Getting Start Using TensorFlowBoard Part I

Instruction

codes as follows 

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# Copyright 2015 Google Inc. 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.  
# ==============================================================================  
  
"""A simple MNIST classifier which displays summaries in TensorBoard. 
 
 This is an unimpressive MNIST model, but it is a good example of using 
tf.name_scope to make a graph legible in the TensorBoard graph explorer, and of 
naming summary tags so that they are grouped meaningfully in TensorBoard. 
 
It demonstrates the functionality of every TensorBoard dashboard. 
"""  
from __future__ import absolute_import  
from __future__ import division  
from __future__ import print_function  
  
import tensorflow as tf  
  
from tensorflow.examples.tutorials.mnist import input_data  
  
  
flags = tf.app.flags  
FLAGS = flags.FLAGS  
flags.DEFINE_boolean('fake_data', False, 'If true, uses fake data '  
                     'for unit testing.')  
flags.DEFINE_integer('max_steps', 1000, 'Number of steps to run trainer.')  
flags.DEFINE_float('learning_rate', 0.001, 'Initial learning rate.')  
flags.DEFINE_float('dropout', 0.9, 'Keep probability for training dropout.')  
flags.DEFINE_string('data_dir', '/tmp/data', 'Directory for storing data')  
flags.DEFINE_string('summaries_dir', '/tmp/mnist_logs', 'Summaries directory')  
  
  

def tb2():
  hello = tf.constant('Hello, TensorFlow!')
  sess = tf.Session()
  sess.run(hello)
  train_writer = tf.train.SummaryWriter(FLAGS.summaries_dir+ '/train',
                                              sess.graph)
  a = tf.constant(10)
  b = tf.constant(32)
  tf.scalar_summary('accuracy', b)  
  tf.histogram_summary( 'sss/activations', b)  
  sess.run(a+b)
  merged = tf.merge_all_summaries()  
  summary_str = sess.run(merged)
  train_writer.add_summary(summary_str, 1)

if __name__ == '__main__':  
  tb2()

ref

tensorfly.cn