What does the tf_exported_symbols.lds do

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• Training SVM with images of different categories

  I am new to machine learning, my aim is to classify traffic/road signs using SVM.

  My question is: Can the training data set of positive images contains different categories of images?

  ex: I want to classify those two type of images to turn RIGHT, is it possible ? would be the data still linearly separable?

  1

  2
• Benefit of applying tf.image.per_image_standardization() over batch_norm layer in Tensorflow?

  What is the benefit in applying tf.image.per_image_standardization() before the first layer of the deep neural network over adding the Batch_Norm layer as the first layer?

  In order to normalize the [0.0, 255.0] float value image pixels before feeding into the network, which method would be suitable?

  1. tf.image.per_image_standardization()

  2. Batch_Norm - layer
• Set "training=False" of "tf.layers.batch_normalization" when training will get a better validation result

  I use TensorFlow to train DNN. I learned that Batch Normalization is very helpful for DNN , so I used it in DNN.

  I use "tf.layers.batch_normalization" and follow the instructions of the API document to build the network: when training, set its parameter "training=True", and when validate, set "training=False". And add tf.get_collection(tf.GraphKeys.UPDATE_OPS).

  Here is my code:

  # -*- coding: utf-8 -*-
  import tensorflow as tf
  import numpy as np
  
  input_node_num=257*7
  output_node_num=257
  
  tf_X = tf.placeholder(tf.float32,[None,input_node_num])
  tf_Y = tf.placeholder(tf.float32,[None,output_node_num])
  dropout_rate=tf.placeholder(tf.float32)
  flag_training=tf.placeholder(tf.bool)
  hid_node_num=2048
  
  h1=tf.contrib.layers.fully_connected(tf_X, hid_node_num, activation_fn=None)
  h1_2=tf.nn.relu(tf.layers.batch_normalization(h1,training=flag_training))
  h1_3=tf.nn.dropout(h1_2,dropout_rate)
  
  h2=tf.contrib.layers.fully_connected(h1_3, hid_node_num, activation_fn=None)
  h2_2=tf.nn.relu(tf.layers.batch_normalization(h2,training=flag_training))
  h2_3=tf.nn.dropout(h2_2,dropout_rate)
  
  h3=tf.contrib.layers.fully_connected(h2_3, hid_node_num, activation_fn=None)
  h3_2=tf.nn.relu(tf.layers.batch_normalization(h3,training=flag_training))
  h3_3=tf.nn.dropout(h3_2,dropout_rate)
  
  tf_Y_pre=tf.contrib.layers.fully_connected(h3_3, output_node_num, activation_fn=None)
  
  loss=tf.reduce_mean(tf.square(tf_Y-tf_Y_pre))
  
  update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
  with tf.control_dependencies(update_ops):
      train_step = tf.train.AdamOptimizer(1e-4).minimize(loss)
  
  with tf.Session() as sess:
      sess.run(tf.global_variables_initializer())
  
      for i1 in range(3000*num_batch):
          train_feature=... # Some processing
          train_label=...  # Some processing
          sess.run(train_step,feed_dict={tf_X:train_feature,tf_Y:train_label,flag_training:True,dropout_rate:1}) # when train , set "training=True" , when validate ,set "training=False" , get a bad result . However when train , set "training=False" ,when validate ,set "training=False" , get a better result .
  
          if((i1+1)%277200==0):# print validate loss every 0.1 epoch
              validate_feature=... # Some processing
              validate_label=... # Some processing
  
              validate_loss = sess.run(loss,feed_dict={tf_X:validate_feature,tf_Y:validate_label,flag_training:False,dropout_rate:1})
              print(validate_loss)
  

  Is there any error in my code ? if my code is right , I think I get a strange result:

  when training, I set "training = True", when validate, set "training = False", the result is not good . I print validate loss every 0.1 epoch , the validate loss in 1st to 3st epoch is

   0.929624
   0.992692
   0.814033
   0.858562
   1.042705
   0.665418
   0.753507
   0.700503
   0.508338
   0.761886
   0.787044
   0.817034
   0.726586
   0.901634
   0.633383
   0.783920
   0.528140
   0.847496
   0.804937
   0.828761
   0.802314
   0.855557
   0.702335
   0.764318
   0.776465
   0.719034
   0.678497
   0.596230
   0.739280
   0.970555
  

  However , when I change the code "sess.run(train_step,feed_dict={tf_X:train_feature,tf_Y:train_label,flag_training:True,dropout_rate:1})" , that : set "training=False" when training, set "training=False" when validate . The result is good . The validate loss in 1st epoch is

   0.474313
   0.391002
   0.369357
   0.366732
   0.383477
   0.346027
   0.336518
   0.368153
   0.330749
   0.322070
   0.335551
  

  Why does this result appear ? Is it necessary to set "training=True" when training, set "training=False" when validate ?