Is this neural network architecture appropriate for CIFAR-10? 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?
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.