fix in local_argmax_pushdown

theano/tensor/nnet/nnet.py

@@ -86,8 +86,8 @@ class SoftmaxWithBias(gof.Op):
 
         x_plus_b = x + b[None, :]
         e_x = numpy.exp(x_plus_b - x_plus_b.max(axis=1)[:, None])
-        sm = e_x / e_x.sum(axis=1)[:, None]
-        output_storage[0][0] = sm
+        e_x *= 1.0 / e_x.sum(axis=1)[:, None]
+        output_storage[0][0] = e_x
 
     def grad(self, inp, grads):
         x, b = inp
@@ -1469,7 +1469,7 @@ def local_softmax_grad_to_crossentropy_with_softmax_grad(node):
 def local_argmax_pushdown(node):
     if node.op == tensor._max_and_argmax and node.inputs[0].owner and \
             len(node.outputs[0].clients) > 0 and node.inputs[0].owner.op in \
-            (softmax, softplus, tensor.exp, tensor.log, tensor.tanh, sigmoid,
+            (softmax_op, softplus, tensor.exp, tensor.log, tensor.tanh, sigmoid,
              softmax_with_bias):
         if theano.config.warn.argmax_pushdown_bug:
             logging.getLogger('theano.tensor.nnet.nnet').warn("WARNING: there "
@@ -1485,7 +1485,7 @@ def local_argmax_pushdown(node):
         x_max, x_argmax = node.outputs
         x, axis = node.inputs
         # TODO: Make a list/set of monotonic ops...
-        if x.owner and x.owner.op in (softmax, softplus, tensor.exp,
+        if x.owner and x.owner.op in (softmax_op, softplus, tensor.exp,
                                       tensor.log, tensor.tanh, sigmoid):
             pre_x, = x.owner.inputs
             return tensor._max_and_argmax(pre_x, axis)

theano/tensor/nnet/tests/test_nnet.py

@@ -1011,7 +1011,7 @@ class T_CrossentropyCategorical1Hot(utt.InferShapeTester):
             try:
                 g = theano.function([x, b, y], T.grad(expr, x), mode=mode)
             finally:
-                config.warn.sum_div_dimshuffle_qbug = backup
+                config.warn.sum_div_dimshuffle_bug = backup
 
             if verbose:
                 printing.debugprint(g)
@@ -1026,7 +1026,7 @@ class T_CrossentropyCategorical1Hot(utt.InferShapeTester):
                 theano.printing.debugprint(g)
                 raise
 
-    def test_scrossentropy_softmax_1hot_with_bias_dxcale_cost(self):
+    def test_crossentropy_softmax_1hot_with_bias_dxcale_cost(self):
         # TODO: add the optimization in FAST_COMPILE?
         # In the mean time, run it as 'FAST_RUN' instead
         mode = theano.compile.mode.get_default_mode()
@@ -1130,7 +1130,7 @@ def test_argmax_pushdown():
 
     # test that the max_and_argmax is pushed down if the max is not used
     out = tensor.max_and_argmax(
-            softmax(tensor.exp(tensor.tanh(sigmoid(x)))),
+            softmax_graph(tensor.exp(tensor.tanh(sigmoid(x)))),
             axis=-1)[1]
     fgraph = gof.FunctionGraph(
             [x],
@@ -1147,7 +1147,7 @@ def test_argmax_pushdown():
     x = tensor.matrix()
     # test that the max_and_argmax is not pushed down if the max is used
     out = tensor.max_and_argmax(
-            softmax(tensor.exp(tensor.tanh(sigmoid(x)))),
+            softmax_op(tensor.exp(tensor.tanh(sigmoid(x)))),
             axis=-1)[0]
     fgraph = gof.FunctionGraph(
             [x],