tensorflow MNIST数据集的训练(线性模型)及tensorboard计算结果可视化

淩亂°似流年 2022-06-09 04:06 160阅读 0赞

#下载MNIST数据
    from tensorflow.examples.tutorials.mnist import input_data
    mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
    #参数设置
    import tensorflow as tf
    x = tf.placeholder(tf.float32,[None, 784])
    W = tf.Variable(tf.zeros([784, 10]))
    b = tf.Variable(tf.zeros([10]))
    y = tf.nn.softmax(tf.matmul(x,W)+b)
    y_ = tf.placeholder(tf.float32, [None, 10])
    #交叉熵
    cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))
    #梯度下降法最小化交叉熵
    train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
    #sess图表
    sess = tf.InteractiveSession()
    #初始化所有参数
    tf.global_variables_initializer().run()
    for _ in range(1000):
       batch_xs, batch_ys = mnist.train.next_batch(100)
       sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
    correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
    #精度预测
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
    #tensorboard
    writer = tf.summary.FileWriter('./graphs',sess.graph)
    #sess.run
    sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels})
    print(sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels}))

运行方法：  
命令提示符下运行

python name.py
    tensorboard --logdir="./graphs" --port 6006

chrome浏览器打开localhost:6006  
![这里写图片描述][SouthEast]  
![这里写图片描述][SouthEast 1]  
可视化后整个计算过程一目了然，包括softmax regression回归，Wx+b等等操作。计算的步骤如下所示。softmax的主要作用在于将计算结果转化成概率。

evidencei=∑jWi,jxj\+bi

y=softmax(evidence)

softmax(x)i=exp(xi)∑jexp(xj)

[SouthEast]: /images/20220609/7bd83f3fc6914e7d8f9bd27758a3e916.png
[SouthEast 1]: /images/20220609/739fc169e5d041ba982ebaacf8a23383.png