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_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_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_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_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_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_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)