CNTK v2 library: Fix a bug in Reshape where it failed to handle the new shape's leading axis dimensionality being 1

Source/ComputationNetworkLib/ReshapingNodes.h

@@ -150,12 +150,16 @@ public:
 
     virtual void /*ComputationNode::*/ ForwardProp(const FrameRange& fr) override
     {
-        ValueFor(fr).AssignValuesOf(InputRef(0).ValueFor(fr));
+        auto result = ValueFor(fr);
+        auto inputValue = InputRef(0).ValueFor(fr);
+        result.AssignValuesOf(inputValue.Reshaped(result.GetNumRows(), result.GetNumCols()));
     }
 
     virtual void /*ComputationNode::*/ BackpropTo(const size_t inputIndex, const FrameRange& fr) override
     {
-        InputRef(inputIndex).GradientFor(fr) += GradientFor(fr);
+        auto gradient = GradientFor(fr);
+        auto inputGradient = InputRef(inputIndex).GradientFor(fr);
+        inputGradient += gradient.Reshaped(inputGradient.GetNumRows(), inputGradient.GetNumCols());
     }
 
     virtual bool OutputUsedInComputingInputNodesGradients() const override { return false; }

bindings/python/cntk/ops/tests/reshaping_test.py

@@ -157,13 +157,29 @@ def test_op_reshape_gradient_accumulation(device_id, precision):
                     device_id=device_id, precision=precision)
 
 
+def test_op_reshape_parameter():
+    from .. import reshape, parameter
+
+    param_shape = (4,2)
+    param_value = np.random.random(param_shape)
+    param = parameter(init=param_value)
+    param_new_shape = (8,1)
+    param_reshaped = reshape(param, param_new_shape)
+
+    expected_forward = np.copy(param_value).reshape(param_new_shape)
+    state, result = param_reshaped.forward({}, [param_reshaped.output], [param_reshaped.output])
+    np.allclose(result[param_reshaped.output], expected_forward)
+    
+    grad = param_reshaped.backward(state, np.ones(param_new_shape), [param])
+    np.allclose(grad[param], np.ones(param_shape))
+
+
 SLICE_TEST_CASES_STATIC = [
     #(input_data, slice_params(beg_index, end_index, axis), expected_result)
     ([[1, 2], [-3, 4]], (1, 2, 0), [[-3, 4]]),
     ([[1,2],[-3,4]], (1,2,1), [[2],[4]]),
 ]
 
-
 @pytest.mark.parametrize("input_data, slice_params, expected_result",
                          SLICE_TEST_CASES_STATIC)
 def test_op_slice(input_data, slice_params, expected_result, device_id, precision):