Tensorflow 张量不是这个图的元素

我得到的

张量张量（“占位符：0”，shape=（128784），dtype=float32）不是这个图的元素。

当我想训练和测试我的图时。堆栈跟踪没有真正的帮助，我无法从中获得更多信息。我真的不明白这个错误是怎么发生的，每个数据集都应该通过

with graphFully自动添加到
graphFully
。作为默认值（）：

，然后我用

with tf.Session（graph=graph）作为Session:

欢迎提供关于如何简化图表的建议。我想定义几个图表，并对它们进行比较，从而得到“复杂”的结构

我的图表：

##fully connected with hidden layer
def createFullyConnected():

    graphFully = tf.Graph()
    with graphFully.as_default():

        def constructGraph(dataset, weights1, biases1, weights2, biases2):
            logits1 = tf.matmul(dataset, weights1) + biases1
            hiddenl = tf.nn.relu(logits1)  
            logits2 = tf.matmul(hiddenl, weights2) + biases2
            return logits2

        def weight_variable(shape):
            initial = tf.truncated_normal(shape, stddev=0.01)
            return tf.Variable(initial, name='weights')


        def bias_variable(shape):
            initial = tf.constant(0.0, shape=shape)
            return tf.Variable(initial, name='biases')

        # Input data. For the training data, we use a placeholder that will be fed
        # at run time with a training minibatch.
        tf_train_dataset = tf.placeholder(tf.float32,
                                        shape=(batch_size, image_size * image_size), name='train_data')
        tf_train_labels = tf.placeholder(tf.float32, shape=(batch_size, num_labels), name='train_labels')
        tf_valid_dataset = tf.constant(validation[0], name='valid_labels')
        tf_test_dataset = tf.constant(test[0], name='test_labels')

        # Variables.
        with tf.name_scope('hidden') as scope:
            weights1 = weight_variable([image_size * image_size, 1024])
            biases1 = bias_variable([1024])
        weights2 = weight_variable([1024, num_labels])
        biases2 = bias_variable([num_labels])

        # Training computation.
        logits = constructGraph(tf_train_dataset, weights1, biases1, weights2, biases2)
        loss = tf.reduce_mean(
        tf.nn.softmax_cross_entropy_with_logits(labels=tf_train_labels, logits=logits))

        # Optimizer.
        optimizer = tf.train.GradientDescentOptimizer(0.5).minimize(loss)

        # Predictions for the training, validation, and test data.
        train_prediction = tf.nn.softmax(logits)
        valid_prediction = tf.nn.softmax(constructGraph(tf_valid_dataset, weights1, biases1, weights2, biases2))
        test_prediction = tf.nn.softmax(constructGraph(tf_test_dataset, weights1, biases1, weights2, biases2))
        # We write the graph out to the `logs` directory
        tf.summary.FileWriter("logs", graphFully).close()
        return (graphFully, optimizer, train_prediction, valid_prediction, test_prediction)  

以及评价：

def evaluate(graph, optimizer, train_prediction, valid_prediction, test_prediction):
    num_steps = 3001
    train_dataset = train[0]
    train_labels = train[1]
    valid_labels = validation[1]
    test_labels = test[1]
    outlier_labels = outlier[1]

    with tf.Session(graph=graph) as session:
        tf.global_variables_initializer().run()
        print("Initialized")
        for step in range(num_steps):
            # Pick an offset within the training data, which has been randomized.
            # Note: we could use better randomization across epochs.
            offset = (step * batch_size) % (train_labels.shape[0] - batch_size)
            # Generate a minibatch.
            batch_data = train_dataset[offset:(offset + batch_size), :]
            batch_labels = train_labels[offset:(offset + batch_size), :]
            # Prepare a dictionary telling the session where to feed the minibatch.
            # The key of the dictionary is the placeholder node of the graph to be fed,
            # and the value is the numpy array to feed to it.
            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 % 500 == 0):
                print("Minibatch loss at step %d: %f" % (step, l))
                print("Minibatch accuracy: %.1f%%" % accuracy(predictions, batch_labels))
                print("Validation accuracy: %.1f%%" % accuracy(
                    valid_prediction.eval(), valid_labels))
        print("Test accuracy: %.1f%%" % accuracy(test_prediction.eval(), test_labels))

---

这里出现的错误是由于两个占位符

tf\u train\u数据集

和

tf\u train\u标签

超出了范围。您需要从

evaluate

函数中的th图访问这两个张量

 def evaluate(...):
     ... 
     tf_train_dataset = graph.get_tensor_by_name('train_data:0')
     tf_train_labels = graph.get_tensor_by_name('train_labels:0')
     with tf.Session(graph=graph) as session:
     ...