def test_diagonal_gaussian_default_output_layer():
"""
DiagonalGaussian's default output layer is compatible with its required
output space
"""
mlp = MLP(layers=[Linear(layer_name="h", dim=5, irange=0.01, max_col_norm=0.01)])
conditional = DiagonalGaussian(mlp=mlp, name="conditional")
vae = DummyVAE()
conditional.set_vae(vae)
input_space = VectorSpace(dim=5)
conditional.initialize_parameters(input_space=input_space, ndim=5)
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_diagonal_gaussian_sample_from_epsilon():
"""
DiagonalGaussian.sample_from_epsilon doesn't crash
"""
mlp = MLP(layers=[Linear(layer_name='h', dim=5, irange=0.01,
max_col_norm=0.01)])
conditional = DiagonalGaussian(mlp=mlp, name='conditional')
vae = DummyVAE()
conditional.set_vae(vae)
input_space = VectorSpace(dim=5)
conditional.initialize_parameters(input_space=input_space, ndim=5)
conditional.sample_from_epsilon((2, 10, 5))
def test_diagonal_gaussian_conditional_expectation():
"""
DiagonalGaussian.conditional_expectation doesn't crash
"""
mlp = MLP(layers=[Linear(layer_name='h', dim=5, irange=0.01,
max_col_norm=0.01)])
conditional = DiagonalGaussian(mlp=mlp, name='conditional')
vae = DummyVAE()
conditional.set_vae(vae)
input_space = VectorSpace(dim=5)
conditional.initialize_parameters(input_space=input_space, ndim=5)
mu = T.matrix('mu')
log_sigma = T.matrix('log_sigma')
conditional.conditional_expectation([mu, log_sigma])
def test_diagonal_gaussian_sample_from_conditional():
"""
DiagonalGaussian.sample_from_conditional works when num_samples is provided
"""
mlp = MLP(layers=[Linear(layer_name='h', dim=5, irange=0.01,
max_col_norm=0.01)])
conditional = DiagonalGaussian(mlp=mlp, name='conditional')
vae = DummyVAE()
conditional.set_vae(vae)
input_space = VectorSpace(dim=5)
conditional.initialize_parameters(input_space=input_space, ndim=5)
mu = T.matrix('mu')
log_sigma = T.matrix('log_sigma')
conditional.sample_from_conditional([mu, log_sigma], num_samples=2)
def test_diagonal_gaussian_sample_from_epsilon():
"""
DiagonalGaussian.sample_from_epsilon doesn't crash
"""
mlp = MLP(layers=[Linear(layer_name="h", dim=5, irange=0.01, max_col_norm=0.01)])
conditional = DiagonalGaussian(mlp=mlp, name="conditional")
vae = DummyVAE()
conditional.set_vae(vae)
input_space = VectorSpace(dim=5)
conditional.initialize_parameters(input_space=input_space, ndim=5)
conditional.sample_from_epsilon((2, 10, 5))
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))
def test_diagonal_gaussian_reparametrization_trick():
"""
DiagonalGaussian.sample_from_conditional works when asked to sample using
the reparametrization trick
"""
mlp = MLP(layers=[Linear(layer_name='h', dim=5, irange=0.01,
max_col_norm=0.01)])
conditional = DiagonalGaussian(mlp=mlp, name='conditional')
vae = DummyVAE()
conditional.set_vae(vae)
input_space = VectorSpace(dim=5)
conditional.initialize_parameters(input_space=input_space, ndim=5)
mu = T.matrix('mu')
log_sigma = T.matrix('log_sigma')
epsilon = T.tensor3('epsilon')
conditional.sample_from_conditional([mu, log_sigma], epsilon=epsilon)
def test_diagonal_gaussian_conditional_expectation():
"""
DiagonalGaussian.conditional_expectation doesn't crash
"""
mlp = MLP(layers=[Linear(layer_name="h", dim=5, irange=0.01, max_col_norm=0.01)])
conditional = DiagonalGaussian(mlp=mlp, name="conditional")
vae = DummyVAE()
conditional.set_vae(vae)
input_space = VectorSpace(dim=5)
conditional.initialize_parameters(input_space=input_space, ndim=5)
mu = T.matrix("mu")
log_sigma = T.matrix("log_sigma")
conditional.conditional_expectation([mu, log_sigma])
def test_diagonal_gaussian_sample_from_conditional():
"""
DiagonalGaussian.sample_from_conditional works when num_samples is provided
"""
mlp = MLP(layers=[Linear(layer_name="h", dim=5, irange=0.01, max_col_norm=0.01)])
conditional = DiagonalGaussian(mlp=mlp, name="conditional")
vae = DummyVAE()
conditional.set_vae(vae)
input_space = VectorSpace(dim=5)
conditional.initialize_parameters(input_space=input_space, ndim=5)
mu = T.matrix("mu")
log_sigma = T.matrix("log_sigma")
conditional.sample_from_conditional([mu, log_sigma], num_samples=2)
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_diagonal_gaussian_reparametrization_trick():
"""
DiagonalGaussian.sample_from_conditional works when asked to sample using
the reparametrization trick
"""
mlp = MLP(layers=[Linear(layer_name="h", dim=5, irange=0.01, max_col_norm=0.01)])
conditional = DiagonalGaussian(mlp=mlp, name="conditional")
vae = DummyVAE()
conditional.set_vae(vae)
input_space = VectorSpace(dim=5)
conditional.initialize_parameters(input_space=input_space, ndim=5)
mu = T.matrix("mu")
log_sigma = T.matrix("log_sigma")
epsilon = T.tensor3("epsilon")
conditional.sample_from_conditional([mu, log_sigma], epsilon=epsilon)
def test_diagonal_gaussian_default_output_layer():
"""
DiagonalGaussian's default output layer is compatible with its required
output space
"""
mlp = MLP(layers=[Linear(layer_name='h', dim=5, irange=0.01,
max_col_norm=0.01)])
conditional = DiagonalGaussian(mlp=mlp, name='conditional')
vae = DummyVAE()
conditional.set_vae(vae)
input_space = VectorSpace(dim=5)
conditional.initialize_parameters(input_space=input_space, ndim=5)