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Add feedforward network tests.
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‎tests/test_feedforward_network.py‎

Lines changed: 131 additions & 63 deletions
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import os
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import neat
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from neat import activations
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from neat.genes import DefaultConnectionGene, DefaultNodeGene
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from neat.nn import FeedForwardNetwork
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@@ -44,71 +48,135 @@ def test_basic():
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assert result[0] == r.values[0]
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# TODO: Update this test for the current implementation.
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# def test_simple_nohidden():
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# config_params = {
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# 'num_inputs':2,
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# 'num_outputs':1,
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# 'num_hidden':0,
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# 'feed_forward':True,
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# 'compatibility_threshold':3.0,
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# 'excess_coefficient':1.0,
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# 'disjoint_coefficient':1.0,
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# 'compatibility_weight_coefficient':1.0,
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# 'conn_add_prob':0.5,
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# 'conn_delete_prob':0.05,
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# 'node_add_prob':0.1,
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# 'node_delete_prob':0.05}
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# config = DefaultGenomeConfig(config_params)
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# config.genome_config.set_input_output_sizes(2, 1)
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# g = DefaultGenome(0, config)
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# g.add_node(0, 0.0, 1.0, 'sum', 'tanh')
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# g.add_connection(-1, 0, 1.0, True)
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# g.add_connection(-2, 0, -1.0, True)
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#
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# net = nn.create_feed_forward_phenotype(g, config)
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#
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# v00 = net.serial_activate([0.0, 0.0])
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# assert_almost_equal(v00[0], 0.0, 1e-3)
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#
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# v01 = net.serial_activate([0.0, 1.0])
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# assert_almost_equal(v01[0], -0.76159, 1e-3)
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#
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# v10 = net.serial_activate([1.0, 0.0])
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# assert_almost_equal(v10[0], 0.76159, 1e-3)
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#
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# v11 = net.serial_activate([1.0, 1.0])
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# assert_almost_equal(v11[0], 0.0, 1e-3)
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# TODO: Update this test for the current implementation.
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# def test_simple_hidden():
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# config = Config()
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# config.genome_config.set_input_output_sizes(2, 1)
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# g = DefaultGenome(0, config)
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#
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# g.add_node(0, 0.0, 1.0, 'sum', 'identity')
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# g.add_node(1, -0.5, 5.0, 'sum', 'sigmoid')
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# g.add_node(2, -1.5, 5.0, 'sum', 'sigmoid')
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# g.add_connection(-1, 1, 1.0, True)
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# g.add_connection(-2, 2, 1.0, True)
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# g.add_connection(1, 0, 1.0, True)
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# g.add_connection(2, 0, -1.0, True)
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# net = nn.create_feed_forward_phenotype(g, config)
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#
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# v00 = net.serial_activate([0.0, 0.0])
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# assert_almost_equal(v00[0], 0.195115, 1e-3)
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#
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# v01 = net.serial_activate([0.0, 1.0])
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# assert_almost_equal(v01[0], -0.593147, 1e-3)
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#
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# v10 = net.serial_activate([1.0, 0.0])
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# assert_almost_equal(v10[0], 0.806587, 1e-3)
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#
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# v11 = net.serial_activate([1.0, 1.0])
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# assert_almost_equal(v11[0], 0.018325, 1e-3)
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def _create_simple_nohidden_network():
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"""Small genome-built feedforward net: 2 inputs -> 1 tanh output, no hidden layer."""
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local_dir = os.path.dirname(__file__)
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config_path = os.path.join(local_dir, "test_configuration")
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config = neat.Config(
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neat.DefaultGenome,
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neat.DefaultReproduction,
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neat.DefaultSpeciesSet,
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neat.DefaultStagnation,
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config_path,
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)
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genome = neat.DefaultGenome(0)
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# Single output node 0 with tanh activation and sum aggregation.
