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

mnist=input_data.read_data_sets('MNIST_data',one_hot=True)

def add_layer(inputs,in_size,out_size,activation_function=None):
    Weights=tf.Variable(tf.random_normal([in_size,out_size]))
    biases=tf.Variable(tf.zeros([1,out_size]))+0.1
    Wx_plus_b=tf.matmul(inputs,Weights)+biases
    
    if activation_function is None:
        outputs=Wx_plus_b
        
    else:
        
        outputs=activation_function(Wx_plus_b)
    return outputs
def compute_accuracy(v_xs,v_ys):
    global prediction
    y_pre=sess.run(prediction,feed_dict={xs:v_xs})
    correct_prediction=tf.equal(tf.argmax(y_pre,1),tf.argmax(v_ys,1))#對比位置
    accuracy=tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
    result=sess.run(accuracy,feed_dict={xs:v_xs,ys:v_ys})
    return result
#define placeholder for inputs to network
xs=tf.placeholder(tf.float32,[None,784])#28x28
ys=tf.placeholder(tf.float32,[None,10])
#add output layer
prediction=add_layer(xs,784,10,activation_function=tf.nn.softmax)

#the error between prediction and real data
cross_entropy=tf.reduce_mean(-tf.reduce_sum(ys*tf.log(prediction),reduction_indices=[1]))
train_step=tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)

sess=tf.Session()
#important step
sess.run(tf.global_variables_initializer())
for i in range(1000):
    batch_xs,batch_ys=mnist.train.next_batch(100)
    sess.run(train_step,feed_dict={xs:batch_xs,ys:batch_ys})
    if i%50==0:
        print(compute_accuracy(mnist.test.images,mnist.test.labels))