disable activation in the output node of network;

we recommend to use output_transform;
change hyperparameters (strong) in XOR example;

tensorneat/algorithm/hyperneat/hyperneat.py

@@ -1,3 +1,5 @@
+from typing import Callable
+
 import jax, jax.numpy as jnp
 
 from utils import State, Act, Agg
@@ -18,6 +20,7 @@ class HyperNEAT(BaseAlgorithm):
             activation=Act.sigmoid,
             aggregation=Agg.sum,
             activate_time: int = 10,
+            output_transform: Callable = Act.sigmoid,
     ):
         assert substrate.query_coors.shape[1] == neat.num_inputs, \
             "Substrate input size should be equal to NEAT input size"
@@ -34,6 +37,7 @@ class HyperNEAT(BaseAlgorithm):
             node_gene=HyperNodeGene(activation, aggregation),
             conn_gene=HyperNEATConnGene(),
             activate_time=activate_time,
+            output_transform=output_transform
         )
 
     def setup(self, randkey):
@@ -102,11 +106,13 @@ class HyperNodeGene(BaseNodeGene):
         self.activation = activation
         self.aggregation = aggregation
 
-    def forward(self, attrs, inputs):
-        return self.activation(
-            self.aggregation(inputs)
-        )
+    def forward(self, attrs, inputs, is_output_node=False):
+        return jax.lax.cond(
+            is_output_node,
+            lambda: self.aggregation(inputs),  # output node does not need activation
+            lambda: self.activation(self.aggregation(inputs))
 
+        )
 
 class HyperNEATConnGene(BaseConnGene):
     custom_attrs = ['weight']

tensorneat/algorithm/neat/gene/node/default.py

@@ -92,7 +92,10 @@ class DefaultNodeGene(BaseNodeGene):
         z = bias + res * z
 
         # the last output node should not be activated
-        if not is_output_node:
-            z = act(act_idx, z, self.activation_options)
+        z = jax.lax.cond(
+            is_output_node,
+            lambda: z,
+            lambda: act(act_idx, z, self.activation_options)
+        )
 
         return z

tensorneat/algorithm/neat/genome/default.py

@@ -58,7 +58,7 @@ class DefaultGenome(BaseGenome):
 
             def hit():
                 ins = jax.vmap(self.conn_gene.forward, in_axes=(1, 0))(conns[:, :, i], values)
-                z = self.node_gene.forward(nodes_attrs[i], ins)
+                z = self.node_gene.forward(nodes_attrs[i], ins, is_output_node=jnp.isin(i, self.output_idx))
                 new_values = values.at[i].set(z)
                 return new_values
 

tensorneat/algorithm/neat/genome/recurrent.py

@@ -1,3 +1,5 @@
+from typing import Callable
+
 import jax, jax.numpy as jnp
 from utils import unflatten_conns
 
@@ -18,10 +20,18 @@ class RecurrentGenome(BaseGenome):
                  node_gene: BaseNodeGene = DefaultNodeGene(),
                  conn_gene: BaseConnGene = DefaultConnGene(),
                  activate_time: int = 10,
+                 output_transform: Callable = None
                  ):
         super().__init__(num_inputs, num_outputs, max_nodes, max_conns, node_gene, conn_gene)
         self.activate_time = activate_time
 
+        if output_transform is not None:
+            try:
+                _ = output_transform(jnp.zeros(num_outputs))
+            except Exception as e:
+                raise ValueError(f"Output transform function failed: {e}")
+        self.output_transform = output_transform
+
     def transform(self, nodes, conns):
         u_conns = unflatten_conns(nodes, conns)
 
@@ -52,7 +62,11 @@ class RecurrentGenome(BaseGenome):
             )(conns, values)
 
             # calculate nodes
-            values = jax.vmap(self.node_gene.forward)(nodes_attrs, node_ins.T)
+            is_output_nodes = jnp.isin(
+                jnp.arange(N),
+                self.output_idx
+            )
+            values = jax.vmap(self.node_gene.forward)(nodes_attrs, node_ins.T, is_output_nodes)
             return values
 
         vals = jax.lax.fori_loop(0, self.activate_time, body_func, vals)