private static void LoadModel(KerasLayersJson layersJson, List <NNLayerBase> Layers)
{
foreach (var layer in layersJson.layers)
{
switch (layer.class_name)
{
case "InputLayer":
var Input = new InputLayer();
Input.LoadConfig(layer.config);
Layers.Add(Input);
break;
case "Activation":
if (layer.config.activation == "relu")
{
Layers.Add(new ReLU());
}
if (layer.config.activation == "tanh")
{
Layers.Add(new Tanh());
}
break;
case "Conv2D":
var Conv2DLayer = new Conv2D();
Conv2DLayer.LoadConfig(layer.config);
Layers.Add(Conv2DLayer);
if (layer.config.activation == "relu")
{
Layers.Add(new ReLU());
}
if (layer.config.activation == "tanh")
{
Layers.Add(new Tanh());
}
break;
case "LeakyReLU":
var LeakyReLULayer = new LeakyReLU();
LeakyReLULayer.LoadConfig(layer.config);
Layers.Add(LeakyReLULayer);
break;
case "BatchNormalization":
Layers.Add(new BatchNormalization());
break;
case "UpSampling2D":
var UpSampling2DLayer = new UpSampling2D();
UpSampling2DLayer.LoadConfig(layer.config);
Layers.Add(UpSampling2DLayer);
break;
case "Concatenate":
var thislayer = new Concatenate();
string alternativeLayerName = layer.inbound_nodes[0][1][0] as string;
thislayer.AlternativeInputId =
Layers.FindIndex(ly => string.Compare(ly.Name, alternativeLayerName) == 0);
Layers.Add(thislayer);
break;
case "Add":
var addlayer = new Add();
int alterinput = layer.inbound_nodes[0].FindIndex(node =>
string.Compare(node[0] as string, Layers[Layers.Count - 1].Name) != 0);
string addalternativeLayerName = layer.inbound_nodes[0][alterinput][0] as string;
addlayer.AlternativeInputId =
Layers.FindIndex(ly => string.Compare(ly.Name, addalternativeLayerName) == 0);
Layers.Add(addlayer);
break;
}
Layers[Layers.Count - 1].Name = layer.name;
}
}
public static void LoadConfig(this UpSampling2D layer, KerasLayerConfigJson config)
{
layer.Size = new Vector2Int(config.size[0], config.size[1]);
}
