Add opt local_reshape_lift to enable softplus opt with reshape in the middle.

theano/tensor/nnet/tests/test_sigm.py

@@ -382,6 +382,17 @@ class T_softplus_opts(unittest.TestCase):
         assert isinstance(topo[2].op.scalar_op, theano.scalar.Neg)
         f(numpy.random.rand(54, 11).astype(config.floatX))
 
+        # Same test with a reshape
+        out = T.log(1 - sigmoid(x).reshape([x.size]))
+        f = theano.function([x], out, mode=self.m)
+        topo = f.maker.fgraph.toposort()
+        #assert len(topo) == 3
+        assert any(isinstance(node.op, T.Reshape) for node in topo)
+        assert any(isinstance(getattr(node.op, 'scalar_op', None),
+                              theano.tensor.nnet.sigm.ScalarSoftplus)
+                   for node in topo)
+        f(numpy.random.rand(54, 11).astype(config.floatX))
+
     def test_log1pexp_to_softplus(self):
         m = theano.config.mode
         if m == 'FAST_COMPILE':

theano/tensor/opt.py

@@ -2436,6 +2436,26 @@ def local_reshape_chain(node):
         return False
 register_canonicalize(local_reshape_chain)
 
+
+@register_canonicalize
+@register_stabilize
+@gof.local_optimizer([])
+def local_reshape_lift(node):
+    """
+    Reshape(UnaryElemwise(x)) -> UnaryElemwise(Reshape(x))
+
+    This optimization is needed by optimization
+    nnet/sigm.py:log1msigm_to_softplus to get applied when there is a reshape.
+    """
+    if (isinstance(node.op, T.Reshape) and
+        node.inputs[0].owner and
+        isinstance(node.inputs[0].owner.op, T.Elemwise) and
+        len(node.inputs[0].owner.inputs) == 1):
+        r = node.op(node.inputs[0].owner.inputs[0], node.inputs[1])
+        e = node.inputs[0].owner.op(r)
+        return [e]
+
+
 if 0:
     # TODO: Test that this optimziation works.
     @register_canonicalize

theano/tensor/tests/test_opt.py

@@ -4004,6 +4004,19 @@ def test_local_flatten_lift():
         assert isinstance(topo[1].op, tensor.Elemwise)
 
 
+def test_local_reshape_lift():
+    x = tensor.tensor4()
+    out = T.exp(x).reshape([x.size])
+    assert out.ndim == 1
+    mode = compile.mode.get_default_mode()
+    mode = mode.including('local_reshape_lift')
+    f = theano.function([x], out, mode=mode)
+    f(numpy.random.rand(5, 4, 3, 2).astype(config.floatX))
+    topo = f.maker.fgraph.toposort()
+    assert isinstance(topo[-2].op, tensor.Reshape)
+    assert isinstance(topo[-1].op, tensor.Elemwise)
+
+
 class Test_lift_transpose_through_dot(unittest.TestCase):
     def simple_optimize(self, g):
         out2in(opt.local_useless_elemwise).optimize(g)