import tensorflow as tf 
from tensorflow.examples.tutorials.mnist import input_data 
 
#載入數(shù)據(jù)集 
mnist = input_data.read_data_sets("MNIST_data",one_hot=True) 
 
#每個(gè)批次的大小和總共有多少個(gè)批次 
batch_size = 100 
n_batch = mnist.train.num_examples // batch_size 
 
#定義函數(shù)
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) #標(biāo)準(zhǔn)差
    tf.summary.scalar('max', tf.reduce_max(var))
    tf.summary.scalar('min', tf.reduce_min(var))
    tf.summary.histogram('histogram', var) #直方圖
    
#命名空間
with tf.name_scope("input"):
  #定義兩個(gè)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"):
  #創(chuàng)建一個(gè)簡(jiǎn)單的神經(jīng)網(wǎng)絡(luò) 
  with tf.name_scope('weights'):
    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
  with tf.name_scope('softmax'):
    prediction = tf.nn.softmax(wx_plus_b) 
 
with tf.name_scope('loss'):
  #交叉熵代價(jià)函數(shù) 
  loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=prediction)) 
  tf.summary.scalar('loss', loss)
with tf.name_scope('train'):
  #使用梯度下降法 
  train_step = tf.train.GradientDescentOptimizer(0.2).minimize(loss) 
 
#初始化變量 
init = tf.global_variables_initializer() 
 
with tf.name_scope('accuracy'):
  with tf.name_scope('correct_prediction'):
    #結(jié)果存放在一個(gè)布爾型列表中 
    correct_prediction = tf.equal(tf.argmax(y,1),tf.argmax(prediction,1))#argmax返回一維張量中最大的值所在的位置 
  with tf.name_scope('accuracy'):
    #求準(zhǔn)確率 
    accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32)) 
    tf.summary.scalar('accuracy', accuracy)
 
#合并所有的summary
merged = tf.summary.merge_all()
 
with tf.Session() as sess: 
  sess.run(init) 
  writer = tf.summary.FileWriter("log/", sess.graph) #寫(xiě)入到的位置
  for epoch in range(51): 
    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("epoch " + str(epoch)+ "  acc " +str(acc))