I want to receive a 5*5 array in Tensorflow, learn and print out the 1*5 answer.

1000
0　1　0　0　0
0　1　0　0　0　　→　　0　1　0　0　0
0　0　1　0　0
0　0　0　0　1
　input data answer data

1　0　0　0　0
0　0　1　0　0
0　0　1　0　0　　→　　0　0　0　1　0
0　0　0　1　0
0　0　0　0　1
　input data answer data

1000
0　1　0　0　0
0　0　1　0　0　
0　0　0　0　1
0　0　0　0　1

import input_data
mnist=input_data.read_data_sets('MNIST_data', one_hot=True)

import tensorflow as tf
sess=tf.InteractiveSession()

x = tf.placeholder (tf.float32,shape=[None,25])
y_=tf.placeholder(tf.float32,shape=[None,5])

W = tf.Variable(tf.zeros([25,5]))
b=tf.Variable(tf.zeros([5]))

sess.run(tf.initialize_all_variables())

y = tf.nn.softmax(tf.matmul(x,W)+b)

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')

W_conv1 = weight_variable ([5,5,1,32])
b_conv1 = bias_variable([32])

x_image=tf.reshape(x,[-1,5,5,1])

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

W_conv2 = weight_variable ([5, 5, 32, 64])
b_conv2 = bias_variable ([64])

h_conv2 = tf.nn.relu(conv2d(h_conv1, W_conv2) + b_conv2)
h_pool2 = max_pool_2x2(h_conv2)

W_fc1 = weight_variable ([5*5*64,512])
b_fc1 = bias_variable ([512])

h_pool2_flat=tf.reshape(h_conv2, [-1,5*5*64])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)

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

W_fc2 = weight_variable ([512,5])
b_fc2 = bias_variable([5])

y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop,W_fc2)+b_fc2)

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.argmax(y_conv,1), tf.argmax(y_,1))
acuracy=tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
sess.run(tf.initialize_all_variables())

for i in range (10000):
  batch=mnist.train.next_batch(500)
  if i%100 == 0:
    train_accuracy=accuracy.eval(feed_dict={
        x:batch[0], y_:batch[1], keep_prob:1.0})
    print("step%d, training acuity%g"%(i,train_accuracy))
  train_step.run (feed_dict={x:batch[0], y_:batch[1], keep_prob:0.5})

print("test acuity%g"%accuracy.eval(feed_dict={
    x:mnist.test.images, y_:mnist.test.labels, keep_prob:1.0}))