Tensorflow machine learning data sets to identify entry --MINIST

Reference website: http://www.tensorfly.cn/tfdoc/tutorials/mnist_beginners.html

# Automatically download and loading data 
from tensorflow.examples.tutorials.mnist Import Input_Data 
MNIST = input_data.read_data_sets ( " MNIST_data / " , one_hot = True) 

# Construction FIG calculated 
Import tensorflow TF AS 
X = tf.placeholder ( " a float " , [ none, 784 ]) 
Y_ = tf.placeholder ( " a float " , [none, 10 ]) 
W is = tf.Variable (tf.zeros ([784,10 ])) 
B = tf.Variable (tf.zeros ([10 ])) 
Y = tf.nn.softmax (tf.matmul (X, W is) + b)
cross_entropy = -tf.reduce_sum(y_*tf.log(y))
train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)

#训练1000步
init = tf.initialize_all_variables()
sess = tf.Session()
sess.run(init)
for i in range(1000):
  batch_xs, batch_ys = mnist.train.next_batch(100)
  sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
 
#验证准确率
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}))