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Fix legacy optimizer handling in compile_from_config(). #18492

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merged 2 commits into from
Sep 25, 2023
Merged

Fix legacy optimizer handling in compile_from_config(). #18492

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dfeaf41
Fix legacy optimizer handling in `compile_from_config()`.
nkovela1 Sep 25, 2023
4c87b8b
Add associated test
nkovela1 Sep 25, 2023
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7 changes: 6 additions & 1 deletion keras/engine/training.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3680,7 +3680,12 @@ def compile_from_config(self, config):
return
config = saving_lib.deserialize_keras_object(config)
self.compile(**config)
if hasattr(self, "optimizer") and self.built:
if (
hasattr(self, "optimizer")
# Exempt legacy optimizers.
and isinstance(self.optimizer, optimizer.Optimizer)
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Is it every possible to pass an optimizer that isn't an Optimizer instance? Wouldn't that blow up?

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All custom optimizers that need to be built on deserialization must subclass from Optimizer afaik, and legacy optimizers (which do not) are exempted here because they do not have a corresponding build method.

Is there any edge case in particular you are referring to? The corresponding CL did not cause any TGP issues.

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Legacy optimizers simply don't exist in Keras 3, do they?

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This PR is actually for the Keras 2.15 release, so legacy optimizers still exist in this revision actually (merging into the cherrypick branch).

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Got it, I had not noticed this was in the 2.15 branch.

and self.built
):
# Create optimizer variables.
self.optimizer.build(self.trainable_variables)

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28 changes: 28 additions & 0 deletions keras/optimizers/legacy/optimizer_v2_test.py
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Expand Up @@ -15,6 +15,7 @@
"""Functional test for OptimizerV2."""

import collections
import os
from copy import deepcopy

import numpy as np
Expand Down Expand Up @@ -568,6 +569,33 @@ def testOptimizerWithKerasModel(self):
batch_size=5,
)

@test_combinations.generate(test_combinations.combine(mode=["eager"]))
def testOptimizerSaving(self):
np.random.seed(1331)
input_np = np.random.random((10, 3))
output_np = np.random.random((10, 4))
a = input_layer.Input(shape=(3,), name="input_a")
model = sequential.Sequential()
model.add(core.Dense(4, kernel_initializer="zeros", name="dense"))
model.add(regularization.Dropout(0.5, name="dropout"))
model(a)
optimizer = gradient_descent.SGD(learning_rate=0.1)
model.compile(optimizer, loss="mse", metrics=["mae"])

model.fit(
input_np,
output_np,
batch_size=10,
validation_data=(input_np, output_np),
epochs=2,
verbose=0,
)

temp_filepath = os.path.join(self.get_temp_dir(), "optv2_model.keras")
model.save(temp_filepath)
loaded_model = keras.models.load_model(temp_filepath)
self.assertAllClose(model(input_np), loaded_model(input_np), atol=1e-6)

@test_combinations.generate(
test_combinations.combine(mode=["graph", "eager"])
)
Expand Down