Neural Networks: Modifiying NN structures?

Question

From this nice answer one can import pretrained NN models and structures for state of art models.

URLDownload[
  "http://data.dmlc.ml/mxnet/models/imagenet/inception-v3.tar.gz",
   FileNameJoin[{$UserDocumentsDirectory, "inception-v3.tar.gz"}]
];

ExtractArchive["inception-v3.tar.gz"];
Needs["NeuralNetworks`"]

net = NeuralNetworks`ImportMXNetModel[
 "model//Inception-7-symbol.json",
 "model//Inception-7-0001.params"
]

The returned result is NetGraph object. In this object, imported models uses NetPort for inputs, as a result one must enter tensor with given dimensions. However, Mathematica has some nice encoders:

enc = NetEncoder[{"Image", {150, 150}}]

My question:

Is it possible to change ports inputting tensors to these Mathematica defined encoders? If so, how?

Upadate:

In addition, I would like to know if it's possible in the inception model to change the number of output classes. Currently, I want to use the structure but not pretrained model with 1008 classes. Lets say I want 10 new classes for output. How can I modify DotPlusLayer("fc1"), SoftMaxLayer("softmax") and Port "Output" for this 10 classes?

Answer 1

net = NeuralNetworks`ImportMXNetModel[
  "model//Inception-7-symbol.json",
  "model//Inception-7-0001.params"
  ]

This network accepts images in resolution 299x299.

SeedRandom[1];
img = RandomImage[1, {299, 299}, ColorSpace -> "RGB"];

net2 = NetGraph[
  {
   net,
   SoftmaxLayer[]
   },
  {1 -> 2},
  "Input" -> NetEncoder[{"Image", {299, 299}, ColorSpace -> "RGB"}],
  "Output" -> NetDecoder[{"Class", Range[1008]}]
  ]

net2[img]

920

UPDATE

If we want to keep neural architecture but change the number of classes (for example, add 10 new), we can trim original architecture using Take.

net3 = NetGraph[
  {
   Take[net, {NetPort["Input"], "flatten"}],
   1008 + 10,
   SoftmaxLayer[]
   },
  {1 -> 2 -> 3},
  "Input" -> NetEncoder[{"Image", {299, 299}, ColorSpace -> "RGB"}], 
  "Output" -> NetDecoder[{"Class", Range[1008 + 10]}]
  ]

But this network should be trained again, because it doesn't keep information about previously learned classes.

NetExtract[net3, {2, "Weights"}]

Automatic