tensorflow训练自己的tfreocd文件问题

import os
import tensorflow as tf 
from PIL import Image
import matplotlib.pyplot as plt
def weight_varible(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')




#mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
print("Loading Done!")


sess = tf.InteractiveSession()


# paras
W_conv1 = weight_varible([5, 5, 1, 32])
b_conv1 = bias_variable([32])


# conv layer-1
x = tf.placeholder(tf.float32, [None, 784])
x_image = tf.reshape(x, [-1, 28, 28, 1])


h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)


# conv layer-2
W_conv2 = weight_varible([5, 5, 32, 64])
b_conv2 = bias_variable([64])


h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
h_pool2 = max_pool_2x2(h_conv2)


# full connection
W_fc1 = weight_varible([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)


# dropout
keep_prob = tf.placeholder(tf.float32)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)


# output layer: softmax
W_fc2 = weight_varible([1024, 10])
b_fc2 = bias_variable([10])


y_conv = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)
y_ = tf.placeholder(tf.float32, [None, 10])


# model training
cross_entropy = -tf.reduce_sum(y_ * tf.log(y_conv))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)


correct_prediction = tf.equal(tf.arg_max(y_conv, 1), tf.arg_max(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))


sess.run(tf.initialize_all_variables())
filename_queue = tf.train.string_input_producer(["train_min.tfrecords"])
reader = tf.TFRecordReader()
_,serialized_example = reader.read(filename_queue)
features = tf.parse_single_example(serialized_example,
features={
'label': tf.FixedLenFeature([],tf.int64),
'img_raw': tf.FixedLenFeature([],tf.string),
})
image = tf.decode_raw(features['img_raw'],tf.uint8)
image = tf.reshape(image,[28,28,1])
label = tf.cast(features['label'], tf.int32)
################
with tf.Session() as sess:
init_op = tf.initialize_all_variables()
sess.run(init_op)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
for i in range(30):
example, l = sess.run([image,label])
#img = Image.fromarray(example,'RGB')
#print(img)
#img.save(str(i)+'_'+str(l)+'.jpg')
#print(example,l)
#print(sess.run(train_step,feed_dict={x: example,y_: l}))
#print("loss",sess.run(cross_entropy,feed_dict={x: example,y_: l}))
train_accuacy = accuracy.eval(feed_dict={x: example, y_: l, keep_prob: 1.0})
        print("step %d, training accuracy %g"%(i, train_accuacy))
        sess.graph.finalize()
    train_step.run(feed_dict = {x: example, y_: l, keep_prob: 1.0})
    sess.close()
print(image)
#print (example)
coord.request_stop()

coord.join(threads)

报错：

ValueError: Cannot feed value of shape (28, 28, 1) for Tensor u'Placeholder:0', which has shape '(?, 784)'