卷积神经网络中密集层和输出层的尺寸如何计算？

Question

Can someone please tell me that why the size of dense layer and the output layer is 256 and 10 respectively?

input = 1x28x28                      
conv2d1   (28-(5-1))=24 -> 32x24x24   
maxpool1                    32x12x12                           
conv2d2   (12-(3-1))=10 ->  32x10x10    
maxpool2                     32x5x5                           
dense                           256    
output                           10  

Answer 1

Convolution layers are different from Fully Connected layers. For fully connected, you reshape the vector to one single dimension and apply matrix multiplication with fc layer weights (W*x+B).

You should clearly read and understand concepts here (best tutorial to understand how convnets works) : http://cs231n.github.io/convolutional-networks/#conv

For Dense Layer :

In your case, first dense layer has size of weights [32*5*5,256]. Reshape the output of pool layer to one vector and feed it through dense layers. Output of first dense layer is 256 dim vector - feed it through second FC layer (weights_size = [256,10]) to get 10 dim vector

All the details of Conv, Pool, Relu, fully-connected layers and calculation of output sizes of each layer are clearly explained in the above link.

Please go through it. I hope that helps.