TensorBoard的官网教程如下： https://www.tensorflow.org/versions/r0.7/how_tos/summaries_and_tensorboard/index.html
简单解释下：TensorBoard是个可视化工具，可以用来查看TensorFlow的图以及过程中的各种值和图像等。 1. 在tensorflow程序中给需要的节点添加“summary operations”，“summary operations”会收集该节点的数据，并标记上第几步、时间戳等标识，写入事件文件。 事件文件的形式如下所示：
2. TensorBoard读取事件文件，并可视化Tensorflow的流程。
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_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 train(): # Import data mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True, fake_data=FLAGS.fake_data) sess = tf.InteractiveSession() # Create a multilayer model. # Input placehoolders with 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 layer with 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) + biases tf.histogram_summary(layer_name + '/pre_activations', preactivate) activations = act(preactivate, 'activation') tf.histogram_summary(layer_name + '/activations', activations) return activations hidden1 = 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 summaries def 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.dropout else: xs, ys = mnist.test.images, mnist.test.labels k = 1.0 return {x: xs, y_: ys, keep_prob: k} for i in range(FLAGS.max_steps): if i % 10 == 0: # Record summaries and test-set accuracy summary, 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 train if i % 100 == 99: # Record execution stats run_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 summary summary, _ = 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。 TensorBoard的不过是个调试工具，看起来很酷炫有没有，但怎么充分利用，我想还是要对tensorflow充分了解。下面要转向对tensorflow的学习中了。通过pip方式安装的tensorflow，在使用tensorboard的时候，可能会出现如下Bug：
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解决方案： 下载tensorflow的github的源代码，将tensorflow的tensorboard目录下的TAG文件拷贝到Python下面的tensorboard目录下即可，我的目录如下：
sudo cp ~/libsource/tensorflow/tensorflow/tensorflow/tensorboard/TAG /usr/local/lib/python2.7/dist-packages/tensorflow/tensorboard/