<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>

CC BY-SA 4.0
2020-02-25T16:03:24.797Z
I am using TensorFlow 2.0 Conv2D layer and I am using ReLU activation function
Arun
CC BY-SA 4.0
2020-02-25T13:23:07.437Z
2020-02-25T13:23:07.437Z
keshav
<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>

CC BY-SA 4.0
2020-10-31T10:40:14.880Z
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.
nbro
<neural-networks><convolutional-neural-networks><tensorflow>
Is this neural network architecture appropriate for CIFAR-10?
Arun
# 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.  

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## 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  

---

