如果要最小化单个参数，则可以执行以下操作（由于要尝试训练参数，因此我避免使用占位符-占位符通常用于超参数和输入，不被视为可训练参数）：
import tensorflow as tfx = tf.Variable(10.0, trainable=True)f_x = 2 * x* x - 5 *x + 4loss = f_xopt = tf.train.GradientDescentOptimizer(0.1).minimize(f_x)with tf.Session() as sess: sess.run(tf.global_variables_initializer()) for i in range(100): print(sess.run([x,loss])) sess.run(opt)
这将输出以下对（x，损耗）对的列表：
[10.0, 154.0][6.5, 56.0][4.4000001, 20.720001][3.1400001, 8.0192013][2.3840001, 3.4469128][1.9304, 1.8008881][1.65824, 1.2083197][1.494944, 0.99499512][1.3969663, 0.91819811][1.3381798, 0.89055157][1.3029079, 0.88059855][1.2817447, 0.87701511][1.2690468, 0.87572551][1.2614281, 0.87526155][1.2568569, 0.87509394][1.2541142, 0.87503386][1.2524685, 0.87501216][1.2514811, 0.87500429][1.2508886, 0.87500143][1.2505331, 0.87500048][1.2503198, 0.875][1.2501919, 0.87500024][1.2501152, 0.87499976][1.2500691, 0.875][1.2500415, 0.875][1.2500249, 0.87500024][1.2500149, 0.87500024][1.2500089, 0.875][1.2500054, 0.87500024][1.2500032, 0.875][1.2500019, 0.875][1.2500012, 0.87500024][1.2500007, 0.87499976][1.2500005, 0.875][1.2500002, 0.87500024][1.2500001, 0.87500024][1.2500001, 0.87500024][1.2500001, 0.87500024][1.2500001, 0.87500024][1.2500001, 0.87500024][1.2500001, 0.87500024][1.2500001, 0.87500024][1.2500001, 0.87500024][1.2500001, 0.87500024][1.2500001, 0.87500024][1.2500001, 0.87500024][1.2500001, 0.87500024][1.2500001, 0.87500024][1.2500001, 0.87500024][1.2500001, 0.87500024]
