张量数据的分类别随机抽样

Question

在进行深度学习Fashin-MNIST数据集的训练时，我想先抽取10%的数据，由于这是一个有类别的张量数据，我想知道如何分类别随机抽样，每个类别的数据都抽到10%呢？

Answer 1

• 这篇博客: 5.1 提高mnist数据分类器准确率到98%以上中的 程序： 部分也许能够解决你的问题, 你可以仔细阅读以下内容或跳转源博客中阅读:
  

• mnist = input_data.read_data_sets("C:/Users/WangT/Desktop/MNIST_data",one_hot=True)#导入数据
  
  batch_size = 100#定义每一次批次处理的数据大小
  n_batch = mnist.train.num_examples // batch_size
  #计算分批处理次数，//是整除的除数，结果始终为整数，区别于/
  #mnist.train.num_examples是训练集的数据大小，类似还有mnist.validation.num_examples, mnist.test.num_examples.
  
  x = tf.placeholder(tf.float32,[None,784])
  y = tf.placeholder(tf.float32,[None,10])
  keep_prob = tf.placeholder(tf.float32)
  #placeholder占位符，希望能输入任意数量的MNIST图像，每一张图像展平为784维的向量，用2维浮点数张量来表示这些图，这个张量的形状是【none，784】，此处None表示此张量的第一个维度可以是任意长度的。
  
  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)
  #模型的参数，可以用Variable表示，可以计算输入值，也可以在计算中被修改
  
  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,W2)+b2)
  
  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,W3)+b3)
  #得到预测结果
  # loss = tf.reduce_mean(tf.square(y - prediction))
  loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y,logits=prediction))
  #损失函数，评估模型好坏，tf.square是平方，tf.reduce_mean是取平均值
  #train_step = tf.train.GradientDescentOptimizer(0.2).minimize(loss)
  train_step = tf.train.AdamOptimizer(lr).minimize(loss)
  
  #tf使用梯度下降法，以lr的学习速率，不断修改模型参数来最小化loss
  
  init = tf.global_variables_initializer()
  #添加一个操作来初始化变量
  correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(prediction,1))
  #tf.equal()比对两个数，相同返回true不同返回false，tf.argmax(y，1)返回y最大时对应的x
  accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
  #tf.cast()将上述结果每一次转换成浮点型，累加并取平均值，得到准确率
  
  with tf.Session() as sess:#定义对话
      sess.run(init)
      for epoch in range (51):#模型循环训练51次
          for batch in range(n_batch):#每次训练要循环n_batch批次
              batch_xs,batch_ys = mnist.train.next_batch(batch_size)#读取训练集的下一批数据
              sess.run(tf.assign(lr,0.001*(0.95**epoch)))
              sess.run(train_step, feed_dict={x:batch_xs,y:batch_ys})#运行模型训练
          
          acc1 = sess.run(accuracy, feed_dict={x:mnist.test.images, y:mnist.test.labels})#每训练一次输出一次准确率，利用的是测试集的数据
          acc2 = sess.run(accuracy,feed_dict={x:mnist.train.images,y:mnist.train.labels})
          print("Iter"+str(epoch)+",Testing Accuracy"+str(acc1)+",Training accuracy"+str(acc2))
  
  
  Extracting C:/Users/WangT/Desktop/MNIST_data\train-images-idx3-ubyte.gz
  Extracting C:/Users/WangT/Desktop/MNIST_data\train-labels-idx1-ubyte.gz
  Extracting C:/Users/WangT/Desktop/MNIST_data\t10k-images-idx3-ubyte.gz
  Extracting C:/Users/WangT/Desktop/MNIST_data\t10k-labels-idx1-ubyte.gz
  Iter0,Testing Accuracy0.9499,Training accuracy0.955691
  Iter1,Testing Accuracy0.9627,Training accuracy0.970036
  Iter2,Testing Accuracy0.9661,Training accuracy0.977127
  Iter3,Testing Accuracy0.9705,Training accuracy0.981327
  Iter4,Testing Accuracy0.9759,Training accuracy0.986345
  Iter5,Testing Accuracy0.9755,Training accuracy0.988509
  Iter6,Testing Accuracy0.9749,Training accuracy0.989545
  Iter7,Testing Accuracy0.9783,Training accuracy0.991491
  Iter8,Testing Accuracy0.9771,Training accuracy0.992618
  Iter9,Testing Accuracy0.9793,Training accuracy0.992564
  Iter10,Testing Accuracy0.9789,Training accuracy0.993691
  Iter11,Testing Accuracy0.9796,Training accuracy0.994327
  Iter12,Testing Accuracy0.9779,Training accuracy0.994382
  Iter13,Testing Accuracy0.9788,Training accuracy0.995036
  Iter14,Testing Accuracy0.9795,Training accuracy0.995073
  Iter15,Testing Accuracy0.9801,Training accuracy0.995491
  Iter16,Testing Accuracy0.9806,Training accuracy0.995727