从0开始--可视化调试工具tensorboard

Demo演示

• 利用官网提供的例子进行演示，官方例子提供了一个基于mnist的例子，我的文件的路径如下： 
  ~/libsource/tensorflow/tensorflow/examples/tutorials/mnist， 
  其中~/libsource/tensorflow/改为用户自己的tensorflow路径即可。 
  上述目录下有一个mnist_with_summaries.py文件，即为加入了“summary operations”的mnist demo。
• 启动mnist_with_summaries.py，

python mnist_with_summaries.py

mnist_with_summaries.py的源码如下：

# Copyright 2015 The TensorFlow Authors. 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_functionimport tensorflow as tffrom tensorflow.examples.tutorials.mnist import input_dataflags = 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 train():# Import datamnist = input_data.read_data_sets(FLAGS.data_dir,one_hot=True,fake_data=FLAGS.fake_data)sess = tf.InteractiveSession()# Create a multilayer model.# Input placehoolderswith tf.name_scope('input'):x = tf.placeholder(tf.float32, [None, 784], name='x-input')y_ = tf.placeholder(tf.float32, [None, 10], name='y-input')with tf.name_scope('input_reshape'):image_shaped_input = tf.reshape(x, [-1, 28, 28, 1])tf.image_summary('input', image_shaped_input, 10)# We can't initialize these variables to 0 - the network will get stuck.def weight_variable(shape):"""Create a weight variable with appropriate initialization."""initial = tf.truncated_normal(shape, stddev=0.1)return tf.Variable(initial)def bias_variable(shape):"""Create a bias variable with appropriate initialization."""initial = tf.constant(0.1, shape=shape)return tf.Variable(initial)def variable_summaries(var, name):"""Attach a lot of summaries to a Tensor."""with tf.name_scope('summaries'):mean = tf.reduce_mean(var)tf.scalar_summary('mean/' + name, mean)with tf.name_scope('stddev'):stddev = tf.sqrt(tf.reduce_sum(tf.square(var - mean)))tf.scalar_summary('sttdev/' + name, stddev)tf.scalar_summary('max/' + name, tf.reduce_max(var))tf.scalar_summary('min/' + name, tf.reduce_min(var))tf.histogram_summary(name, var)def nn_layer(input_tensor, input_dim, output_dim, layer_name, act=tf.nn.relu):"""Reusable code for making a simple neural net layer.It does a matrix multiply, bias add, and then uses relu to nonlinearize.It also sets up name scoping so that the resultant graph is easy to read,and adds a number of summary ops."""# Adding a name scope ensures logical grouping of the layers in the graph.with tf.name_scope(layer_name):# This Variable will hold the state of the weights for the layerwith tf.name_scope('weights'):weights = weight_variable([input_dim, output_dim])variable_summaries(weights, layer_name + '/weights')with tf.name_scope('biases'):biases = bias_variable([output_dim])variable_summaries(biases, layer_name + '/biases')with tf.name_scope('Wx_plus_b'):preactivate = tf.matmul(input_tensor, weights) + biasestf.histogram_summary(layer_name + '/pre_activations', preactivate)activations = act(preactivate, 'activation')tf.histogram_summary(layer_name + '/activations', activations)return activationshidden1 = nn_layer(x, 784, 500, 'layer1')with tf.name_scope('dropout'):keep_prob = tf.placeholder(tf.float32)tf.scalar_summary('dropout_keep_probability', keep_prob)dropped = tf.nn.dropout(hidden1, keep_prob)y = nn_layer(dropped, 500, 10, 'layer2', act=tf.nn.softmax)with tf.name_scope('cross_entropy'):diff = y_ * tf.log(y)with tf.name_scope('total'):cross_entropy = -tf.reduce_mean(diff)tf.scalar_summary('cross entropy', cross_entropy)with tf.name_scope('train'):train_step = tf.train.AdamOptimizer(FLAGS.learning_rate).minimize(cross_entropy)with tf.name_scope('accuracy'):with tf.name_scope('correct_prediction'):correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))with tf.name_scope('accuracy'):accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))tf.scalar_summary('accuracy', accuracy)# Merge all the summaries and write them out to /tmp/mnist_logs (by default)merged = tf.merge_all_summaries()train_writer = tf.train.SummaryWriter(FLAGS.summaries_dir + '/train',sess.graph)test_writer = tf.train.SummaryWriter(FLAGS.summaries_dir + '/test')tf.initialize_all_variables().run()# Train the model, and also write summaries.# Every 10th step, measure test-set accuracy, and write test summaries# All other steps, run train_step on training data, & add training summariesdef feed_dict(train):"""Make a TensorFlow feed_dict: maps data onto Tensor placeholders."""if train or FLAGS.fake_data:xs, ys = mnist.train.next_batch(100, fake_data=FLAGS.fake_data)k = FLAGS.dropoutelse:xs, ys = mnist.test.images, mnist.test.labelsk = 1.0return {x: xs, y_: ys, keep_prob: k}for i in range(FLAGS.max_steps):if i % 10 == 0:  # Record summaries and test-set accuracysummary, acc = sess.run([merged, accuracy], feed_dict=feed_dict(False))test_writer.add_summary(summary, i)print('Accuracy at step %s: %s' % (i, acc))else:  # Record train set summaries, and trainif i % 100 == 99:  # Record execution statsrun_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)run_metadata = tf.RunMetadata()summary, _ = sess.run([merged, train_step],feed_dict=feed_dict(True),options=run_options,run_metadata=run_metadata)train_writer.add_run_metadata(run_metadata, 'step%d' % i)train_writer.add_summary(summary, i)print('Adding run metadata for', i)else:  # Record a summarysummary, _ = sess.run([merged, train_step], feed_dict=feed_dict(True))train_writer.add_summary(summary, i)def main(_):if tf.gfile.Exists(FLAGS.summaries_dir):tf.gfile.DeleteRecursively(FLAGS.summaries_dir)tf.gfile.MakeDirs(FLAGS.summaries_dir)train()if __name__ == '__main__':tf.app.run()

其中

flags.DEFINE_string('summaries_dir', '/tmp/mnist_logs', 'Summaries directory')

标识了事件文件的输出路径。该例中，输出路径为/tmp/mnist_logs

• 打开TensorBoard服务

tensorboard --logdir=/tmp/mnist_logs/

• 在浏览器中进行浏览http://0.0.0.0:6006，在这个可视化界面中，可以查看tensorflow图和各种中间输出等。

WARNING:tensorflow:IOError [Errno 2] No such file or directory: '/usr/local/lib/python2.7/dist-packages/tensorflow/tensorboard/TAG' on path /usr/local/lib/python2.7/dist-packages/tensorflow/tensorboard/TAG
WARNING:tensorflow:Unable to read TensorBoard tag
Starting TensorBoard  on port 6006

sudo cp ~/libsource/tensorflow/tensorflow/tensorflow/tensorboard/TAG /usr/local/lib/python2.7/dist-packages/tensorflow/tensorboard/