变量与操作

高级API

• 高级API
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     # 1）准备数据
    x = tf.random_normal(shape=[100, 1])
    y_true = tf.matmul(x, [[0.8]]) + 0.7
    # 2）构造模型
    # 定义模型参数
    weights = tf.Variable(initial_value=tf.random_normal(shape=[1, 1]))
    bias = tf.Variable(initial_value=tf.random_normal(shape=[1, 1]))
    y_predict = tf.matmul(x, weights) + bias
    # 3）构造损失函数
    loss = tf.reduce_mean(tf.square(y_predict - y_true))
    # 4）优化损失
    optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01).minimize(loss)
    # 显式的初始化变量
    init = tf.global_variables_initializer()
    # 开启会话
    with tf.Session() as sess:
        sess.run(init)

        # 查看初始化模型参数之后的值
        print("训练前模型参数为：Weights:%f,bias:%f,loss:%f" % (weights.eval(), bias.eval(), loss.eval()))
        # 开始训练
        for i in range(1000):
            if i % 100 == 0:
                sess.run(optimizer)
                print("第%f训练后模型参数为：Weights:%f,bias:%f,loss:%f" % (i, weights.eval(), bias.eval(), loss.eval()))

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