public static string[] load_attributes_from_hdf5_group(long group, string name)
{
if (Hdf5.AttributeExists(group, name))
{
var(success, attr) = Hdf5.ReadStringAttributes(group, name, "");
if (success)
{
return(attr.ToArray());
}
}
return(null);
}
public static List <(IVariableV1, NDArray)> load_weights_from_hdf5_group(long f, List <ILayer> layers)
{
string original_keras_version = "2.5.0";
string original_backend = null;
if (Hdf5.AttributeExists(f, "keras_version"))
{
var(success, attr) = Hdf5.ReadStringAttributes(f, "keras_version", "");
if (success)
{
original_keras_version = attr.First();
}
// keras version should be 2.5.0+
var ver_major = int.Parse(original_keras_version.Split('.')[0]);
var ver_minor = int.Parse(original_keras_version.Split('.')[1]);
if (ver_major < 2 || (ver_major == 2 && ver_minor < 5))
{
throw new ValueError("keras version should be 2.5.0 or later.");
}
}
if (Hdf5.AttributeExists(f, "backend"))
{
var(success, attr) = Hdf5.ReadStringAttributes(f, "backend", "");
if (success)
{
original_backend = attr.First();
}
}
var filtered_layers = new List <ILayer>();
foreach (var layer in layers)
{
var weights = _legacy_weights(layer);
if (weights.Count > 0)
{
filtered_layers.append(layer);
}
}
string[] layer_names = load_attributes_from_hdf5_group(f, "layer_names");
var filtered_layer_names = new List <string>();
foreach (var name in layer_names)
{
if (!filtered_layers.Select(x => x.Name).Contains(name))
{
continue;
}
long g = H5G.open(f, name);
var weight_names = load_attributes_from_hdf5_group(g, "weight_names");
if (weight_names.Count() > 0)
{
filtered_layer_names.Add(name);
}
H5G.close(g);
}
layer_names = filtered_layer_names.ToArray();
if (layer_names.Length != filtered_layers.Count())
{
throw new ValueError("You are trying to load a weight file " +
$"containing {layer_names}" +
$" layers into a model with {filtered_layers.Count} layers.");
}
var weight_value_tuples = new List <(IVariableV1, NDArray)>();
foreach (var(k, name) in enumerate(layer_names))
{
var weight_values = new List <NDArray>();
long g = H5G.open(f, name);
var weight_names = load_attributes_from_hdf5_group(g, "weight_names");
foreach (var i_ in weight_names)
{
(bool success, Array result) = Hdf5.ReadDataset <float>(g, i_);
if (success)
{
weight_values.Add(np.array(result));
}
}
H5G.close(g);
var layer = filtered_layers[k];
var symbolic_weights = _legacy_weights(layer);
preprocess_weights_for_loading(layer, weight_values, original_keras_version, original_backend);
if (weight_values.Count() != symbolic_weights.Count())
{
throw new ValueError($"Layer #{k} (named {layer.Name}" +
"in the current model) was found to " +
$"correspond to layer {name} in the save file." +
$"However the new layer {layer.Name} expects " +
$"{symbolic_weights.Count()} weights, but the saved weights have " +
$"{weight_values.Count()} elements.");
}
weight_value_tuples.AddRange(zip(symbolic_weights, weight_values));
}
keras.backend.batch_set_value(weight_value_tuples);
return(weight_value_tuples);
}
