def crossover(self, parent_1, parent_2, crossover_index=-1):
parent_1_co = parent_1[:-2]
parent_2_co = parent_2[:-2]
if self.weight_inheritance:
parent_1_state_dict = parent_1.state_dict()
parent_2_state_dict = parent_2.state_dict()
parent_1_layer_list = sequential_to_layer_list(parent_1_co)
parent_2_layer_list = sequential_to_layer_list(parent_2_co)
valid_1, valid_2 = False, False
n_attempts = 0
while not valid_1 and not valid_2:
if crossover_index == -1:
crossover_index = random.randrange(1, len(parent_1_co))
child_1_layer_list = parent_1_layer_list[:
crossover_index] + parent_2_layer_list[
crossover_index:]
child_2_layer_list = parent_2_layer_list[:
crossover_index] + parent_1_layer_list[
crossover_index:]
valid_1 = calculate_activation_sizes(child_1_layer_list,
self.X_train.shape[1:]) != -1
valid_2 = calculate_activation_sizes(child_2_layer_list,
self.X_train.shape[1:]) != -1
if n_attempts > self.max_crossover_attempts:
break
if valid_1 and valid_2:
child_1, child_2 = generate_sequential(child_1_layer_list, self.X_train.shape[1:],
len(np.unique(self.y_train)), append_flatten=True),\
generate_sequential(child_2_layer_list, self.X_train.shape[1:],
len(np.unique(self.y_train)), append_flatten=True)
if self.weight_inheritance:
load_state(child_1, parent_1_state_dict, parent_2_state_dict,
crossover_index)
load_state(child_2, parent_2_state_dict, parent_1_state_dict,
crossover_index)
return child_1, child_2
elif valid_1 and not valid_2:
child_1 = generate_sequential(child_1_layer_list,
self.X_train.shape[1:],
len(np.unique(self.y_train)),
append_flatten=True)
if self.weight_inheritance:
load_state(child_1, parent_1_state_dict, parent_2_state_dict,
crossover_index)
return child_1, parent_2
elif valid_2 and not valid_1:
child_2 = generate_sequential(child_2_layer_list,
self.X_train.shape[1:],
len(np.unique(self.y_train)),
append_flatten=True)
if self.weight_inheritance:
load_state(child_2, parent_2_state_dict, parent_1_state_dict,
crossover_index)
return parent_1, child_2
else:
logging.debug(
f'Failed crossover attempt after {self.max_crossover_attempts} tries'
)
return parent_1, parent_2
def get_best_individual(self):
if len(self.ga.current_generation) == 0:
raise Exception(
'Cannot return best individual before running the GA')
return generate_sequential(self.ga.best_individual()[1],
self.X_train.shape[1:],
len(np.unique(self.y_train)),
append_flatten=False)
def test_sequential_generation(self):
input_shape = [3, 100, 100]
layer_list = model_generation.generate_random_model(
10, (100, 100), AVAILABLE_MODULES, MAX_N_KERNELS,
MAX_CONV_KERNEL_SIZE, MAX_CONV_STRIDE, MAX_CONV_DILATION,
MAX_CONV_OUT_CHANNELS, MAX_POOLING_KERNEL_SIZE, MAX_POOLING_STRIDE)
sequential = generate_sequential(layer_list,
input_shape=input_shape,
output_size=10)
self.assertEqual(len(sequential), len(layer_list) + 2)
def create_individual(self, data):
individual = generate_random_model(
self.model_n_layers, self.X_train.shape[1:],
self.available_modules, self.max_n_kernels,
self.max_conv_kernel_size, self.max_conv_stride,
self.max_conv_dilation, self.max_conv_out_channels,
self.max_pooling_kernel_size, self.max_pooling_stride)
return generate_sequential(individual,
input_shape=self.X_train.shape[1:],
output_size=len(np.unique(self.y_train)))
def test_sequential_to_layer_list(self):
input_shape = [3, 100, 100]
layer_list = model_generation.generate_random_model(
30, (100, 100), AVAILABLE_MODULES, MAX_N_KERNELS,
MAX_CONV_KERNEL_SIZE, MAX_CONV_STRIDE, MAX_CONV_DILATION,
MAX_CONV_OUT_CHANNELS, MAX_POOLING_KERNEL_SIZE, MAX_POOLING_STRIDE)
sequential = generate_sequential(layer_list,
