interpret cnn structure I am trying to interpret the CNN model from the below settings. AS I am new to deep learning and I am not able to fully comprehend the layer structure . Could someone please tell me is these two settings are similar because I read in one research paper they used first setting and wrote that is similar to the second setting

A CNN for MNIST with two 5x5 convolution layers (the first with 32 channels, the second with 64, each followed with 2x2 max pooling), a fully connected layer with 512 units and ReLu activation, and a final softmax output layer (1,663,370 total parameters).

The CNN for MNIST has 8 layers with the following structure: 3×3×32 Convolutional → 3×3×64 Convolutional → 2×2 MaxPool → Dropout → Flatten → 1 × 128 Full connected → Dropout → 128 × 10 Fully connected → Softmax.
The CNN for MNIST: 5x5x32 Convolutional -&gt; 2x2 MaxPool -&gt; 5x5x64 Convolutional -&gt; 2x2 MaxPool -&gt; Flatten -&gt; 1x512 Full connected -&gt; ReLu -&gt; 512 x 10 Full connected -&gt; Softmax.
The CNN for MNIST has 8 layers with the following structure: 3×3×32 Convolutional → 3×3×64 Convolutional → 2×2 MaxPool → Dropout → Flatten → 1 × 128 Full connected → Dropout → 128 × 10 Fully connected → Softmax.

Converted to both to same formats. They are not the same.