def build(self):
vnode = VariableNode()
self.connect(self.input_nodes[0], vnode)
vnode.add_op(Dense(1))
return self
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
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
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)
def test_mirror_node():
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
def test_mime_node():
vnode = VariableNode()
vop = Dense(10)
vnode.add_op(vop)
vnode.add_op(Dense(20))
mnode = MimeNode(vnode)
mop = Dense(30)
mnode.add_op(mop)
mnode.add_op(Dense(40))
vnode.set_op(0)
assert vnode.op == vop
assert mnode.op == mop
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
def build(self):
source = prev_input = self.input_nodes[0]
# look over skip connections within a range of the 3 previous nodes
anchor_points = collections.deque([source], maxlen=3)
for _ in range(self.num_layers):
vnode = VariableNode()
self.add_dense_to_(vnode)
self.connect(prev_input, vnode)
# * Cell output
cell_output = vnode
cmerge = ConstantNode()
cmerge.set_op(
AddByProjecting(self, [cell_output], activation="relu"))
for anchor in anchor_points:
skipco = VariableNode()
skipco.add_op(Zero())
skipco.add_op(Connect(self, anchor))
self.connect(skipco, cmerge)
prev_input = cmerge
# ! for next iter
anchor_points.append(prev_input)
if self.dropout >= 0.0:
dropout_node = ConstantNode(op=Dropout(rate=self.dropout))
self.connect(prev_input, dropout_node)
prev_input = dropout_node
output_node = ConstantNode(
Dense(self.output_shape[0],
activation=None if self.regression else "softmax"))
self.connect(prev_input, output_node)
return self
def build(self):
prev_node = self.input_nodes[0]
for _ in range(self.num_layers):
vnode = VariableNode()
vnode.add_op(Identity())
for i in range(*self.num_units):
vnode.add_op(Dense(i, tf.nn.relu))
self.connect(prev_node, vnode)
prev_node = vnode
output_node = ConstantNode(
Dense(
self.output_shape[0], activation=None if self.regression else "softmax"
)
)
self.connect(prev_node, output_node)
return self
def build(self):
inp = self.input_nodes[0]
# auto-encoder
units = [128, 64, 32, 16, 8, 16, 32, 64, 128]
prev_node = inp
d = 1
for i in range(len(units)):
vnode = VariableNode()
vnode.add_op(Identity())
if d == 1 and units[i] < units[i + 1]:
d = -1
for u in range(min(2, units[i]), max(2, units[i]) + 1, 2):
vnode.add_op(Dense(u, tf.nn.relu))
latente_space = vnode
else:
for u in range(min(units[i], units[i + d]),
max(units[i], units[i + d]) + 1, 2):
vnode.add_op(Dense(u, tf.nn.relu))
self.connect(prev_node, vnode)
prev_node = vnode
out2 = ConstantNode(op=Dense(self.output_shape[0][0], name="output_0"))
self.connect(prev_node, out2)
# regressor
prev_node = latente_space
# prev_node = inp
for _ in range(self.num_layers):
vnode = VariableNode()
for i in range(16, 129, 16):
vnode.add_op(Dense(i, tf.nn.relu))
self.connect(prev_node, vnode)
prev_node = vnode
out1 = ConstantNode(op=Dense(self.output_shape[1][0], name="output_1"))
self.connect(prev_node, out1)
return self
def gen_vnode(self) -> VariableNode:
vnode = VariableNode()
for i in range(1, 1000):
vnode.add_op(Dense(i, tf.nn.relu))
return vnode