失败的sess.run错误“无法为Tensor 'Placeholder:0'提供形状值（50,2352），其形状为'(?, 784)'”

请帮我...

我学习使用我自己的数据基于教程专家的张量流 .

遵循我的代码：

#datasets define
NUM_CLASSES = 65535 
IMAGE_SIZE = 28 
IMAGE_PIXELS = IMAGE_SIZE*IMAGE_SIZE*1

#read datasets
with open(FLAGS.train, 'r') as f: # train.txt
    train_image = []
    train_label = []
    num = 0
    for line in f:
        if num == 500:
            break
        line = line.rstrip()
        l = line.split(',')
        print(l[0])
        img = cv2.imread(l[0])
        img = cv2.resize(img, (IMAGE_SIZE, IMAGE_SIZE))
        train_image.append(img.flatten().astype(np.float32)/255.0)
        tmp = np.zeros(NUM_CLASSES)
        tmp[int(l[1])] = 1
        train_label.append(tmp)
        num += 1
    train_image = np.asarray(train_image)
    train_label = np.asarray(train_label)
    train_len = len(train_image)

def inference(images_placeholder, keep_prob):
    def weight_variable(shape):
        initial = tf.truncated_normal(shape, stddev=0.1)
        return tf.Variable(initial)
def bias_variable(shape):
    initial = tf.constant(0.1, shape=shape)
    return tf.Variable(initial)
def conv2d(x, W):
    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
def max_pool_2x2(x):
    return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
                          strides=[1, 2, 2, 1], padding='SAME')
x_images = tf.reshape(images_placeholder, [-1, IMAGE_SIZE, IMAGE_SIZE, 1])
with tf.name_scope('conv1') as scope:
    W_conv1 = weight_variable([5, 5, 1, 32])
    b_conv1 = bias_variable([32])
    h_conv1 = tf.nn.relu(conv2d(x_images, W_conv1) + b_conv1)
with tf.name_scope('pool1') as scope:
    h_pool1 = max_pool_2x2(h_conv1)
with tf.name_scope('conv2') as scope:
    W_conv2 = weight_variable([5, 5, 32, 64])
    b_conv2 = bias_variable([64])
    h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
with tf.name_scope('pool2') as scope:
    h_pool2 = max_pool_2x2(h_conv2)
with tf.name_scope('fc1') as scope:
    W_fc1 = weight_variable([7*7*64, 1024])
    b_fc1 = bias_variable([1024])
    h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])
    h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
    h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
with tf.name_scope('fc2') as scope:
    W_fc2 = weight_variable([1024, NUM_CLASSES])
    b_fc2 = bias_variable([NUM_CLASSES])
with tf.name_scope('softmax') as scope:
    y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)
return y_conv

#learn
with tf.Graph().as_default():
    images_placeholder = tf.placeholder("float", shape=(None, IMAGE_PIXELS))
    labels_placeholder = tf.placeholder("float", shape=(None, NUM_CLASSES))
    keep_prob = tf.placeholder("float")

    logits = inference(images_placeholder, keep_prob)
    loss_value = loss(logits, labels_placeholder)
    train_op = training(loss_value, FLAGS.learning_rate)
    print("train_op =", train_op)

    acc = accuracy(logits, labels_placeholder)

    saver = tf.train.Saver()
    sess = tf.Session()
    sess.run(tf.initialize_all_variables())
    summary_op = tf.merge_all_summaries()
    summary_writer = tf.train.SummaryWriter(FLAGS.train_dir, sess.graph_def)

    if train_len % FLAGS.batch_size is 0:
        train_batch = train_len/FLAGS.batch_size
    else:
        train_batch = (train_len/FLAGS.batch_size)+1
    print("train_batch = %d",str(train_batch))
    for step in range(FLAGS.max_steps):
        for i in range(int(train_batch)):
            batch = FLAGS.batch_size*i
            batch_plus = FLAGS.batch_size*(i+1)
            print("batch_plus =", batch_plus)
            if batch_plus > train_len: batch_plus = train_len
            sess.run(train_op, feed_dict={
                     images_placeholder: train_image[batch:batch_plus],
                     labels_placeholder: train_label[batch:batch_plus],
                     keep_prob: 0.5})

        if step % 10 == 0:
            train_accuracy = 0.0
            for i in range(train_batch):
                batch = FLAGS.batch_size*i
                batch_plus = FLAGS.batch_size*(i+1)
                if batch_plus > train_len: batch_plus = train_len
                train_accuracy += sess.run(acc, feed_dict={
                                           images_placeholder: train_image[batch:batch_plus],
                                           labels_placeholder: train_label[batch:batch_plus],
                                           keep_prob: 1.0})
                if i is not 0: train_accuracy /= 2.0
            #summary_str = sess.run(summary_op, feed_dict={
            #    images_placeholder: train_image,
            #    labels_placeholder: train_label,
            #    keep_prob: 1.0})
            #summary_writer.add_summary(summary_str, step)
            print("step %d, training accuracy %g",(step, train_accuracy))

if test_len % FLAGS.batch_size is 0:
    test_batch = test_len/FLAGS.batch_size
else:
    test_batch = (test_len/FLAGS.batch_size)+1
    print("test_batch = ",str(test_batch))
    test_accuracy = 0.0
for i in range(test_batch):
    batch = FLAGS.batch_size*i
    batch_plus = FLAGS.batch_size*(i+1)
    if batch_plus > train_len: batch_plus = train_len
    test_accuracy += sess.run(acc, feed_dict={
                              images_placeholder: test_image[batch:batch_plus],
                              labels_placeholder: test_label[batch:batch_plus],
                              keep_prob: 1.0})
    if i is not 0: test_accuracy /= 2.0
print("test accuracy %g",(test_accuracy))
save_path = saver.save(sess, FLAGS.save_model)

但是当我尝试运行它时，我给出了一个错误：ValueError：无法为Tensor'占位符：0'提供形状值（50,2352），其形状为'（？，784）'

我觉得我忽视了一些小事但我没有看到它 .