def celeb(): X = util.get_celeb() dim = 64 colors = 3 d_sizes = { 'conv_layers': [(64, 5, 2, False), (128, 5, 2, True), (256, 5, 2, True), (512, 5, 2, True)], 'dense_layers': [] } g_sizes = { 'z': 100, 'projection': 512, 'bn_after_project': True, 'conv_layers': [(256, 5, 2, True), (128, 5, 2, True), (64, 5, 2, True), (colors, 5, 2, False)], 'dense_layers': [], 'output_activation': tf.tanh } gan = DCGAN(dim, colors, d_sizes, g_sizes) gan.fit(X)
def celeb(): X = util.get_celeb() # just loads a list of filenames, we will load them in dynamically # because there are many dim = 64 colors = 3 # for celeb d_sizes = { 'conv_layers': [(64, 5, 2, False), (128, 5, 2, True), (256, 5, 2, True), (512, 5, 2, True)], 'dense_layers': [], } g_sizes = { 'z': 100, 'projection': 512, 'bn_after_project': True, 'conv_layers': [(256, 5, 2, True), (128, 5, 2, True), (64, 5, 2, True), (colors, 5, 2, False)], 'dense_layers': [], 'output_activation': tf.tanh, } # setup gan # note: assume square images, so only need 1 dim gan = DCGAN(dim, colors, d_sizes, g_sizes) gan.fit(X)
def celeb(): X = util.get_celeb() # just loads a list of filenames, we will load them in dynamically # because there are many dim = 64 colors = 3 # for celeb d_sizes = { 'conv_layers': [ (64, 5, 2, False), (128, 5, 2, True), (256, 5, 2, True), (512, 5, 2, True) ], 'dense_layers': [], } g_sizes = { 'z': 100, 'projection': 512, 'bn_after_project': True, 'conv_layers': [ (256, 5, 2, True), (128, 5, 2, True), (64, 5, 2, True), (colors, 5, 2, False) ], 'dense_layers': [], 'output_activation': tf.tanh, } # setup gan # note: assume square images, so only need 1 dim gan = DCGAN(dim, colors, d_sizes, g_sizes) gan.fit(X)