InvalidArgumentError：dtype doubleを使用してプレースホルダテンソル 'ground_truth'の値を入力する必要があります。

Question

Tensorflowで転送の学習を理解しようとしています。しかし、私は述べられたエラーを取得しています。
これは私のコードです

def add_final_training_ops(graph, class_count, final_tensor_name, 
          ground_truth_tensor_name): 
    """Adds a new softmax and fully-connected layer for training. 
    We need to retrain the top layer to identify our new classes, so this function 
    adds the right operations to the graph, along with some variables to hold the 
    weights, and then sets up all the gradients for the backward pass. 
    The set up for the softmax and fully-connected layers is based on: 
    https://tensorflow.org/versions/master/tutorials/mnist/beginners/index.html 
    Args: 
     graph: Container for the existing model's Graph. 
     class_count: Integer of how many categories of things we're trying to 
     recognize. 
     final_tensor_name: Name string for the new final node that produces results. 
     ground_truth_tensor_name: Name string of the node we feed ground truth data 
     into. 
    Returns: 
     Nothing. 
    """ 
    bottleneck_tensor1 = graph.get_tensor_by_name(ensure_name_has_port(
     BOTTLENECK_TENSOR_NAME)) 
    bottleneck_tensor = tf.placeholder_with_default(bottleneck_tensor1, shape=[None, 2048]) 
    layer_weights = tf.Variable(
     tf.truncated_normal([BOTTLENECK_TENSOR_SIZE, class_count], stddev=0.001), 
     name='final_weights') 
    layer_biases = tf.Variable(tf.zeros([class_count]), name='final_biases') 
    logits = tf.matmul(bottleneck_tensor, layer_weights, 
         name='final_matmul') + layer_biases 
    tf.nn.softmax(logits, name=final_tensor_name) 
    ground_truth_placeholder = tf.placeholder(tf.float64, 
               [None, class_count], 
               name=ground_truth_tensor_name) 
    cross_entropy = tf.nn.softmax_cross_entropy_with_logits(
     logits=logits, labels=ground_truth_placeholder) 
    cross_entropy_mean = tf.reduce_mean(cross_entropy) 
    train_step = tf.train.GradientDescentOptimizer(FLAGS.learning_rate).minimize(
     cross_entropy_mean) 
    return train_step, cross_entropy_mean 

def do_train(sess,X_input, Y_input, X_validation, Y_validation): 
    ground_truth_tensor_name = 'ground_truth' 
    mini_batch_size = 10 
    n_train = X_input.shape[0] 

    graph = create_graph() 

    train_step, cross_entropy = add_final_training_ops(
     graph, len(classes), FLAGS.final_tensor_name, 
     ground_truth_tensor_name) 

    init = tf.initialize_all_variables() 
    sess.run(init) 

    evaluation_step = add_evaluation_step(graph, FLAGS.final_tensor_name, ground_truth_tensor_name) 

    # Get some layers we'll need to access during training. 
    bottleneck_tensor1 = graph.get_tensor_by_name(ensure_name_has_port(BOTTLENECK_TENSOR_NAME)) 
    bottleneck_tensor = tf.placeholder_with_default(bottleneck_tensor1, shape=[None, 2048]) 
    ground_truth_tensor1 = graph.get_tensor_by_name(ensure_name_has_port(ground_truth_tensor_name)) 
    ground_truth_tensor = tf.placeholder_with_default(ground_truth_tensor1, shape=[None, len(classes)]) 

    i=0 
    epocs = 1 
    for epoch in range(epocs): 
     shuffledRange = np.random.permutation(n_train) 
     y_one_hot_train = encode_one_hot(len(classes), Y_input) 
     y_one_hot_validation = encode_one_hot(len(classes), Y_validation) 
     shuffledX = X_input[shuffledRange,:] 
     shuffledY = y_one_hot_train[shuffledRange] 
     for Xi, Yi in iterate_mini_batches(shuffledX, shuffledY, mini_batch_size): 
      print Xi.shape 
      print type(Xi) 
      print type(Yi) 
      print Yi.shape 
      print Yi.dtype 
      print Yi[0] 
      sess.run(train_step, 
        feed_dict={bottleneck_tensor: Xi, 
           ground_truth_tensor: Yi}) 

print文以下の出力があります。

(10, 2048) 
<type 'numpy.ndarray'> 
<type 'numpy.ndarray'> 
(10, 5) 
float64 
[ 0. 0. 0. 1. 0.] 

私がエラーを取得しています：私はこれを直面していますなぜ誰かが私を伝えることができ

sess.run(train_step,feed_dict={bottleneck_tensor: Xi,ground_truth_tensor: Yi}) 

エラー？

Answer 1

問題は、フィードしていないadd_final_training_opsにプレースホルダを作成したことです。 add_final_training_opsに作成するプレースホルダーground_truth_tensorは同じものだとは思うかもしれませんが、前者によって初期化されていても新しいものです。

おそらく最も簡単な解決策は、プレースホルダをadd_final_training_opsから戻し、代わりにこのプレースホルダを使用することです。