学习笔记TF039:TensorBoard
学习笔记TF039:TensorBoard
利炳根 发表于4个月前
学习笔记TF039:TensorBoard
  • 发表于 4个月前
  • 阅读 4
  • 收藏 1
  • 点赞 0
  • 评论 0

腾讯云 技术升级10大核心产品年终让利>>>   

首先向大家和《TensorFlow实战》的作者说句不好意思。我现在看的书是《TensorFlow实战》。但从TF024开始,我在学习笔记的参考资料里一直写的是《TensorFlow实践》,我自己粗心搞错了,希望不至于对大家造成太多误导。

TensorBoard,TensorFlow官方可视化工具。展示模型训练过程各种汇总数据。标量(Scalars)、图片(Images)、音频(audio)、计算图(Graphs)、数据分布(Distributions)、直方图(Histograms)、嵌入向量(Embeddings)。TensorBoard展示数据,执行TensorFlow计算图过程,各种类型数据汇总并记录到日志文件。TensorBoard读取日志文件,解析数据,生成数据可视化Web页,浏览器观察各种汇总数据。

载入TesnsorFlow,设置训练最大步数1000,学习速率0.001,dropout保留比率0.9。设置MNIST数据下载地址data_dir、汇总数据日志存放路径log_dir。日志路径log_dir存所有汇总数据。

input_data.read_data_sets下载MNIST数据,创建TensorFlow默认Session。

with tf.name_scope限定命名空间。定义输入x、y placeholder。输入一维数据变形28x28图片储存到tensor,tf.summary.image汇总图片数据TensorBoard展示。

定义神经网络模型参数初始化方法,权重用truncated_normal初始化,偏置赋值0.1。

定义Variable变量数据汇总函数,计算Variable mean、stddev、max、min,tf.summary.scalar记录、汇总。tf.summary.histogram记录变量var直方图数据。

设计MLP多层神经网络训练数据,每一层汇总模型参数数据。定义创建一层神经网络数据汇总函数nn_layer。输入参数,输入数据input_tensor、输入维度input_dim、输出维度output_dim、层名称layer_name。激活函数act用ReLU。初始化神经网络权重、偏置,用variable_summaries汇总variable数据。输入,矩阵乘法,加偏置,未激活结果用tf.summary.histogram统计直方图。用激活函数后,tf.summary.histogram再统计一次。

nn_layer创建一层神经网络,输入维度图片尺寸28x28=784,输出维度隐藏节点数500。创建Dropout层,用tf.summary.scalar记录keep_prob。用nn_layer定义神经网络输出层,输入维度为上层隐含节点数500,输出维度类别数10,激活涵数全等映射identity。

tf.nn.softmax_cross_entropy_with_logits()对前面输出层结果Softmax处理,计算交叉熵损失cross_entropy。计算平均损失,tf.summary.scalar统计汇总。

Adma优化器优化损失。统计预测正确样本数,计算正确率accury, tf.summary.scalar统计汇总accuracy。

tf.summary.merger_all()获取所有汇总操作。定义两个tf.summary.FileWriter(文件记录器)在不同子目录,分别存放训练和测试日志数据。Session计算图sess.graph加入训练过程记录器,TensorBoard GRAPHS窗口展示整个计算图可视化效果。tf.global_variables_initializer().run()初始化全部变量。

定义feed_dict损失函数。先判断训练标记,True,从mnist.train获取一个batch样本,设置dropout值;False,获取测试数据,设置keep_prob 1,没有dropout效果。

实际执行具体训练、测试、日志记录操作。tf.train.Saver()创建模型保存器。进入训练循环,每隔10步执行merged(数据汇总)、accuracy(求测试集预测准确率)操作,test_writer.add_sumamry将汇总结果summary和循环步数i写入日志文件。每隔100步,tf.RunOptions定义TensorFlow运行选项,设置trace_lever FULL_TRACE。tf.RunMetadata()定义TensorFlow运行元信息,记录训练运算时间和内存占用等信息。执行merged数据汇总操作,train_step训练操作,汇总结果summary、训练元信息run_metadata添加到train_writer。执行merged、train_step操作,添加summary到train_writer。所有训练全部结束,关闭train_writer、test_writer。

切换Linux命令行,执行TensorBoard程序,--logdir指定TensorFlow日志路径,TensorBoard自动生成所有汇总数据可视化结果。 tensorboard --logdir=/tmp/tensorflow/mnist/logs/mnist_with_summaries 复制网址到浏览器。

打开标量SCALARS窗口,打开accuracy图表。调整Smoothing参数,控制曲线平滑处理,数值越小越接近实际值,波动大;数值越大曲线越平缓。图表下方按钮放大图片,右边按钮调整坐标轴范围。

