import tensorflow as tf
from deephyper.nas import KSearchSpace
from deephyper.nas.node import ConstantNode, VariableNode
from deephyper.nas.operation import Identity, operation
Dense = operation(tf.keras.layers.Dense)
[docs]class SupervisedRegAutoEncoderSpace(KSearchSpace):
def __init__(
self,
input_shape,
output_shape,
batch_size=None,
seed=None,
units=[128, 64, 32, 16, 8, 16, 32, 64, 128],
num_layers=5,
):
super().__init__(input_shape, output_shape, batch_size=batch_size, seed=seed)
self.units = units
self.num_layers = num_layers
[docs] 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
if __name__ == "__main__":
from tensorflow.keras.utils import plot_model
shapes = dict(input_shape=(100,), output_shape=[(100,), (10,)])
space = SupervisedRegAutoEncoderSpace(**shapes).build()
model = space.sample()
plot_model(model)
