声明一个函数来计算图层的激活Tensorflow版本如何工作而我的版本没有？

所以我尝试以类似的方式在MNIST数据集上实现卷积神经网络：https://github.com/tensorflow/tensorflow/blob/r1.1/tensorflow/examples/tutorials/mnist/mnist_with_summaries.py

但是，在这样做时，我注意到由于某种原因我的第二个max_pool没有发生 . 另外，我不明白上面链接中的代码是如何工作的，更具体地说，nn_layer方法如何重用，因为权重仅存在于该范围内并且调用它两次会改变它们？

我的代码：

import tensorflow as tf  
from tensorflow.examples.tutorials.mnist import input_data
import os 
from tensorflow.contrib.tensorboard.plugins import projector

current_path = os.path.dirname(os.path.realpath(__file__))
current_path = current_path+"/logs"

def train():
    mnist = input_data.read_data_sets("MNIST_data", one_hot = True)

    def initializer(shape):
        initial = tf.truncated_normal(shape, stddev=0.1)
        return tf.Variable(initial)

    def conv2d(x,W):
        return tf.nn.conv2d(x , W , [1,1,1,1] , padding="SAME")

    def max_pool(x):
        return tf.nn.max_pool(x , [1,2,2,1] , [1,2,2,1] , padding="SAME")

    def conv_layer(x,length,width,input_channels,output_channels,layer_name,act=tf.nn.relu):
        with tf.name_scope(layer_name):
            with tf.name_scope('weights'):
                weights = initializer([length,width,input_channels,output_channels])
                tf.summary.histogram(layer_name+"_weights",weights)
            with tf.name_scope('biases'):
                biases = initializer([output_channels])
                tf.summary.histogram(layer_name+"_biases",biases)
            with tf.name_scope('activations'):
                activations = act(conv2d(x,weights) + biases)
                activations = max_pool(activations)
                tf.summary.histogram(layer_name+"_activations",activations)
            return activations

    def dense_layer(x,input_size,output_size,layer_name,act=tf.nn.relu):
        with tf.name_scope(layer_name):
            with tf.name_scope('weights'):
                weights = initializer([input_size,output_size])
                tf.summary.histogram(layer_name+"_weights",weights)
            with tf.name_scope('biases'):
                biases = initializer([output_size])
                tf.summary.histogram(layer_name+"_biases",biases)
            with tf.name_scope('activations'):
                activations = act(tf.matmul(x,weights) + biases)
                tf.summary.histogram(layer_name+"_activations",activations)
            return activations

    def dropout(x,keep_prob):
        with tf.name_scope('Dropout'):
            dropped =tf.nn.dropout(x,keep_prob)
        return dropped

    with tf.name_scope('input'):
        x = tf.placeholder(tf.float32, [None,784],name='image_inputs')
        y = tf.placeholder(tf.float32, [None,10],name='image_labels')
        keep_prob = tf.placeholder(tf.float32,name='keep_probability')

    with tf.name_scope('input_reshape'):
        x_image = tf.reshape(x , [-1,28,28,1])
        tf.summary.image('input',x_image,50)

    h1 = conv_layer(x_image,3,3,1,32,"first_convolution_layer")
    h2 = conv_layer(h1,3,3,32,64,"second_convolution_layer")

    h2 = tf.reshape(h1,[-1,7*7*64])
    h2 = dropout(h2,keep_prob)

    h3 = dense_layer(h2,7*7*64,1024,"first_dense_layer")
    h3 = dropout(h3,keep_prob)

    h4 = dense_layer(h3,1024,1024,"second_dense_layer")
    h4 = dropout(h4,keep_prob)

    h_out = dense_layer(h4,1024,10,"output_dense_layer",act=tf.nn.sigmoid)

    with tf.name_scope("Loss"):
        cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=h_out))
        tf.summary.scalar('Loss',cost)

    train = tf.train.AdamOptimizer().minimize(cost)

    with tf.name_scope("Accuracy"):
        correct_pred = tf.equal(tf.argmax(h_out, 1), tf.argmax(y, 1))
        accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
        summary = tf.summary.merge_all()

    init = tf.global_variables_initializer()
    sess = tf.InteractiveSession()
    sess.run(init)
    saver = tf.train.Saver()

    summary_writer = tf.summary.FileWriter(current_path, sess.graph)

    for i in range(500):
        batch = mnist.train.next_batch(500)
        if(i%100 == 0):
            summary_str = sess.run(summary,feed_dict={x:batch[0], y:batch[1], keep_prob:1.0})
            summary_writer.add_summary(summary_str, i)
            summary_writer.flush()
            train_accuracy = accuracy.eval(feed_dict={x:batch[0], y:batch[1], keep_prob:1.0})
            saver.save(sess, os.path.join(current_path,'model.ckpt'), i)
            print("Step %d   Training Accuracy: %f" %((i/100 + 1), train_accuracy))
        train.run(feed_dict={x:batch[0], y:batch[1], keep_prob:0.5})

    sum=0.0
    for i in range(10):
        batch_x = mnist.test.images[(i*1000):((i+1)*1000)-1]
        batch_y = mnist.test.labels[(i*1000):((i+1)*1000)-1]
        sum = sum + accuracy.eval(feed_dict={x:batch_x, y:batch_y, keep_prob:1.0})
    print("Test Accuracy: %f" %(sum/10.0))

if tf.gfile.Exists(current_path):
    tf.gfile.DeleteRecursively(current_path)
tf.gfile.MakeDirs(current_path)
train()