Link between the trained and inference model

Here you will be transferring the trained weights from the trained model to the inference model. In the encoder decoder model, there are three layers with parameters. They are,

The encoder GRU layer
The decoder GRU layer
The decoder Dense layer

The other layers, such as TimeDistributed do not have any parameters, thus don't require the copying of weights.

For this exercise, you have been provided with the trained encoder GRU layer (tr_en_gru), trained decoder GRU (tr_de_gru) and the trained Dense layer (tr_de_dense). You also have access to all the layers of the inference model (including the encoder) such as the encoder GRU layer (en_gru), decoder GRU (de_gru) and the Dense layer (de_dense).

Deze oefening maakt deel uit van de cursus

Machine Translation with Keras

Cursus bekijken

Oefeninstructies

Load the weights of the trained encoder GRU layer.
Set the weights of the encoder GRU layer of the inference model.
Load the weights for the decoder GRU layer (trained) and set the weights in the inference model.
Load the weights of the decoder Dense layer (trained) and set the weights in the inference model.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Load the weights to the encoder GRU from the trained model
en_gru_w = ____.get_weights()
# Set the weights of the encoder GRU of the inference model
en_gru.____(____)
# Load and set the weights to the decoder GRU
de_gru.____(tr_de_gru.____)
# Load and set the weights to the decoder Dense
____.set_weights(____.____)

Code bewerken en uitvoeren