def create_full_model()

in models.py [0:0]

• 18 lines of code
• 6 McCabe index (conditional complexity)

    def create_full_model(self, layer_to_explore, layer_to_explore_size, image_size):

        all_layers = dict(list(self.pretrained_model.named_children()))
        all_keys = list(
            all_layers.keys()
        )  # TODO: I am not sure the order is preserved ?
        max_index = all_keys.index(layer_to_explore)

        ##### ENCODER
        # take all layers up to the one we want to explore for the encoder
        encoder_layers = [
            (all_keys[i], all_layers[layer])
            for i, layer in enumerate(all_layers)
            if i <= max_index
        ]
        layers = OrderedDict()
        for layer in encoder_layers:
            name = layer[0]
            layers[name] = layer[1]

        # create a new model with it (saves time during feed-forward if we take other layers than the last one)
        self.fixed_encoder = nn.Sequential(layers)

        ##### Linear layer to learn the mapping
        self.linear = nn.Linear(layer_to_explore_size, layer_to_explore_size)

        ##### DECODER
        self.decoder = nn.Linear(layer_to_explore_size, image_size)