fix bug in restore genome.
This commit is contained in:
wls2002
@@ -56,6 +56,8 @@ class DefaultGenome(BaseGenome):
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def restore(self, state, transformed):
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seqs, nodes, u_conns = transformed
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conns = flatten_conns(nodes, u_conns, C=self.max_conns)
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# restore enable
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conns = jnp.insert(conns, obj=2, values=1, axis=1)
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return nodes, conns
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def forward(self, state, inputs, transformed):
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@@ -57,6 +57,9 @@ class RecurrentGenome(BaseGenome):
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def restore(self, state, transformed):
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nodes, u_conns = transformed
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conns = flatten_conns(nodes, u_conns, C=self.max_conns)
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# restore enable
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conns = jnp.insert(conns, obj=2, values=1, axis=1)
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return nodes, conns
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def forward(self, state, inputs, transformed):
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@@ -50,6 +50,11 @@ class Pipeline:
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self.fetch_data = lambda data: data
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else:
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raise NotImplementedError
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else:
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if isinstance(problem, RLEnv):
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assert not problem.record_episode, "record_episode must be False"
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elif isinstance(problem, FuncFit):
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assert not problem.return_data, "return_data must be False"
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def setup(self, state=State()):
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print("initializing")
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@@ -90,6 +95,13 @@ class Pipeline:
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self.problem.evaluate, in_axes=(None, 0, None, 0)
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)(state, keys, self.algorithm.forward, pop_transformed)
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# update population
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pop_nodes, pop_conns = jax.vmap(self.algorithm.restore, in_axes=(None, 0))(
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state, pop_transformed
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)
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state = state.update(pop_nodes=pop_nodes, pop_conns=pop_conns)
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# update data for next generation
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data = self.fetch_data(raw_data)
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assert (
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data.ndim == 3
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@@ -119,9 +131,10 @@ class Pipeline:
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# replace nan with -inf
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fitnesses = jnp.where(jnp.isnan(fitnesses), -jnp.inf, fitnesses)
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previous_pop = self.algorithm.ask(state)
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state = self.algorithm.tell(state, fitnesses)
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return state.update(randkey=randkey), fitnesses
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return state.update(randkey=randkey), previous_pop, fitnesses
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def auto_run(self, state):
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print("start compile")
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@@ -135,9 +148,7 @@ class Pipeline:
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self.generation_timestamp = time.time()
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previous_pop = self.algorithm.ask(state)
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state, fitnesses = compiled_step(state)
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state, previous_pop, fitnesses = compiled_step(state)
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fitnesses = jax.device_get(fitnesses)
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@@ -7,8 +7,8 @@
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"metadata": {
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"collapsed": true,
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"ExecuteTime": {
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"end_time": "2024-05-30T11:40:55.584592400Z",
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"start_time": "2024-05-30T11:40:53.016051600Z"
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"end_time": "2024-05-31T09:01:41.824974900Z",
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"start_time": "2024-05-31T09:01:39.138674100Z"
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}
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},
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"outputs": [],
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@@ -25,7 +25,7 @@
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"outputs": [
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{
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"data": {
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"text/plain": "((10, 5), (10, 4))"
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"text/plain": "((5, 5), (5, 4))"
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},
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"execution_count": 2,
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"metadata": {},
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@@ -33,7 +33,7 @@
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}
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],
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"source": [
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"genome = DefaultGenome(num_inputs=3, num_outputs=2, max_nodes=10, max_conns=10)\n",
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"genome = DefaultGenome(num_inputs=3, num_outputs=1, max_nodes=5, max_conns=5)\n",
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"state = genome.setup()\n",
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"key = jax.random.PRNGKey(0)\n",
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"nodes, conns = genome.initialize(state, key)\n",
