def test_create_multiple_inputs_with_one_vnode(self):
import tensorflow as tf
from deephyper.nas import KSearchSpace
from deephyper.nas.node import ConstantNode, VariableNode
from deephyper.nas.operation import operation, Concatenate
Dense = operation(tf.keras.layers.Dense)
class TestSpace(KSearchSpace):
def __init__(self, input_shape, output_shape):
super().__init__(input_shape, output_shape)
def build(self):
merge = ConstantNode()
merge.set_op(Concatenate(self, self.input_nodes))
vnode1 = VariableNode()
self.connect(merge, vnode1)
vnode1.add_op(Dense(1))
return self
space = TestSpace([(5, ), (5, )], (1, )).build()
model = space.sample()
def test_create_more_nodes(self):
import tensorflow as tf
from deephyper.nas import KSearchSpace
from deephyper.nas.node import VariableNode
from deephyper.nas.operation import operation
Dense = operation(tf.keras.layers.Dense)
class TestSpace(KSearchSpace):
def __init__(self, input_shape, output_shape):
super().__init__(input_shape, output_shape)
def build(self):
vnode1 = VariableNode()
self.connect(self.input_nodes[0], vnode1)
vnode1.add_op(Dense(10))
vnode2 = VariableNode()
vnode2.add_op(Dense(1))
self.connect(vnode1, vnode2)
return self
space = TestSpace((5, ), (1, )).build()
model = space.sample()
def test_basic_space(verbose=0):
import tensorflow as tf
from deephyper.nas import KSearchSpace
from deephyper.nas.node import VariableNode, ConstantNode
from deephyper.nas.operation import operation, Identity
Dense = operation(tf.keras.layers.Dense)
class BasicSpace(KSearchSpace):
def __init__(self,
input_shape,
output_shape,
batch_size=None,
*args,
**kwargs):
super().__init__(input_shape,
output_shape,
batch_size=batch_size,
*args,
**kwargs)
def build(self):
input_node = self.input[0]
dense = VariableNode()
dense.add_op(Identity())
for i in range(1, 1000):
dense.add_op(Dense(i))
self.connect(input_node, dense)
output_node = ConstantNode(Dense(self.output_shape[0]))
self.connect(dense, output_node)
space = BasicSpace(input_shape=(1, ), output_shape=(1, ))
space.build()
model_1 = space.sample([1])
if verbose:
model_1.summary()
model_2 = space.sample()
if verbose:
model_2.summary()
def test_mirror_node(self):
import tensorflow as tf
from deephyper.nas.node import MirrorNode, VariableNode
from deephyper.nas.operation import operation
Dense = operation(tf.keras.layers.Dense)
vnode = VariableNode()
vop = Dense(10)
vnode.add_op(vop)
vnode.add_op(Dense(20))
mnode = MirrorNode(vnode)
vnode.set_op(0)
assert vnode.op == vop
assert mnode.op == vop
import pytest
import tensorflow as tf
from deephyper.nas import KSearchSpace
from deephyper.nas.node import ConstantNode, VariableNode
from deephyper.nas.operation import operation, Concatenate
Dense = operation(tf.keras.layers.Dense)
@pytest.mark.incremental
class TestKSearchSpace:
def test_create(self):
class TestSpace(KSearchSpace):
def __init__(self, input_shape, output_shape):
super().__init__(input_shape, output_shape)
def build(self):
vnode = VariableNode()
self.connect(self.input_nodes[0], vnode)
vnode.add_op(Dense(1))
return self
space = TestSpace((5, ), (1, )).build()
model = space.sample()
def test_create_more_nodes(self):
class TestSpace(KSearchSpace):
def __init__(self, input_shape, output_shape):
import tensorflow as tf
from deephyper.nas import KSearchSpace
from deephyper.nas.node import ConstantNode, VariableNode
from deephyper.nas.operation import operation, Concatenate
Dense = operation(tf.keras.layers.Dense)
Dropout = operation(tf.keras.layers.Dropout)
class OneLayerSpace(KSearchSpace):
def __init__(self,
input_shape,
output_shape,
batch_size=None,
seed=None,
regression=True):
super().__init__(input_shape,
output_shape,
batch_size=batch_size,
seed=seed)
self.regression = regression
def build(self):
if type(self.input_shape) is list:
vnodes = []
for i in range(len(self.input_shape)):
vn = self.gen_vnode()
vnodes.append(vn)
self.connect(self.input_nodes[i], vn)
