Skip to content

Instantly share code, notes, and snippets.

What would you like to do?
Newton's optimization method for multivariate function in tensorflow
import numpy as np
import tensorflow as tf
# Newton's optimization method for multivariate function in tensorflow
def cons(x):
return tf.constant(x, dtype=tf.float32)
def compute_hessian(fn, vars):
mat = []
for v1 in vars:
temp = []
for v2 in vars:
temp.append(tf.gradients(tf.gradients(fn, v2)[0], v1)[0])
temp = [cons(0) if t == None else t for t in temp]
temp = tf.pack(temp)
mat = tf.pack(mat)
return mat
def compute_grads(fn, vars):
grads = []
for v in vars:
grads.append(tf.gradients(fn, v)[0])
return tf.reshape(tf.pack(grads), shape=[2, -1])
def optimize(all_variables, update):
optmize_variables = []
for i in range(len(all_variables)):
optmize_variables.append(all_variables[i].assign(all_variables[i] - alpha * tf.squeeze(update[i])))
return tf.pack(optmize_variables)
x = tf.Variable(np.random.random_sample(), dtype=tf.float32)
y = tf.Variable(np.random.random_sample(), dtype=tf.float32)
alpha = cons(0.1)
# f = tf.pow(x, cons(2)) + cons(2) * x * y + cons(3) * tf.pow(y, cons(2)) + cons(4) * x + cons(5) * y + cons(6)
f = cons(0.5) * tf.pow(x, 2) + cons(2.5) * tf.pow(y, 2)
all_variables = [x, y]
hessian = compute_hessian(f, all_variables)
hessian_inv = tf.matrix_inverse(hessian)
g = compute_grads(f, all_variables)
update = tf.unpack(tf.matmul(hessian_inv, g))
optimize_op = optimize(all_variables, update)
sess = tf.Session()
func = np.inf
for i in range(10):
prev = func
v1, v2, func =[x, y, f])
print v1, v2, func
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
You can’t perform that action at this time.