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)
"""
