Question

我尝试使用tensorboard来使用DNN可视化图像分类器。我非常确定目录路径是正确的，但是没有显示数据。当我尝试 SELECT table_a.*, ( SELECT count(*) FROM table_b WHERE table_b.id_b = table_a.id_A ) AS totala FROM table_a 返回：在logdir＆＃39; PATH /＆＃39;

中找不到任何事件文件

我想我的编码肯定有问题。

图形

tensorboard --inspect --logdir='PATH/'

运行

batch_size = 500

graph = tf.Graph()
with graph.as_default():

  # Input data. For the training data, we use a placeholder that will be fed
  # at run time with a training minibatch.
  with tf.name_scope('train_input'):
    tf_train_dataset = tf.placeholder(tf.float32,
                                      shape=(batch_size, image_size * image_size),
                                      name = 'train_x_input')

    tf_train_labels = tf.placeholder(tf.float32, shape=(batch_size, num_labels),
                                     name = 'train_y_input')
  with tf.name_scope('validation_input'):
    tf_valid_dataset = tf.constant(valid_dataset, name = 'valid_x_input')
    tf_test_dataset = tf.constant(test_dataset, name = 'valid_y_input')

  # Variables.
  with tf.name_scope('layer'):
    with tf.name_scope('weights'):
        weights = tf.Variable(
            tf.truncated_normal([image_size * image_size, num_labels]),
            name = 'W')
        variable_summaries(weights)
    with tf.name_scope('biases'):
        biases = tf.Variable(tf.zeros([num_labels]), name = 'B')
        variable_summaries(biases)
  # Training computation.
  with tf.name_scope('Wx_plus_b'):
    logits = tf.matmul(tf_train_dataset, weights) + biases
    tf.summary.histogram('logits', logits)
  with tf.name_scope('loss'):
    loss = tf.reduce_mean(
        tf.nn.softmax_cross_entropy_with_logits(labels=tf_train_labels, logits=logits),
        name = 'loss')
    tf.summary.histogram('loss', loss)
    tf.summary.scalar('loss_scalar', loss)

  # Optimizer.
  with tf.name_scope('optimizer'):
    optimizer = tf.train.GradientDescentOptimizer(0.5).minimize(loss)

  # Predictions for the training, validation, and test data.
  train_prediction = tf.nn.softmax(logits)
  valid_prediction = tf.nn.softmax(tf.matmul(tf_valid_dataset, weights) + biases)
  test_prediction = tf.nn.softmax(tf.matmul(tf_test_dataset, weights) + biases)

Answer 1

解决。对于那些在我的命令行上不好的人来说，问题是在命令行中，不要使用quote（''）来标记你的目录。假设您的数据位于'X：\ X \ file.x' 首先进入X：\命令行。然后输入： tensorboard --logdir=X/ 不 tensorboard --logdir='.X/'

Answer 2

with tf.Session() as sess:
     writer = tf.summary.FileWriter("output", sess.graph)

Windows OS.Tensorboard输出文件夹是在file.py所处的文件夹中创建的。因此，如果从Windows Documents文件夹运行example.py，可以在命令提示符下尝试：tensorboard --logdir=C:\Users\YourName\Documents\output

tensorboard无法找到事件文件

2 个答案: