def run():
fine_tune_weights = fine_tune.fine_tune(lm,
init_weights,
seqs,
num_epochs=1,
batch_size=2,
shuffle=False,
learning_rate=0.001)
return _compute_logprob(seqs, lm, fine_tune_weights).mean()
def run(n_copies):
fine_tune_weights = fine_tune.fine_tune(lm,
init_weights,
np.tile(
seq, [n_copies, 1]),
num_epochs=1,
batch_size=n_copies,
shuffle=False,
learning_rate=0.001)
return _compute_logprob(seq, lm, fine_tune_weights).mean()
def test_fine_tuning_zero_learning_rate(self, model_cls):
domain = _test_domain()
seqs = domain.sample_uniformly(6, seed=0)
lm = _get_lm(model_cls, domain=domain)
init_weights = lm.get_weights()
init_logprob = _compute_logprob(seqs, lm, init_weights).mean()
fine_tune_weights = fine_tune.fine_tune(
lm, init_weights, seqs, num_epochs=1, batch_size=2, learning_rate=0.)
final_logprob = _compute_logprob(seqs, lm, fine_tune_weights).mean()
self.assertAllClose(final_logprob, init_logprob)
def test_fine_tuning_with_zero_example_weight(self, model_cls):
domain = _test_domain()
lm = _get_lm(model_cls, domain=domain, use_dropout=False)
init_weights = lm.get_weights()
# Use 2 sequences of equal length, such that their contribution to
# the loss (which is averages over non-pad tokens) is equal).
seqs = domain.sample_uniformly(2, seed=0)
# Fine tune with a weight of zero on the second example.
fine_tune_weights_from_zero_weight = fine_tune.fine_tune(
lm,
init_weights,
seqs,
num_epochs=1,
batch_size=2,
example_weights=np.array([1., 0.]),
shuffle=False,
learning_rate=0.001)
logprobs_from_zero_weight = _compute_logprob(
seqs, lm, fine_tune_weights_from_zero_weight)
# Fine tune using only the first example. Use an example weight of 2.0
# to compensate for halving the number of tokens that the per-token
# loss is computed over vs. above.
fine_tune_weights_from_one_example = fine_tune.fine_tune(
lm,
init_weights,
seqs[:1],
num_epochs=1,
batch_size=1,
example_weights=np.array([2.]),
shuffle=False,
learning_rate=0.001)
logprobs_from_one_example = _compute_logprob(
seqs, lm, fine_tune_weights_from_one_example)
self.assertAllClose(logprobs_from_zero_weight,
logprobs_from_one_example)
def test_fine_tuning_increases_likelihood(self):
domain = _test_domain()
seqs = domain.sample_uniformly(6, seed=0)
lm = _get_lm(domain=domain)
init_weights = lm.get_weights()
init_logprob = _compute_logprob(seqs, lm, init_weights).mean()
fine_tune_weights = fine_tune.fine_tune(lm,
init_weights,
seqs,
num_epochs=1,
batch_size=2,
learning_rate=0.001)
final_logprob = _compute_logprob(seqs, lm, fine_tune_weights).mean()
self.assertGreater(final_logprob, init_logprob)
def test_fine_tuning_with_example_weights(self, model_cls):
domain = _test_domain()
seqs = domain.sample_uniformly(2, seed=0)
lm = _get_lm(model_cls, domain=domain)
init_weights = lm.get_weights()
init_logprobs = _compute_logprob(seqs, lm, init_weights)
# Select the sequence that has higher initial likelihood and push
# down its likelihood and pull up the likelihood of the other sequence.
if init_logprobs[0] > init_logprobs[1]:
seqs = seqs[::-1]
example_weights = np.array([-1., 1.])
fine_tune_weights = fine_tune.fine_tune(
lm,
init_weights,
seqs,
num_epochs=1,
batch_size=2,
example_weights=example_weights,
learning_rate=0.001)
final_logprobs = _compute_logprob(seqs, lm, fine_tune_weights)
# Check that the ranking of their likelihoods is reversed.
self.assertGreater(final_logprobs[1], final_logprobs[0])
