Tensorflowによる中間層の可視化

###前提・実現したいこと
Tensorflowによる画像識別で、中間層の可視化を行いたいです。

###該当のソースコード

import sys
import cv2
import random
import os 
import numpy as np
import tensorflow as tf
import tensorflow.python.platform

NUM_CLASSES = 10
IMAGE_SIZE = 28
IMAGE_PIXELS = IMAGE_SIZE*IMAGE_SIZE*3

flags = tf.app.flags
FLAGS = flags.FLAGS

flags.DEFINE_string('train', './data/train/data.txt', 'File name of train data')
flags.DEFINE_string('test', './data/test/data.txt', 'File name of train data')
flags.DEFINE_string('train_dir', './data/tensorboard', 'Directory to put the training data.')
flags.DEFINE_integer('max_steps', 200, 'Number of steps to run trainer.')
flags.DEFINE_integer('batch_size', 20, 'Batch size Must divide evenly into the dataset sizes.')
flags.DEFINE_float('learning_rate', 1e-4, 'Initial learning rate.')

def inference(images_placeholder, keep_prob):

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

    def bias_variable(shape):
      initial = tf.constant(0.1, shape=shape)
      return tf.Variable(initial)

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

    def max_pool_2x2(x):
      return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
                            strides=[1, 2, 2, 1], padding='SAME')
    
    x_image = tf.reshape(images_placeholder, [-1, 28, 28, 3])

    with tf.name_scope('conv1') as scope:
        W_conv1 = weight_variable([5, 5, 3, 32])
        b_conv1 = bias_variable([32])
        h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)

    with tf.name_scope('pool1') as scope:
        h_pool1 = max_pool_2x2(h_conv1)
    
    with tf.name_scope('conv2') as scope:
        W_conv2 = weight_variable([5, 5, 32, 64])
        b_conv2 = bias_variable([64])
        h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)

    with tf.name_scope('pool2') as scope:
        h_pool2 = max_pool_2x2(h_conv2)

    with tf.name_scope('fc1') as scope:
        W_fc1 = weight_variable([7*7*64, 1024])
        b_fc1 = bias_variable([1024])
        h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])
        h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)

        h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

    with tf.name_scope('fc2') as scope:
        W_fc2 = weight_variable([1024, NUM_CLASSES])
        b_fc2 = bias_variable([NUM_CLASSES])

    with tf.name_scope('softmax') as scope:
        y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)

    return y_conv

def loss(logits, labels):

    cross_entropy = -tf.reduce_sum(labels*tf.log(logits))

    tf.summary.scalar("cross_entropy", cross_entropy)
    return cross_entropy

def training(loss, learning_rate):

    train_step = tf.train.AdamOptimizer(learning_rate).minimize(loss)
    return train_step

def accuracy(logits, labels):

    correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(labels, 1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
    tf.summary.scalar("accuracy", accuracy)
    return accuracy

if __name__ == '__main__':
   
    f = open(FLAGS.train, 'r')
    
    train_image = []
    train_label = []
    for line in f:
       
        line = line.rstrip()
        l = line.split()
       
        img = cv2.imread(l[0])
        img = cv2.resize(img, (28, 28))
        
        train_image.append(img.flatten().astype(np.float32)/255.0)
        
        tmp = np.zeros(NUM_CLASSES)
        tmp[int(l[1])] = 1
        train_label.append(tmp)
   
    train_image = np.asarray(train_image)
    train_label = np.asarray(train_label)
    f.close()

    f = open(FLAGS.test, 'r')
    test_image = []
    test_label = []
    for line in f:
        line = line.rstrip()
        l = line.split()
        img = cv2.imread(l[0])
        img = cv2.resize(img, (28, 28))
        test_image.append(img.flatten().astype(np.float32)/255.0)
        tmp = np.zeros(NUM_CLASSES)
        tmp[int(l[1])] = 1
        test_label.append(tmp)
    test_image = np.asarray(test_image)
    test_label = np.asarray(test_label)
    f.close()
    
    with tf.Graph().as_default():
       
        images_placeholder = tf.placeholder("float", shape=(None, IMAGE_PIXELS))
       
        labels_placeholder = tf.placeholder("float", shape=(None, NUM_CLASSES))
       
        keep_prob = tf.placeholder("float")

        
        logits = inference(images_placeholder, keep_prob)
        
        loss_value = loss(logits, labels_placeholder)
        
        train_op = training(loss_value, FLAGS.learning_rate)
        
        acc = accuracy(logits, labels_placeholder)

        saver = tf.train.Saver()
        cwd = os.getcwd()

        sess = tf.Session()

        sess.run(tf.initialize_all_variables())

        summary_op = tf.summary.merge_all()
        summary_writer = tf.summary.FileWriter(FLAGS.train_dir, sess.graph_def)
        
        for step in range(FLAGS.max_steps):
            for i in range(int(len(train_image)/FLAGS.batch_size)):

                batch = FLAGS.batch_size*i

                sess.run(train_op, feed_dict={
                  images_placeholder: train_image[batch:batch+FLAGS.batch_size],
                  labels_placeholder: train_label[batch:batch+FLAGS.batch_size],
                  keep_prob: 0.5})

            train_accuracy = sess.run(acc, feed_dict={
                images_placeholder: train_image,
                labels_placeholder: train_label,
                keep_prob: 1.0})
            print ("step %d, training accuracy %g"%(step, train_accuracy))

            summary_str = sess.run(summary_op, feed_dict={
                images_placeholder: train_image,
                labels_placeholder: train_label,
                keep_prob: 1.0})
            summary_writer.add_summary(summary_str, step)

    print ("test accuracy %g"%sess.run(acc, feed_dict={
        images_placeholder: test_image,
        labels_placeholder: test_label,
        keep_prob: 1.0}))

    save_path = saver.save(sess, cwd + "//model.ckpt")

###試したこと
上記のコードは
http://kivantium.hateblo.jp/entry/2015/11/18/233834 - TensorFlowでアニメゆるゆりの制作会社を識別するを参考にさせていただきました。
http://enakai00.hatenablog.com/entry/2016/02/29/090112 - TensorFlow Tutorialの数学的背景 − Deep MNIST for Experts（その3）ではMNISTのデータセットより中間層を表示しているようですが、自前のデータセットについての表示を行う記述に苦労しています。プログラミング初心者で申し訳ないのですが、お分かりの方ご教授いただけると助かります。