Tensorflowでモデルを再利用する方法

Question

TensorFlowを初めて使用しています。私は次のグラフを持っています。私が得るテスト精度は90％です。私はモデルを再利用したいと思います。私がそれを理解した1つの方法は、学習された重みから変数を開始することです（下のREUSE_MODELを参照）。しかし、モデルを使ってテストデータセットを実行すると、2.0％の精度が得られます。

私はそれをやっている方法で何が問題なのですか、それを行うにはどうすればよいですか？

GRAPHのビルドして実行

graph = tf.Graph() 

with graph.as_default(): 

    # input data 
    tf_train_dataset = tf.placeholder(tf.float32, shape=(batch_size, image_size, image_size, num_channels)) 
    tf_train_labels = tf.placeholder(tf.float32, shape=(batch_size, num_labels)) 
    tf_test_dataset = tf.constant(test_dataset) 

    layer1_weights = tf.Variable(tf.truncated_normal([kernel_size, kernel_size, num_channels, num_kernels])) 
    layer1_biases = tf.Variable(tf.zeros([num_kernels])) 
    layer2_weights = tf.Variable(tf.truncated_normal([kernel_size, kernel_size, num_kernels, num_kernels])) 
    layer2_biases = tf.Variable(tf.constant(1.0, shape=[num_kernels])) 
    layer3_weights = tf.Variable(tf.truncated_normal([image_size // 4 * image_size // 4 * num_kernels, num_hidden], stddev=0.1)) 
    layer3_biases = tf.Variable(tf.constant(1.0, shape=[num_hidden])) 
    layer4_weights = tf.Variable(tf.truncated_normal([num_hidden, num_labels], stddev=0.1)) 
    layer4_biases = tf.Variable(tf.constant(1.0, shape=[num_labels])) 

    # model 
    def model(data): 
    conv = tf.nn.conv2d(data, layer1_weights, [1, 2, 2, 1], padding='SAME') 
    hidden = tf.nn.relu(conv + layer1_biases)  
    conv = tf.nn.conv2d(hidden, layer2_weights, [1, 2, 2, 1], padding='SAME') 
    hidden = tf.nn.relu(conv + layer2_biases)  
    shape = hidden.get_shape().as_list() 
    # reshape is of size batch_size X features_vector. We flatten the output of the layer2 to a features vector 
    reshape = tf.reshape(hidden, [shape[0], shape[1] * shape[2] * shape[3]])  
    hidden = tf.nn.relu(tf.matmul(reshape, layer3_weights) + layer3_biases) 
    return tf.matmul(hidden, layer4_weights) + layer4_biases 

    # training computation 
    logits = model(tf_train_dataset) 
    loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=tf_train_labels, logits=logits)) 

    optimizer = tf.train.GradientDescentOptimizer(0.05).minimize(loss) 

    # predictions 
    train_prediction = tf.nn.softmax(logits) 
    test_prediction = tf.nn.softmax(model(tf_test_dataset)) 

def accuracy(predictions, labels): 
    return (100.0 * np.sum(np.argmax(predictions, 1) == np.argmax(labels, 1)) 
     /predictions.shape[0]) 

num_steps = 1001 
num_epochs = 100 

with tf.Session(graph=graph) as session: 

    tf.global_variables_initializer().run() 
    print('Initialized')  
    for epoch in range(num_epochs): 
     for step in range(num_steps): 

     offset = (step * batch_size) % (train_labels.shape[0] - batch_size) 
     batch_data = train_dataset[offset:(offset + batch_size), :, :, :] 
     batch_labels = train_labels[offset:(offset + batch_size), :] 
     feed_dict = {tf_train_dataset : batch_data, tf_train_labels : batch_labels} 
     _, l, predictions = session.run([optimizer, loss, train_prediction], feed_dict=feed_dict) 

     if (step % 50 == 0): 
      print('Minibatch loss at step %d: %f' % (step, l)) 
      print('Minibatch accuracy: %.1f%%' % accuracy(predictions, batch_labels)) 

    print('Test accuracy: %.1f%%' % accuracy(test_prediction.eval(), test_labels)) 

REUSEモデル

with graph.as_default(): 

    tf_test_dataset2 = tf.constant(test_dataset) 

    layer1_weights2 = tf.Variable(layer1_weights.initialized_value()) 
    layer1_biases2 = tf.Variable(layer1_biases.initialized_value()) 
    layer2_weights2 = tf.Variable(layer2_weights.initialized_value()) 
    layer2_biases2 = tf.Variable(layer2_biases.initialized_value()) 
    layer3_weights2 = tf.Variable(layer3_weights.initialized_value()) 
    layer3_biases2 = tf.Variable(layer3_biases.initialized_value()) 
    layer4_weights2 = tf.Variable(layer4_weights.initialized_value()) 
    layer4_biases2 = tf.Variable(layer4_biases.initialized_value()) 

    # model 
    def model(data): 
    conv = tf.nn.conv2d(data, layer1_weights2, [1, 2, 2, 1], padding='SAME') 
    hidden = tf.nn.relu(conv + layer1_biases2)  
    conv = tf.nn.conv2d(hidden, layer2_weights2, [1, 2, 2, 1], padding='SAME') 
    hidden = tf.nn.relu(conv + layer2_biases2)  
    shape = hidden.get_shape().as_list() 
    # reshape is of size batch_size X features_vector. We flatten the output of the layer2 to a features vector 
    reshape = tf.reshape(hidden, [shape[0], shape[1] * shape[2] * shape[3]])  
    hidden = tf.nn.relu(tf.matmul(reshape, layer3_weights2) + layer3_biases2) 
    return tf.matmul(hidden, layer4_weights2) + layer4_biases2 

    test_prediction2 = tf.nn.softmax(model(tf_test_dataset2)) 

with tf.Session(graph=graph) as session:  
    tf.global_variables_initializer().run() 
    session.run(test_prediction2) 
    print('Test accuracy: %.1f%%' % accuracy(test_prediction2.eval(), test_labels)) 

Answer 1

私は正しい方法は、これがこの上の公式ドキュメントで、メタグラフを保存して復元することであると思う： https://www.tensorflow.org/api_docs/python/state_ops/exporting_and_importing_meta_graphs