Answers.Body: <p>Dataset and model refer to different things. Dataset means part of data available for training (training dataset) or validation (validation dataset). Model is the learning process goal, the state of the computer "brain" after it has been fully educated (or made its learning). Model size refers to size of the container which contains the model. In deep learning it can be measured by width and depth of the network used, I also found a <a href="https://www.deeplearningmodel.net/" rel="nofollow noreferrer">site</a> comparing different models by npy file size, that physically contains the generated model as computer code. In that case model contained a more complex structure which was documented and size in bytes was for comparison purposes.</p>
  
  <p>So in short, it is roughly speaking the size of layers and neurons, if I have to take one of your options. Dataset is a different thing.</p>
  
  <p>More precise explanation about what is model and what is dataset:</p>
  
  <p><a href="https://www.quora.com/What-are-different-models-in-machine-learning" rel="nofollow noreferrer">https://www.quora.com/What-are-different-models-in-machine-learning</a></p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-08-22T06:58:05.773Z
Answers.Text: Thank you for your answer
  I understand that size of dataset doesn't effect the size of model.
  For example I have keras sequential model with 3 layers of `Dense()` with 64 units each. This amount of layers and units detmermine the size of model and this size is not depend on what size of dataset I use (1 GB or 50GB) right?
Answers.UserDisplayName: T K
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-08-22T08:49:16.513Z
Answers.Text: That is my understanding. The model is more accurate the more you train it, but that does not add the size, at least on Deep Learning where I am familiar with.
Answers.UserDisplayName: mico
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Answers.CreationDate: 2019-08-21T18:23:38.073Z
Answers.LastActivityDate: 2019-08-21T18:23:38.073Z
Answers.UserDisplayName: mico
Body: <p>I mean what is determine my model size, connection amount between layers and neurons, or size of my dataset? </p>
  
Tags: <tensorflow><keras><models><artificial-neuron><sequence-modeling>
Title: Is "dataset size" and "model size" same thing?
UserDisplayName: T K
ThreadText: # Is "dataset size" and "model size" same thing?
  
  (Asked by: username_0 on 2019-08-21)
  
  I mean what is determine my model size, connection amount between layers and neurons, or size of my dataset?
  
  **Tags:** tensorflow, keras, models, artificial-neuron, sequence-modeling
  
  
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  ## 1 answers
  
  ### Answer by username_1 on 2019-08-21. Score: 0 | Accepted answer
  
  Dataset and model refer to different things. Dataset means part of data available for training (training dataset) or validation (validation dataset). Model is the learning process goal, the state of the computer "brain" after it has been fully educated (or made its learning). Model size refers to size of the container which contains the model. In deep learning it can be measured by width and depth of the network used, I also found a [site](https://www.deeplearningmodel.net/) comparing different models by npy file size, that physically contains the generated model as computer code. In that case model contained a more complex structure which was documented and size in bytes was for comparison purposes.
  
  So in short, it is roughly speaking the size of layers and neurons, if I have to take one of your options. Dataset is a different thing.
  
  More precise explanation about what is model and what is dataset:
  
  <https://www.quora.com/What-are-different-models-in-machine-learning>
  
  > **Comments:**
  > **username_0**: Thank you for your answer I understand that size of dataset doesn't effect the size of model. For example I have keras sequential model with 3 layers of `Dense()` with 64 units each. This amount of layers and units detmermine the size of model and this size is not depend on what size of dataset I use (1 GB or 50GB) right?  
  > **username_1**: That is my understanding. The model is more accurate the more you train it, but that does not add the size, at least on Deep Learning where I am familiar with.  
  
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