def convert_stochastic_network_to_genome(network: ComplexStochasticNetwork, original_genome: Genome = None, fitness=None,
fix_std=True) -> Genome:
if original_genome is None:
raise ValueError('Not implemented')
genome = original_genome.copy()
for layer_index in range(network.n_layers):
stochastic_linear_layer = getattr(network, f'layer_{layer_index}')
layer = network.layers[layer_index]
for bias_index, node_key in enumerate(layer.output_keys):
# if node_key in genome.node_genes.keys():
genome.node_genes[node_key].set_mean(stochastic_linear_layer.qb_mean[bias_index])
if fix_std:
bias_logvar = DEFAULT_LOGVAR
else:
bias_logvar = stochastic_linear_layer.qb_logvar[bias_index]
genome.node_genes[node_key].set_log_var(bias_logvar)
for connection_input_index, input_node_key in enumerate(layer.input_keys):
for connection_output_index, output_node_key in enumerate(layer.output_keys):
connection_key = (input_node_key, output_node_key)
mean = stochastic_linear_layer.qw_mean[connection_output_index, connection_input_index]
if fix_std:
weight_logvar = DEFAULT_LOGVAR
else:
weight_logvar = stochastic_linear_layer.qw_logvar[connection_output_index, connection_input_index]
if connection_key in genome.connection_genes.keys():
genome.connection_genes[connection_key].set_mean(mean)
genome.connection_genes[connection_key].set_log_var(weight_logvar)
genome.fitness = fitness
return genome
pool = Pool(processes=N_PROCESSES, initializer=process_initialization, initargs=(config.dataset, True))
for genome in genomes:
logger.debug(f'Genome {genome.key}: {genome.get_graph()}')
# x = torch.zeros(2, 784).float()
# y = torch.zeros(2).long()
# x=dataset.x.clone().detach()
# y=dataset.y.clone().detach()
loss = get_loss('classification')
beta_type = 'other'
problem_type = 'classification'
batch_size = 100000
n_samples = 20
is_gpu = False
x = (genome.copy(),
# x, y,
loss, beta_type, problem_type,
batch_size, n_samples, is_gpu)
tasks.append(x)
# TODO: fix logging when using multiprocessing. Easy fix is to disable
fitnesses = list(pool.imap(evaluate_genome_parallel, tasks, chunksize=max([len(genomes)//N_PROCESSES, 1])))
pool.close()
for i, genome in enumerate(genomes):
print(fitnesses[i])
genome.fitness = fitnesses[i]
print('finished')
exit_queue = manager.Queue()
exception_queue = manager.Queue()
results_queue = manager.Queue()
workers = []
for i in range(N_PROCESSES):
worker = Worker(task_queue=task_queue,
exit_queue=exit_queue,
exception_queue=exception_queue,
results_queue=results_queue)
worker.start()
workers.append(worker)
for genome in genomes:
task_queue.put(Task(genome=genome.copy(), dataset=None,
x=torch.zeros((2, 784)).float(),
y=torch.zeros(2).long(),
# x=self.dataset.x.clone().detach(),
# y=self.dataset.y.clone().detach(),
loss=get_loss('classification'),
beta_type='other',
problem_type='classification',
batch_size=100000,
n_samples=20,
is_gpu=False))
while not task_queue.empty():
print('reading results from workers')
print(task_queue.qsize())
# for worker in workers: