| import random | |
| class Domain(): | |
| def __init__(self, minimum, maximum): | |
| self.min = minimum | |
| self.max = maximum | |
| assert self.min < self.max | |
| def scale(self, r): | |
| """ |
Some simulations, such as convergence calculations, need trials and errors for selecting appropriate parameters until an expected result is obtained.
In this sample, we demonstrate how to automate this kind of iterations. This sample tries candidate parameters, which are given by us in advance, one by one until an expected result is obtained.
Register simulator as follows.
This is a sample of optimizing parameters using OACIS watcher. This program iteratively search for parameters which minimizes the results of the simulations. For the optimization, we adopted a differential evolutiion algorithm.
Register simulator as follows.
| Gemfile.lock |
| from tensorflow.examples.tutorials.mnist import input_data | |
| mnist = input_data.read_data_sets("MNIST_data/", one_hot=True) | |
| import tensorflow as tf | |
| x = tf.placeholder(tf.float32, [None, 784]) | |
| W = tf.Variable(tf.zeros([784, 10])) | |
| b = tf.Variable(tf.zeros([10])) | |
| y = tf.nn.softmax(tf.matmul(x, W) + b) | |
| y_ = tf.placeholder(tf.float32, [None, 10]) |