def test_convolutional_compatible():
"""
VAE allows convolutional encoding networks
"""
encoding_model = MLP(
layers=[
SpaceConverter(layer_name="conv2d_converter", output_space=Conv2DSpace(shape=[4, 4], num_channels=1)),
ConvRectifiedLinear(
layer_name="h",
output_channels=2,
kernel_shape=[2, 2],
kernel_stride=[1, 1],
pool_shape=[1, 1],
pool_stride=[1, 1],
pool_type="max",
irange=0.01,
),
]
)
decoding_model = MLP(layers=[Linear(layer_name="h", dim=16, irange=0.01)])
prior = DiagonalGaussianPrior()
conditional = BernoulliVector(mlp=decoding_model, name="conditional")
posterior = DiagonalGaussian(mlp=encoding_model, name="posterior")
vae = VAE(nvis=16, prior=prior, conditional=conditional, posterior=posterior, nhid=16)
X = T.matrix("X")
lower_bound = vae.log_likelihood_lower_bound(X, num_samples=10)
f = theano.function(inputs=[X], outputs=lower_bound)
rng = make_np_rng(default_seed=11223)
f(as_floatX(rng.uniform(size=(10, 16))))
def test_convolutional_compatible():
"""
VAE allows convolutional encoding networks
"""
encoding_model = MLP(
layers=[
SpaceConverter(
layer_name='conv2d_converter',
output_space=Conv2DSpace(shape=[4, 4], num_channels=1)
),
ConvRectifiedLinear(
layer_name='h',
output_channels=2,
kernel_shape=[2, 2],
kernel_stride=[1, 1],
pool_shape=[1, 1],
pool_stride=[1, 1],
pool_type='max',
irange=0.01)
]
)
decoding_model = MLP(layers=[Linear(layer_name='h', dim=16, irange=0.01)])
prior = DiagonalGaussianPrior()
conditional = BernoulliVector(mlp=decoding_model, name='conditional')
posterior = DiagonalGaussian(mlp=encoding_model, name='posterior')
vae = VAE(nvis=16, prior=prior, conditional=conditional,
posterior=posterior, nhid=16)
X = T.matrix('X')
lower_bound = vae.log_likelihood_lower_bound(X, num_samples=10)
f = theano.function(inputs=[X], outputs=lower_bound)
rng = make_np_rng(default_seed=11223)
f(as_floatX(rng.uniform(size=(10, 16))))
def test_multiple_samples_allowed():
"""
VAE allows multiple samples per data point
"""
encoding_model = MLP(layers=[Linear(layer_name="h", dim=10, irange=0.01)])
decoding_model = MLP(layers=[Linear(layer_name="h", dim=10, irange=0.01)])
prior = DiagonalGaussianPrior()
conditional = BernoulliVector(mlp=decoding_model, name="conditional")
posterior = DiagonalGaussian(mlp=encoding_model, name="posterior")
vae = VAE(nvis=10, prior=prior, conditional=conditional, posterior=posterior, nhid=5)
X = T.matrix("X")
lower_bound = vae.log_likelihood_lower_bound(X, num_samples=10)
f = theano.function(inputs=[X], outputs=lower_bound)
rng = make_np_rng(default_seed=11223)
f(as_floatX(rng.uniform(size=(10, 10))))
def test_multiple_samples_allowed():
"""
VAE allows multiple samples per data point
"""
encoding_model = MLP(layers=[Linear(layer_name='h', dim=10, irange=0.01)])
decoding_model = MLP(layers=[Linear(layer_name='h', dim=10, irange=0.01)])
prior = DiagonalGaussianPrior()
conditional = BernoulliVector(mlp=decoding_model, name='conditional')
posterior = DiagonalGaussian(mlp=encoding_model, name='posterior')
vae = VAE(nvis=10, prior=prior, conditional=conditional,
posterior=posterior, nhid=5)
X = T.matrix('X')
lower_bound = vae.log_likelihood_lower_bound(X, num_samples=10)
f = theano.function(inputs=[X], outputs=lower_bound)
rng = make_np_rng(default_seed=11223)
f(as_floatX(rng.uniform(size=(10, 10))))
def test_vae_automatically_finds_kl_integrator():
"""
VAE automatically finds the right KLIntegrator
"""
encoding_model = MLP(layers=[Linear(layer_name='h', dim=10, irange=0.01)])
decoding_model = MLP(layers=[Linear(layer_name='h', dim=10, irange=0.01)])
prior = DiagonalGaussianPrior()
conditional = BernoulliVector(mlp=decoding_model, name='conditional')
posterior = DiagonalGaussian(mlp=encoding_model, name='posterior')
vae = VAE(nvis=10, prior=prior, conditional=conditional,
posterior=posterior, nhid=5)
assert (vae.kl_integrator is not None and
isinstance(vae.kl_integrator, DiagonalGaussianPriorPosteriorKL))
