tf.nn.softmax_cross_entropy_with_logits()错误: logits和标签的大小必须相同

Question

我是新手，正在尝试编写一种算法来对TensorFlow -10数据集中的图像进行分类。我得到了这个错误：

InvalidArgumentError (see above for traceback): logits and labels must be same size: logits_size=[10000,10] labels_size=[1,10000]
     [[Node: SoftmaxCrossEntropyWithLogits = SoftmaxCrossEntropyWithLogits[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"](Reshape, Reshape_1)]]

下面是我的代码：

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import cPickle

n_nodes_hl1 = 500
n_nodes_hl2 = 500
n_nodes_hl3 = 500

n_classes = 10
batch_size = 100
image_size = 32*32*3 # because 3 channels

x = tf.placeholder('float', shape=(None, image_size)) 
y = tf.placeholder('float')

def neural_network_model(data):
    hidden_1_layer = {'weights':tf.Variable(tf.random_normal([image_size, n_nodes_hl1])), 'biases':tf.Variable(tf.random_normal([n_nodes_hl1]))}
    hidden_2_layer = {'weights':tf.Variable(tf.random_normal([n_nodes_hl1, n_nodes_hl2])), 'biases':tf.Variable(tf.random_normal([n_nodes_hl2]))}
    hidden_3_layer = {'weights':tf.Variable(tf.random_normal([n_nodes_hl2, n_nodes_hl3])), 'biases':tf.Variable(tf.random_normal([n_nodes_hl3]))}
    output_layer = {'weights':tf.Variable(I am new to TensorFlow and tf.random_normal([n_nodes_hl3, n_classes])), 'biases':tf.Variable(tf.random_normal([n_classes]))}
    # input_data * weights + biases
    l1 = tf.add(tf.matmul(data, hidden_1_layer['weights']), hidden_1_layer['biases'])
    # activation function
    l1 = tf.nn.relu(l1)

    l2 = tf.add(tf.matmul(l1, hidden_2_layer['weights']), hidden_2_layer['biases'])
    l2 = tf.nn.relu(l2)

    l3 = tf.add(tf.matmul(l2, hidden_3_layer['weights']), hidden_3_layer['biases'])
    l3 = tf.nn.relu(l3)

    output = tf.matmul(l3, output_layer['weights']) + output_layer['biases']
    return output

def train_neural_network(x):
    prediction = neural_network_model(x)
    cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(prediction, y))//THIS IS LINE 48 WHERE THE ERROR OCCURS
    #learning rate = 0.001
    optimizer = tf.train.AdamOptimizer().minimize(cost)
    hm_epochs = 10
    with tf.Session() as sess:
        sess.run(tf.initialize_all_variables())
        for epoch in range(hm_epochs):
            epoch_loss = 0
            for i in range(5):
                with open('data_batch_'+str(i+1),'rb') as f:
                    train_data = cPickle.load(f)
                print train_data
                print prediction.get_shape()
                #print len(y)
                _, c = sess.run([optimizer, cost], feed_dict={x:train_data['data'],y:train_data['labels']})
                epoch_loss += c
            print 'Epoch ' + str(epoch) + ' completed out of ' + str(hm_epochs) + ' loss: ' + str(epoch_loss)
        correct = tf.equal(tf.argmax(prediction,1), tf.argmax(y,1))
        accuracy = tf.reduce_mean(tf.cast(correct, 'float'))
        with open('test_batch','rb') as f:
            test_data = cPickle.load(f)
            accuracy = accuracy.eval({x:test_data['data'],y:test_data['labels']})
        print 'Accuracy: ' + str(accuracy)

train_neural_network(x)

我很确定这意味着在第48行(如上所示)，prediction和y的形状不同，但我对TensorFlow的理解还不够深入，无法知道如何修复它。我甚至不知道y是在哪里设置的，我从教程中获得了大部分代码，并摆弄了一下，将其应用于不同的数据集。如何修复此错误？