// https://github.com/keras-team/keras/blob/49f5b931410bc2e56378f20a15e8ac919e0efb88/keras/applications/vgg16.py
// WEIGHTS_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5'
/// <summary>
/// モデル作って返します。
/// </summary>
/// <returns></returns>
public static Model CreateModel(string weightFile)
{
var img_input = Python.Input(new int?[] { 224, 224, 3 });
var x = new Conv2D(64, new[] { 3, 3 }, activation: new ReLU(), padding: PaddingType.Same).Call(img_input);
x = new Conv2D(64, new[] { 3, 3 }, activation: new ReLU(), padding: PaddingType.Same).Call(x);
x = new MaxPooling2D(new int[] { 2, 2 }).Call(x);
x = new Conv2D(128, new[] { 3, 3 }, activation: new ReLU(), padding: PaddingType.Same).Call(x);
x = new Conv2D(128, new[] { 3, 3 }, activation: new ReLU(), padding: PaddingType.Same).Call(x);
x = new MaxPooling2D(new int[] { 2, 2 }).Call(x);
x = new Conv2D(256, new[] { 3, 3 }, activation: new ReLU(), padding: PaddingType.Same).Call(x);
x = new Conv2D(256, new[] { 3, 3 }, activation: new ReLU(), padding: PaddingType.Same).Call(x);
x = new Conv2D(256, new[] { 3, 3 }, activation: new ReLU(), padding: PaddingType.Same).Call(x);
x = new MaxPooling2D(new int[] { 2, 2 }).Call(x);
x = new Conv2D(512, new[] { 3, 3 }, activation: new ReLU(), padding: PaddingType.Same).Call(x);
x = new Conv2D(512, new[] { 3, 3 }, activation: new ReLU(), padding: PaddingType.Same).Call(x);
x = new Conv2D(512, new[] { 3, 3 }, activation: new ReLU(), padding: PaddingType.Same).Call(x);
x = new MaxPooling2D(new int[] { 2, 2 }).Call(x);
x = new Conv2D(512, new[] { 3, 3 }, activation: new ReLU(), padding: PaddingType.Same).Call(x);
x = new Conv2D(512, new[] { 3, 3 }, activation: new ReLU(), padding: PaddingType.Same).Call(x);
x = new Conv2D(512, new[] { 3, 3 }, activation: new ReLU(), padding: PaddingType.Same).Call(x);
x = new MaxPooling2D(new int[] { 2, 2 }).Call(x);
var model = new Model(img_input, x, "vgg16");
model.LoadWeightsH5(weightFile);
return(model);
}
