CGAN convergence time

[oliverzhang42]

I've noticed an inefficiency in the CGAN code. When we append the one-hot encoded labels to the image, they influence the training gradients a lot. Instead, I've noticed that scaling the one-hot encoded labels down by a factor of 0.01 or even 0.001 helps the CGAN converge around twice as fast.

That would mean changing opts.py's conv_cond_concat function. My hack was to change return concat([x, y*tf.ones([x_shapes[0], x_shapes[1], x_shapes[2], y_shapes[3]])], 3) to return concat([x, 0.001*y*tf.ones([x_shapes[0], x_shapes[1], x_shapes[2], y_shapes[3]])], 3) and that worked well for me. I'm not too sure about in general though, perhaps try adding batch norm?