要点：
我目前正在将Apache + PHP堆栈上的网站迁移到Node + Express，并且想知道在新版本下动态注入元标记的最佳方法/最佳实践（如果有的话）是什么叠加。
详细信息：
在现有堆栈下，通过直接将PHP代码添加到HTML文件中来动态注入元标记。由于渲染是在服务器端完成的，因此Facebook / Google + /任何网络抓取工具都可以正确解释标签。
在新筹码下，经过一些研究后，我发现了两个选择：
在这两个选项之间，哪一个会有更好的表现还是没有实质性差异？您还有其他方式可以推荐吗？谢谢！
答案 0 :(得分：4)
EJS可能是最简单的，与PHP非常相似。
您还可以查看Mustache和Handlebars以获取其他选项，只需对现有HTML进行最少的更改。
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
#Imported Data set
mnist = input_data.read_data_sets("/tmp/data/", one_hot = True)
#ammount of output classes
n_classes = 10
#ammount of examples processed at once
#memory impact of ~500MB for 128 with more on eval runs
batch_size = 128
#Times to cycle through the entire imput data set
epoch_amm =20
#Input and outputs placeholders
x = tf.placeholder(tf.float32, [None, 784])
y = tf.placeholder(tf.float32)
#Dropout is 1-keeprate; fc- fully conected layer dropout;conv conv layer droupout
keep_rate_fc=.5
keep_rate_conv=.75
keep_prob=tf.placeholder(tf.float32)
#Regularization paramaters
Regularization_active= False #True and False MUST be capitalized
Lambda= 1.0 #'weight' of the weights on the loss function
# counter for total steps taken by trainer
training_steps = 1
#Learning Rate For Network
base_Rate = .03
decay_steps = 64
decay_rate = .96
Staircase = True
Learning_Rate = tf.train.exponential_decay(base_Rate, training_steps, decay_steps, decay_rate, staircase='Staircase', name='Exp_decay' )
#Convolution Function returns neuronns that act on a section of prev. layer
def conv2d(x,W):
return tf.nn.conv2d(x,W,strides=[1,1,1,1],padding='SAME')
#Pooling function returns max value in 2 by 2 sections
def maxpool2d(x):
return tf.nn.max_pool(x,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME')
def relu(x):
return tf.nn.relu(x,'relu')
def add(x, b):
return tf.add(x,b)
#'Main' method, contains the Neural Network
def convolutional_neural_network(x):
weights = {'W_conv1':tf.Variable(tf.random_normal([5,5,1,32])),
'W_conv2':tf.Variable(tf.random_normal([5,5,32,64])),
'W_fc':tf.Variable(tf.random_normal([7*7*64,1024])),
'W_out':tf.Variable(tf.random_normal([1024,n_classes]))}
biases = {'B_conv1':tf.Variable(tf.random_normal([32])),
'B_conv2':tf.Variable(tf.random_normal([64])),
'B_fc':tf.Variable(tf.random_normal([1024])),
'B_out':tf.Variable(tf.random_normal([n_classes]))}
# Input layer
x = tf.reshape(x, shape=[-1,28,28,1])
#first layer. pass inputs through conv2d and save as conv1 then apply maxpool2d
conv1 = conv2d(x,weights['W_conv1'])
conv1 = add(conv1,biases['B_conv1'])
conv1 = relu(conv1)
conv1 = maxpool2d(conv1)
conv1 = tf.nn.dropout(conv1,keep_rate_conv)
#second layer does same as first layer
conv2 = conv2d(conv1,weights['W_conv2'])
conv2 = add(conv2,biases['B_conv2'])
conv2 = relu(conv2)
conv2 = maxpool2d(conv2)
conv2 = tf.nn.dropout(conv2,keep_rate_conv)
#3rd layer fully connected
fc = tf.reshape(conv2,[-1,7*7*64])
fc = tf.matmul(fc,weights['W_fc'])
fc = add(fc,biases['B_fc'])
fc = relu(fc)
fc = tf.nn.dropout(fc,keep_rate_fc)
#4th and final layer
output = tf.matmul(fc,weights['W_out'])
