clc
% import libs
tf = py.importlib.import_module('tensorflow');
np = py.importlib.import_module('numpy');
% set problem
a = tf.Variable(np.float32(0.001), pyargs('dtype',tf.float32));
k1 = tf.placeholder(tf.float32,pyargs('shape',py.None,'name','k1'));
cost = a^2 + k1*a + 5;
% set optimizer
optimizer = tf.train.RMSPropOptimizer(pyargs('learning_rate',0.1)).minimize(cost);
init = tf.global_variables_initializer();
% solve
trysess = tf.Session();sess.run(init);dict = py.dict(pyargs(k1.name,np.array([4])));idxr = [];costr = [];for i = 1:20sess.run(optimizer,pyargs('feed_dict',dict));a_val = sess.run(a,pyargs('feed_dict',dict));cost_val = sess.run(cost,pyargs('feed_dict',dict));% 显示信息msgstr = ['迭代%d次：变量a=%f,cost=%fn'];fprintf(msgstr,i,a_val,cost_val);idxr = [idxr i];costr = [costr double(cost_val)];endsess.close()
catchsess.close()
end
% plot
plot(idxr,costr)

迭代1次：变量a=-0.252028,cost=4.055407
迭代2次：变量a=-0.439602,cost=3.434840
迭代3次：变量a=-0.593724,cost=2.977612
迭代4次：变量a=-0.726587,cost=2.621581
迭代5次：变量a=-0.844292,cost=2.335660
迭代6次：变量a=-0.950372,cost=2.101719
迭代7次：变量a=-1.047062,cost=1.908090
迭代8次：变量a=-1.135870,cost=1.746720
迭代9次：变量a=-1.217856,cost=1.611750
迭代10次：变量a=-1.293788,cost=1.498735
迭代11次：变量a=-1.364241,cost=1.404189
迭代12次：变量a=-1.429650,cost=1.325299
迭代13次：变量a=-1.490354,cost=1.259739
迭代14次：变量a=-1.546619,cost=1.205554
迭代15次：变量a=-1.598660,cost=1.161074
迭代16次：变量a=-1.646657,cost=1.124851
迭代17次：变量a=-1.690764,cost=1.095627
迭代18次：变量a=-1.731121,cost=1.072296
迭代19次：变量a=-1.767859,cost=1.053890
迭代20次：变量a=-1.801107,cost=1.039558