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  • tensorflow版线性回归

    import os
    os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
    
    import tensorflow as tf
    
    def linearregression():
        X = tf.random_normal([100,1],mean=0.0,stddev=1.0)
        y_true = tf.matmul(X,[[0.8]]) + [[0.7]]
    
        weights = tf.Variable(initial_value=tf.random_normal([1,1]))
        bias = tf.Variable(initial_value=tf.random_normal([1,1]))
    
        y_predict = tf.matmul(X,weights)+bias
    
        loss = tf.reduce_mean(tf.square(y_predict-y_true))
        optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01).minimize(loss)
        init = tf.global_variables_initializer()
    
        with tf.Session() as sess:
            sess.run(init)
    
            for i in range(1000):
                sess.run(optimizer)
                print("loss:", sess.run(loss))
                print("weight:", sess.run(weights))
                print("bias:", sess.run(bias))
    
    if __name__ == '__main__':
        linearregression()
    

      

    多思考也是一种努力,做出正确的分析和选择,因为我们的时间和精力都有限,所以把时间花在更有价值的地方。
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  • 原文地址:https://www.cnblogs.com/LiuXinyu12378/p/12246138.html
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