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_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_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_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)