#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 # 向量的維度數目  m = None # 樣本數，這里可以獲取，也可以不獲取  c = 10 # 類別數目   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)