FCNN example working

DeepINN/model.py

@@ -47,7 +47,9 @@ def compile_network(self):
         print("Network compiled", file=sys.stderr, flush=True)
 
     def train(self, iterations : int = None, display_every : int = None):
+
         if self.iter == 0: # We are running a fresh training
+            self.training_history = []  # Initialize an empty list for storing loss values
             self.iterations = iterations
             # Load all the seeds, data types, devices etc.
             self.config.apply_seeds()
@@ -85,6 +87,9 @@ def train(self, iterations : int = None, display_every : int = None):
             if self.iter % (self.iterations/10) == 0:
                 print(f"Iteration: {self.iter+1} \t BC Loss: {self.BC_loss:0.4f}\t PDE Loss: {self.PDE_loss:0.4f} \t Loss: {self.total_loss:0.4f}")
 
+            # Append the total loss value to the training history list
+            self.training_history.append(self.total_loss.item())
+
             self.iter = self.iter + 1
         else:
             print('Training finished')

DeepINN/nn/__init__.py

@@ -1,2 +1,2 @@
 from .FCNN import BaseNetwork, FullyConnected
-from.utils import activation, initialiser
+from .utils import activation, initialiser

DeepINN/utils/__init__.py

@@ -20,7 +20,7 @@
 
 from .data import PointsDataset, PointsDataLoader, DeepONetDataLoader
 
-from .user_fun import UserFunction
+from .user_fun import UserFunction, tensor2numpy
 from .plotting import plot, animate, scatter
 from .evaluation import compute_min_and_max
 

DeepINN/utils/plotting/scatter_points.py

@@ -12,10 +12,10 @@ def scatter(subspace, *samplers, dpi=100, save=False):
 
     Parameters
     ----------
-    subspace : torchphysics.problem.Space
+    subspace : dp.problem.Space
         The (sub-)space of which the points should be plotted.
         Only plotting for dimensions <= 3 is possible.
-    *samplers : torchphysics.problem.Samplers
+    *samplers : dp.problem.Samplers
         The diffrent samplers for which the points should be plotted.
         The plot for each sampler will be created in the order there were
         passed in.
@@ -96,4 +96,6 @@ def _scatter_3D(points, labels, dpi, save):
     ax.set_ylabel(labels[1])
     ax.set_zlabel(labels[2])
     if save:
-        plt.savefig('geom.jpg', dpi = dpi,bbox_inches='tight',transparent=True)
+        plt.savefig('geom.jpg', dpi = dpi,bbox_inches='tight',transparent=True)
+
+

DeepINN/utils/user_fun.py

@@ -310,4 +310,10 @@ def evaluate_function(self, device='cpu', **inp):
                 self.fun = self.fun.to(device)
                 return self.fun
             else: 
-                return torch.tensor(self.fun, device=device).float()
+                return torch.tensor(self.fun, device=device).float()
+
+def tensor2numpy(tensor_list):
+    """
+    Converts a list of torch.tensors to numpy arrays.
+    """
+    return [tensor.detach().cpu().numpy() for tensor in tensor_list]

Tutorials/3. Gradients/1. Gradients.ipynb

@@ -1251,7 +1251,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.8.10"
   },
   "orig_nbformat": 4
  },