test.py

from tensorflow.examples.tutorials.mnist 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,[None,784])
w = tf.Variable(tf.zeros([784,10]))
b = tf. Variable(tf.zeros([10]))
y = tf.nn.softmax(tf.matmul(x,w)+b)
y_ = tf.placeholder(tf.float32,[None,10])
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_*tf.log(y), reduction_indices=[1]))
train_step = tf.train.GradientDescentOptimizer(0.5).minimiz(cross_entropy)
tf.global_variables_initializer().run()
for i in range(1000):
    batch_xs,batch_ys = mnist.train,next_batch(100)
    train_step.run({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, tf.float32))
print(accuracy.eval({x:mnist.test.image,y_:mnist.test.labels}))