Github action: auto-update.

Author: github-actions[bot]

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -197,7 +197,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.14"
+      "version": "3.9.19"
     }
   },
   "nbformat": 4,

dev/_downloads/082e73328a5caf8c1fe9ad7fe05cf68f/plot_incremental_FNO_darcy.ipynb

@@ -78,7 +78,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.14"
+      "version": "3.9.19"
     }
   },
   "nbformat": 4,

dev/_downloads/0ac9f102e4f1903984e3b4a7d517c88f/plot_darcy_flow.ipynb

@@ -1,18 +1,19 @@
 """
-Training an FNO on Darcy-Flow
+Training a TFNO on Darcy-Flow
 =============================
 
 In this example, we demonstrate how to use the small Darcy-Flow example we ship with the package
-to train a Fourier-Neural Operator
+to train a Tensorized Fourier-Neural Operator
 """
 
 # %%
 # 
 
+
 import torch
 import matplotlib.pyplot as plt
 import sys
-from neuralop.models import FNO
+from neuralop.models import TFNO
 from neuralop import Trainer
 from neuralop.training import AdamW
 from neuralop.data.datasets import load_darcy_flow_small
@@ -33,13 +34,9 @@
 
 
 # %%
-# We create a simple FNO model
+# We create a tensorized FNO model
 
-model = FNO(n_modes=(16, 16),
-             in_channels=1, 
-             out_channels=1,
-             hidden_channels=32, 
-             projection_channels=64)
+model = TFNO(n_modes=(16, 16), in_channels=1, hidden_channels=32, projection_channels=64, factorization='tucker', rank=0.42)
 model = model.to(device)
 
 n_params = count_model_params(model)

dev/_downloads/0d78e075dd52a34e158d7f5f710dfe89/plot_incremental_FNO_darcy.zip

@@ -33,14 +33,7 @@
 # %%
 # We create a tensorized FNO model
 
-model = TFNO(n_modes=(16, 16),
-             in_channels=1, 
-             out_channels=1, 
-             hidden_channels=32, 
-             projection_channels=64, 
-             factorization='tucker', 
-             rank=0.42)
-
+model = TFNO(n_modes=(16, 16), in_channels=1, hidden_channels=32, projection_channels=64, factorization='tucker', rank=0.42)
 model = model.to(device)
 
 n_params = count_model_params(model)

dev/_downloads/1a3050d57a180b92b424ce128dfe1d36/plot_FNO_darcy.py

@@ -51,7 +51,7 @@
       },
       "outputs": [],
       "source": [
-        "model = TFNO(n_modes=(16, 16),\n             in_channels=1, \n             out_channels=1, \n             hidden_channels=32, \n             projection_channels=64, \n             factorization='tucker', \n             rank=0.42)\n\nmodel = model.to(device)\n\nn_params = count_model_params(model)\nprint(f'\\nOur model has {n_params} parameters.')\nsys.stdout.flush()"
+        "model = TFNO(n_modes=(16, 16), in_channels=1, hidden_channels=32, projection_channels=64, factorization='tucker', rank=0.42)\nmodel = model.to(device)\n\nn_params = count_model_params(model)\nprint(f'\\nOur model has {n_params} parameters.')\nsys.stdout.flush()"
       ]
     },
     {
@@ -154,7 +154,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.14"
+      "version": "3.9.19"
     }
   },
   "nbformat": 4,