def test_fixed_seed_dim_sample_quadratic(self):
"""Ensures that passing `seed` produces the same loss."""
dims = 10
initial_dist = quadratic_helper.ConstantDistribution(
np.random.normal(0, 1, [dims]))
specturm = np.linspace(1.0, 2.0, dims).astype(np.float32)
A_dist = quadratic_helper.FixedEigenSpectrumMatrixDistribution(
specturm)
B_dist = quadratic_helper.ConstantDistribution(
tf.ones([dims], dtype=tf.float32))
C_dist = quadratic_helper.ConstantDistribution(1.0)
lossmod = quadratic_helper.QuadraticBasedTask(
dims=dims,
initial_dist=initial_dist,
A_dist=A_dist,
B_dist=B_dist,
C_dist=C_dist,
seed=23,
)
s = lossmod.initial_params()
loss = lossmod.call_split(s, None)
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
loss1 = sess.run(loss)
sess.run(tf.global_variables_initializer())
loss2 = sess.run(loss)
self.assertNear(loss1, loss2, 1e-9)
def get_quadratic_family(cfg, seed=None):
"""Get a task for the given cfg.
Args:
cfg: config specifying the model generated by `sample_quadratic_family_cfg`.
seed: optional int Seed used to generate the instance of a given task. Note
this is not the seed used to generate the cfg, but just an instance of the
given cfg.
Returns:
base.BaseTask for the given config.
"""
dims = cfg["dims"]
# pylint: disable=invalid-name
A_dist = _get_distribution_over_matrix(dims, cfg["A_dist"])
initial_dist = _get_vector_dist(dims, cfg["initial_dist"])
B_dist = quadratic_helper.ConstantDistribution(tf.zeros([dims]))
C_dist = quadratic_helper.ConstantDistribution(0.)
output_fn = _get_output_fn(cfg["output_fn"], cfg["loss_scale"])
seed = cfg["seed"]
if cfg["noise"]:
A_noise_dist = _get_distribution_over_matrix(dims,
cfg["noise"]["A_noise"])
else:
A_noise_dist = None
return quadratic_helper.QuadraticBasedTask(dims=dims,
initial_dist=initial_dist,
A_dist=A_dist,
A_noise_dist=A_noise_dist,
B_dist=B_dist,
C_dist=C_dist,
seed=seed,
output_fn=output_fn)