tensorflow library

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ @author: tarodz """ import numpy as np import matplotlib.pyplot as plt import scipy as sp import tensorflow as tf; minimum=[-.25,2] # THIS SCRIPT PERFORMS PROJECTED GRADIENT DESCENT ON FUNCTION F, # ASSUMING Q(the feasible region) is w1>=0, w2>=0 def f(w): shiftedW=w-np.array(minimum); return tf.reduce_sum(tf.multiply(shiftedW,shiftedW)); #define starting value of W for gradient descent #here, W is a 2D vector initialW=np.random.randn(2) #create a shared variable (i.e. a variable that persists between calls to a tensorflow function) w = tf.Variable(initialW,name="w"); #define output of applying f to w #out goal will be to minimize f(w), i.e. find w with lowest possible f(w) z=f(w); # if you want more accurate result, replace step size 0.01 with something smaller optimizer = tf.train.GradientDescentOptimizer(0.01) train = optimizer.minimize(z) #initialize tensorflow session sess = tf.Session() sess.run(tf.global_variables_initializer()) with sess: # hard-coded number of steps, could be too little, may need to be increased for i in range(300): #perform gradient step train.run(); #get the numpy vector with current value of w w_value=w.eval(); # run proximal operator (here it's simple, just replace negative values with 0) new_w_value=np.maximum(w_value,0); print((w_value,new_w_value)) # update tensorflow value using numpy value new_w_assign = tf.assign(w,new_w_value); sess.run(new_w_assign); #sess.close() print("True minimum: "+str(np.maximum(minimum,0))); print("Found minimum:"+str(new_w_value));