import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import numpy as np
import matplotlib.pyplot as plt

mnist=input_data.read_data_sets("MNIST_d",one_hot=False)

batch_size=100
n_batch=mnist.train.num_examples//batch_size
x=tf.placeholder(tf.float32,[None,784])
y=tf.placeholder(tf.float32,[None,10])
#creat NN
W=tf.Variable(tf.zeros([784,10]))
b=tf.Variable(tf.zeros([10]))
prediction=tf.nn.softmax(tf.matmul(x,W)+b)

loss=tf.reduce_mean(tf.square(y-prediction))

train_step=tf.train.GradientDescentOptimizer(0.2).minimize(loss)
init=tf.global_variables_initializer()
corpre=tf.equal(tf.arg_max(y,1),tf.arg_max(prediction,1))
accuracy=tf.reduce_mean(tf.cast(corpre,tf.float32))
with tf.Session() as sess:
    sess.run(init)
    for epoch in range(21):
        for batch in range(n_batch):
            batch_xs,batch_ys=mnist.train.next_batch(batch_size)
            sess.run(train_step,feed_dict={x:batch_xs,y:batch_ys})

        acc=sess.run(accuracy,feed_dict={x:mnist.test.images,y:mnist.test.labels})
        print("Iter"+str(epoch)+",testing accuracy"+str(acc))