Answers.Body: <p>after reading some literature in the area I'd recommend the following:</p>
  <ul>
  <li>Try using Convolution Neural Networks (CNNs) <a href="https://wiki.tum.de/download/attachments/242090417/IMU_Sensor_Fusion_With_ML.pdf?version=1&amp;modificationDate=1562329774913&amp;api=v2" rel="nofollow noreferrer">this</a> paper outlines some really good points on why should you use CNNs.</li>
  <li>Try a Combination of the different layers in the same model. Start with some Convolution Layers, then some LSTMs and then a couple of Dense Layers followed by Dropout</li>
  </ul>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-08-27T00:14:38.397Z
Answers.LastActivityDate: 2020-08-27T00:14:38.397Z
Answers.UserDisplayName: Saurav Maheshkar
Body: <p>Currently, I'm working on 6-axis IMU(Inertial Measurment Unit) dataset. <a href="https://www.kaggle.com/dasmehdixtr/carla-driver-behaviour-dataset" rel="nofollow noreferrer">This dataset</a> contain 6 axis IMU data of 7 different drivers. The Imu sensor attached on vehicle. The drivers drives same path. So, the dataset include 6 feature columns and a label column.</p>
  <p>I tried multiple neural network models.The sensor data is a sequential data so I tried LSTM(Long Short Term Memory) &amp; classical fully-connected layers.
  Some of my architecture(in keras framework):</p>
  <hr />
  <pre><code>Layer (type)                 Output Shape              Param #   
  
  lstm_4 (LSTM)                (None, 1, 128)            69120     
  _________________________________________________________________
  lstm_5 (LSTM)                (None, 1, 64)             49408     
  _________________________________________________________________
  lstm_6 (LSTM)                (None, 1, 32)             12416     
  _________________________________________________________________
  dense_8 (Dense)              (None, 1, 64)             2112      
  _________________________________________________________________
  dropout_2 (Dropout)          (None, 1, 64)             0         
  _________________________________________________________________
  dense_9 (Dense)              (None, 1, 7)              455       
  
  
  2nd Architecture:
  
  =================================================================
  dense_10 (Dense)             (None, 32)                224       
  _________________________________________________________________
  dense_11 (Dense)             (None, 64)                2112      
  _________________________________________________________________
  dense_12 (Dense)             (None, 128)               8320      
  _________________________________________________________________
  dense_13 (Dense)             (None, 256)               33024     
  _________________________________________________________________
  dropout_3 (Dropout)          (None, 256)               0         
  _________________________________________________________________
  dense_14 (Dense)             (None, 512)               131584    
  _________________________________________________________________
  dense_15 (Dense)             (None, 256)               131328    
  _________________________________________________________________
  dense_16 (Dense)             (None, 128)               32896     
  _________________________________________________________________
  dense_17 (Dense)             (None, 64)                8256      
  _________________________________________________________________
  dropout_4 (Dropout)          (None, 64)                0         
  _________________________________________________________________
  dense_18 (Dense)             (None, 128)               8320      
  _________________________________________________________________
  dense_19 (Dense)             (None, 7)                 903     
  </code></pre>
  <p>The best accuracy in my models was %70 which is not good. How style of layers should I use to handle this data? Or, which type of model would increase accuracy?</p>
  
Tags: <deep-learning><recurrent-neural-networks><long-short-term-memory><deep-neural-networks>
Title: What type of model should I fit to increase accuracy?
UserDisplayName: dasmehdix
ThreadText: # What type of model should I fit to increase accuracy?
  
  (Asked by: username_0 on 2020-08-26)
  
  Currently, I'm working on 6-axis IMU(Inertial Measurment Unit) dataset. [This dataset](https://www.kaggle.com/dasmehdixtr/carla-driver-behaviour-dataset) contain 6 axis IMU data of 7 different drivers. The Imu sensor attached on vehicle. The drivers drives same path. So, the dataset include 6 feature columns and a label column.
  
  I tried multiple neural network models.The sensor data is a sequential data so I tried LSTM(Long Short Term Memory) & classical fully-connected layers.
  Some of my architecture(in keras framework):
  
  ---
  
  ```
  Layer (type)                 Output Shape              Param #   
  
  lstm_4 (LSTM)                (None, 1, 128)            69120     
  _________________________________________________________________
  lstm_5 (LSTM)                (None, 1, 64)             49408     
  _________________________________________________________________
  lstm_6 (LSTM)                (None, 1, 32)             12416     
  _________________________________________________________________
  dense_8 (Dense)              (None, 1, 64)             2112      
  _________________________________________________________________
  dropout_2 (Dropout)          (None, 1, 64)             0         
  _________________________________________________________________
  dense_9 (Dense)              (None, 1, 7)              455       
  
  2nd Architecture:
  
  =================================================================
  dense_10 (Dense)             (None, 32)                224       
  _________________________________________________________________
  dense_11 (Dense)             (None, 64)                2112      
  _________________________________________________________________
  dense_12 (Dense)             (None, 128)               8320      
  _________________________________________________________________
  dense_13 (Dense)             (None, 256)               33024     
  _________________________________________________________________
  dropout_3 (Dropout)          (None, 256)               0         
  _________________________________________________________________
  dense_14 (Dense)             (None, 512)               131584    
  _________________________________________________________________
  dense_15 (Dense)             (None, 256)               131328    
  _________________________________________________________________
  dense_16 (Dense)             (None, 128)               32896     
  _________________________________________________________________
  dense_17 (Dense)             (None, 64)                8256      
  _________________________________________________________________
  dropout_4 (Dropout)          (None, 64)                0         
  _________________________________________________________________
  dense_18 (Dense)             (None, 128)               8320      
  _________________________________________________________________
  dense_19 (Dense)             (None, 7)                 903     
  
  ```
  
  The best accuracy in my models was %70 which is not good. How style of layers should I use to handle this data? Or, which type of model would increase accuracy?
  
  **Tags:** deep-learning, recurrent-neural-networks, long-short-term-memory, deep-neural-networks
  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2020-08-27. Score: 2 | Accepted answer
  
  after reading some literature in the area I'd recommend the following:
  
  * Try using Convolution Neural Networks (CNNs) [this](https://wiki.tum.de/download/attachments/242090417/IMU_Sensor_Fusion_With_ML.pdf?version=1&modificationDate=1562329774913&api=v2) paper outlines some really good points on why should you use CNNs.
  * Try a Combination of the different layers in the same model. Start with some Convolution Layers, then some LSTMs and then a couple of Dense Layers followed by Dropout
  
  
  ---
  
  