切换图像IMAGES窗口,可以看到所有tf.summary.image()汇总数据。

计算图GRAPHS窗口,整个TensorFlow计算图结构。网络forward inference流程,backward训练更新参数流程。实线代表数据依赖关系,虚线代表控制条件依赖关系。节点窗口,看属性、输入、输出及tensor尺寸。

"+"按钮,展示node内部细节。所有同一命名空间节点被折叠一起。右键单击节点选择删除。

切换配色风络,基于结构,同结构节点同颜色;基于运算硬件,同运算硬件节点同颜色。

Session runs,选择run_metadata训练元信息。

切换DISTRIBUTIONS窗口,看各个神经网络层输出分布,激活函数前后结果。看看有没有被屏蔽节点(dead neurons)。转为直方图。

EMBEDDINGS窗口,降维嵌入向量可视化效果。tf.save.Saver保存整个模型,TensorBoard自动对模型所有二维Variable可视化(只有Variable可以被保存,Tensor不行)。选择T-SNE或PCA算法对数据列(特征)降维,在3D、2D坐标可视化展示。对Word2Vec计算或Language Model非常有用。

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
max_steps=1000
learning_rate=0.001
dropout=0.9
data_dir='/tmp/tensorflow/mnist/input_data'
log_dir='/tmp/tensorflow/mnist/logs/mnist_with_summaries'
  # Import data
mnist = input_data.read_data_sets(data_dir,one_hot=True)
sess = tf.InteractiveSession()
  # Create a multilayer model.
  # Input placeholders
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.summary.image('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):
  """Attach a lot of summaries to a Tensor (for TensorBoard visualization)."""
  with tf.name_scope('summaries'):
    mean = tf.reduce_mean(var)
    tf.summary.scalar('mean', mean)
    with tf.name_scope('stddev'):
      stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean)))
    tf.summary.scalar('stddev', stddev)
    tf.summary.scalar('max', tf.reduce_max(var))
    tf.summary.scalar('min', tf.reduce_min(var))
    tf.summary.histogram('histogram', 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)
    with tf.name_scope('biases'):
      biases = bias_variable([output_dim])
      variable_summaries(biases)
    with tf.name_scope('Wx_plus_b'):
      preactivate = tf.matmul(input_tensor, weights) + biases
      tf.summary.histogram('pre_activations', preactivate)
    activations = act(preactivate, name='activation')
    tf.summary.histogram('activations', activations)
    return activations
hidden1 = nn_layer(x, 784, 500, 'layer1')
with tf.name_scope('dropout'):
  keep_prob = tf.placeholder(tf.float32)
  tf.summary.scalar('dropout_keep_probability', keep_prob)
  dropped = tf.nn.dropout(hidden1, keep_prob)
  # Do not apply softmax activation yet, see below.
y = nn_layer(dropped, 500, 10, 'layer2', act=tf.identity)
with tf.name_scope('cross_entropy'):
    # The raw formulation of cross-entropy,
    #
    # tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(tf.softmax(y)),
    #                               reduction_indices=[1]))
    #
    # can be numerically unstable.
    #
    # So here we use tf.nn.softmax_cross_entropy_with_logits on the
    # raw outputs of the nn_layer above, and then average across
    # the batch.
  diff = tf.nn.softmax_cross_entropy_with_logits(logits=y, labels=y_)
  with tf.name_scope('total'):
    cross_entropy = tf.reduce_mean(diff)
tf.summary.scalar('cross_entropy', cross_entropy)
with tf.name_scope('train'):
  train_step = tf.train.AdamOptimizer(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.summary.scalar('accuracy', accuracy)
  # Merge all the summaries and write them out to /tmp/mnist_logs (by default)
merged = tf.summary.merge_all()
train_writer = tf.summary.FileWriter(log_dir + '/train', sess.graph)
test_writer = tf.summary.FileWriter(log_dir + '/test')
tf.global_variables_initializer().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:
    xs, ys = mnist.train.next_batch(100)
    k = dropout
  else:
    xs, ys = mnist.test.images, mnist.test.labels
    k = 1.0
  return {x: xs, y_: ys, keep_prob: k}

saver = tf.train.Saver()  
for i in range(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%03d' % i)
      train_writer.add_summary(summary, i)
      saver.save(sess, log_dir+"/model.ckpt", 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)
train_writer.close()
test_writer.close()

参考资料: 《TensorFlow实战》

欢迎付费咨询(150元每小时),我的微信:qingxingfengzi

共有 人打赏支持
粉丝 10
博文 47
码字总数 104940
×
利炳根
如果觉得我的文章对您有用,请随意打赏。您的支持将鼓励我继续创作!
* 金额(元)
¥1 ¥5 ¥10 ¥20 其他金额
打赏人
留言
* 支付类型
微信扫码支付
打赏金额:
已支付成功
打赏金额: