#encoding=utf-8 
__author__ = 'freedom' 
import tensorflow as tf 
 
def loadMNIST(): 
 from tensorflow.examples.tutorials.mnist import input_data 
 mnist = input_data.read_data_sets('MNIST_data',one_hot=True) 
 return mnist 
 
def softmax(mnist,rate=0.01,batchSize=50,epoch=20): 
 n = 784 # 向量的維度數(shù)目 
 m = None # 樣本數(shù)，這里可以獲取，也可以不獲取 
 c = 10 # 類別數(shù)目 
 
 x = tf.placeholder(tf.float32,[m,n]) 
 y = tf.placeholder(tf.float32,[m,c]) 
 
 w = tf.Variable(tf.zeros([n,c])) 
 b = tf.Variable(tf.zeros([c])) 
 
 pred= tf.nn.softmax(tf.matmul(x,w)+b) 
 loss = tf.reduce_mean(-tf.reduce_sum(y*tf.log(pred),reduction_indices=1)) 
 opt = tf.train.GradientDescentOptimizer(rate).minimize(loss) 
 
 init = tf.initialize_all_variables() 
 
 sess = tf.Session() 
 sess.run(init) 
 for index in range(epoch): 
  avgLoss = 0 
  batchNum = int(mnist.train.num_examples/batchSize) 
  for batch in range(batchNum): 
   batch_x,batch_y = mnist.train.next_batch(batchSize) 
   _,Loss = sess.run([opt,loss],{x:batch_x,y:batch_y}) 
   avgLoss += Loss 
  avgLoss /= batchNum 
  print 'every epoch average loss is ',avgLoss 
 
 right = tf.equal(tf.argmax(pred,1),tf.argmax(y,1)) 
 accuracy = tf.reduce_mean(tf.cast(right,tf.float32)) 
 print 'Accracy is ',sess.run(accuracy,({x:mnist.test.images,y:mnist.test.labels})) 
 
 
if __name__ == "__main__": 
 mnist = loadMNIST() 
 softmax(mnist)