remove unused optimizer

theano/tensor/nnet/nnet.py

@@ -697,115 +697,6 @@ def softmax_simplifier(numerators, denominators):
     return numerators, denominators
 opt.local_mul_canonizer.add_simplifier(softmax_simplifier, 'softmax_simplifier')
 
-# another commit that removes
-if 0:
-    @opt.register_specialize
-    @gof.local_optimizer([tensor.add])
-    def local_softmax_grad(node):
-        '''dy*sm - DimShuffle{0,'x'}(sum{1}(dy*sm))*sm -> softmax_grad(dy,sm)'''
-        # TODO what if the signs are changed?
-        # TODO and if a scalar is distributed before each of the terms?
-        # TODO 'dy' could also be a product
-        if node.op == tensor.add and node.out.ndim == 2:
-            add_inputs = node.inputs
-            # Trying to locate two nodes in the sum:
-            #   dy * sm, prod_term
-            #   - DimShuffle{0,'x'}(sum{1}(dy*sm))*sm
-            prod_term = None
-            other_terms = []
-            # First, prod_term
-            for add_in in add_inputs:
-                if (add_in.owner and
-                        add_in.owner.op == tensor.mul and
-                        prod_term is None):
-                    mul_inputs = add_in.owner.inputs
-                    if (len(mul_inputs) == 2 and
-                            all([mul_in.ndim == 2 for mul_in in mul_inputs])):
-                        prod_term = add_in
-                    else:
-                        other_terms.append(add_in)
-                else:
-                    other_terms.append(add_in)
-            if prod_term is None:
-                # print 'no prod_term'
-                return
-            assert len(other_terms) == len(add_inputs) - 1
-
-            ds_term = None
-            rest = []
-            for add_in in other_terms:
-                if add_in.owner and add_in.owner.op == tensor.neg:
-                    neg_input = add_in.owner.inputs[0]
-                    if neg_input.owner and neg_input.owner.op == tensor.mul:
-                        mul2_inputs = neg_input.owner.inputs
-                        if len(mul2_inputs) != 2:
-                            rest.append(add_in)
-                            # print 'len(mul2_inputs) =', len(mul2_inputs)
-                            continue
-                        # Try and find DimShuffle(Sum)
-                        maybe_ds = None
-                        for i, mul2_in in enumerate(mul2_inputs):
-                            if mul2_in.owner and isinstance(mul2_in.owner.op,
-                                                            elemwise.DimShuffle):
-                                maybe_ds = mul2_in
-                                maybe_sm = mul2_inputs[1 - i]  # The other one
-                        if (maybe_ds is None or
-                                maybe_ds.ndim != 2 or
-                                maybe_sm.ndim != 2):
-                            rest.append(add_in)
-                            # print 'maybe_ds =', maybe_ds
-                            # if maybe_ds:
-                            # print 'maybe_ds.ndim =', maybe_ds.ndim, ', maybe_sm.ndim =', maybe_sm.ndim
-                            continue
-
-                        if maybe_sm is mul_inputs[0]:
-                            maybe_dy = mul_inputs[1]
-                        elif maybe_sm is mul_inputs[1]:
-                            maybe_dy = mul_inputs[0]
-                        else:
-                            rest.append(add_in)
-                            # print 'maybe_sm, maybe_dy =', maybe_sm, maybe_dy
-                            # print 'mul_inputs =', mul_inputs
-                            continue
-
-                        ds_order = maybe_ds.owner.op.new_order
-                        ds_input = maybe_ds.owner.inputs[0]
-                        axis = None
-                        if ds_input.owner and isinstance(ds_input.owner.op,
-                                                         elemwise.Sum):
-                            axis = ds_input.owner.op.axis
-                            sum_input = ds_input.owner.inputs[0]
-
-                        if ((ds_order != (0, 'x')) or
-                                (axis != (1,)) or
-                                (sum_input is not prod_term)):
-                            rest.append(add_in)
-                            # print 'ds_order =', ds_order
-                            # print 'axis =', axis
-                            # if axis is not None:
-                            #    print 'sum_input =', sum_input, ', prod_term =', prod_term
-                            # else:
-                            #    print 'ds_input.owner =', ds_input.owner
-                            # print 'add_in =', add_in
-                            continue
-
-                        ds_term = add_in
-
-                    else:
-                        # print 'neg_input.owner =', neg_input.owner
-                        rest.append(add_in)
-                else:
-                    # print 'add_in.owner =', add_in.owner
-                    rest.append(add_in)
-
-            if ds_term is None:
-                # print 'no ds_term'
-                return
-            if len(rest) == 0:
-                return [softmax_grad(maybe_dy, maybe_sm)]
-            else:
-                return [tensor.add(softmax_grad(maybe_dy, maybe_sm), *rest)]
-
 
 class CrossentropySoftmaxArgmax1HotWithBias(gof.Op):
     """