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")
    
```
CC BY-SA 3.0
2017-06-25T15:30:47.510
35881
2018-07-27T05:00:00.763
2017-06-28T15:13:17.517
24018
24018
question
python3 tensorflow
tensorflowでclassとsaver.saveが一緒に使えない。
「うまくいかない」の内容がわからないです。エラー? それ以外?

例えば

```

    from (クラスを定義したファイル) import SingleCNN, mnist
    
    cnn = SingleCNN(16, 1024)
    
    cnn.saver.restore(cnn.sess, "(モデルを保存したディレクトリ)/model.ckpt")
    
    print('Accuracy: %f' % (cnn.sess.run(cnn.accuracy, feed_dict={cnn.x: mnist.test.images, cnn.t: mnist.test.labels})))
    
```

としたら 最後に保存したモデルの Accuracy が出ることは確認できました。

インデントが妙なところは直しましたが。

```

    (simple_cnn.py)
    import tensorflow as tf
    
    
    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
    
```

* * *
```

    (train.py)
    import tensorflow as tf
    import numpy as np
    from tensorflow.examples.tutorials.mnist import input_data
    
    from simple_cnn import SingleCNN
    
    if __name__ == '__main__':
        mnist = input_data.read_data_sets("./tmp/data/", one_hot=True)
    
        np.random.seed(20160703)
        tf.set_random_seed(20160703)
    
        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:
                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")
    
        cnn.saver.save(cnn.sess, "./tmp/model.ckpt")
        print('Accuracy: %f' % (cnn.sess.run(cnn.accuracy, feed_dict={cnn.x: mnist.test.images, cnn.t: mnist.test.labels})))
    
```

* * *
```

    (load_model.py)
    from tensorflow.examples.tutorials.mnist import input_data
    
    from simple_cnn import SingleCNN
    
    if __name__ == '__main__':
        mnist = input_data.read_data_sets("./tmp/data/", one_hot=True)
    
        cnn = SingleCNN(16, 1024)
        cnn.saver.restore(cnn.sess, "./tmp/model.ckpt")
        print('Accuracy: %f' % (cnn.sess.run(cnn.accuracy, feed_dict={cnn.x: mnist.test.images, cnn.t: mnist.test.labels})))
    
```

なんの問題もなく、最後に保存したモデルがロードできていますよ。  
最後に保存していないだけなのでは?

* * *
```

    import numpy as np
    import tensorflow as tf
    
    from tensorflow.examples.tutorials.mnist import input_data
    
    from simple_cnn import SingleCNN
    
    mnist = input_data.read_data_sets("./tmp/data/", one_hot=True)
    
    cnn = SingleCNN(16, 1024)
    cnn.saver.restore(cnn.sess, "./tmp/model.ckpt")
    
    for idx in range(10):
        result = cnn.sess.run(tf.argmax(cnn.p,1), feed_dict={cnn.x: np.array([mnist.test.images[idx]])})
        print('estimated {}, correct {}'.format(result, np.argmax(mnist.test.labels[idx])))
    
    estimated [7], correct 7
    estimated [2], correct 2
    estimated [1], correct 1
    estimated [0], correct 0
    estimated [4], correct 4
    estimated [1], correct 1
    estimated [4], correct 4
    estimated [9], correct 9
    estimated [6], correct 5
    estimated [9], correct 9
    
```

やはり問題なさそうですが。
CC BY-SA 3.0
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2017-06-28T15:20:10.570
2017-06-28T15:20:10.570
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answer