def get_space(cache):
space = HyperSpace()
with space.as_default():
name_prefix = 'test_'
filters = 64
in1 = Input(shape=(
28,
28,
1,
))
in2 = Input(shape=(
28,
28,
1,
))
ic1 = InputChoice([in1, in2], 1)([in1, in2])
or1 = ModuleChoice([
sepconv5x5(name_prefix, filters),
sepconv3x3(name_prefix, filters),
avgpooling3x3(name_prefix, filters),
maxpooling3x3(name_prefix, filters),
identity(name_prefix)
])(ic1)
space.set_inputs([in1, in2])
space.weights_cache = cache
return space
def enas_micro_search_space(arch='NRNR',
input_shape=(28, 28, 1),
init_filters=64,
node_num=4,
data_format=None,
classes=10,
classification_dropout=0,
hp_dict={},
use_input_placeholder=True,
weights_cache=None):
space = HyperSpace()
with space.as_default():
if use_input_placeholder:
input = Input(shape=input_shape, name='0_input')
else:
input = None
stem, input = stem_op(input, init_filters, data_format)
node0 = stem
node1 = stem
reduction_no = 0
normal_no = 0
for l in arch:
if l == 'N':
normal_no += 1
type = 'normal'
cell_no = normal_no
is_reduction = False
else:
reduction_no += 1
type = 'reduction'
cell_no = reduction_no
is_reduction = True
filters = (2**reduction_no) * init_filters
if is_reduction:
node0 = FactorizedReduction(
filters, f'{normal_no + reduction_no}_{type}_C{cell_no}_0',
data_format)(node0)
node1 = FactorizedReduction(
filters, f'{normal_no + reduction_no}_{type}_C{cell_no}_1',
data_format)(node1)
x = conv_layer(hp_dict, f'{normal_no + reduction_no}_{type}',
cell_no, [node0, node1], filters, node_num,
is_reduction)
node0 = node1
node1 = x
logit = classification(x, classes, classification_dropout, data_format)
space.set_inputs(input)
if weights_cache is not None:
space.weights_cache = weights_cache
return space