tensorflowでclassとsaver.saveが一緒に使えない。

初心者です。
「TensorFlowで学ぶディープラーニング入門」
という本で勉強している最中です。
教科書のコードを少しいじって、Tensorflowでクラスを定義してsaver.saveをつかうとなぜかうまくいきません。
結果の保存されたファイルはクラスを定義せずに書いた方に比べるとmodel.ckpt.indexとmodel.ckpt.meta容量が大きいです（下記参照）。
問題となるのは、その後
saver.restore(sess, "./tmp/model.ckpt")
として、データを読み出そうとすると、前者はうまくいかず、後者はうまくいくということです（後者は本に書いてあったコードなのでうまくいくのは当たり前なんですが。。。）。
pythonは3.6を使っています。
理由がわからず、２週間ほどここで躓いています。
宜しくお願いします。

１）クラスを定義したコード（下記参照）で実行
saver.saveで保村されたファイルとサイズ。
77 May 29 23:27 checkpoint
38675264 May 29 23:27 model.ckpt.data-00000-of-00001
720 May 29 23:27 model.ckpt.index
55104 May 29 23:27 model.ckpt.meta

2）クラスを定義してないコード（下記参照）で実行
saver.saveで保村されたファイルとサイズ。
77 May 29 23:35 checkpoint
38675264 May 29 23:35 model.ckpt.data-00000-of-00001
638 May 29 23:35 model.ckpt.index
43869 May 29 23:35 model.ckpt.meta

一応どちらも、最適化の処理は行い、最後の３つの出力を見ると。
１）では
Step: 1800, Loss: 660.897339, Accuracy: 0.980500
Step: 1900, Loss: 671.345215, Accuracy: 0.978200
Step: 2000, Loss: 653.030029, Accuracy: 0.980000
２）では
Step: 1800, Loss: 657.612671, Accuracy: 0.980100
Step: 1900, Loss: 795.096313, Accuracy: 0.976200
Step: 2000, Loss: 720.260742, Accuracy: 0.978500

１）クラスを定義したコード

import tensorflow as tf
import numpy as np
from tensorflow.examples.tutorials.mnist import input_data

np.random.seed(20160703)
tf.set_random_seed(20160703)

mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)

class SingleCNN:
    def __init__(self, num_filters, num_units):
        with tf.Graph().as_default():
            self.prepare_model(num_filters, num_units)
            self.prepare_session()

   def prepare_model(self, num_filters, num_units):
    num_units1 = 14 * 14 * num_filters
    num_units2 = num_units

    with tf.name_scope('input'):
        x = tf.placeholder(tf.float32, [None, 784], name='input')
        x_image = tf.reshape(x, [-1, 28, 28, 1])

    with tf.name_scope('convolution'):
        W_conv = tf.Variable(
            tf.truncated_normal([5, 5, 1, num_filters], stddev=0.1),
            name='conv-filter')
        h_conv = tf.nn.conv2d(
            x_image, W_conv, strides=[1, 1, 1, 1], padding='SAME',
            name='filter-output')

    with tf.name_scope('pooling'):
        h_pool = tf.nn.max_pool(h_conv, ksize=[1, 2, 2, 1],
                                strides=[1, 2, 2, 1], padding='SAME',
                                name='max-pool')
        h_pool_flat = tf.reshape(h_pool, [-1, 14 * 14 * num_filters],
                                 name='pool-output')

    with tf.name_scope('fully-connected'):
        w2 = tf.Variable(tf.truncated_normal([num_units1, num_units2]))
        b2 = tf.Variable(tf.zeros([num_units2]))
        hidden2 = tf.nn.relu(tf.matmul(h_pool_flat, w2) + b2,
                             name='fc-output')

    with tf.name_scope('softmax'):
        w0 = tf.Variable(tf.zeros([num_units2, 10]))
        b0 = tf.Variable(tf.zeros([10]))
        p = tf.nn.softmax(tf.matmul(hidden2, w0) + b0,
                          name='softmax-output')

    with tf.name_scope('optimizer'):
        t = tf.placeholder(tf.float32, [None, 10], name='labels')
        loss = -tf.reduce_sum(t * tf.log(p), name='loss')
        train_step = tf.train.AdamOptimizer(0.0005).minimize(loss)

    with tf.name_scope('evaluator'):
        correct_prediction = tf.equal(tf.argmax(p, 1), tf.argmax(t, 1))
        accuracy = tf.reduce_mean(tf.cast(correct_prediction,
                                          tf.float32), name='accuracy')


