Multi-layer perceptron¶
\[\begin{split}y = f_1(x_1,x_2,x_3,x_4,x_5) \ if \ x_0 == 1 \\
y = f_2(x_1,x_2,x_3,x_4,x_5,x_6) \ if \ x_0 == 2 \\
y = f_3(x_1,x_2,x_3,x_4,x_5,x_6,x_7) \ if \ x_0 == 3 \\\end{split}\]
\(x_0 \in \{1,2,3\} , x_1 \in [-5,-2], x_2 \in [-5,-1], x_3 \in [8,16,32,64,128,256], x_4 \in \{ReLU,SeLU,ISRLU\}, x_{5,6,7} \in [0,5] .\)
Usage¶
import matplotlib.pyplot as plt
from smt.problems import HierarchicalNeuralNetwork
problem = HierarchicalNeuralNetwork()
n_doe = 100
xdoe, x_is_acting = problem.design_space.sample_valid_x(
n_doe
) # If acting information is needed
# xdoe = problem.sample(n_doe) # Also possible
y = problem(xdoe)
plt.scatter(xdoe[:, 0], y)
plt.xlabel("x")
plt.ylabel("y")
plt.show()
Options¶
Option |
Default |
Acceptable values |
Acceptable types |
Description |
|---|---|---|---|---|
ndim |
3 |
None |
[‘int’] |
|
return_complex |
False |
None |
[‘bool’] |
|
name |
CantileverBeam |
None |
[‘str’] |
|
P |
50000.0 |
None |
[‘int’, ‘float’] |
Tip load (50 kN) |
E |
200000000000.0 |
None |
[‘int’, ‘float’] |
Modulus of elast. (200 GPa) |
