此文章代码为Tensorflow官方文档入门模型，python版本为2.7。详见链接：http://wiki.jikexueyuan.com/project/tensorflow-zh/tutorials/mnist_beginners.html
<code>import tensorflow as tf
import input_data </code>

x = tf.placeholder("float", [None, 784])
y_ = tf.placeholder("float", [None, 10])
W = tf.Variable(tf.zeros([784,10]))
b = tf.Variable(tf.zeros([10]))
y = tf.nn.softmax(tf.matmul(x,W) + b)#神经网络模型实现```

```cross_entropy = -tf.reduce_sum(y_*tf.log(y))#交叉熵
train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)```

```init = tf.initialize_all_variables()#初始化变量
sess = tf.Session()
sess.run(init)```

```for i in range(1000):    #训练模型1000次
    batch_xs,batch_ys = mnist.train.next_batch(100)   
    sess.run(train_step,feed_dict={x:batch_xs,y_:batch_ys})
correct_prediction = tf.equal(tf.argmax(y,1),tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction,"float"))
print sess.run(accuracy,feed_dict={x:mnist.test.images,y_:mnist.test.labels})#输出测试数据集正确率```