Why slim.nets.vgg use conv2d instead of fully_connected layers?

Question

In tensorflow/models/slim/nets, here is the link of relative snippets of vgg. I'am curious about why slim.nets.vgg use conv2d instead of fully_connected layers, although it works out the same way actually? Is it for the purpose of speed? I appreciate some explanations. Thanks!

Answer 1

After a while, I think there is at least one reason that it can avoid weights converting mistakes.

Tensorflow/slim as well as other high-level libraries allows tensor formats either BHWC(batch_size, height, width, channel. Same below) by default or BCHW(for better performance).

When converting weights between these two formats, the weights [in_channel, out_channel] of first fc (fully connected layer, after conv layer) have to been reshaped to [last_conv_channel, height, width, out_channel] for example, then transposed to [height, width, last_conv_channel, out_channel] and reshaped again to [in_channel, out_channel].

And if you use conv weights rather than fully connected weights, such transformation will explicitly applied on fc layer (conv weights actually). Surely will avoid converting mistakes.