为什么用卷积神经网络进行数字是被，准确率一直很低啊求大神zhi

import tensorflow as tf
import tensorflow.examples.tutorials.mnist.input_data as input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
x = tf.placeholder("float32", [None, 784])
x_image = tf.reshape(x, [-1, 28, 28, 1])
w1 = tf.Variable(tf.ones([5, 5, 1, 32]))
b1 = tf.Variable(tf.ones([32]))
y_conv = tf.nn.relu(tf.nn.conv2d(x_image, w1, strides=[1, 1, 1, 1], padding="SAME")+b1)
y_max_pool = tf.nn.max_pool(y_conv, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="SAME")
wf1 = tf.Variable(tf.ones([14*14*32, 1024]))
bf1 = tf.Variable(tf.ones([1024]))
yf = tf.reshape(y_max_pool, [-1, 14*14*32])
y_f1 = tf.nn.relu(tf.matmul(yf, wf1) + bf1)
wf2 = tf.Variable(tf.ones([1024, 10]))
bf2 = tf.Variable(tf.ones([10]))
y_out = tf.nn.softmax(tf.matmul(y_f1, wf2)+bf2)
y = tf.placeholder("float32", [None, 10])
loss = -tf.reduce_sum(y*tf.log(y_out))
train_step = tf.train.GradientDescentOptimizer(0.01).minimize(loss)
init = tf.initialize_all_variables()
sess = tf.Session()
sess.run(init)
for i in range(1000):
batch_x, batch_y = mnist.train.next_batch(200)
sess.run(train_step, feed_dict={x: batch_x, y: batch_y})
if i%50 == 0:
prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_out, 1))
accuracy = tf.reduce_mean(tf.cast(prediction, "float"))
print(sess.run(accuracy, feed_dict={x: mnist.test.images, y: mnist.test.labels}))