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Revert changes to optimizer default hyper-params (WIP) (#3415)

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While developing v2.1, I ran a bunch of hyper-parameter search
experiments to find settings that performed well for spaCy's NER and
parser. I ended up changing the default Adam settings from beta1=0.9,
beta2=0.999, eps=1e-8 to beta1=0.8, beta2=0.8, eps=1e-5. This was giving
a small improvement in accuracy (like, 0.4%).

Months later, I run the models with Prodigy, which uses beam-search
decoding even when the model has been trained with a greedy objective.
The new models performed terribly...So, wtf? After a couple of days
debugging, I figured out that the new optimizer settings was causing the
model to converge to solutions where the top-scoring class often had
a score of like, -80. The variance on the weights had gone up
enormously. I guess I needed to update the L2 regularisation as well?

Anyway. Let's just revert the change --- if the optimizer is finding
such extreme solutions, that seems bad, and not nearly worth the small
improvement in accuracy.

Currently training a slate of models, to verify the accuracy change is minimal.
Once the training is complete, we can merge this.

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This commit is contained in:
Matthew Honnibal 2019-03-16 21:39:02 +01:00 committed by Ines Montani
parent 62afa64a8d
commit 61617c64d5
1 changed files with 4 additions and 4 deletions
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8
spacy/_ml.py
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@ -48,11 +48,11 @@ def cosine(vec1, vec2):
def create_default_optimizer(ops, **cfg):
learn_rate = util.env_opt("learn_rate", 0.001)
beta1 = util.env_opt("optimizer_B1", 0.8)
beta2 = util.env_opt("optimizer_B2", 0.8)
eps = util.env_opt("optimizer_eps", 0.00001)
beta1 = util.env_opt("optimizer_B1", 0.9)
beta2 = util.env_opt("optimizer_B2", 0.999)
eps = util.env_opt("optimizer_eps", 1e-8)
L2 = util.env_opt("L2_penalty", 1e-6)
max_grad_norm = util.env_opt("grad_norm_clip", 5.0)
max_grad_norm = util.env_opt("grad_norm_clip", 1.0)
optimizer = Adam(ops, learn_rate, L2=L2, beta1=beta1, beta2=beta2, eps=eps)
optimizer.max_grad_norm = max_grad_norm
optimizer.device = ops.device