def evaluate(args): G = Model(net=mlp_G(), loss=None, optimizer=None) model_path = os.path.join(args.output_dir, args.model_name) print("Loading model from ", model_path) G.load(model_path) noise = get_noise(size=(128, args.nz)) samples = G.forward(noise) if not os.path.exists(args.output_dir): os.makedirs(args.output_dir) save_path = os.path.join(args.output_dir, "evaluate.png") save_batch_as_images(save_path, samples)
def main(args): if args.seed >= 0: random_seed(args.seed) # create output directory for saving result images if not os.path.exists('./output'): os.mkdir('./output') # define network we are going to load net = Net([ Conv2D(kernel=[5, 5, 1, 6], stride=[1, 1], padding="SAME"), ReLU(), MaxPool2D(pool_size=[2, 2], stride=[2, 2]), Conv2D(kernel=[5, 5, 6, 16], stride=[1, 1], padding="SAME"), ReLU(), MaxPool2D(pool_size=[2, 2], stride=[2, 2]), Flatten(), Dense(120), ReLU(), Dense(84), ReLU(), Dense(10) ]) # load the model model = Model(net=net, loss=SoftmaxCrossEntropyLoss(), optimizer=Adam()) print('loading pre-trained model file', args.model_path) model.load(args.model_path) # create pyplot window for on-the-fly visualization img = np.ones((1, 28, 28, 1)) fig = disp_mnist_batch(img) # actual visualization generations layer_name = 'conv-layer-1' print('[ ' + layer_name + ' ]') images = am_visualize_conv_layer(model, 0, fig) save_batch_as_images('output/{}.png'.format(layer_name), images, title='visualized feature maps for ' + layer_name) layer_name = 'conv-layer-2' print('[ ' + layer_name + ' ]') images = am_visualize_conv_layer(model, 3, fig) save_batch_as_images('output/{}.png'.format(layer_name), images, title='visualized feature maps for ' + layer_name)