Backend PyTorch supports more inverse problems

Author: lululxvi

deepxde/model.py

@@ -209,7 +209,8 @@ def outputs(inputs):
         def outputs_losses(inputs, targets):
             outputs_ = outputs(inputs)
             # Data losses
-            targets = torch.from_numpy(targets)
+            if targets is not None:
+                targets = torch.from_numpy(targets)
             losses = self.data.losses(targets, outputs_, loss_fn, self)
             if not isinstance(losses, list):
                 losses = [losses]

examples/Lorenz_inverse.py

@@ -1,20 +1,28 @@
-"""Backend supported: tensorflow.compat.v1, tensorflow
+"""Backend supported: tensorflow.compat.v1, tensorflow, pytorch
 
 Documentation: https://deepxde.readthedocs.io/en/latest/demos/lorenz.inverse.html
 """
 import deepxde as dde
 import numpy as np
+# Backend tensorflow.compat.v1 or tensorflow
 from deepxde.backend import tf
+# Backend pytorch
+# import torch
 
 
 def gen_traindata():
     data = np.load("dataset/Lorenz.npz")
     return data["t"], data["y"]
 
 
+# Backend tensorflow.compat.v1 or tensorflow
 C1 = tf.Variable(1.0)
 C2 = tf.Variable(1.0)
 C3 = tf.Variable(1.0)
+# Backend pytorch
+# C1 = torch.tensor(1.0, requires_grad=True)
+# C2 = torch.tensor(1.0, requires_grad=True)
+# C3 = torch.tensor(1.0, requires_grad=True)
 
 
 def Lorenz_system(x, y):

examples/reaction_inverse.py

@@ -1,7 +1,10 @@
-"""Backend supported: tensorflow.compat.v1, tensorflow"""
+"""Backend supported: tensorflow.compat.v1, tensorflow, pytorch"""
 import deepxde as dde
 import numpy as np
+# Backend tensorflow.compat.v1 or tensorflow
 from deepxde.backend import tf
+# Backend pytorch
+# import torch
 
 
 def gen_traindata():
@@ -15,8 +18,12 @@ def gen_traindata():
     return np.hstack((X, T)), Ca, Cb
 
 
+# Backend tensorflow.compat.v1 or tensorflow
 kf = tf.Variable(0.05)
 D = tf.Variable(1.0)
+# Backend pytorch
+# kf = torch.tensor(0.05, requires_grad=True)
+# D = torch.tensor(1.0, requires_grad=True)
 
 
 def pde(x, y):