfg.DIM_G, cfg.DIM_G, 3, output, resample='up', labels=labels, is_training=is_training) print('Generator_Resnet_32 !!!')

the ResidualBlock can't go on.what should I do something? Can you give me some advice?

[rookiexiao123]

Hi, your gpu is 16g?