Using hydra for function example

config/function.yaml

@@ -0,0 +1,3 @@
+optimizer : sophia # lion, adam, sparse_lion
+epochs: 20
+segments: 5

examples/function_example.py

@@ -3,6 +3,7 @@
 functions using a single input and single output with polynomial
 synaptic weights
 """
+
 import matplotlib.pyplot as plt
 import numpy as np
 import torch
@@ -12,6 +13,10 @@
 from torch.nn import functional as F
 from torch.utils.data import DataLoader, Dataset
 from lion_pytorch import Lion
+from Sophia import SophiaG
+import hydra
+from omegaconf import DictConfig
+
 from high_order_layers_torch.sparse_optimizers import SparseLion
 
 from high_order_layers_torch.layers import *
@@ -139,6 +144,8 @@ def configure_optimizers(self):
             return torch.optim.Adam(self.parameters(), lr=0.001)
         elif self.optimizer == "lion":
             return Lion(self.parameters(), lr=0.001)
+        elif self.optimizer == "sophia":
+            return SophiaG(self.parameters(), lr=0.01, rho=0.035)
         elif self.optimizer == "sparse_lion":
             print(f"Using sparse lion")
             return SparseLion(self.parameters(), lr=0.001)
@@ -248,7 +255,11 @@ def plot_approximation(
 
 
 def plot_results(
-    epochs: int = 20, segments: int = 5, plot: bool = True, first_only: bool = False
+    epochs: int = 20,
+    segments: int = 5,
+    plot: bool = True,
+    first_only: bool = False,
+    optimizer="lion",
 ):
     """
     plt.figure(0)
@@ -289,11 +300,11 @@ def plot_results(
         plot_approximation(
             element["layer"],
             element["model_set"],
-            5,
+            segments,
             epochs,
             accelerator="cpu",
             periodicity=2,
-            opt="sparse_lion",
+            opt="sophia",
             first_only=first_only,
         )
 
@@ -304,5 +315,15 @@ def plot_results(
         plt.show()
 
 
+@hydra.main(config_path="../config", config_name="function")
+def run(cfg: DictConfig):
+    plot_results(
+        epochs=cfg.epochs,
+        segments=cfg.segments,
+        first_only=False,
+        optimizer=cfg.optimizer,
+    )
+
+
 if __name__ == "__main__":
-    plot_results(first_only=False)
+    run()

examples/xor.py

@@ -4,20 +4,14 @@
 synaptic weights
 """
 
-import math
-import os
-
 import matplotlib.pyplot as plt
-import numpy as np
 import torch
 from pytorch_lightning import LightningModule, Trainer
 from torch.nn import functional as F
-from torch.utils.data import DataLoader, Dataset, random_split
-from torchvision import transforms
-from torchvision.datasets import MNIST
+from torch.utils.data import DataLoader, Dataset
 from lion_pytorch import Lion
 import hydra
-from omegaconf import DictConfig, OmegaConf
+from omegaconf import DictConfig
 
 
 import high_order_layers_torch.PolynomialLayers as poly