def run_gan(): (train_images, train_labels), (_, _) = tf.keras.datasets.mnist.load_data() train_images = train_images.reshape(train_images.shape[0], 28, 28, 1).astype('float32') train_images = (train_images - 127.5) / 127.5 # Normalize images to [-1,1] print(train_images.shape) train_labels = to_categorical(train_labels) print(train_labels.shape) # Batch and shuffle the data train_dataset = tf.data.Dataset.from_tensor_slices( (train_images, train_labels)).shuffle(BUFFER_SIZE).batch(BATCH_SIZE) gan = CGAN(gen_lr, disc_lr, noise_dim=NOISE_DIM) gan.create_generator() gan.create_discriminator() if model_test: # Test generator random_noise = tf.random.normal([1, NOISE_DIM]) condition = tf.zeros(shape=(1, 10)) generated_image = gan.generator([random_noise, condition]) plt.imshow(generated_image[0, :, :, 0], cmap='gray') plt.show() # Test Discriminator prob = gan.discriminator([generated_image, condition]) print("Probability of image being real: {}".format(sigmoid(prob))) gan.set_noise_seed(num_examples_to_generate) print(gan.label_seed.shape) gan.set_checkpoint(path=save_ckpt_path) gen_loss_array, disc_loss_array = gan.train(train_dataset, epochs=EPOCHS) # Plot Discriminator Loss plt.plot(range(EPOCHS), gen_loss_array) plt.plot(range(EPOCHS), disc_loss_array) plt.show()
generated_imgs = [] fixed_noise = [torch.randn(1, 100, 1, 1) for _ in range(5)] for i, c in enumerate(conditions): try: noise = fixed_noise[i % 5] except Exception: raise IndexError print(i) print(25 % (i + 1)) print(len(fixed_noise)) exit(-1) c = vocab.encode_feature(c) c = np.expand_dims(c, 0) * 0.9 + 0.05 noise, c = Variable(torch.Tensor(noise)), Variable(torch.Tensor(c)) img_v = model.generator(noise, c) img = cvt_output(img_v) generated_imgs.append(img) imgs = np.array(generated_imgs) save_imgs(imgs) """ imgs = Variable(torch.Tensor(imgs)) torchvision.utils.save_image(imgs.data, 'test.jpg', nrow=5) """