我已经在tensorflow 训练模型如下：

batch_size = 128

graph = tf.Graph()
with graph.as_default():
# 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))
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.
weights = tf.Variable(
    tf.truncated_normal([image_size * image_size, num_labels]))
biases = tf.Variable(tf.zeros([num_labels]))

# Training computation.
logits = tf.matmul(tf_train_dataset, weights) + biases
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(
    tf.matmul(tf_valid_dataset, weights) + biases)
test_prediction = tf.nn.softmax(tf.matmul(tf_test_dataset, weights) + biases)

num_steps = 3001
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))

现在，我想使用一个单一的图像作为输入，这是要重塑到相同的格式作为我的训练图像，并获得预测10类的概率。这个问题已经问了很多次，我很难理解他们的解决方案，其中一个最好的答案是使用以下代码：

feed_dict = {x: [your_image]}
classification = tf.run(y, feed_dict)
print classification

在我的代码中，x和y的等价性是什么？假设我从测试数据集中选择一个图像进行预测：

img = train_dataset[678]

我期待一个概率为10的数组。