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  • tensorboard使用

    import os
    os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
    
    import tensorflow as tf
    
    #tensorboard --logdir="./"
    
    def linearregression():
    
        with tf.variable_scope("original_data"):
            X = tf.random_normal([100,1],mean=0.0,stddev=1.0)
            y_true = tf.matmul(X,[[0.8]]) + [[0.7]]
    
        with tf.variable_scope("linear_model"):
            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
    
        with tf.variable_scope("loss"):
            loss = tf.reduce_mean(tf.square(y_predict-y_true))
    
        with tf.variable_scope("optimizer"):
            optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01).minimize(loss)
    
        #收集观察张量
        tf.summary.scalar("losses",loss)
        tf.summary.histogram("weight",weights)
        tf.summary.histogram("biases",bias)
        #合并收集的张量
        merge = tf.summary.merge_all()
    
        init = tf.global_variables_initializer()
    
        with tf.Session() as sess:
            sess.run(init)
            filewriter = tf.summary.FileWriter("./tmp",graph=sess.graph)
            for i in range(1000):
                sess.run(optimizer)
                print("loss:", sess.run(loss))
                print("weight:", sess.run(weights))
                print("bias:", sess.run(bias))
                summary = sess.run(merge)
                filewriter.add_summary(summary,i)
    
    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/12246318.html
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