TensorFlow - 異なるテストデータセットで訓練されたモデルを予測する方法は？

Question

私はTensorFlowを使って新しく、訓練されたモデルで画像を分類する方法がわかりません。トレーニングとすべての作業のための電車、検証、テストのデータセットをすでに作成していますが、2番目のテストデータセット（test2と呼ばれます）を予測したいと考えています。私は数字の絵を分類しています。

私はこれを試してみましたが、それは動作しません：

def train_and_predict(restore=False, test_set=None): 
    """ 
    Training of the model, posibility to restore a trained model and predict on another dataset. 
    """ 
    batch_size = 50 
    # Regular datasets for training 
    train_dataset, train_labels, test_dataset, test_labels, valid_dataset, valid_labels = load_dataset(dataset_size) 
    if restore: 
     # change the testset if restoring the trained model 
     test_dataset, test_labels = create_dataset(test_set) 
     test_dataset, test_labels = reformat(test_dataset, test_labels) 
     batch_size = number_predictions 

    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_valid_dataset = tf.constant(valid_dataset) 
     tf_test_dataset = tf.constant(test_dataset) 

     # Variables. 
     K = 32 # first convolutional layer output depth 
     L = 64 # second convolutional layer output depth 
     N = 1024 # fully connected layer 

     W1 = tf.Variable(tf.truncated_normal([5, 5, 1, K], stddev=0.1)) # 5x5 patch, 1 input channel 
     B1 = tf.Variable(tf.constant(0.1, tf.float32, [K])) 
     W2 = tf.Variable(tf.truncated_normal([5, 5, K, L], stddev=0.1)) 
     B2 = tf.Variable(tf.constant(0.1, tf.float32, [L])) 

     W3 = tf.Variable(tf.truncated_normal([7 * 7 * L, N], stddev=0.1)) 
     B3 = tf.Variable(tf.constant(0.1, tf.float32, [N])) 
     W4 = tf.Variable(tf.truncated_normal([N, 10], stddev=0.1)) 
     B4 = tf.Variable(tf.constant(0.1, tf.float32, [10])) 

     # Model. 
     def model(data, train = True): 
      stride = 1 
      Y1 = tf.nn.relu(tf.nn.conv2d(data, W1, strides=[1, stride, stride, 1], padding='SAME') + B1) 
      Y1 = tf.nn.max_pool(Y1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') 
      Y2 = tf.nn.relu(tf.nn.conv2d(Y1, W2, strides=[1, stride, stride, 1], padding='SAME') + B2) 
      Y2 = tf.nn.max_pool(Y2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') 
      Y3 = tf.reshape(Y2, [-1, 7*7*64]) 
      Y4 = tf.nn.relu(tf.matmul(Y3, W3) + B3) 
      if train: 
       # drop-out during training 
       Y4 = tf.nn.dropout(Y4, 0.5) 
      return tf.matmul(Y4, W4) + B4 

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

     # Optimizer. 
     optimizer = tf.train.AdamOptimizer(1e-4).minimize(loss) 

     # Predictions for the training, validation, and test data. 
     train_prediction = tf.nn.softmax(logits) 
     valid_prediction = tf.nn.softmax(model(tf_valid_dataset, False)) 
     test_prediction = tf.nn.softmax(model(tf_test_dataset, False)) 

     # Saver 
     saver = tf.train.Saver() 

    num_steps = 1001 
    with tf.Session(graph=graph) as session: 
     if restore: 
      ckpt = tf.train.get_checkpoint_state('./model/') 
      saver.restore(session, ckpt.model_checkpoint_path) 
      _, l, predictions = session.run([optimizer, loss, test_prediction]) 
     else: 
      tf.global_variables_initializer().run() 
      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 % 100 ==0): 
        saver.save(session, './model/' + 'model.ckpt', global_step=step+1) 
       if (step % 1000 == 0): 
        print('\nMinibatch loss at step %d: %f' % (step, l)) 
     test_accuracy = accuracy(test_prediction.eval(), test_labels) 
    return test_accuracy , predictions 

だから、最初の時間を、私はその後、私は他のテストセットに予測したい、モデルとテストを訓練された：

t,p = train_and_predict() #training 
t_test2, p_test2 = train_and_predict(restore=True, test_set='./test2') 

ファンクションload_dataset、create_dataset、reformatは、（nb_pictures、28、28、1）という形状のデータセットと、シェイプのラベル（nb_pictures、10）を提供します。

ありがとうございました

Answer 1

あなたはそこに必要なものをすべてお持ちです。

with tf.Session(graph=graph) as session: 
     ckpt = tf.train.get_checkpoint_state('./model/') 
     saver.restore(session, ckpt.model_checkpoint_path) 
     feed_dict = {tf_train_dataset : batch_data} 
     predictions = session.run([test_prediction], feed_dict)