w = tf.variable(tf.random_uniform([1], -1.0, 1.0), name=『w』)#隨機初始化權重引數-1到1之間
b = tf.variable(tf.zeros([1]), name=『b』)#以0為初始化，[1]表示維度
y = w * x_data + b#目標函式
loss = tf.reduce_mean(tf.square(y - y_data), name=『loss』)#reduce_mean平均值
optimizer = tf.train.gradientdescentoptimizer(0.5)#學習率大了
train = optimizer.minimize(loss, name=『train』)
sess = tf.session()
init = tf.global_variables_initializer()
sess.run(init)
print (「w =」, sess.run(w), 「b =」, sess.run(b), 「loss =」, sess.run(loss))
for step in range(20):
sess.run(train)
# 輸出訓練好的w和b
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