When understanding the inception module, I once saw the following statement from an online post. What's the calculation underline the "`192 28×28 sized feature maps can be reduced to 64 28×28 feature maps through 64 1×1 convolutions`" 


> Inception modules in convolutional networks were designed to allow for
> deeper and larger convolutional layers while at the same time allowing
> for more efficient computation. This is done by using 1×1 convolutions
> with small feature map size, for example, 192 28×28 sized feature maps
> can be reduced to 64 28×28 feature maps through 64 1×1 convolutions.