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node0 = DefaultNodeGene(0)
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node0.bias = 0.0
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node0.response = 1.0
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node0.activation = "tanh"
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node0.aggregation = "sum"
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genome.nodes[0] = node0
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# Connections: input -1 -> 0 (weight 1.0), input -2 -> 0 (weight -1.0).
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conn1_key = (-1, 0)
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conn1 = DefaultConnectionGene(conn1_key, innovation=0)
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conn1.weight = 1.0
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conn1.enabled = True
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conn2_key = (-2, 0)
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conn2 = DefaultConnectionGene(conn2_key, innovation=1)
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conn2.weight = -1.0
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conn2.enabled = True
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genome.connections[conn1_key] = conn1
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genome.connections[conn2_key] = conn2
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return FeedForwardNetwork.create(genome, config)
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def test_simple_nohidden_from_genome():
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"""FeedForwardNetwork.create builds the expected simple no-hidden network."""
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net = _create_simple_nohidden_network()
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v00 = net.activate([0.0, 0.0])
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assert_almost_equal(v00[0], 0.0, 1e-6)
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v01 = net.activate([0.0, 1.0])
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assert_almost_equal(v01[0], -0.9866142981514303, 1e-6)
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v10 = net.activate([1.0, 0.0])
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assert_almost_equal(v10[0], 0.9866142981514303, 1e-6)
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v11 = net.activate([1.0, 1.0])
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assert_almost_equal(v11[0], 0.0, 1e-6)
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def _create_simple_hidden_network():
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"""Small genome-built feedforward net: 2 inputs -> 2 sigmoid hidden -> 1 identity output."""
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local_dir = os.path.dirname(__file__)
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config_path = os.path.join(local_dir, "test_configuration")
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config = neat.Config(
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neat.DefaultGenome,
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neat.DefaultReproduction,
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neat.DefaultSpeciesSet,
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neat.DefaultStagnation,
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config_path,
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)
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genome = neat.DefaultGenome(0)
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# Output node 0 (identity), hidden nodes 1 and 2 (sigmoid).
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node0 = DefaultNodeGene(0)
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node0.bias = 0.0
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node0.response = 1.0
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node0.activation = "identity"
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node0.aggregation = "sum"
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node1 = DefaultNodeGene(1)
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node1.bias = -0.5
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node1.response = 5.0
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node1.activation = "sigmoid"
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node1.aggregation = "sum"
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node2 = DefaultNodeGene(2)
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node2.bias = -1.5
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node2.response = 5.0
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node2.activation = "sigmoid"
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node2.aggregation = "sum"
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genome.nodes[0] = node0
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genome.nodes[1] = node1
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genome.nodes[2] = node2
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# Connections: -1 -> 1, -2 -> 2, and hidden 1/2 to output 0 (second with weight -1.0).
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connections = [
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((-1, 1), 1.0),
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((-2, 2), 1.0),
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((1, 0), 1.0),
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((2, 0), -1.0),
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]
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for innovation, (key, weight) in enumerate(connections):
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cg = DefaultConnectionGene(key, innovation=innovation)
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cg.weight = weight
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cg.enabled = True
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genome.connections[key] = cg
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return FeedForwardNetwork.create(genome, config)
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def test_simple_hidden_from_genome():
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"""FeedForwardNetwork.create builds a simple hidden-layer network with expected behavior."""
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net = _create_simple_hidden_network()
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v00 = net.activate([0.0, 0.0])
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assert_almost_equal(v00[0], 0.07530540138431994, 1e-6)
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v01 = net.activate([0.0, 1.0])
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assert_almost_equal(v01[0], -0.9241417948687655, 1e-6)
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v10 = net.activate([1.0, 0.0])
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assert_almost_equal(v10[0], 0.9994472211938866, 1e-6)
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v11 = net.activate([1.0, 1.0])
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assert_almost_equal(v11[0], 2.4940801202077978e-08, 1e-6)
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if __name__ == '__main__':
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test_unconnected()
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test_basic()
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test_simple_nohidden_from_genome()
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test_simple_hidden_from_genome()

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