tensorboard tensorflow中的可視化組件

在新版本的tensorflow 中tensorboard已經(jīng)被整合,無需下載.其執(zhí)行是利用了一個封裝的內置服務器,性能不錯.
我們可以將神經(jīng)網(wǎng)絡運行時的各類數(shù)據(jù)存儲下來進行可視化展示,我首先展示其功能,然后再分解代碼.本處例子源自tensorflow的官方源碼,如果你需要了解更多,建議直接閱讀官方文檔

展示

最重要的網(wǎng)絡結構的展示

基本數(shù)據(jù)的展示

在本例子中獲取了,mean,stddev,max,min等數(shù)據(jù).其他部分還包括images,圖片本例子中展示的則是,mnist的展示圖.

更多部分建議你運行源碼自己體驗一下

CODE

tf.summary使我們需要的 用來想tensorboard寫入數(shù)據(jù)的方法

tf.summary.scalar(‘a(chǎn)ccuracy’, accuracy) 如代碼,scalar可以將數(shù)據(jù)傳入,并在tensorboard中最終以表格的形式展示

tf.summary.image(‘input’, image_shaped_input, NUM_CLASSES) image方法則是前面圖片中image模塊的數(shù)據(jù)傳入方法

引用,定義基本參數(shù)

from __future__ import absolute_import from __future__ import division from __future__ import print_functionimport argparse import os import sysimport tensorflow as tffrom tensorflow.examples.tutorials.mnist import input_dataos.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'FLAGS = None # The MNIST dataset has 10 classes, representing the digits 0 through 9. NUM_CLASSES = 10# The MNIST images are always 28x28 pixels. IMAGE_SIZE = 28 IMAGE_PIXELS = IMAGE_SIZE * IMAGE_SIZE

官方文檔的代碼寫的咋一看非常復雜,不過結構上并不復雜.

讀取數(shù)據(jù),定義定義可視化節(jié)點

# Import datamnist = input_data.read_data_sets("/home/fonttian/Data/MNIST_data/",one_hot=True,fake_data=FLAGS.fake_data)sess = tf.InteractiveSession()# Create a multilayer model.# Input placeholderswith tf.name_scope('input'): # 此處定義了input可視化節(jié)點,下面則是占位符的聲明,在tensorflow中的函數(shù)一個共有的name,就是聲明的節(jié)點的name(名字),該部分可以在上面的圖片中展示x = tf.placeholder(tf.float32, [None, IMAGE_PIXELS], name='x-input')y_ = tf.placeholder(tf.float32, [None, NUM_CLASSES], name='y-input')with tf.name_scope('input_reshape'):image_shaped_input = tf.reshape(x, [-1, IMAGE_SIZE, IMAGE_SIZE, 1])tf.summary.image('input', image_shaped_input, NUM_CLASSES)# tf.summary 是將數(shù)據(jù)傳入tensorboard的,image將會展示在我們剛剛展示的images部分. ``` 抽取代碼部分內容,封裝為函數(shù)<div class="se-preview-section-delimiter"></div> # 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)

“`

# 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 feed_dict(train):# 需要feed_dict參數(shù)"""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}

定義我們的神經(jīng)網(wǎng)絡

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)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) + biasestf.summary.histogram('pre_activations', preactivate)activations = act(preactivate, name='activation')tf.summary.histogram('activations', activations)return activationshidden1 = nn_layer(x, IMAGE_PIXELS, FLAGS.hidden1_units, 'layer1')with tf.name_scope('dropout'): # 定義dropout的可視化節(jié)點,dropout避免過擬合的方法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, FLAGS.hidden1_units, NUM_CLASSES, 'layer2', act=tf.identity)

定義損失函數(shù)和優(yōu)化算法,準確率

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(labels=y_, logits=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(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.summary.scalar('accuracy', accuracy)

寫入數(shù)據(jù)

# Merge all the summaries and write them out to# /tmp/tensorflow/mnist/logs/mnist_with_summaries (by default)merged = tf.summary.merge_all()train_writer = tf.summary.FileWriter(FLAGS.log_dir + '/train', sess.graph)test_writer = tf.summary.FileWriter(FLAGS.log_dir + '/test')tf.global_variables_initializer().run()

回話設計

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%03d' % 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)# 停止writertrain_writer.close()test_writer.close()

執(zhí)行CODE

def main(_):if tf.gfile.Exists(FLAGS.log_dir):tf.gfile.DeleteRecursively(FLAGS.log_dir)tf.gfile.MakeDirs(FLAGS.log_dir)train()if __name__ == '__main__':parser = argparse.ArgumentParser()parser.add_argument('--fake_data', nargs='?', const=True, type=bool,default=False,help='If true, uses fake data for unit testing.')parser.add_argument('--max_steps', type=int, default=1000,help='Number of steps to run trainer.')parser.add_argument('--hidden1_units', type=float, default=500,help='The number of neurons in the first hidden.')parser.add_argument('--learning_rate', type=float, default=0.001,help='Initial learning rate')parser.add_argument('--dropout', type=float, default=0.9,help='Keep probability for training dropout.')parser.add_argument('--data_dir',type=str,default='/home/fonttian/Data/MNIST_data/',help='Directory for storing input data')parser.add_argument('--log_dir',type=str,default='/home/fonttian/Documents/tensorflow/TensorFlow-Basics/tmp/tensorflow/mnist/logs/mnist_with_summaries',help='Summaries log directory')FLAGS, unparsed = parser.parse_known_args()tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)

tensorboard的運行

建議你運行該代碼,進行更深入的嘗試,

總結

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