在Tensorflow中添加整个列车/测试数据集的准确性摘要

3 回答

• 8

  定义一个接受占位符的 tf.scalar_summary ：

  accuracy_value_ = tf.placeholder(tf.float32, shape=())
  accuracy_summary = tf.scalar_summary('accuracy', accuracy_value_)
  

  然后计算整个数据集的精度（定义一个计算数据集中每个批次的准确度并提取平均值的例程）并将其保存到python变量中，我们称之为 va .

  获得 va 的值后，只需运行 accuracy_summary 操作，输入 accuracy_value_ 占位符：

  sess.run(accuracy_summary, feed_dict={accuracy_value_: va})
  

  回复于 2024-05-04T11:56:25+08:00
• 0

  我实现了一个天真的单层模型作为示例来对MNIST数据集进行分类并在Tensorboard中可视化验证准确性，它对我有用 .

  import tensorflow as tf
  from tensorflow.contrib.learn.python.learn.datasets.mnist import read_data_sets
  import os
  
  # number of epoch
  num_epoch = 1000
  model_dir = '/tmp/tf/onelayer_model/accu_info'
  # mnist dataset location, change if you need
  data_dir = '../data/mnist'
  
  # load MNIST dataset without one hot
  dataset = read_data_sets(data_dir, one_hot=False)
  
  # Create placeholder for input images X and labels y
  X = tf.placeholder(tf.float32, [None, 784])
  # one_hot = False
  y = tf.placeholder(tf.int32)
  
  # One layer model graph
  W = tf.Variable(tf.truncated_normal([784, 10], stddev=0.1))
  b = tf.Variable(tf.constant(0.1, shape=[10]))
  logits = tf.nn.relu(tf.matmul(X, W) + b)
  
  init = tf.initialize_all_variables()
  
  cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits, y)
  # loss function
  loss = tf.reduce_mean(cross_entropy)
  train_op = tf.train.GradientDescentOptimizer(0.01).minimize(loss)
  
  _, top_1_op = tf.nn.top_k(logits)
  top_1 = tf.reshape(top_1_op, shape=[-1])
  correct_classification = tf.cast(tf.equal(top_1, y), tf.float32)
  # accuracy function
  acc = tf.reduce_mean(correct_classification)
  
  # define info that is used in SummaryWritter
  acc_summary = tf.scalar_summary('valid_accuracy', acc)
  valid_summary_op = tf.merge_summary([acc_summary])
  
  with tf.Session() as sess:
      # initialize all the variable
      sess.run(init)
  
      print("Writing Summaries to %s" % model_dir)
      train_summary_writer = tf.train.SummaryWriter(model_dir, sess.graph)
  
      # load validation dataset
      valid_x = dataset.validation.images
      valid_y = dataset.validation.labels
  
      for epoch in xrange(num_epoch):
          batch_x, batch_y = dataset.train.next_batch(100)
          feed_dict = {X: batch_x, y: batch_y}
          _, acc_value, loss_value = sess.run(
              [train_op, acc, loss], feed_dict=feed_dict)
          vsummary = sess.run(valid_summary_op,
                              feed_dict={X: valid_x,
                                         y: valid_y})
  
          # Write validation accuracy summary
          train_summary_writer.add_summary(vsummary, epoch)
  

  回复于 2024-05-04T11:56:25+08:00
• 0

  如果您使用的是使用内部计数器的tf.metrics操作，则可以使用您的验证集进行批处理 . 这是一个简化的例子：

  model = create_model()
  tf.summary.scalar('cost', model.cost_op)
  acc_value_op, acc_update_op = tf.metrics.accuracy(labels,predictions)
  
  summary_common = tf.summary.merge_all()
  
  summary_valid = tf.summary.merge([
      tf.summary.scalar('accuracy', acc_value_op),
      # other metrics here...
  ])
  
  with tf.Session() as sess:
      train_writer = tf.summary.FileWriter(logs_path + '/train',
                                           sess.graph)
      valid_writer = tf.summary.FileWriter(logs_path + '/valid')
  

  虽然 training ，但只能使用您的列车编写者编写常见摘要：

  summary = sess.run(summary_common)
  train_writer.add_summary(summary, tf.train.global_step(sess, gstep_op))
  train_writer.flush()
  

  After every validation ，使用valid-writer编写两个摘要：

  gstep, summaryc, summaryv = sess.run([gstep_op, summary_common, summary_valid])
  valid_writer.add_summary(summaryc, gstep)
  valid_writer.add_summary(summaryv, gstep)
  valid_writer.flush()
  

  使用tf.metrics时，不要忘记在每个验证步骤之前重置内部计数器（局部变量） .

  回复于 2024-05-04T11:56:25+08:00