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dataangelOceania2018
dataangel
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Oceania2018
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update:Keras
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6 files changed

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6 files changed

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‎src/TensorFlowNET.Core/Keras/Layers/ILayer.cs‎

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@@ -13,6 +13,8 @@ public interface ILayer
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List<INode> OutboundNodes { get; }
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Tensors Apply(Tensors inputs, Tensor state = null, bool is_training = false);
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List<IVariableV1> trainable_variables { get; }
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List<IVariableV1> trainable_weights { get; }
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List<IVariableV1> non_trainable_weights { get; }
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TensorShape output_shape { get; }
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int count_params();
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LayerArgs get_config();

‎src/TensorFlowNET.Core/Operations/NnOps/RNNCell.cs‎

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@@ -67,6 +67,8 @@ public abstract class RnnCell : ILayer
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public bool Trainable => throw new NotImplementedException();
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public List<IVariableV1> trainable_variables => throw new NotImplementedException();
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public List<IVariableV1> trainable_weights => throw new NotImplementedException();
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public List<IVariableV1> non_trainable_weights => throw new NotImplementedException();
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public TensorShape output_shape => throw new NotImplementedException();
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‎src/TensorFlowNET.Keras/Engine/Layer.cs‎

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@@ -239,6 +239,21 @@ public int count_params()
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return layer_utils.count_params(this, weights);
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return 0;
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}
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List<IVariableV1> ILayer.trainable_weights
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{
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get
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{
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return trainable_weights;
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}
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}
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List<IVariableV1> ILayer.non_trainable_weights
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{
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get
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{
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return non_trainable_weights;
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}
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}
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public List<IVariableV1> weights
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{

‎src/TensorFlowNET.Keras/Engine/Model.Training.cs‎

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@@ -0,0 +1,56 @@
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using System;
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using System.Collections.Generic;
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using System.Text;
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using HDF.PInvoke;
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using HDF5CSharp;
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using Tensorflow.Keras.Saving;
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namespace Tensorflow.Keras.Engine
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{
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public partial class Model
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{
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private long fileId = -1;
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private long f = -1;
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public void load_weights(string filepath ="",bool by_name= false, bool skip_mismatch=false, object options = null)
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{
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long root = Hdf5.OpenFile(filepath, true);
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long fileId = root;
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//try
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//{
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bool msuccess = Hdf5.GroupExists(fileId, "model_weights");
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bool lsuccess = Hdf5.GroupExists(fileId, "layer_names");
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if (!lsuccess && msuccess)
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{
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f = H5G.open(fileId, "model_weights");
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}
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if (by_name)
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{
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//fdf5_format.load_weights_from_hdf5_group_by_name();
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}
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else
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{
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fdf5_format.load_weights_from_hdf5_group(f, this);
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}
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H5G.close(f);
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//}
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//catch (Exception ex)
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//{
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// if (fileId != -1)
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// {
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// Hdf5.CloseFile(fileId);
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// }
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// if (f != -1)
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// {
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// H5G.close(f);
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// }
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// throw new Exception(ex.ToString());
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//}
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}
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}
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}
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‎src/TensorFlowNET.Keras/Saving/fdf5_format.cs‎

