beundling weights
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zomseffen
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bd56173379
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b0d22f6bf1
1 changed files with 64 additions and 40 deletions
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@ -51,28 +51,32 @@ class EvolutionModel(nn.Module):
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self.incoming_connections[connection.end].append(connection)
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self.layers = {}
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self.layer_non_recurrent_inputs = {}
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self.layer_recurrent_inputs = {}
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self.layer_results = {}
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self.layer_num = 1
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self.indices = {}
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self.has_recurrent = False
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self.non_recurrent_indices = {}
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self.recurrent_indices = {}
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with torch.no_grad():
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for key, value in self.incoming_connections.items():
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value.sort(key=lambda element: element.start)
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lin = nn.Linear(len(value), 1, bias=self.genes.nodes[key].bias is not None)
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for index, connection in enumerate(value):
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lin.weight[0, index] = value[index].weight
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if self.genes.nodes[key].bias is not None:
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lin.bias[0] = self.genes.nodes[key].bias
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non_lin = nn.ELU()
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sequence = nn.Sequential(
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lin,
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non_lin
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)
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self.add_module('layer_' + str(key), sequence)
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self.layers[key] = sequence
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# lin = nn.Linear(len(value), 1, bias=self.genes.nodes[key].bias is not None)
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# for index, connection in enumerate(value):
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# lin.weight[0, index] = value[index].weight
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# if self.genes.nodes[key].bias is not None:
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# lin.bias[0] = self.genes.nodes[key].bias
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#
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# non_lin = nn.ELU()
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# sequence = nn.Sequential(
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# lin,
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# non_lin
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# )
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# self.add_module('layer_' + str(key), sequence)
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# self.layers[key] = sequence
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self.indices[key] = list(map(lambda element: element.start, value))
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self.non_recurrent_indices[key] = list(filter(lambda element: not element.recurrent, value))
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@ -101,20 +105,39 @@ class EvolutionModel(nn.Module):
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rank_of_node[key] = max_rank + 1
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layers_to_add = list(filter(lambda element: element[0] not in rank_of_node.keys(), layers_to_add))
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ranked_layers = list(rank_of_node.items())
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ranked_layers.sort(key=lambda element: element[1])
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ranked_layers = list(filter(lambda element: element[1] > 0, ranked_layers))
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ranked_layers = list(map(lambda element: (element, 0),
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filter(lambda recurrent_element:
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recurrent_element not in list(
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map(lambda ranked_layer: ranked_layer[0], ranked_layers)
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),
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list(filter(lambda recurrent_keys:
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len(self.recurrent_indices[recurrent_keys]) > 0,
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self.recurrent_indices.keys()))))) + ranked_layers
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with torch.no_grad():
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self.layer_num = max_rank = max(map(lambda element: element[1], rank_of_node.items()))
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#todo: handle solely recurrent nodes
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for rank in range(1, max_rank + 1):
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# get nodes
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nodes = list(map(lambda element: element[0], filter(lambda item: item[1] == rank, rank_of_node.items())))
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non_recurrent_inputs = list(set.union(*map(lambda node: set(self.non_recurrent_indices[node]), nodes)))
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non_recurrent_inputs.sort()
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recurrent_inputs = list(set.union(*map(lambda node: set(self.recurrent_indices[node]), nodes)))
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recurrent_inputs.sort()
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lin = nn.Linear(len(non_recurrent_inputs) + len(recurrent_inputs), len(nodes), bias=True)
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# todo: load weights
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# for index, connection in enumerate(value):
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# lin.weight[0, index] = value[index].weight
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# if self.genes.nodes[key].bias is not None:
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# lin.bias[0] = self.genes.nodes[key].bias
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#
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non_lin = nn.ELU()
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sequence = nn.Sequential(
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lin,
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non_lin
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)
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self.add_module('layer_' + str(rank), sequence)
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self.layers[rank] = sequence
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self.layer_results[rank] = nodes
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self.layer_non_recurrent_inputs[rank] = non_recurrent_inputs
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self.layer_recurrent_inputs[rank] = recurrent_inputs
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self.layer_order = list(map(lambda element: element[0], ranked_layers))
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self.memory_size = (max(map(lambda element: element[1].node_id, self.genes.nodes.items())) + 1)
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self.memory = torch.Tensor(self.memory_size)
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self.output_range = range(self.num_input_nodes, self.num_input_nodes + self.action_num * 2)
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@ -130,24 +153,25 @@ class EvolutionModel(nn.Module):
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outs = []
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for batch_index, batch_element in enumerate(x_flat):
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memory[0:self.num_input_nodes] = batch_element
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for layer_index in self.layer_order:
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non_recurrent_in = memory[self.non_recurrent_indices[layer_index]]
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for layer_index in range(1, self.layer_num + 1):
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non_recurrent_in = memory[self.layer_non_recurrent_inputs[layer_index]]
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non_recurrent_in = torch.stack([non_recurrent_in])
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if self.has_recurrent and len(self.recurrent_indices[layer_index]) > 0:
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recurrent_in = last_memory_flat[batch_index, self.recurrent_indices[layer_index]]
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if self.has_recurrent and len(self.layer_recurrent_inputs[layer_index]) > 0:
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recurrent_in = last_memory_flat[batch_index, self.layer_recurrent_inputs[layer_index]]
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recurrent_in = torch.stack([recurrent_in])
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combined_in = torch.concat([non_recurrent_in, recurrent_in], dim=1)
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else:
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combined_in = non_recurrent_in
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memory[layer_index] = self.layers[layer_index](combined_in)
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outs.append(memory[self.num_input_nodes: self.num_input_nodes + self.action_num * 2])
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memory[self.layer_results[layer_index]] = self.layers[layer_index](combined_in)
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outs.append(memory[self.output_range])
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outs = torch.stack(outs)
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self.memory = torch.Tensor(memory)
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return torch.reshape(outs, (x.shape[0], outs.shape[1]//2, 2))
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def update_genes_with_weights(self):
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# todo rework
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for key, value in self.incoming_connections.items():
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value.sort(key=lambda element: element.start)
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