Create a randomly initialized layer:

Extract the weight matrix from the layer:

Create a NetChain:

Extract the third layer:

Create an operator containing a net:

Extract the net from inside the operator:

Create a NetEncoder:

Extract the type of encoder:

Extract the output dimensions of the encoder:

Extract the color space of the encoder:

Create a NetChain with named layers:

Extract the first layer:

Extract the last layer:

Extract a named layer:

Extract the function used in the elementwise layer:

Extract several layers:

Extract all layers:

Create an initialized NetChain:

Extract the weights from a specific layer:

Extract the weights from all layers:

Create a NetGraph object with named layers:

Extract a named layer:

Extract all the layers:

Create a layer with specific input dimensions:

Extract the input dimensions:

Extract the output dimensions:

Create a layer with an attached encoder:

Extract the input encoder:

Extract a parameter of the input encoder:

Extract the type and output dimensions of the input encoder:

Extract the value of all shared arrays in a network:

In this example, NetArray["Weights"] corresponds to the value of the weights in all layers:

Use a NetExtract operator:

The part specifications supported by NetExtract are identical to those used by NetReplacePart. Extract the weights of the third layer of a NetChain:

Use the same specification to replace the weights:

Create a ConvolutionLayer:

Attempting to extract the weights of this uninitialized layer returns Automatic:

Known parameters can be extracted: