使用tensorflow实现VGG网络,训练mnist数据集方式

weights ={
  'wc1':tf.Variable(tf.random_normal([3,3,1,64])),
  'wc2':tf.Variable(tf.random_normal([3,3,64,64])),
  'wc3':tf.Variable(tf.random_normal([3,3,64,128])),
  'wc4':tf.Variable(tf.random_normal([3,3,128,128])),
  
  'wc5':tf.Variable(tf.random_normal([3,3,128,256])),
  'wc6':tf.Variable(tf.random_normal([3,3,256,256])),
  'wc7':tf.Variable(tf.random_normal([3,3,256,256])),
  'wc8':tf.Variable(tf.random_normal([3,3,256,256])),
  
  'wc9':tf.Variable(tf.random_normal([3,3,256,512])),
  'wc10':tf.Variable(tf.random_normal([3,3,512,512])),
  'wc11':tf.Variable(tf.random_normal([3,3,512,512])),
  'wc12':tf.Variable(tf.random_normal([3,3,512,512])),
  'wc13':tf.Variable(tf.random_normal([3,3,512,512])),
  'wc14':tf.Variable(tf.random_normal([3,3,512,512])),
  'wc15':tf.Variable(tf.random_normal([3,3,512,512])),
  'wc16':tf.Variable(tf.random_normal([3,3,512,256])),
  
  'wd1':tf.Variable(tf.random_normal([4096,4096])),
  'wd2':tf.Variable(tf.random_normal([4096,4096])),
  'out':tf.Variable(tf.random_normal([4096,nn_classes])),
}
 
biases ={
  'bc1':tf.Variable(tf.zeros([64])),
  'bc2':tf.Variable(tf.zeros([64])),
  'bc3':tf.Variable(tf.zeros([128])),
  'bc4':tf.Variable(tf.zeros([128])),
  'bc5':tf.Variable(tf.zeros([256])),
  'bc6':tf.Variable(tf.zeros([256])),
  'bc7':tf.Variable(tf.zeros([256])),
  'bc8':tf.Variable(tf.zeros([256])),
  'bc9':tf.Variable(tf.zeros([512])),
  'bc10':tf.Variable(tf.zeros([512])),
  'bc11':tf.Variable(tf.zeros([512])),
  'bc12':tf.Variable(tf.zeros([512])),
  'bc13':tf.Variable(tf.zeros([512])),
  'bc14':tf.Variable(tf.zeros([512])),
  'bc15':tf.Variable(tf.zeros([512])),
  'bc16':tf.Variable(tf.zeros([256])),
  
  
  'bd1':tf.Variable(tf.zeros([4096])),
  'bd2':tf.Variable(tf.zeros([4096])),
  'out':tf.Variable(tf.zeros([nn_classes])),
}

def convLevel(i,input,type):
  num = i
  out = conv2D('conv'+str(num),input,weights['wc'+str(num)],biases['bc'+str(num)])
  if type=='p':
    out = maxPool2D('pool'+str(num),out, k=2) 
    out = norm('norm'+str(num),out, lsize=4)
  return out 
 
def VGG(x,weights,biases,dropout):
  x = tf.reshape(x,shape=[-1,28,28,1])
 
  input = x
 
  for i in range(16):
    i += 1
    if(i==2) or (i==4) or (i==12) : # 根据模型定义还需要更多的POOL化，但mnist图片大小不允许。
      input = convLevel(i,input,'p')
    else:
      input = convLevel(i,input,'c')

pred = VGG(x, weights, biases, keep_prob)
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred,labels=y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
 
correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1))
accuracy_ = tf.reduce_mean(tf.cast(correct_pred,tf.float32))
 
init = tf.global_variables_initializer()
with tf.Session() as sess:
  sess.run(init)
  step = 1
  while step*batch_size < train_iters:
    batch_x,batch_y = mnist.train.next_batch(batch_size)
    sess.run(optimizer,feed_dict={x:batch_x,y:batch_y,keep_prob:dropout})
    print(step*batch_size)
    if step % display_step == 0 :
      #loss,acc = sess.run([cost,accuracy],feed_dict={x:batch_x,y:batch_y,keep_prob=1.0})
      acc = sess.run(accuracy_, feed_dict={x: batch_x, y: batch_y, keep_prob: 1.})
      # 计算损失值
      
      loss = sess.run(cost, feed_dict={x: batch_x, y: batch_y, keep_prob: 1.})
      print("iter: "+str(step*batch_size)+"mini batch Loss="+"{:.6f}".format(loss)+",acc="+"{:6f}".format(acc))
 
    step += 1 
   
  print("training end!")