input_shape=input_shape,
output_size=10)
new_layer_list = sequential_to_layer_list(sequential)
for new_layer, layer in zip(new_layer_list, layer_list):
self.assertEqual(type(new_layer), type(layer))
def test_score_model(self):
input_shape = [1, 20]
layer_list = model_generation.generate_random_model(
10, input_shape, AVAILABLE_MODULES, MAX_N_KERNELS,
MAX_CONV_KERNEL_SIZE, MAX_CONV_STRIDE, MAX_CONV_DILATION,
MAX_CONV_OUT_CHANNELS, MAX_POOLING_KERNEL_SIZE, MAX_POOLING_STRIDE)
X, y = make_classification(100)
X = X[:, None, :, None]
sequential = generate_sequential(layer_list,
input_shape=input_shape,
output_size=10)
print(
get_model_score(sequential, X[:50], y[:50], X[50:], y[50:], 32, 1))
def mutate(self, individual):
if self.weight_inheritance:
individual_state_dict = individual.state_dict()
individual_layer_list = sequential_to_layer_list(individual)
mutate_index = random.randrange(len(individual))
valid_model = False
while not valid_model: # this will eventually work so infinite loop is OK
individual_layer_list[mutate_index] = random_initialize_layer(
random.choice(self.available_modules), self.X_train.shape[1:],
self.max_n_kernels, self.max_conv_kernel_size,
self.max_conv_stride, self.max_conv_dilation,
self.max_conv_out_channels, self.max_pooling_kernel_size,
self.max_pooling_stride)
valid_model = calculate_activation_sizes(
individual_layer_list, self.X_train.shape[1:]) != -1
individual = generate_sequential(individual_layer_list,
self.X_train.shape[1:],
len(np.unique(self.y_train)),
append_flatten=False)
if self.weight_inheritance:
load_state(individual, individual_state_dict,
individual_state_dict, 0)
return individual
def test_weight_inheritance(self):
X, y = make_classification(100, n_features=100)
X = X[:, None, :, None]
ea = EasyNASGA(X,
y,
population_size=2,
generations=1,
weight_inheritance=True)
input_shape = [1, 100, 1]
layer_list_1 = [
init_conv_layer(input_shape,
n_kernels=2,
conv_kernel_size=(10, 1),
conv_stride=(1, 1),
conv_dilation=(1, 1),
conv_out_channels=10,
random_sizes=False),
init_conv_layer(input_shape,
n_kernels=2,
conv_kernel_size=(10, 1),
conv_stride=(1, 1),
conv_dilation=(1, 1),
conv_out_channels=10,
random_sizes=False),
init_conv_layer(input_shape,
n_kernels=2,
conv_kernel_size=(10, 1),
conv_stride=(1, 1),
conv_dilation=(1, 1),
conv_out_channels=10,
random_sizes=False)
]
layer_list_2 = [
init_conv_layer(input_shape,
n_kernels=2,
conv_kernel_size=(20, 1),
conv_stride=(1, 1),
conv_dilation=(1, 1),
conv_out_channels=10,
random_sizes=False),
init_conv_layer(input_shape,
n_kernels=2,
conv_kernel_size=(20, 1),
conv_stride=(1, 1),
conv_dilation=(1, 1),
conv_out_channels=10,
random_sizes=False),
init_conv_layer(input_shape,
n_kernels=2,
conv_kernel_size=(20, 1),
conv_stride=(1, 1),
conv_dilation=(1, 1),
conv_out_channels=10,
random_sizes=False)
]
seq_1 = generate_sequential(layer_list_1,
input_shape=input_shape,
output_size=10)
seq_2 = generate_sequential(layer_list_2,
input_shape=input_shape,
output_size=10)
cut_point = 1
child_1, child_2 = ea.crossover(seq_1, seq_2, cut_point)
seq_1_child = generate_sequential(child_1,
input_shape=input_shape,
output_size=10,
append_flatten=False)
seq_2_child = generate_sequential(child_2,
input_shape=input_shape,
output_size=10,
append_flatten=False)
assert (seq_1_child._modules['0'].weight == seq_1._modules['0'].weight
).all()
assert (seq_1_child._modules['2'].weight == seq_2._modules['2'].weight
).all()
assert (seq_2_child._modules['0'].weight == seq_2._modules['0'].weight
).all()
assert (seq_2_child._modules['2'].weight == seq_1._modules['2'].weight
).all()
