add sympy support; which can transfer your network into sympy expression;

add visualize in genome;
add related tests.

tensorneat/utils/activation/act_jnp.py

@@ -0,0 +1,89 @@
+import jax
+import jax.numpy as jnp
+
+
+class Act:
+    @staticmethod
+    def name2func(name):
+        return getattr(Act, name)
+
+    @staticmethod
+    def sigmoid(z):
+        z = jnp.clip(5 * z, -10, 10)
+        return 1 / (1 + jnp.exp(-z))
+
+    @staticmethod
+    def tanh(z):
+        return jnp.tanh(0.6 * z)
+
+    @staticmethod
+    def sin(z):
+        return jnp.sin(z)
+
+    @staticmethod
+    def relu(z):
+        return jnp.maximum(z, 0)
+
+    @staticmethod
+    def lelu(z):
+        leaky = 0.005
+        return jnp.where(z > 0, z, leaky * z)
+
+    @staticmethod
+    def identity(z):
+        return z
+
+    @staticmethod
+    def clamped(z):
+        return jnp.clip(z, -1, 1)
+
+    @staticmethod
+    def inv(z):
+        z = jnp.where(z > 0, jnp.maximum(z, 1e-7), jnp.minimum(z, -1e-7))
+        return 1 / z
+
+    @staticmethod
+    def log(z):
+        z = jnp.maximum(z, 1e-7)
+        return jnp.log(z)
+
+    @staticmethod
+    def exp(z):
+        z = jnp.clip(z, -10, 10)
+        return jnp.exp(z)
+
+    @staticmethod
+    def abs(z):
+        return jnp.abs(z)
+
+
+ACT_ALL = (
+    Act.sigmoid,
+    Act.tanh,
+    Act.sin,
+    Act.relu,
+    Act.lelu,
+    Act.identity,
+    Act.clamped,
+    Act.inv,
+    Act.log,
+    Act.exp,
+    Act.abs,
+)
+
+
+def act_func(idx, z, act_funcs):
+    """
+    calculate activation function for each node
+    """
+    idx = jnp.asarray(idx, dtype=jnp.int32)
+    # change idx from float to int
+
+    # -1 means identity activation
+    res = jax.lax.cond(
+        idx == -1,
+        lambda: z,
+        lambda: jax.lax.switch(idx, act_funcs, z),
+    )
+
+    return res