def _mutate_layout_rows(layout, parameters):
current_rows = len(layout.get_rows())
new_rows = mutate_param(
parameters.get_layout_parameter('rows'),
current_rows)
if new_rows > current_rows:
for row_idx in range(current_rows, new_rows):
layout.rows.append(
_random_layout_row(
layout,
row_idx,
parameters,
init_layer_parameters=True))
else:
while len(layout.get_rows()) > new_rows:
idx = rand.randint(0, len(layout.rows) - 1)
layout.rows = [
r
for i, r in enumerate(layout.rows)
if i != idx]
def _mutate_layout_layers(layout, parameters):
row = random_list_element(layout.rows)
block = random_list_element(row.blocks)
current_layers = len(block.get_layers())
new_layers = mutate_param(
parameters.get_layout_parameter('layers'),
current_layers)
if new_layers > current_layers:
for _ in range(current_layers, new_layers):
layers = get_allowed_new_block_layers(block.layers, parameters)
block.layers += _create_template_layers(
[random_list_element(layers)],
parameters,
init_layer_parameters=True)
else:
while len(block.layers) > new_layers:
idx = rand.randint(0, len(block.layers) - 1)
block.layers = [
l
for i, l in enumerate(block.layers)
if i != idx]
def _mutate_layout_blocks(layout, parameters):
row = random_list_element(layout.rows)
row_idx = layout.rows.index(row)
current_blocks = len(row.get_blocks())
new_blocks = mutate_param(
parameters.get_layout_parameter('blocks'),
current_blocks)
if new_blocks > current_blocks:
for _ in range(current_blocks, new_blocks):
row.blocks.append(
_random_layout_block(
layout,
row_idx,
parameters,
init_layer_parameters=True))
else:
while len(row.blocks) > new_blocks:
idx = rand.randint(0, len(row.blocks) - 1)
row.blocks = [
b
for i, b in enumerate(row.blocks)
if i != idx]
def _mutate_optimizer(optimizer, parameters, p_mutate_param=0.1):
param_space = deepcopy(parameters.get_optimizer_parameters(optimizer.optimizer))
for name, value in optimizer.parameters.items():
if rand.random() < p_mutate_param:
optimizer.parameters[name] = mutate_param(param_space[name], value)
def _mutate_layer(layer, parameters, p_mutate_param=0.1):
param_space = deepcopy(parameters.get_layer_parameters(layer.layer_type))
for name, value in layer.parameters.items():
if rand.random() < p_mutate_param:
layer.parameters[name] = mutate_param(param_space[name], value)
layer.apply_constraints()