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function[]=visualize_2D_optimizer(f,traj)

%input - trajectory matrix of size (2,n) containing n iterates of 2D optimizer's trajectory, f is a function which evaluates pointwise.
    x = linspace(-3,3,30);
    y = linspace(-3,3,30);
    [X,Y] = meshgrid(x,y); %creating grid of points to evaluate f for the contour.
    val=zeros(size(X));
    for m=1:size(X,1)
        for n=1:size(X,2)
            val(m,n)=f([X(m,n);Y(m,n)]);

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#improved GIST of https://gist.github.com/MikeOuimet/f50904374651c9c15f236aef3109d57
#allowed backwards compatability by refactoring code involving tf.diag gradients

import numpy as np
import gym
import tensorflow as tf
import matplotlib.pyplot as plt

def weight_variable(shape):
  initial = tf.truncated_normal(shape, stddev=0.1)

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