def create_search_space(input_shape=(2,), output_shape=(3,), *args, **kwargs):
ss = KSearchSpace(input_shape, output_shape)
x = ss.input_nodes[0]
out_xor = ConstantNode(op=Dense(1, activation="sigmoid"), name="XOR")
out_and = ConstantNode(op=Dense(1, activation="sigmoid"), name="AND")
ss.connect(x, out_and)
out_or = ConstantNode(op=Dense(1, activation="sigmoid"), name="OR")
ss.connect(x, out_or)
in_xor = VariableNode(name="in_XOR")
in_xor.add_op(Concatenate(ss, [x]))
in_xor.add_op(Concatenate(ss, [x, out_and]))
in_xor.add_op(Concatenate(ss, [x, out_or]))
in_xor.add_op(Concatenate(ss, [x, out_and, out_or]))
ss.connect(in_xor, out_xor)
out = ConstantNode(name="OUT")
out.set_op(Concatenate(ss, stacked_nodes=[out_xor, out_and, out_or]))
return ss
def test_create_multiple_inputs_with_one_vnode(self):
from deephyper.search.nas.model.space import KSearchSpace
from deephyper.search.nas.model.space.node import VariableNode, ConstantNode
from deephyper.search.nas.model.space.op.op1d import Dense
from deephyper.search.nas.model.space.op.merge import Concatenate
struct = KSearchSpace([(5, ), (5, )], (1, ))
merge = ConstantNode()
merge.set_op(Concatenate(struct, struct.input_nodes))
vnode1 = VariableNode()
struct.connect(merge, vnode1)
vnode1.add_op(Dense(1))
struct.set_ops([0])
struct.create_model()
def set_output_node(self, graph, output_nodes):
"""Set the output node of the search_space.
Args:
graph (nx.DiGraph): graph of the search_space.
output_nodes (Node): nodes of the current search_space without successors.
Returns:
Node: output node of the search_space.
"""
if len(output_nodes) == 1:
node = ConstantNode(op=Identity(), name='Structure_Output')
graph.add_node(node)
graph.add_edge(output_nodes[0], node)
else:
node = ConstantNode(name='Structure_Output')
op = Concatenate(self, output_nodes)
node.set_op(op=op)
return node
def create_search_space(
input_shape=(2, ), output_shape=(3, ), *args, **kwargs):
ss = KSearchSpace(input_shape, output_shape)
x = ss.input_nodes[0]
out_xor = ConstantNode(op=Dense(1), name="XOR")
ss.connect(x, out_xor)
out_and = ConstantNode(op=Dense(1), name="AND")
ss.connect(x, out_and)
out_or = ConstantNode(op=Dense(1), name="OR")
ss.connect(x, out_or)
out = ConstantNode(name="OUT")
out.set_op(Concatenate(ss, stacked_nodes=[out_xor, out_and, out_or]))
return ss
def create_structure(input_shape=[(1, ), (942, ), (5270, ), (2048, )], output_shape=(1,), num_cells=2, *args, **kwargs):
struct = AutoOutputStructure(input_shape, output_shape, regression=True)
input_nodes = struct.input_nodes
output_submodels = [input_nodes[0]]
for i in range(1, 4):
vnode1 = VariableNode('N1')
add_mlp_op_(vnode1)
struct.connect(input_nodes[i], vnode1)
vnode2 = VariableNode('N2')
add_mlp_op_(vnode2)
struct.connect(vnode1, vnode2)
vnode3 = VariableNode('N3')
add_mlp_op_(vnode3)
struct.connect(vnode2, vnode3)
output_submodels.append(vnode3)
merge1 = ConstantNode(name='Merge')
merge1.set_op(Concatenate(struct, merge1, output_submodels))
vnode4 = VariableNode('N4')
add_mlp_op_(vnode4)
struct.connect(merge1, vnode4)
prev = vnode4
for i in range(num_cells):
vnode = VariableNode(f'N{i+1}')
add_mlp_op_(vnode)
struct.connect(prev, vnode)
merge = ConstantNode(name='Merge')
merge.set_op(AddByPadding(struct, merge, [vnode, prev]))
prev = merge
return struct
def create_structure(input_shape=[(1, ), (942, ), (5270, ), (2048, )],
output_shape=(1, ),
num_cells=2,
*args,
**kwargs):
struct = AutoOutputStructure(input_shape, output_shape, regression=True)
input_nodes = struct.input_nodes
output_submodels = [input_nodes[0]]
for i in range(1, 4):
cnode1 = ConstantNode(name='N', op=Dense(1000, tf.nn.relu))
struct.connect(input_nodes[i], cnode1)
cnode2 = ConstantNode(name='N', op=Dense(1000, tf.nn.relu))
struct.connect(cnode1, cnode2)
cnode3 = ConstantNode(name='N', op=Dense(1000, tf.nn.relu))
struct.connect(cnode2, cnode3)
output_submodels.append(cnode3)
