Backend Tensorflow 1.x: DeepONet & DeepONetCartesianProd support multiple outputs (#1532)

Author: vl-dud

deepxde/data/pde_operator.py

@@ -236,8 +236,10 @@ def _losses(self, outputs, loss_fn, inputs, model, num_func):
 
         losses = []
         for i in range(num_func):
-            out = outputs[i][:, None]
-
+            out = outputs[i]
+            # Single output
+            if bkd.ndim(out) == 1:
+                out = out[:, None]
             f = []
             if self.pde.pde is not None:
                 f = self.pde.pde(inputs[1], out, model.net.auxiliary_vars[i][:, None])

deepxde/data/triple.py

@@ -59,11 +59,11 @@ class TripleCartesianProd(Data):
     """
 
     def __init__(self, X_train, y_train, X_test, y_test):
-        if len(X_train[0]) * len(X_train[1]) != y_train.size:
+        if len(X_train[0]) != y_train.shape[0] or len(X_train[1]) != y_train.shape[1]:
             raise ValueError(
                 "The training dataset does not have the format of Cartesian product."
             )
-        if len(X_test[0]) * len(X_test[1]) != y_test.size:
+        if len(X_test[0]) != y_test.shape[0] or len(X_test[1]) != y_test.shape[1]:
             raise ValueError(
                 "The testing dataset does not have the format of Cartesian product."
             )