   #        tf.scalar_summary("loss", loss)
   #        tf.scalar_summary("accuracy", accuracy)
    tf.summary.scalar("loss", loss)
    tf.summary.scalar("accuracy", accuracy)

   #        tf.histogram_summary("convolution_filters", W_conv)
            tf.summary.histogram("convolution_filters", W_conv)

    self.x, self.t, self.p = x, t, p
    self.train_step = train_step
    self.loss = loss
    self.accuracy = accuracy

    def prepare_session(self):
        sess = tf.InteractiveSession()
        #sess.run(tf.initialize_all_variables())
        sess.run(tf.global_variables_initializer())
        saver = tf.train.Saver()
        #summary = tf.merge_all_summaries()
        summary = tf.summary.merge_all()
        #writer = tf.train.SummaryWriter("/tmp/mnist_df_logs", sess.graph)
        writer = tf.summary.FileWriter("/tmp/mnist_df_logs", sess.graph)

        self.sess = sess
        self.summary = summary
        self.writer = writer
        self.saver = saver

cnn = SingleCNN(16, 1024)
i = 0
for _ in range(2000):
i += 1
batch_xs, batch_ts = mnist.train.next_batch(100)
cnn.sess.run(cnn.train_step, feed_dict={cnn.x:batch_xs, cnn.t:batch_ts})
if (i % 100 == 0) & (i > 100):
    summary, loss_val, acc_val = cnn.sess.run(
        [cnn.summary, cnn.loss, cnn.accuracy],
        feed_dict={cnn.x:mnist.test.images, cnn.t:mnist.test.labels})
    print ('Step: %d, Loss: %f, Accuracy: %f'
           % (i, loss_val, acc_val))
    #cnn.writer.add_summary(summary, i)
    #'mdc_session',global_step=i)
    cnn.saver.save(cnn.sess,"/tmp/model.ckpt")


    #!rm -rf /tmp/mnist_df_logs
    #tensorboard --logdir=/tmp/mnist_df_logs

2)クラスを定義していないコード

import tensorflow as tf
import numpy as np
from tensorflow.examples.tutorials.mnist import input_data

np.random.seed(20160703)
tf.set_random_seed(20160703)

mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)

num_filters = 16

x = tf.placeholder(tf.float32, [None, 784])
x_image = tf.reshape(x, [-1,28,28,1])

W_conv = tf.Variable(tf.truncated_normal([5,5,1,num_filters],
                                     stddev=0.1))
h_conv = tf.nn.conv2d(x_image, W_conv,
                  strides=[1,1,1,1], padding='SAME')
h_pool =tf.nn.max_pool(h_conv, ksize=[1,2,2,1],
                   strides=[1,2,2,1], padding='SAME')

h_pool_flat = tf.reshape(h_pool, [-1, 14*14*num_filters])

num_units1 = 14*14*num_filters
num_units2 = 1024

w2 = tf.Variable(tf.truncated_normal([num_units1, num_units2]))
b2 = tf.Variable(tf.zeros([num_units2]))
hidden2 = tf.nn.relu(tf.matmul(h_pool_flat, w2) + b2)

w0 = tf.Variable(tf.zeros([num_units2, 10]))
b0 = tf.Variable(tf.zeros([10]))
p = tf.nn.softmax(tf.matmul(hidden2, w0) + b0)

t = tf.placeholder(tf.float32, [None, 10])
loss = -tf.reduce_sum(t * tf.log(p))
train_step = tf.train.AdamOptimizer(0.0005).minimize(loss)
correct_prediction = tf.equal(tf.argmax(p, 1), tf.argmax(t, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

sess = tf.InteractiveSession()
sess.run(tf.global_variables_initializer())
#saver = tf.train.Saver(write_version = saver_pb2.SaverDef.V1)
saver = tf.train.Saver()

i = 0
for _ in range(2000):
    i += 1
    batch_xs, batch_ts = mnist.train.next_batch(100)
    sess.run(train_step, feed_dict={x: batch_xs, t: batch_ts})
    if (i % 100 == 0) & (i > 1800):
        loss_val, acc_val = sess.run([loss, accuracy],
             feed_dict={x:mnist.test.images, t:mnist.test.labels})
        print ('Step: %d, Loss: %f, Accuracy: %f'
           % (i, loss_val, acc_val))
        saver.save(sess, "./tmp/model.ckpt")

