Answers.Body: <p>To be honest, your model is not very clear. But basically after the convolution, you need to add non-linear layers. Otherwise, there is no point of Neural Networks.</p>
  
  <p>You can add a Relu layer for sure. </p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-02-25T16:03:24.797Z
Answers.Text: I am using TensorFlow 2.0 Conv2D layer and I am using ReLU activation function
Answers.UserDisplayName: Arun
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-02-25T13:23:07.437Z
Answers.LastActivityDate: 2020-02-25T13:23:07.437Z
Answers.UserDisplayName: keshav
Body: <p>I have a CNN architecture for CIFAR-10 dataset which is as follows:</p>
  
  <blockquote>
    <p>Convolutions:           64, 64, pool</p>
    
    <p>Fully Connected Layers: 256, 256, 10</p>
    
    <p>Batch size:             60</p>
    
    <p>Optimizer:              Adam(2e-4)</p>
    
    <p>Loss:                   Categorical Cross-Entropy</p>
  </blockquote>
  
  <p>When I train this model, training and testing accuracy along with loss has a very jittery behavior and does not converge properly.</p>
  
  <p>Is the defined architecture correct? Should I have a max-pooling layer after every convolution layer?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-10-31T10:40:14.880Z
Comments.Text: I know this is an old question, but could you please edit it to include the result of [`plot_model(your_model_object)`](https://www.tensorflow.org/api_docs/python/tf/keras/utils/plot_model)? Moreover, you should provide the plots that show the behavior of the accuracy and loss. You should explain for how long you have been training.
Comments.UserDisplayName: nbro
Tags: <neural-networks><convolutional-neural-networks><tensorflow>
Title: Is this neural network architecture appropriate for CIFAR-10?
UserDisplayName: Arun
ThreadText: # Is this neural network architecture appropriate for CIFAR-10?
  
  (Asked by: username_0 on 2020-02-17)
  
  I have a CNN architecture for CIFAR-10 dataset which is as follows:
  
  > Convolutions: 64, 64, pool
  >
  > Fully Connected Layers: 256, 256, 10
  >
  > Batch size: 60
  >
  > Optimizer: Adam(2e-4)
  >
  > Loss: Categorical Cross-Entropy
  
  When I train this model, training and testing accuracy along with loss has a very jittery behavior and does not converge properly.
  
  Is the defined architecture correct? Should I have a max-pooling layer after every convolution layer?
  
  **Tags:** neural-networks, convolutional-neural-networks, tensorflow
  > **Comments:**
  > **username_1**: I know this is an old question, but could you please edit it to include the result of [`plot_model(your_model_object)`](https://www.tensorflow.org/api_docs/python/tf/keras/utils/plot_model)? Moreover, you should provide the plots that show the behavior of the accuracy and loss. You should explain for how long you have been training.  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_2 on 2020-02-25. Score: 1
  
  To be honest, your model is not very clear. But basically after the convolution, you need to add non-linear layers. Otherwise, there is no point of Neural Networks.
  
  You can add a Relu layer for sure.
  
  > **Comments:**
  > **username_0**: I am using TensorFlow 2.0 Conv2D layer and I am using ReLU activation function  
  
  ---
  
  