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@@ -42,8 +42,8 @@
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"end_time": "2024-05-30T11:40:59.021858400Z",
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"start_time": "2024-05-30T11:40:55.592593400Z"
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"end_time": "2024-05-31T09:01:45.179170400Z",
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"start_time": "2024-05-31T09:01:41.832976100Z"
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}
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},
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"id": "89fb5cd0e77a028d"
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@@ -54,7 +54,7 @@
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"outputs": [
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{
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"data": {
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"text/plain": "(2, 10, 10)"
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"text/plain": "(Array([0, 1, 2, 4, 3], dtype=int32, weak_type=True),\n Array([[ 0. , -1.013169 , 1. , 0. , 0. ],\n [ 1. , -0.3775248 , 1. , 0. , 0. ],\n [ 2. , 0.7407059 , 1. , 0. , 0. ],\n [ 3. , -0.66817343, 1. , 0. , 0. ],\n [ 4. , 0.5336131 , 1. , 0. , 0. ]], dtype=float32, weak_type=True),\n Array([[[ nan, nan, nan, nan,\n 0.13149254],\n [ nan, nan, nan, nan,\n 0.02001922],\n [ nan, nan, nan, nan,\n -0.79229796],\n [ nan, nan, nan, nan,\n nan],\n [ nan, nan, nan, -0.57102853,\n nan]]], dtype=float32, weak_type=True))"
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},
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"execution_count": 3,
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"metadata": {},
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@@ -62,14 +62,14 @@
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}
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],
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"source": [
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"unflatten = unflatten_conns(nodes, conns)\n",
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"unflatten.shape"
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"transformed = genome.transform(state, nodes, conns)\n",
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"transformed"
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],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"end_time": "2024-05-30T11:40:59.472701700Z",
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"start_time": "2024-05-30T11:40:59.021858400Z"
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"end_time": "2024-05-31T09:01:45.729969500Z",
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"start_time": "2024-05-31T09:01:45.178173400Z"
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}
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},
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"id": "aaa88227bbf29936"
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@@ -80,7 +80,7 @@
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"outputs": [
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{
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"data": {
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"text/plain": "(Array([[ 0. , 5. , 1. , -0.41923347],\n [ 1. , 5. , 1. , -3.1815007 ],\n [ 2. , 5. , 1. , 0.5184341 ],\n [ 5. , 3. , 1. , -1.9784615 ],\n [ 5. , 4. , 1. , 0.7132204 ],\n [ nan, nan, nan, nan],\n [ nan, nan, nan, nan],\n [ nan, nan, nan, nan],\n [ nan, nan, nan, nan],\n [ nan, nan, nan, nan]], dtype=float32, weak_type=True),\n Array([[ 0. , 5. , 1. , -0.41923347],\n [ 1. , 5. , 1. , -3.1815007 ],\n [ 2. , 5. , 1. , 0.5184341 ],\n [ 5. , 3. , 1. , -1.9784615 ],\n [ 5. , 4. , 1. , 0.7132204 ],\n [ nan, nan, nan, nan],\n [ nan, nan, nan, nan],\n [ nan, nan, nan, nan],\n [ nan, nan, nan, nan],\n [ nan, nan, nan, nan]], dtype=float32))"
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"text/plain": "(Array([[ 0. , -1.013169 , 1. , 0. , 0. ],\n [ 1. , -0.3775248 , 1. , 0. , 0. ],\n [ 2. , 0.7407059 , 1. , 0. , 0. ],\n [ 3. , -0.66817343, 1. , 0. , 0. ],\n [ 4. , 0.5336131 , 1. , 0. , 0. ]], dtype=float32, weak_type=True),\n Array([[ 1. , 0. , 4. , 0.13149254],\n [ 1. , 1. , 4. , 0.02001922],\n [ 1. , 2. , 4. , -0.79229796],\n [ 1. , 4. , 3. , -0.57102853],\n [ 1. , nan, nan, nan]], dtype=float32))"
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},
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"execution_count": 4,
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"metadata": {},
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@@ -89,18 +89,44 @@
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],
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"source": [
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"# single flatten\n",
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"flatten = flatten_conns(nodes, unflatten, C=10)\n",
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"conns, flatten"
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"nodes, conns = genome.restore(state, transformed)\n",
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"nodes, conns"
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],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"end_time": "2024-05-30T11:41:00.308954100Z",
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"start_time": "2024-05-30T11:40:59.469541500Z"
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"end_time": "2024-05-31T09:01:46.660023600Z",
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"start_time": "2024-05-31T09:01:45.724970700Z"
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}
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},
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"id": "f2c65de38fdcff8f"
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"outputs": [
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{
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"data": {
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"text/plain": "Array([[ 1. , 3. , 0. , 1. , 4. ,\n 0.13149254],\n [ 1. , 3. , 1. , 1. , 4. ,\n 0.02001922],\n [ 1. , 3. , 2. , 1. , 4. ,\n -0.79229796],\n [ 1. , 3. , 4. , 1. , 3. ,\n -0.57102853],\n [ 1. , 3. , nan, 1. , nan,\n nan]], dtype=float32)"
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"conns = jnp.insert(conns, obj=3, values=1, axis=1)\n",
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"conns"
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],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"end_time": "2024-05-31T09:03:35.665080500Z",
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"start_time": "2024-05-31T09:03:35.013654700Z"
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}
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},
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"id": "10bcb665c32fb728"
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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