Obtain a trained model:

Obtain a new model in which the input NetEncoder has been removed:

Obtain a new model that can be trained on a different set of classes:

Obtain a new model in which the first activation layer has been replaced:

Obtain a new model in which the first convolution biases have been randomized:

Create an existing model that contains a DropoutLayer with dropout probability 0.5:

Update the dropout probability:

Create a linear layer with no weight matrix specified:

Insert specific weights and biases:

Evaluate the layer on an input:

Create a layer without an input encoder:

Attach a "Class" encoder to the input of the layer, which embeds the classes as {1,0} and {0,1}:

The resulting layer can now take the values True and False as inputs:

Add type information to an existing net:

Remove a decoder from a net:

Reshape an existing layer to have different input and output dimensions. Create a layer with a specific NetEncoder :

Apply the layer to an input:

Replace the input NetEncoder and output NetDecoder :

Apply the resized layer to an input:

Replace the second layer within a NetChain :

Replace a property of an existing NetEncoder :

Replace the value of a shared array in a network:

The weights have been replaced by the new value and are still shared:

Turn a network that processes fixed-length sequences into an equivalent network that handles variable-length sequences:

Turn a network that processes strings in a fixed-length fashion into an equivalent network that handles variable-length strings without padding nor clipping:

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

Use the same specification to extract the weights: