def eval_genome(genome, config, batch_data):
"""
Most important part of NEAT since it is here that we adapt NEAT to our problem.
We tell what is the phenotype of a genome and how to calculate its fitness
(same idea than a loss)
:param config: config from the config file
:param genome: one genome to get evaluated
:param batch_data: data to use to evaluate the genomes
:return fitness: returns the fitness of the genome
this version is intented to use ParallelEvaluator and should be much faster
"""
net = neat.nn.RecurrentNetwork.create(genome, config)
cross_entropy = 0
for data in batch_data:
inputs, output = data[0], data[2]
net.reset()
mask, scores = gate_activation_ce(net, inputs)
selected_score = scores[mask]
if selected_score.size == 0:
scores = 1 / 7 * np.ones(7)
else:
xo = np.sum(selected_score, axis=0) / selected_score.size
xo[np.isinf(xo)] = 100
xo[np.isnan(xo)] = 100
scores = softmax(xo)
cross_entropy -= np.log(scores[output] + 10**-20)
return 1 - cross_entropy / normalize_fitness
def evaluate(net, data_loader):
correct = 0
total = 0
net.reset()
target_scores = []
non_target_scores = []
for data in tqdm(data_loader):
inputs, output = data[0], data[2]
mask, scores = gate_activation_ce(net, inputs)
selected_score = scores[mask]
if selected_score.size == 0:
scores = 1 / 7 * np.ones(7)
else:
xo = np.sum(selected_score, axis=0) / selected_score.size
scores = softmax(xo)
total += 1
correct += (scores.argmax() == output)
if output == 0:
target_scores.append(scores[0])
else:
non_target_scores.append(scores[0])
target_scores = np.array(target_scores)
non_target_scores = np.array(non_target_scores)
pmiss, pfa = rocch(target_scores, non_target_scores)
eer = rocch2eer(pmiss, pfa)
return float(correct) / total, eer
def eval_genomes(genomes, config_, batch_data):
"""
Most important part of NEAT since it is here that we adapt NEAT to our problem.
We tell what is the phenotype of a genome and how to calculate its fitness (same idea than a loss)
:param config_: config from the config file
:param genomes: list of all the genomes to get evaluated
"""
for _, genome in tqdm(genomes):
net = neat.nn.RecurrentNet.create(genome, config_)
cross_entropy = 0
for data in batch_data:
inputs, output = data[0], data[2]
net.reset()
mask, scores = gate_activation_ce(net, inputs)
selected_score = scores[mask]
if selected_score.size == 0:
scores = 1 / 7 * np.ones(7)
else:
xo = np.sum(selected_score, axis=0) / selected_score.size
print("xo =", xo)
scores = softmax(xo)
print("scores =", scores)
cross_entropy -= np.log(scores[output] + 10**-20)
genome.fitness = 1 - cross_entropy / normalize_fitness