Tensorflow—tensorboard网络结构

代码：

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data


#载入数据集
#当前路径
mnist = input_data.read_data_sets("MNISt_data", one_hot=True)

运行结果：

Extracting MNISt_data/train-images-idx3-ubyte.gz
Extracting MNISt_data/train-labels-idx1-ubyte.gz
Extracting MNISt_data/t10k-images-idx3-ubyte.gz
Extracting MNISt_data/t10k-labels-idx1-ubyte.gz

代码：

#每个批次的大小
#以矩阵的形式放进去
batch_size = 100
#计算一共有多少个批次
n_batch = mnist.train.num_examples // batch_size



#命名空间
with tf.name_scope('input'):
    #定义两个placeholder
    #28 x 28 = 784
    x = tf.placeholder(tf.float32, [None, 784], name='x_input')
    y = tf.placeholder(tf.float32, [None, 10], name='y_input')

with tf.name_scope('layer'):
    #创建一个简单的神经网络
    #输入层784，没有隐藏层，输出层10个神经元
    with tf.name_scope('weights'):
        W = tf.Variable(tf.zeros([784, 10]), name='W')
    with tf.name_scope('biases'):
        b = tf.Variable(tf.zeros([1, 10]), name='b')
    with tf.name_scope('wx_plus_b'):   
        wx_plus_b = tf.matmul(x, W) + b
    with tf.name_scope('softmax'):
        prediction = tf.nn.softmax(wx_plus_b)

with tf.name_scope('loss'):
    #二次代价函数
    loss = tf.reduce_mean(tf.square(y - prediction))
    #交叉熵
    #loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=prediction))

with tf.name_scope('train'):
    #使用梯度下降法
    train_step = tf.train.GradientDescentOptimizer(0.2).minimize(loss)

#初始化变量
init = tf.global_variables_initializer()


with tf.name_scope('accuracy'):
    with tf.name_scope('correct_prediction'):
        #结果存放在一个布尔型列表中
        #tf.argmax(y, 1)与tf.argmax(prediction, 1)相同返回True,不同则返回False
        #argmax返回一维张量中最大的值所在的位置
        correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(prediction, 1))
    with tf.name_scope('accuracy'):
        #求准确率
        #tf.cast(correct_prediction, tf.float32) 将布尔型转换为浮点型
        accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))


with tf.Session() as sess:
    sess.run(init)
    
    
    #当前路径logs文件夹
    writer = tf.summary.FileWriter('logs/', sess.graph)
    #总共1个周期
    for epoch in range(1):
        #总共n_batch个批次
        for batch in range(n_batch):
            #获得一个批次
            batch_xs, batch_ys = mnist.train.next_batch(batch_size)
            sess.run(train_step, feed_dict={x:batch_xs, y:batch_ys})
        
        #训练完一个周期后准确率
        acc = sess.run(accuracy, feed_dict={x:mnist.test.images, y:mnist.test.labels})
        print("Iter" + str(epoch) + ", Testing Accuracy" + str(acc))

在命令行：(注意切换到当前路径下)

tensorboard --logdir=logs

效果展示：