cifar10_dcgan.py 文件源码

def generate(epoch):
    latent = np.random.uniform(-1, 1, (batch_size, 100))
    generated = (get_image(latent) + 1) / 2
    manifold = np.zeros((32*8, 32*8, 3), dtype=theano.config.floatX)
    for indx in range(8):
        for indy in range(8):
            current_img = np.swapaxes(generated[indx * 8 + indy], 0, 2)
            current_img = np.swapaxes(current_img, 0, 1)
            manifold[indx * 32:(indx+1) * 32, indy * 32:(indy+1) * 32, :] = current_img
    manifold = np.asarray(manifold * 255, dtype='int32')
    manifold = scipy.misc.toimage(manifold, cmin=0, cmax=255)
    scipy.misc.imsave(result_folder + str(epoch) + '.png', manifold)


#================Train================#

# pretrain()