merge1 = ConstantNode(name='Merge')
merge1.set_op(Concatenate(struct, merge1, output_submodels))
cnode4 = ConstantNode(name='N', op=Dense(1000, tf.nn.relu))
struct.connect(merge1, cnode4)
prev = cnode4
for i in range(num_cells):
cnode = ConstantNode(name='N', op=Dense(1000, tf.nn.relu))
struct.connect(prev, cnode)
merge = ConstantNode(name='Merge')
merge.set_op(AddByPadding(struct, merge, [cnode, prev]))
prev = merge
return struct
def create_search_space(
input_shape=(2, ), output_shape=(5, ), *args, **kwargs):
ss = KSearchSpace(input_shape, output_shape)
x = ss.input_nodes[0]
nunits = 10
hid_2 = ConstantNode(op=Dense(nunits, "relu"), name="hid_2")
out_2 = ConstantNode(op=Dense(1), name="out_2")
ss.connect(hid_2, out_2)
hid_3 = ConstantNode(op=Dense(nunits, "relu"), name="hid_3")
out_3 = ConstantNode(op=Dense(1), name="out_3")
ss.connect(hid_3, out_3)
hid_4 = ConstantNode(op=Dense(nunits, "relu"), name="hid_4")
out_4 = ConstantNode(op=Dense(1), name="out_4")
ss.connect(hid_4, out_4)
hid_5 = ConstantNode(op=Dense(nunits, "relu"), name="hid_5")
out_5 = ConstantNode(op=Dense(1), name="out_5")
ss.connect(hid_5, out_5)
hid_6 = ConstantNode(op=Dense(nunits, "relu"), name="hid_6")
out_6 = ConstantNode(op=Dense(1), name="out_6")
ss.connect(hid_6, out_6)
# L1 DEPENDENT ON DATA OVERALL
in_2 = VariableNode(name="in_2")
in_2.add_op(Concatenate(ss, [x]))
in_2.add_op(Concatenate(ss, [x, out_3]))
in_2.add_op(Concatenate(ss, [x, out_4]))
in_2.add_op(Concatenate(ss, [x, out_5]))
in_2.add_op(Concatenate(ss, [x, out_6]))
ss.connect(in_2, hid_2)
# L2 DEPENDANT ON DATA, L1 AND L3
in_3 = VariableNode(name="in_3")
in_3.add_op(Concatenate(ss, [x, out_2]))
in_3.add_op(Concatenate(ss, [x, out_4]))
ss.connect(in_3, hid_3)
# L3 DEPENDANT ON DATA, L1 L2 AND WIDTH
in_4 = VariableNode(name="in_4")
in_4.add_op(Concatenate(ss, [x]))
in_4.add_op(Concatenate(ss, [x, out_2]))
in_4.add_op(Concatenate(ss, [x, out_3]))
in_4.add_op(Concatenate(ss, [x, out_5]))
in_4.add_op(Concatenate(ss, [x, out_6]))
in_4.add_op(Concatenate(ss, [x, out_2, out_3]))
in_4.add_op(Concatenate(ss, [x, out_2, out_5]))
in_4.add_op(Concatenate(ss, [x, out_2, out_6]))
in_4.add_op(Concatenate(ss, [x, out_3, out_5]))
in_4.add_op(Concatenate(ss, [x, out_3, out_6]))
in_4.add_op(Concatenate(ss, [x, out_5, out_6]))
in_4.add_op(Concatenate(ss, [x, out_3, out_5, out_6]))
in_4.add_op(Concatenate(ss, [x, out_2, out_5, out_6]))
in_4.add_op(Concatenate(ss, [x, out_2, out_3, out_6]))
in_4.add_op(Concatenate(ss, [x, out_2, out_3, out_5]))
in_4.add_op(Concatenate(ss, [x, out_2, out_3, out_5, out_6]))
ss.connect(in_4, hid_4)
# HEIGHT DEPENDANT ON ALL MEASURES COMBINED AND DATA
in_5 = VariableNode(name="in_5")
in_5.add_op(Concatenate(ss, [x]))
in_5.add_op(Concatenate(ss, [x, out_2]))
in_5.add_op(Concatenate(ss, [x, out_3]))
in_5.add_op(Concatenate(ss, [x, out_4]))
in_5.add_op(Concatenate(ss, [x, out_6]))
in_5.add_op(Concatenate(ss, [x, out_2, out_3]))
in_5.add_op(Concatenate(ss, [x, out_2, out_4]))
in_5.add_op(Concatenate(ss, [x, out_2, out_6]))
in_5.add_op(Concatenate(ss, [x, out_3, out_4]))
in_5.add_op(Concatenate(ss, [x, out_3, out_6]))
in_5.add_op(Concatenate(ss, [x, out_4, out_6]))
in_5.add_op(Concatenate(ss, [x, out_3, out_4, out_6]))
in_5.add_op(Concatenate(ss, [x, out_2, out_4, out_6]))
in_5.add_op(Concatenate(ss, [x, out_2, out_3, out_6]))
in_5.add_op(Concatenate(ss, [x, out_2, out_3, out_4]))
in_5.add_op(Concatenate(ss, [x, out_2, out_3, out_4, out_6]))
ss.connect(in_5, hid_5)
# WIDTH DEPENDANT ON DATA AND CROSS SECTION
in_6 = VariableNode(name="in_6")
in_6.add_op(Concatenate(ss, [x]))
in_6.add_op(Concatenate(ss, [x, out_2]))
in_6.add_op(Concatenate(ss, [x, out_3]))
in_6.add_op(Concatenate(ss, [x, out_4]))
in_6.add_op(Concatenate(ss, [x, out_5]))
ss.connect(in_6, hid_6)
out = ConstantNode(name="OUT")
out.set_op(
Concatenate(ss, stacked_nodes=[out_2, out_3, out_4, out_5, out_6]))
return ss