查看变量空间，或者变量结构
当你不想直接进行训练，但打算查看模型结构时，当你把模型结构写进文件logs时就会生成模型文件。之重要的是这句写文件的：
writer = tf.summary.FileWriter('logs/',sess.graph)
import tensorflow as tf
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
with tf.name_scope('wights'):
W = tf.Variable(tf.zeros([784,10]),name='W')
with tf.name_scope('biases'):
b = tf.Variable(tf.zeros([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)
#二次代价函数
# loss = tf.reduce_mean(tf.square(y-prediction))
with tf.name_scope('loss'):
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
correct_prediction = tf.equal(tf.argmax(y,1),tf.argmax(prediction,1))#argmax返回一维张量中最大的值所在的位置
with tf.name_scope('accuracy'):
#求准确率
accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
with tf.Session() as sess:
sess.run(init)
writer = tf.summary.FileWriter('logs/',sess.graph)
