What's the dimensionality of the output space in Tensorflow's docs?

Question

I've been going through the docs recently and in many different functions like tf.layers.dense or the tf.nn.conv2d, I came across with the arguments units and filters respectively and I can't understand the point of them. Can someone clearly describe the meaning of

dimensionality of the output space

in the above cases or maybe more general terms? Thanks in advance.

Answer 1

from my opinion:

units in tf.layers.dense:

• means that how many output nodes of dense layer should be returned.
• Because the fully connected layer(dense layer) should consist of input and output.
• Then , the mean of dimensionality of the output space could be translated to the number of ouput nodes.
• if the units = 1 , it means all the input nodes connected to one output nodes
• in inception v3 or other classifier model, we could found the units of dense layer always be the classifier number.

filters in tf.nn.conv2d:

• like the state in api doc :

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

• maybe the confused point is out_channels

  for out_channels , I try to understand it as how many filters we want to scan the input tensors.

• so out_channels is regarded as the number of kernel.