Skip to content
Branch: master
Find file Copy path
Find file Copy path
Fetching contributors…
Cannot retrieve contributors at this time
98 lines (77 sloc) 3.82 KB
import itertools
from autokeras.constant import Constant
from import Searcher
def assert_search_space(search_space):
grid = search_space
value_list = []
if Constant.LENGTH_DIM not in list(grid.keys()):
print('No length dimension found in search Space. Using default values')
elif not isinstance(grid[Constant.LENGTH_DIM][0], int):
print('Converting String to integers. Next time please make sure to enter integer values for Length Dimension')
grid[Constant.LENGTH_DIM] = list(map(int, grid[Constant.LENGTH_DIM]))
if Constant.WIDTH_DIM not in list(grid.keys()):
print('No width dimension found in search Space. Using default values')
grid[Constant.WIDTH_DIM] = Constant.DEFAULT_WIDTH_SEARCH
elif not isinstance(grid[Constant.WIDTH_DIM][0], int):
print('Converting String to integers. Next time please make sure to enter integer values for Width Dimension')
grid[Constant.WIDTH_DIM] = list(map(int, grid[Constant.WIDTH_DIM]))
grid_key_list = list(grid.keys())
for key in grid_key_list:
dimension = list(itertools.product(*value_list))
# print(dimension)
return grid, dimension
class GridSearcher(Searcher):
""" Class to search for neural architectures using Greedy search strategy.
search_space: A dictionary. Specifies the search dimensions and their possible values
def __init__(self, n_output_node, input_shape, path, metric, loss, generators, verbose, search_space={},
trainer_args=None, default_model_len=None, default_model_width=None):
super(GridSearcher, self).__init__(n_output_node, input_shape, path, metric, loss, generators, verbose,
trainer_args, default_model_len, default_model_width)
self.search_space, self.search_dimensions = assert_search_space(search_space)
self.search_space_counter = 0
def get_search_dimensions(self):
return self.search_dimensions
def search_space_exhausted(self):
""" Check if Grid search has exhausted the search space """
if self.search_space_counter == len(self.search_dimensions):
return True
return False
def search(self, train_data, test_data, timeout=60 * 60 * 24):
"""Run the search loop of training, generating and updating once.
Call the base class implementation for search with
train_data: An instance of DataLoader.
test_data: An instance of Dataloader.
timeout: An integer, time limit in seconds.
if self.search_space_exhausted():
super().search(train_data, test_data, timeout)
def update(self, other_info, model_id, graph, metric_value):
def generate(self, multiprocessing_queue):
"""Generate the next neural architecture.
multiprocessing_queue: the Queue for multiprocessing return value.
list of 2-element tuples: generated_graph and other_info,
for grid searcher the length of list is 1.
generated_graph: An instance of Graph.
other_info: Always 0.
grid = self.get_grid()
self.search_space_counter += 1
generated_graph = self.generators[0](self.n_classes, self.input_shape). \
generate(grid[Constant.LENGTH_DIM], grid[Constant.WIDTH_DIM])
return [(generated_graph, 0)]
def get_grid(self):
""" Return the next grid to be searched """
if self.search_space_counter < len(self.search_dimensions):
return self.search_dimensions[self.search_space_counter]
return None
You can’t perform that action at this time.