• test_learning.py

add test_two_output_ports_on_OUTPUT_Node
PrincetonUniversity · Nov 4, 2023 · 531d818 · 531d818
1 parent fc7b12a
commit 531d818
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diff --git a/tests/composition/test_autodiffcomposition.py b/tests/composition/test_autodiffcomposition.py
@@ -2655,7 +2655,6 @@ def test_two_output_ports_on_OUTPUT_Node(self):
         result_autodiff_nodes = autodiff.learn(inputs={input_A: [[0, 0], [0, 1], [1, 0], [1, 1]],
                                                        input_B: [[1, 2], [1, 2], [1, 2], [1, 2]]},
                                                learning_rate = .01, epochs=3)
-
         for port, node in zip(result_autodiff_ports, result_autodiff_nodes):
             np.testing.assert_allclose(port, node)
 

diff --git a/tests/composition/test_learning.py b/tests/composition/test_learning.py
@@ -2107,6 +2107,29 @@ def test_backprop_fct_with_3_inputs_to_linear_combination_product(self, test_var
                     [[0.05755718, 0.06642539]], [[0.08571825, 0.10452682]]]
         np.testing.assert_allclose(actual=comp.results, desired=expected, atol=1e-8)
 
+    def test_two_output_ports_on_OUTPUT_Node(self):
+
+        input_A = pnl.ProcessingMechanism(name='INPUT_A', size=2)
+        input_B = pnl.ProcessingMechanism(name='INPUT_B', size=2)
+        output = pnl.ProcessingMechanism(name='OUTPUT', size=(2,3))
+        comp = Composition(name='comp')
+
+        with pytest.raises(CompositionError) as error_text:
+            comp.add_backpropagation_learning_pathway([input_A,
+                                                       pnl.MappingProjection(input_A, output.input_ports[0]),
+                                                       output])
+        error_msg = ("'OUTPUT', which is the terminal node of a learning pathway in 'comp', "
+                     "has a more than one output_port (2), which is not currently supported for learning. "
+                     "Trying using a different Mechanism for each output.")
+        assert error_msg in str(error_text.value)
+        # # FIX: Add these when/if learning is supported for multiple output_ports
+        # comp.add_backpropagation_learning_pathway([input_B,
+        #                                            pnl.MappingProjection(input_B, output.input_ports[1]),
+        #                                            output])
+        # comp.learn(inputs={input_A: [[0, 0], [0, 1], [1, 0], [1, 1]],
+        #                    input_B: [[1, 2], [1, 2], [1, 2], [1, 2]]},
+        #            learning_rate = .01, epochs=3)
+
 
     expected_quantities = [
         (