11.tensorboard网络结构

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

# 载入数据集
mnist = input_data.read_data_sets("MNIST_data", one_hot=True)
 
# 批次大小
batch_size = 64
# 计算一个周期一共有多少个批次
n_batch = mnist.train.num_examples // batch_size
 
with tf.name_scope('input'):
    # 定义两个placeholder
    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:784-10
    with tf.name_scope('weights'):
        W = tf.Variable(tf.truncated_normal([784,10], stddev=0.1))
    with tf.name_scope('biases'):
        b = tf.Variable(tf.zeros([10]) + 0.1)
    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.losses.mean_squared_error(y, prediction)
with tf.name_scope('train'):
    # 使用梯度下降法
    train = tf.train.GradientDescentOptimizer(0.3).minimize(loss)
 
with tf.name_scope('accuracy'):
    with tf.name_scope('correct_prediction'):
        # 结果存放在一个布尔型列表中
        correct_prediction = tf.equal(tf.argmax(y,1),tf.argmax(prediction,1))
    with tf.name_scope('accuracy'):
        # 求准确率
        accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
 
with tf.Session() as sess:
    # 变量初始化
    sess.run(tf.global_variables_initializer())
    writer = tf.summary.FileWriter('logs/',sess.graph)
#     # 周期epoch：所有数据训练一次，就是一个周期
#     for epoch in range(21):
#         for batch in range(n_batch):
#             # 获取一个批次的数据和标签
#             batch_xs,batch_ys = mnist.train.next_batch(batch_size)
#             sess.run(train,feed_dict={x:batch_xs,y:batch_ys})
#         # 每训练一个周期做一次测试
#         acc = sess.run(accuracy,feed_dict={x:mnist.test.images,y:mnist.test.labels})
#         print("Iter " + str(epoch) + ",Testing Accuracy " + str(acc))