読み出してテストするコード

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data


mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)

num_filters = 16

x = tf.placeholder(tf.float32, [None, 784])
x_image = tf.reshape(x, [-1,28,28,1])

W_conv = tf.Variable(tf.truncated_normal([5,5,1,num_filters], stddev=0.1))
h_conv = tf.nn.conv2d(x_image, W_conv,
                  strides=[1,1,1,1], padding='SAME')
h_pool =tf.nn.max_pool(h_conv, ksize=[1,2,2,1],
                   strides=[1,2,2,1], padding='SAME')

h_pool_flat = tf.reshape(h_pool, [-1, 14*14*num_filters])

num_units1 = 14*14*num_filters
num_units2 = 1024

w2 = tf.Variable(tf.truncated_normal([num_units1, num_units2]))
b2 = tf.Variable(tf.zeros([num_units2]))  
hidden2 = tf.nn.relu(tf.matmul(h_pool_flat, w2) + b2)

w0 = tf.Variable(tf.zeros([num_units2, 10]))
b0 = tf.Variable(tf.zeros([10]))
p = tf.nn.softmax(tf.matmul(hidden2, w0) + b0)

t = tf.placeholder(tf.float32, [None, 10])
loss = -tf.reduce_sum(t * tf.log(p))
train_step = tf.train.AdamOptimizer(0.0005).minimize(loss)
correct_prediction = tf.equal(tf.argmax(p, 1), tf.argmax(t, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

sess = tf.InteractiveSession()
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver()
saver.restore(sess, "/tmp/model.ckpt")

filter_vals, conv_vals, pool_vals = sess.run(
  [W_conv, h_conv, h_pool], feed_dict={x:mnist.test.images[:9]})

fig = plt.figure(figsize=(10,num_filters+1))

for i in range(num_filters):
    subplot = fig.add_subplot(num_filters+1, 10, 10*(i+1)+1)
    subplot.set_xticks([])
    subplot.set_yticks([])
    subplot.imshow(filter_vals[:,:,0,i],
               cmap=plt.cm.gray_r, interpolation='nearest')

for i in range(9):
    subplot = fig.add_subplot(num_filters+1, 10, i+2)
    subplot.set_xticks([])
    subplot.set_yticks([])
    subplot.set_title('%d' % np.argmax(mnist.test.labels[i]))
    subplot.imshow(mnist.test.images[i].reshape((28,28)),
               vmin=0, vmax=1,
               cmap=plt.cm.gray_r, interpolation='nearest')

    for f in range(num_filters):
        subplot = fig.add_subplot(num_filters+1, 10, 10*(f+1)+i+2)
        subplot.set_xticks([])
        subplot.set_yticks([])
        subplot.imshow(conv_vals[i,:,:,f],
                   cmap=plt.cm.gray_r, interpolation='nearest')


fig.show()

fig = plt.figure(figsize=(10,num_filters+1))

for i in range(num_filters):
    subplot = fig.add_subplot(num_filters+1, 10, 10*(i+1)+1)
    subplot.set_xticks([])
    subplot.set_yticks([])
    subplot.imshow(filter_vals[:,:,0,i],
               cmap=plt.cm.gray_r, interpolation='nearest')

for i in range(9):
    subplot = fig.add_subplot(num_filters+1, 10, i+2)
    subplot.set_xticks([])
    subplot.set_yticks([])
    subplot.set_title('%d' % np.argmax(mnist.test.labels[i]))
    subplot.imshow(mnist.test.images[i].reshape((28,28)),
               vmin=0, vmax=1,
               cmap=plt.cm.gray_r, interpolation='nearest')

    for f in range(num_filters):
        subplot = fig.add_subplot(num_filters+1, 10, 10*(f+1)+i+2)
        subplot.set_xticks([])
        subplot.set_yticks([])
        subplot.imshow(pool_vals[i,:,:,f],
                   cmap=plt.cm.gray_r, interpolation='nearest')

fig.show()

input("何か入力してください")