def get_dataset(input_params, dataset_type: problem_type.ProblemType):
if dataset_type == problem_type.ProblemType.VANILLA_MNIST.name:
return mnist.MnistDataset(input_params)
elif dataset_type == problem_type.ProblemType.VANILLA_FASHION_MNIST.name:
return fashion_mnist.FashionMnistDataset(input_params)
elif dataset_type == problem_type.ProblemType.VANILLA_CIFAR10.name:
return cifar10.Cifar10Dataset(input_params)
elif dataset_type == problem_type.ProblemType.CONDITIONAL_MNIST.name:
return mnist.MnistDataset(input_params, with_labels=True)
elif dataset_type == problem_type.ProblemType.CONDITIONAL_FASHION_MNIST.name:
return fashion_mnist.FashionMnistDataset(input_params,
with_labels=True)
elif dataset_type == problem_type.ProblemType.CONDITIONAL_CIFAR10.name:
return cifar10.Cifar10Dataset(input_params, with_labels=True)
elif dataset_type == problem_type.ProblemType.CYCLE_SUMMER2WINTER.name:
return summer2winter.SummerToWinterDataset(input_params)
else:
raise NotImplementedError
from gans.trainers import vanilla_gan_trainer
model_parameters = edict({
'img_height': 28,
'img_width': 28,
'num_channels': 1,
'batch_size': 16,
'num_epochs': 10,
'buffer_size': 1000,
'latent_size': 100,
'learning_rate_generator': 0.0001,
'learning_rate_discriminator': 0.0001,
'save_images_every_n_steps': 10
})
dataset = mnist.MnistDataset(model_parameters)
def validation_dataset():
return tf.random.normal(
[model_parameters.batch_size, model_parameters.latent_size])
validation_dataset = validation_dataset()
generator = sequential.SequentialModel(layers=[
keras.Input(shape=[model_parameters.latent_size]),
layers.Dense(units=7 * 7 * 256, use_bias=False),
layers.BatchNormalization(),
layers.LeakyReLU(),
layers.Reshape((7, 7, 256)),
model_parameters = edict({
'img_height': 28,
'img_width': 28,
'num_channels': 1,
'batch_size': 16,
'num_epochs': 10,
'buffer_size': 1000,
'latent_size': 100,
'num_classes': 10,
'learning_rate_generator': 0.0001,
'learning_rate_discriminator': 0.0001,
'save_images_every_n_steps': 10
})
dataset = mnist.MnistDataset(model_parameters, with_labels=True)
def validation_dataset():
test_batch_size = model_parameters.num_classes ** 2
labels = np.repeat(list(range(model_parameters.num_classes)), model_parameters.num_classes)
validation_samples = [tf.random.normal([test_batch_size, model_parameters.latent_size]), np.array(labels)]
return validation_samples
validation_dataset = validation_dataset()
generator = latent_to_image.LatentToImageGenerator(model_parameters)
discriminator = discriminator.Discriminator(model_parameters)
generator_optimizer = optimizers.Adam(