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RectifiedAdam: Store 'total_steps' hyperparameter as float #2369

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Feb 7, 2021
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RectifiedAdam: Store 'total_steps' hyperparameter as float #2369

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84e67f0
RectifiedAdam: Store 'total_steps' hyperparameter as float
olesalscheider Jan 22, 2021
ffb37e4
Woraround on serialize
bhack Jan 30, 2021
6ad6c64
Remove unused import
bhack Jan 30, 2021
73bbbd0
Revert docstring change
bhack Jan 30, 2021
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6 changes: 3 additions & 3 deletions tensorflow_addons/optimizers/rectified_adam.py
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Original file line number Diff line number Diff line change
Expand Up @@ -103,7 +103,7 @@ def __init__(
beyond".
sma_threshold. A float value.
The threshold for simple mean average.
total_steps: An integer. Total number of training steps.
total_steps: An integer value. Total number of training steps.
Enable warmup by setting a positive value.
warmup_proportion: A floating point value.
The proportion of increasing steps.
Expand Down Expand Up @@ -131,7 +131,7 @@ def __init__(
self._set_hyper("decay", self._initial_decay)
self._set_hyper("weight_decay", weight_decay)
self._set_hyper("sma_threshold", sma_threshold)
self._set_hyper("total_steps", int(total_steps))
self._set_hyper("total_steps", float(total_steps))
self._set_hyper("warmup_proportion", warmup_proportion)
self._set_hyper("min_lr", min_lr)
self.epsilon = epsilon or tf.keras.backend.epsilon()
Expand Down Expand Up @@ -316,7 +316,7 @@ def get_config(self):
"sma_threshold": self._serialize_hyperparameter("sma_threshold"),
"epsilon": self.epsilon,
"amsgrad": self.amsgrad,
"total_steps": self._serialize_hyperparameter("total_steps"),
"total_steps": int(self._serialize_hyperparameter("total_steps")),
"warmup_proportion": self._serialize_hyperparameter(
"warmup_proportion"
),
Expand Down
23 changes: 23 additions & 0 deletions tensorflow_addons/optimizers/tests/rectified_adam_test.py
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Expand Up @@ -209,3 +209,26 @@ def test_scheduler_serialization():
"class_name": "InverseTimeDecay",
"config": wd_scheduler.get_config(),
}


def test_checkpoint_serialization(tmpdir):
optimizer = RectifiedAdam()
optimizer2 = RectifiedAdam()

var_0 = tf.Variable([1.0, 2.0], dtype=tf.dtypes.float32)
var_1 = tf.Variable([3.0, 4.0], dtype=tf.dtypes.float32)

grad_0 = tf.constant([0.1, 0.2], dtype=tf.dtypes.float32)
grad_1 = tf.constant([0.03, 0.04], dtype=tf.dtypes.float32)

grads_and_vars = list(zip([grad_0, grad_1], [var_0, var_1]))

optimizer.apply_gradients(grads_and_vars)

checkpoint = tf.train.Checkpoint(optimizer=optimizer)
checkpoint2 = tf.train.Checkpoint(optimizer=optimizer2)
model_path = str(tmpdir / "rectified_adam_chkpt")
checkpoint.write(model_path)
checkpoint2.read(model_path)

optimizer2.apply_gradients(grads_and_vars)