TensorBoard可视化网络结构和参数Tensorboard windows显示空白
Tensorboard显示空白,或者graphs中显示“No graph definition files were found”,在数据正确的前提下,最可能是路径的问题。 Windows 下通过cmd启动tensorboard,采用如下两种方法可以避免路径造成的问题(假设文件在D盘的logs文件夹下): 1.文件夹之间使用 // 分割
>tensorboard --logdir=D://logs
2.将路径直接切换到文件的上一级目录下
>cd D: >tensorboard --logdir=logs
在学习深度网络框架的过程中,我们发现一个问题,就是如何输出各层网络参数,用于更好地理解,调试和优化网络?针对这个问题,TensorFlow开发了一个特别有用的可视化工具包:TensorBoard,既可以显示网络结构,又可以显示训练和测试过程中各层参数的变化情况。本博文分为四个部分,第一部分介绍相关函数,第二部分是代码测试,第三部分是运行结果,第四部分介绍相关参考资料。
一. 相关函数
TensorBoard的输入是tensorflow保存summary data的日志文件。日志文件名的形式如:events.out.tfevents.1467809796.lei-All-Series 或 events.out.tfevents.1467809800.lei-All-Series。TensorBoard可读的summary data有scalar,images,audio,histogram和graph。那么怎么把这些summary data保存在日志文件中呢?
数值如学习率,损失函数用scalar_summary函数。tf.scalar_summary(节点名称,获取的数据)
- accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
- tf.scalar_summary('accuracy', accuracy)
各层网络权重,偏置的分布,用histogram_summary函数
- preactivate = tf.matmul(input_tensor, weights) + biases
- tf.histogram_summary(layer_name + '/pre_activations', preactivate)
其他几种summary data也是同样的方式获取,只是对应的获取函数名称换一下。这些获取summary data函数节点和graph是独立的,调用的时候也需要运行session。当需要获取的数据较多的时候,我们一个一个去保存获取到的数据,以及一个一个去运行会显得比较麻烦。tensorflow提供了一个简单的方法,就是合并所有的summary data的获取函数,保存和运行只对一个对象进行操作。比如,写入默认路径中,比如/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')
SummaryWriter从tensorflow获取summary data,然后保存到指定路径的日志文件中。以上是在建立graph的过程中,接下来执行,每隔一定step,写入网络参数到默认路径中,形成最开始的文件:events.out.tfevents.1467809796.lei-All-Series 或 events.out.tfevents.1467809800.lei-All-Series。
- 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 summarieis, and train
- summary, _ = sess.run([merged, train_step], feed_dict=feed_dict(True))
- train_writer.add_summary(summary, i)
二. 代码测试
- # 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 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')
- image_shaped_input = tf.reshape(x, [-1, 28, 28, 1])
- tf.image_summary('input', image_shaped_input, 10)
- y_ = tf.placeholder(tf.float32, [None, 10], name='y-input')
- keep_prob = tf.placeholder(tf.float32)
- tf.scalar_summary('dropout_keep_probability', keep_prob)
- # 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')
- 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 summarieis, and train
- 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()
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