Tensorboard嵌入式投影机毛坯

Question

即使日志目录中存在检查点文件，嵌入式投影机也不会显示任何内容。

我的代码文件的修改版本：https://github.com/normanheckscher/mnist-tensorboard-embeddings：从mnist_t-sine.py。它已被修改为绘制嵌入直方图（没有太大意义，但只是为了检查张量板是否工作）。

这里是我的代码：https://drive.google.com/open?id=0B8ZdOStUW_DCb3Uwbm9VTnQ5SzA

# Copyright 2016 Norman Heckscher. All Rights Reserved. 
# 
# Licensed under the Apache License, Version 2.0 (the 'License'); 
# you may not use this file except in compliance with the License. 
# You may obtain a copy of the License at 
# 
#  http://www.apache.org/licenses/LICENSE-2.0 
# 
# Unless required by applicable law or agreed to in writing, software 
# distributed under the License is distributed on an 'AS IS' BASIS, 
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 
# See the License for the specific language governing permissions and 
# limitations under the License. 
# ============================================================================== 
"""MNIST dimensionality reduction with TensorFlow and TensorBoard. 

This demonstrates the functionality of the TensorBoard Embedding Visualization dashboard using MNIST. 

https://www.tensorflow.org/versions/r0.12/how_tos/embedding_viz/index.html#tensorboard-embedding-visualization 
""" 
from __future__ import absolute_import 
from __future__ import division 
from __future__ import print_function 

import argparse 
import sys 

import os 
import numpy as np 
from tensorflow.contrib.tensorboard.plugins import projector 

import tensorflow as tf 

from tensorflow.examples.tutorials.mnist import input_data 
FLAGS = None 

def generate_embeddings(): 
    # Import data 
    mnist = input_data.read_data_sets(FLAGS.data_dir, 
             one_hot=True, 
             fake_data=FLAGS.fake_data) 
    sess = tf.InteractiveSession() 

    # Input set for Embedded TensorBoard visualization 
    # Performed with cpu to conserve memory and processing power 
    with tf.device("/cpu:0"): 
     embedding = tf.Variable(tf.stack(mnist.test.images[:FLAGS.max_steps], axis=0), trainable=False, name='embedding') 
    tf.summary.histogram("Embedding",embedding) 
    tf.summary.tensor_summary("Embeddings", embedding) 

    saver = tf.train.Saver() 
    writer = tf.summary.FileWriter(FLAGS.log_dir + '/projector', sess.graph) 

    merged_summ = tf.summary.merge_all() 
    tf.global_variables_initializer().run() 
    # Add embedding tensorboard visualization. Need tensorflow version 
    # >= 0.12.0RC0 
    config = projector.ProjectorConfig() 
    embed= config.embeddings.add() 
    embed.tensor_name = 'embedding' 
    embed.metadata_path = os.path.join(FLAGS.log_dir + '/projector/metadata.tsv') 
    embed.sprite.image_path = os.path.join(FLAGS.data_dir + '/mnist_10k_sprite.png') 
    summ = sess.run(merged_summ) 
    for i in range(20): 
     writer.add_summary(summ,i) 
    # Specify the width and height of a single thumbnail. 
    embed.sprite.single_image_dim.extend([28, 28]) 
    projector.visualize_embeddings(writer, config) 
    print(sess.run(embedding)) 
    saver.save(sess, os.path.join(
     FLAGS.log_dir, 'projector/a_model.ckpt'), global_step=FLAGS.max_steps) 

def generate_metadata_file(): 
    # Import data 
    mnist = input_data.read_data_sets(FLAGS.data_dir, 
             one_hot=True, 
             fake_data=FLAGS.fake_data) 
    def save_metadata(file): 
     with open(file, 'w') as f: 
      for i in range(FLAGS.max_steps): 
       c = np.nonzero(mnist.test.labels[::1])[1:][0][i] 
       f.write('{}\n'.format(c)) 

    save_metadata(FLAGS.log_dir + '/projector/metadata.tsv') 

def main(_): 
    if tf.gfile.Exists(FLAGS.log_dir + '/projector'): 
     tf.gfile.DeleteRecursively(FLAGS.log_dir + '/projector') 
     tf.gfile.MkDir(FLAGS.log_dir + '/projector') 
    tf.gfile.MakeDirs(FLAGS.log_dir + '/projector') # fix the directory to be created 
    generate_metadata_file() 
    generate_embeddings() 

if __name__ == '__main__': 
    parser = argparse.ArgumentParser() 
    parser.add_argument('--fake_data', nargs='?', const=True, type=bool, 
         default=False, 
         help='If true, uses fake data for unit testing.') 
    parser.add_argument('--max_steps', type=int, default=50, 
         help='Number of steps to run trainer.') 
    parser.add_argument('--data_dir', type=str, default='/Users/norman/Documents/workspace/mnist-tensorboard-embeddings/mnist_data', 
         help='Directory for storing input data') 
    parser.add_argument('--log_dir', type=str, default='/Users/norman/Documents/workspace/mnist-tensorboard-embeddings/logs', 
         help='Summaries log directory') 
    FLAGS, unparsed = parser.parse_known_args() 
    tf.app.run(main=main, argv=[sys.argv[0]] + unparsed) 

这些Tensorboard图表，直方图的正常工作的截图和分配标签，其中曲面嵌入标签的例外：

空白曲面嵌入标签：

我已经卸载，并从与GPU支持的来源重新安装tensorflow。下面是输出：tensorboard --inspect --logdir=logs：

====================================================================== 
Processing event files... (this can take a few minutes) 
====================================================================== 

Found event files in: 
logs/projector 

These tags are in logs/projector: 
audio - 
histograms 
    Embedding 
images - 
scalars - 
tensor 

====================================================================== 

Event statistics for logs/projector: 
audio - 
graph 
    first_step   0 
    last_step   0 
    max_step    0 
    min_step    0 
    num_steps   1 
    outoforder_steps  [] 
histograms 
    first_step   0 
    last_step   19 
    max_step    19 
    min_step    0 
    num_steps   20 
    outoforder_steps  [] 
images - 
scalars - 
sessionlog:checkpoint - 
sessionlog:start - 
sessionlog:stop - 
tensor 
    first_step   0 
    last_step   19 
    max_step    19 
    min_step    0 
    num_steps   20 
    outoforder_steps  [] 
====================================================================== 

Answer 1

我已经重新安装tensorboard从PIP解决问题为：pip install tensorflow-tensorboard，在这个问题GitHub的建议：https://github.com/tensorflow/tensorflow/issues/10756，通过eiennohito开放。