output = add(output,biases['B_out'])
return output
#Trains The neural Network
def train_neural_network(x):
training_steps = 0
#Initiate The Network
prediction = convolutional_neural_network(x)
#Define the Cost and Cost function
#tf.reduce_mean averages the values of a tensor into one value
cost = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits(prediction,y) )
#Apply Regularization if active
#if Regularization_active :
# print('DEBUG!! LINE 84 REGULARIZATION ACTIVE')
# cost = (tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits(prediction,y))+
# (Lambda*(tf.nn.l2_loss(weight['W_conv1'])+
# tf.nn.l2_loss(weight['W_conv2'])+
# tf.nn.l2_loss(weight['W_fc'])+
# tf.nn.l2_loss(weight['W_out'])+
# tf.nn.l2_loss(biases['B_conv1'])+
# tf.nn.l2_loss(biases['B_conv2'])+
# tf.nn.l2_loss(biases['B_fc'])+
# tf.nn.l2_loss(biases['B_out']))))
#Optimizer + Learning_Rate passthrough
optimizer = tf.train.AdamOptimizer().minimize(cost)
#Get Epoch Ammount
hm_epochs = epoch_amm
#Starts C++ Training session
print('Session Started')
with tf.Session() as sess:
#Initiate all Variables
sess.run(tf.global_variables_initializer())
#Begin Logs
summary_writer = tf.summary.FileWriter('/tmp/logs',sess.graph)
#Start Training
for epoch in range(hm_epochs):
epoch_loss = 0
for count in range(int(mnist.train.num_examples/batch_size)):
training_steps = (training_steps+1)
epoch_x, epoch_y = mnist.train.next_batch(batch_size)
count, c = sess.run([optimizer, cost], feed_dict={x: epoch_x, y: epoch_y})
epoch_loss += c
print('Epoch', epoch, 'current epoch loss', epoch_loss, 'batch loss', c,'ts',training_steps,' ', end='\r')
#Log the loss per epoch
print('Epoch', epoch, 'completed out of',hm_epochs,'loss:',epoch_loss,' ')
acc_total = 0
correct = tf.equal(tf.argmax(prediction, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct, 'float'))
for _ in range(int(mnist.test.num_examples/batch_size)):
test_x, test_y = mnist.test.next_batch(batch_size)
acc = accuracy.eval(feed_dict={x: test_x, y: test_y})
acc_total += acc
print('Accuracy:',acc_total*batch_size/float(mnist.test.num_examples),end='\r')
print('Epoch', epoch, 'current test set accuracy : ',acc_total*batch_size/float(mnist.test.num_examples))
acc_total=0
correct = tf.equal(tf.argmax(prediction, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct, 'float'))
for _ in range(int(mnist.train.num_examples/batch_size)):
train_x, train_y = mnist.train.next_batch(batch_size)
acc = accuracy.eval(feed_dict={x: train_x, y: train_y})
acc_total += acc
print('Accuracy:',acc_total*batch_size/float(mnist.train.num_examples),end='\r')
print('Epoch', epoch, 'current train set accuracy : ',acc_total*batch_size/float(mnist.test.num_examples))
print('Complete')
sess.close()
#Run the Neural Network
train_neural_network(x)
<html><head><%= yourMetaTags %> ... <html><head>{{ yourMetaTags }} ... doT.js也很快。
请参阅：
解析HTML并使用DOM API操作它只是为了插入元标记在我看来是一种矫枉过正。
另一方面，如果您只需要插入元标记，那么您可以使用类似<html><head>{{ yourMetaTags }} ...的内容进行简单的正则表达式替换，但是当您需要更多功能时，它可能会随着时间的推移而变得更加复杂。毕竟，每个人都在生活的某个阶段制造了一个模板引擎。