def run_gan(): (train_images, train_labels), (_, _) = tf.keras.datasets.mnist.load_data() print(train_images.shape) 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) # Batch and shuffle the data train_dataset = tf.data.Dataset.from_tensor_slices(train_images).shuffle( BUFFER_SIZE).batch(BATCH_SIZE) gan = DCGAN(gen_lr, disc_lr, batch_size=BATCH_SIZE, noise_dim=NOISE_DIM) gan.create_generator() gan.create_discriminator() # Test generator random_noise = tf.random.normal([1, NOISE_DIM]) generated_image = gan.generator(random_noise) #plt.imshow(generated_image[0,:,:,0],cmap='gray') #plt.show() # Test Discriminator prob = gan.discriminator(generated_image) print("Probability of image being real: {}".format(sigmoid(prob))) gan.set_noise_seed(num_examples_to_generate) 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()