python - tf.nn.conv2d vs tf.layers.conv2d

Question

Is there any advantage in using tf.nn.* over tf.layers.*?

Most of the examples in the doc use tf.nn.conv2d, for instance, but it is not clear why they do so.

Answer 1

As GBY mentioned, they use the same implementation.

There is a slight difference in the parameters.

For tf.nn.conv2d:

filter: A Tensor. Must have the same type as input. A 4-D tensor of shape [filter_height, filter_width, in_channels, out_channels]

For tf.layers.conv2d:

filters: Integer, the dimensionality of the output space (i.e. the number of filters in the convolution).

I would use tf.nn.conv2d when loading a pretrained model (example code: https://github.com/ry/tensorflow-vgg16), and tf.layers.conv2d for a model trained from scratch.