梯度下降优化被认为是数据科学中的重要概念，考虑以下所示的步骤，以了解梯度下降优化的实现-
第1步 - 包括必要的模块以及x和y变量的声明，通过它们无涯教程将定义梯度下降优化。
import tensorflow as tfx = tf.Variable(2, name = x, dtype = tf.float32) log_x = tf.log(x) log_x_squared = tf.square(log_x)
optimizer = tf.train.GradientDescentOptimizer(0.5) train = optimizer.minimize(log_x_squared)
第2步 - 初始化必要的变量，并调用优化器以使用相应的函数进行定义和调用。
init = tf.initialize_all_variables()
def optimize():
with tf.Session() as session:
session.run(init)
print("starting at", "x:", session.run(x), "log(x)^2:", session.run(log_x_squared))
</span><span class="kwd">for</span><span class="pln"> step </span><span class="kwd">in</span><span class="pln"> range</span><span class="pun">(</span><span class="lit">10</span><span class="pun">):</span><span class="pln">
session</span><span class="pun">.</span><span class="pln">run</span><span class="pun">(</span><span class="pln">train</span><span class="pun">)</span><span class="pln">
</span><span class="kwd">print</span><span class="pun">(</span><span class="str">"step"</span><span class="pun">,</span><span class="pln"> step</span><span class="pun">,</span><span class="pln"> </span><span class="str">"x:"</span><span class="pun">,</span><span class="pln"> session</span><span class="pun">.</span><span class="pln">run</span><span class="pun">(</span><span class="pln">x</span><span class="pun">),</span><span class="pln"> </span><span class="str">"log(x)^2:"</span><span class="pun">,</span><span class="pln"> session</span><span class="pun">.</span><span class="pln">run</span><span class="pun">(</span><span class="pln">log_x_squared</span><span class="pun">))</span><span class="pln">
optimize()
上面的代码行生成输出，如下面的屏幕快照所示-
