From 2bca6bdbe71d8c123eb0ab940075479a94e76b28 Mon Sep 17 00:00:00 2001
From: Paul Andrey <paul.andrey@inria.fr>
Date: Fri, 27 Jan 2023 12:40:36 +0100
Subject: [PATCH] Update the RMSProp-implementation example.
---
examples/adding_rmsprop/readme.md | 29 +++++++++++++++--------------
1 file changed, 15 insertions(+), 14 deletions(-)
diff --git a/examples/adding_rmsprop/readme.md b/examples/adding_rmsprop/readme.md
index 3a4e37a5..7d47d6e9 100644
--- a/examples/adding_rmsprop/readme.md
+++ b/examples/adding_rmsprop/readme.md
@@ -56,7 +56,7 @@ The RMSProp optimizer is part of the adaptative optimizer family, and was thus
added to `_adaptative.py` file.
```python
-from declearn.optimizer.modules import OptiModule
+from declearn.optimizer.modules import OptiModule, EWMAModule
class RMSPropModule(OptiModule):
@@ -82,37 +82,38 @@ class RMSPropModule(OptiModule):
to the (divisor) adapative scaling term.
"""
- # Reuse the existing momemtum module, see below
+ # Reuse the existing EWMA module, see below
- self.mom = MomentumModule(beta=beta)
+ self.mom = EWMAModule(beta=beta)
self.eps = eps
# Allow access to the module's parameters
def get_config(self,) -> Dict[str, Any]:
- """Return a JSON-serializable dict with this module's parameters."""
- return {"beta": self.mom.beta, "eps": self.eps}
+ return {"beta": self.ewma.beta, "eps": self.eps}
# Define the actual transformations of the gradient
def run(self, gradients: Vector) -> Vector:
- """Apply RMSProp adaptation to input (pseudo-)gradients."""
- v_t = self.mom.run(gradients**2)
+ v_t = self.ewma.run(gradients**2)
scaling = (v_t**0.5) + self.eps
return gradients / scaling
+
+ # Define the state-access methods; here states are handled by the EWMA
+
+ def get_state(self) -> Dict[str, Any]:
+ return self.ewma.get_state()
+
+ def set_state(self, state: Dict[str, Any],) -> None:
+ self.ewma.set_state(state)
```
-We here reuse the Momemtum module, defined in `modules/_base.py`. As a
+We here reuse the EWMA module, defined in the `modules/_momentum.py` file. As a
module, it takes in a `Vector` and outputs a `Vector`. It has one parameter,
-$`\beta`$, and its `run` method looks like this:
+$`\beta`$, manages a state vector `state` and its `run` method looks like this:
```python
def run(self, gradients: Vector) -> Vector:
- """Apply Momentum acceleration to input (pseudo-)gradients."""
-
- # Iteratively update the state class attribute with input gradients
-
self.state = (self.beta * self.state) + ((1 - self.beta) * gradients)
return self.state
-
```
--
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