在tensorboaed画神经网络结构和查询变量(可视化)

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

#下载并加载数据
mnist = input_data.read_data_sets(r"C:\Users\Administrator\Desktop\mnist_data",one_hot=True)

batch_size=100
n_batch=mnist.train.num_examples//batch_size


#参数摘要,这块是查询变量
def variable_summaries(var):
    with tf.name_scope('summaries'):
        mean=tf.reduce_mean(var)
        tf.summary.scalar('mean',mean)#平均值
        with tf.name_scope('stddev'):
            stddev=tf.sqrt(tf.reduce_mean(tf.square(var - mean)))
        tf.summary.scalar('stddev',stddev)#标准差
        tf.summary.scalar('max',tf.reduce_max(var))#最大值
        tf.summary.scalar('min',tf.reduce_min(var))#最小值
        tf.summary.histogram('%histogram',var)#直方图

#命名空间
with tf.name_scope('input'):#将下面两个包含在此框架底下
#数据与标签的占位
    x = tf.placeholder(tf.float32,shape = [None,784],name='x-input')
    y = tf.placeholder(tf.float32,shape=[None,10],name='y-input')

with tf.name_scope('layer'):
    #创建一个简单的神经网络
    with tf.name_scope('wight'):
        W = tf.Variable(tf.zeros([784,10]),name='W')
        variable_summaries(W)
    with tf.name_scope('biases'):
        b = tf.Variable(tf.zeros([10]),name='b')
        variable_summaries(b)
    with tf.name_scope('wx_plus_b'):
        wx_plus_b=tf.matmul(x,W)+b
#softmax回归,得到预测概率
    with tf.name_scope('softmax'):
        y_predict = tf.nn.softmax(wx_plus_b)
#求交叉熵得到残差
#loss = tf.reduce_mean(tf.square(y-y_predict))(这个与下面那个可以相互交替)
with tf.name_scope('loss'):
    loss=tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y,logits=y_predict))
    tf.summary.scalar('loss',loss)
#梯度下降法使得残差最小
#train_step = tf.train.GradientDescentOptimizer(0.2).minimize(loss)
with tf.name_scope('train'):
    train_step=tf.train.AdamOptimizer(1e-2).minimize(loss)
#测试阶段,测试准确度计算
with tf.name_scope('accuracy'):
    correct_prediction = tf.equal(tf.argmax(y_predict,1),tf.argmax(y,1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))#多个批次的准确度均值
    tf.summary.scalar('accuracy',accuracy)
    
#合并所有的summary
merged=tf.summary.merge_all()
init = tf.global_variables_initializer()
with tf.Session() as sess:
    sess.run(init)
    writer=tf.summary.FileWriter(r"C:\Users\Administrator\Desktop\logs",sess.graph)
    for epoch in range(10):
        for batch in range(n_batch):
            batch_xs,batch_ys = mnist.train.next_batch(batch_size)
            summary,_=sess.run([merged,train_step],feed_dict={x:batch_xs,y:batch_ys})
            
        writer.add_summary(summary,epoch)
            
        acc=sess.run(accuracy,feed_dict={x: mnist.test.images, y: mnist.test.labels})
        print('Iter:'+ str(epoch)+ 'testing accuracy'+ str(acc))