Body: <p>I am doing a network architecture search (NAS).</p>
  <p>I have two directories, <code>train_data_npy</code> and <code>valid_data_npy</code> where there are 3013 and 1506 <code>*.npy</code> files, respectively.</p>
  <p>Each <code>*.npy</code> file has 11 columns of float types, of which the first eight columns are features and the last three columns are one-hot-encoded labels (characters) of three classes.</p>
  <pre><code>----------------------------------------------------------------------
  f1      f2      f3      f4   f5   f6   f7   f8          ---classes---
  ----------------------------------------------------------------------
  0.0     0.0     0.0     1.0  1.0  1.0  1.0  1.0         0.0  0.0  1.0
  6.559   9.22    0.0     1.0  1.0  1.0  1.0  1.0         0.0  0.0  1.0
  5.512   6.891   10.589  0.0  0.0  0.0  0.0  1.0         0.0  0.0  1.0
  7.082   8.71    7.227   0.0  0.0  0.0  0.0  0.0         0.0  0.0  1.0
  6.352   9.883   12.492  0.0  0.0  0.0  0.0  0.0         0.0  0.0  1.0
  6.711   10.422  13.44   0.0  0.0  0.0  0.0  1.0         0.0  0.0  1.0
  7.12    9.283   12.723  0.0  0.0  0.0  0.0  0.0         0.0  0.0  1.0
  6.408   9.277   12.542  0.0  0.0  0.0  0.0  0.0         0.0  0.0  1.0
  6.608   9.686   12.793  0.0  0.0  0.0  0.0  0.0         0.0  0.0  1.0
  6.723   8.602   12.168  0.0  0.0  0.0  0.0  0.0         0.0  0.0  1.0
  ... ... ... ... ...
  </code></pre>
  <p>Given the format of the data, I have written two scripts.</p>
  <p><code>cnn_autokeras_by_chunk_with_ohe.py</code> uses OHE labels as they are, and <code>cnn_autokeras_by_chunk_without_ohe.py</code> converts OHE data into integers. I.e., I use eight columns for features and only one column for the labels, and OHE is converted into numbers <span class="math-container">$1$</span>, <span class="math-container">$2$</span>, <span class="math-container">$3$</span>. I want to predict <span class="math-container">$1$</span>, <span class="math-container">$2$</span>, <span class="math-container">$3$</span>.</p>
  <p>The first one achieves an accuracy of <code>0.40</code>, and the second one achieves an accuracy of <code>0.97</code>.</p>
  <p>Should I consider this a normal phenomenon, or should I consider it an anomaly?</p>
  <p>Two scripts are using the same features and classes and looking for the best architecture to solve a classification problem. One is representing the classes as OHE, and the other is representing the classes as integers. My expectation was that they would produce similar accuracy, as the only practical difference between these specifications was the representation of the labels. However, since the accuracies are different, I am curious what might have caused this issue.</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2023-12-06T23:03:02.387Z
Comments.Text: https://meta.stackexchange.com/a/64069
Comments.UserDisplayName: Dave
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2023-12-07T01:10:44.550Z
Comments.Text: [Answered on Stack Overflow](https://stackoverflow.com/a/77617059/11751799)
Comments.UserDisplayName: Dave
Tags: <neural-networks><classification><neural-architecture-search>
Title: Should I consider this a normal phenomenon, or should I consider it an anomaly?
UserDisplayName: user366312
ThreadText: # Should I consider this a normal phenomenon, or should I consider it an anomaly?
  
  (Asked by: username_0 on 2023-12-06)
  
  I am doing a network architecture search (NAS).
  I have two directories, train_data_npy and valid_data_npy where there are 3013 and 1506 *.npy files, respectively.
  Each *.npy file has 11 columns of float types, of which the first eight columns are features and the last three columns are one-hot-encoded labels (characters) of three classes.
  ----------------------------------------------------------------------
  f1      f2      f3      f4   f5   f6   f7   f8          ---classes---
  ----------------------------------------------------------------------
  0.0     0.0     0.0     1.0  1.0  1.0  1.0  1.0         0.0  0.0  1.0
  6.559   9.22    0.0     1.0  1.0  1.0  1.0  1.0         0.0  0.0  1.0
  5.512   6.891   10.589  0.0  0.0  0.0  0.0  1.0         0.0  0.0  1.0
  7.082   8.71    7.227   0.0  0.0  0.0  0.0  0.0         0.0  0.0  1.0
  6.352   9.883   12.492  0.0  0.0  0.0  0.0  0.0         0.0  0.0  1.0
  6.711   10.422  13.44   0.0  0.0  0.0  0.0  1.0         0.0  0.0  1.0
  7.12    9.283   12.723  0.0  0.0  0.0  0.0  0.0         0.0  0.0  1.0
  6.408   9.277   12.542  0.0  0.0  0.0  0.0  0.0         0.0  0.0  1.0
  6.608   9.686   12.793  0.0  0.0  0.0  0.0  0.0         0.0  0.0  1.0
  6.723   8.602   12.168  0.0  0.0  0.0  0.0  0.0         0.0  0.0  1.0
  ... ... ... ... ...
  
  Given the format of the data, I have written two scripts.
  cnn_autokeras_by_chunk_with_ohe.py uses OHE labels as they are, and cnn_autokeras_by_chunk_without_ohe.py converts OHE data into integers. I.e., I use eight columns for features and only one column for the labels, and OHE is converted into numbers $1$, $2$, $3$. I want to predict $1$, $2$, $3$.
  The first one achieves an accuracy of 0.40, and the second one achieves an accuracy of 0.97.
  Should I consider this a normal phenomenon, or should I consider it an anomaly?
  Two scripts are using the same features and classes and looking for the best architecture to solve a classification problem. One is representing the classes as OHE, and the other is representing the classes as integers. My expectation was that they would produce similar accuracy, as the only practical difference between these specifications was the representation of the labels. However, since the accuracies are different, I am curious what might have caused this issue.
  
  **Tags:** neural-networks, classification, neural-architecture-search
  > **Comments:**
  > **username_1**: https://meta.stackexchange.com/a/64069  
  > **username_1**: [Answered on Stack Overflow](https://stackoverflow.com/a/77617059/11751799)  
  
  ---
  
  ## 0 answers
  
  