def __init__(self, distribution, image_dims, vae_model=DefaultVAEModel()):
super(VariationalAutoencoder, self).__init__()
self.xinference_net = vae_model.inference_net()
self.xgenerative_net = vae_model.generative_net(
image_dims, distribution.no_of_parameters())
self.distribution = distribution
self.vae_model = vae_model
self.latent_distribution = nb.RealGauss()
def __init__(self, name='autoencoder', **kwargs):
super(VariationalAutoEncoder, self).__init__(name=name, **kwargs)
self.vae = gen_vae.YZVAEModel()
self.encoder = self.vae.inference_net()
self.decoder = self.vae.generative_net([64, 64, 3], 1)
self.sampling = Sampling()
self.oldVAE = gen_vae.VariationalAutoencoder(nb.RealStd(), [64, 64, 3],
self.vae)
self.z_sampler = nb.RealGauss()
def test_pixelvae( image_range=512, no_epoch=10, learning_rate=.0001 ):
test_model( pvae.PixelVAE( nb.Binary(), [ 28, 28, 1 ] ), "PixelVAE Bin", train_bin_images[:image_range], test_z, 'bin', no_epoch, learning_rate )
test_model( pvae.PixelVAE( nb.RealGauss(), [ 32, 32, 3 ] ), "PixelVAE RealGauss", deq_train_col_images[:image_range], test_z, 'col', no_epoch, learning_rate )
test_model( pvae.PixelVAE( nb.Discrete(), [ 32, 32, 3 ] ), "PixelVAE Discrete", deq_train_col_images[:image_range], test_z, 'col', no_epoch, learning_rate )
def test_cnn( image_range=512, no_epoch=10, learning_rate=.0001 ):
test_model( cnn.PixelCNN( nb.Binary(), [ 28, 28, 1] ), "CNN Bin", train_bin_images[:image_range], None, 'bin', no_epoch, learning_rate )
test_model( cnn.PixelCNN( nb.RealGauss(), [ 32, 32, 3 ] ), "CNN RealGauss", deq_train_col_images[:image_range], None, 'col', no_epoch, learning_rate )
test_model( cnn.PixelCNN( nb.Discrete(), [ 32, 32, 3 ] ), "CNN Discrete", deq_train_col_images[:image_range], None, 'col', no_epoch, learning_rate )
def test_vae( image_range=512, no_epoch=10, learning_rate=.0001 ):
test_model( vae.VariationalAutoencoder( nb.Binary(), [ 28, 28, 1 ] ), "VAE Bin", train_bin_images[:image_range], test_z, 'bin', no_epoch, learning_rate )
test_model( vae.VariationalAutoencoder( nb.RealGauss(), [ 32, 32, 3 ] ), "VAE RealGauss", deq_train_col_images[:image_range], test_z, 'col', no_epoch, learning_rate )
test_model( vae.VariationalAutoencoder( nb.Discrete(), [ 32, 32, 3 ] ), "VAE Dscrete", deq_train_col_images[:image_range], test_z, 'col', no_epoch, learning_rate )
def test_nb( image_range=512, no_epoch=10, learning_rate=.0001 ):
test_model( nb.NBModel( nb.Binary(), [ 28, 28, 1 ] ), "NB Bin", train_bin_images[:image_range], None, 'bin', no_epoch, learning_rate )
test_model( nb.NBModel( nb.RealGauss(), [ 32, 32, 3 ] ), "NB RealGauss", deq_train_col_images[:image_range], None, 'col', no_epoch, learning_rate )
test_model( nb.NBModel( nb.Discrete(), [ 32, 32, 3 ] ), "NB Discrete", deq_train_col_images[:image_range], None, 'col', no_epoch, learning_rate )