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=True)

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
keep_prob=tf.placeholder(tf.float32)
lr=tf.Variable(0.001,dtype=tf.float32)

W1=tf.Variable(tf.truncated_normal([784,500],stddev=0.1))
b1=tf.Variable(tf.zeros([500])+0.1)
L1=tf.nn.tanh(tf.matmul(x,W1)+b1)
L1_drop=tf.nn.dropout(L1,keep_prob)


W2=tf.Variable(tf.truncated_normal([500,300],stddev=0.1))
b2=tf.Variable(tf.zeros([300])+0.1)
L2=tf.nn.tanh(tf.matmul(L1_drop,W2)+b2)
L2_drop=tf.nn.dropout(L2,keep_prob)

W3=tf.Variable(tf.truncated_normal([300,10],stddev=0.1))
b3=tf.Variable(tf.zeros([10])+0.1)
prediction=tf.nn.softmax(tf.matmul(L2_drop,W3)+b3)




loss=tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y,logits=prediction))

train_step=tf.train.AdamOptimizer(lr).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):
        sess.run(tf.assign(lr,0.001*(0.95**epoch)))
        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,keep_prob:1.0})
        learning_rate=sess.run(lr)

acc=sess.run(accuracy,feed_dict={x:mnist.test.images,y:mnist.test.labels,keep_prob:1.0})
        print("Iter"+str(epoch)+",testing
accuracy"+str(acc)+",learningrate"+str(learning_rate))