Merge pull request #3643 from SinaHonari/issue3420

theano/tensor/basic.py

@@ -4401,6 +4401,11 @@ class Reshape(Op):
         # Here, we only simplify if the shape (node.inputs[1]) is a constant,
         # ideally it would suffice to check that it is always non-negative.
 
+        # If current variable is a scalar and its dimensionality should
+        # change to self.ndim, then use size 1 for all new dimensions.
+        if len(ishapes[0]) == 0:
+            return [(1,) * self.ndim]
+
         requ = node.inputs[1]
         if isinstance(requ, theano.tensor.TensorConstant):
             requ = list(requ.data)
@@ -4418,17 +4423,12 @@ class Reshape(Op):
                                  ' must have at most one entry equal to -1')
             return [requ]
         else:
-            oshape = []
-            for i in xrange(self.ndim):
-                default_os_i = theano.tensor.opt.Shape_i(i)(node.outputs[0])
-                try:
-                    os_i = get_scalar_constant_value(node.inputs[1][i]).item()
-                    if os_i == -1:
-                        os_i = default_os_i
-                except NotScalarConstantError:
-                    os_i = default_os_i
-                oshape.append(os_i)
-            return [tuple(oshape)]
+            new_dims = [node.inputs[1][i] for i in xrange(self.ndim)]
+            return [tuple([switch(eq(new_dims[i], -1),
+                                  theano.tensor.mul(*ishapes[0]) //
+                                  (-theano.tensor.mul(*new_dims)),
+                                  new_dims[i])
+                           for i in xrange(self.ndim)])]
 
     def c_code_cache_version(self):
         return (6,)

theano/tensor/tests/test_basic.py

@@ -5112,14 +5112,14 @@ class T_reshape(utt.InferShapeTester, utt.TestOptimizationMixin):
         r = a.reshape(shapes, ndim=1)
         z = zeros_like(r)
 
-        f = self.function([a, shapes], z.shape)
+        f = self.function([a, shapes], r)
         self.assertRaises(ValueError, f, a_val, [13])
 
         # Test reshape to 2 dim
         r = a.reshape(shapes, ndim=2)
         z = zeros_like(r)
 
-        f = self.function([a, shapes], z.shape)
+        f = self.function([a, shapes], r)
 
         self.assertRaises(ValueError, f, a_val, [-1, 5])
         self.assertRaises(ValueError, f, a_val, [7, -1])