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@@ -0,0 +1,179 @@
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using System;
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using System.Collections.Generic;
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using System.Text;
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using HDF.PInvoke;
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using NumSharp;
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using Tensorflow.Keras.Engine;
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using HDF5CSharp;
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using static Tensorflow.Binding;
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using static Tensorflow.KerasApi;
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namespace Tensorflow.Keras.Saving
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{
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public class fdf5_format
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{
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public static void load_model_from_hdf5(string filepath = "", Dictionary<string, object> custom_objects = null, bool compile = false)
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{
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long root = Hdf5.OpenFile(filepath,true);
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load_model_from_hdf5(root, custom_objects, compile);
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}
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public static void load_model_from_hdf5(long filepath = -1, Dictionary<string, object> custom_objects = null, bool compile = false)
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{
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//long fileId = filepath;
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//try
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//{
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// groupId = H5G.open(fileId, "/");
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// (bool success, string[] attrId) = Hdf5.ReadStringAttributes(groupId, "model_config", "");
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// H5G.close(groupId);
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// if (success == true) {
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// Console.WriteLine(attrId[0]);
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// }
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//}
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//catch (Exception ex)
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//{
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// if (filepath != -1) {
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// Hdf5.CloseFile(filepath);
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// }
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// if (groupId != -1) {
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// H5G.close(groupId);
39+
// }
40+
// throw new Exception(ex.ToString());
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//}
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}
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public static void save_model_to_hdf5(long filepath = -1, Dictionary<string, object> custom_objects = null, bool compile = false)
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{
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}
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public static void preprocess_weights_for_loading(long filepath = -1, Dictionary<string, object> custom_objects = null, bool compile = false)
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{
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}
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public static void _convert_rnn_weights(long filepath = -1, Dictionary<string, object> custom_objects = null, bool compile = false)
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{
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}
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public static void save_optimizer_weights_to_hdf5_group(long filepath = -1, Dictionary<string, object> custom_objects = null, bool compile = false)
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{
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}
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public static void load_optimizer_weights_from_hdf5_group(long filepath = -1, Dictionary<string, object> custom_objects = null, bool compile = false)
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{
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}
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public static void save_weights_to_hdf5_group(long filepath = -1, Dictionary<string, object> custom_objects = null, bool compile = false)
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{
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}
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public static void load_weights_from_hdf5_group(long f=-1,Model model=null)
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{
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string original_keras_version = "1";
71+
string original_backend = null;
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if (Hdf5.AttributeExists(f, "keras_version"))
73+
{
74+
(bool success, string[] attr) = Hdf5.ReadStringAttributes(f, "keras_version", "");
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if (success)
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{
77+
original_keras_version = attr[0];
78+
}
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}
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if (Hdf5.AttributeExists(f, "backend"))
81+
{
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(bool success, string[] attr) = Hdf5.ReadStringAttributes(f, "backend", "");
83+
if (success)
84+
{
85+
original_backend = attr[0];
86+
}
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}
88+
List<ILayer> filtered_layers = new List<ILayer>();
89+
List<Tensor> weights;
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foreach (var layer in model.Layers)
91+
{
92+
weights = _legacy_weights(layer);
93+
if (weights.Count>0)
94+
{
95+
filtered_layers.append(layer);
96+
}
97+
}
98+
string[] layer_names = load_attributes_from_hdf5_group(f,"layer_names");
99+
List<NDArray> weight_values=new List<NDArray>();
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foreach (var i in filtered_layers) {
101+
long g = H5G.open(f, i.Name);
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string[] weight_names = null;
103+
if (g != -1)
104+
{
105+
weight_names = load_attributes_from_hdf5_group(g, "weight_names");
106+
}
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if (weight_names != null)
108+
{
109+
foreach (var i_ in weight_names) {
110+
(bool success, Array result) = Hdf5.ReadDataset<float>(g, i_);
111+
//
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weight_values.Add(np.array(result));
113+
}
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}
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H5G.close(g);
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}
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}
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public static void toarrayf4(long filepath = -1, Dictionary<string, object> custom_objects = null, bool compile = false)
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{
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}
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public static void load_weights_from_hdf5_group_by_name(long filepath = -1, Dictionary<string, object> custom_objects = null, bool compile = false)
124+
{
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}
127+
public static void save_attributes_to_hdf5_group(long filepath = -1, Dictionary<string, object> custom_objects = null, bool compile = false)
128+
{
129+
130+
}
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public static string[] load_attributes_from_hdf5_group(long f = -1, string name = "")
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{
133+
if (Hdf5.AttributeExists(f, name))
134+
{
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(bool success, string[] attr) = Hdf5.ReadStringAttributes(f, name, "");
136+
if (success)
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{
138+
return attr;
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}
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}
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return null;
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}
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public static void load_attributes_from_hdf5_group(long filepath = -1, Dictionary<string, object> custom_objects = null, bool compile = false)
144+
{
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146+
}
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public static List<Tensor> _legacy_weights(ILayer layer)
149+
{
150+
151+
List<Tensor> weights= new List<Tensor>();
152+
if (layer.trainable_weights.Count != 0)
153+
{
154+
Tensor[] trainable_weights = Array.ConvertAll<IVariableV1, Tensor>(layer.trainable_weights.ToArray(), s => s.AsTensor());
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Tensor[] non_trainable_weights =null;
156+
if (layer.non_trainable_weights.Count != 0)
157+
{
158+
non_trainable_weights = Array.ConvertAll<IVariableV1, Tensor>(layer.non_trainable_weights.ToArray(), s => s.AsTensor());
159+
}
160+
foreach (var i in trainable_weights) {
161+
if (non_trainable_weights != null)
162+
{
163+
foreach (var i_ in non_trainable_weights)
164+
{
165+
weights.Add(i + i_);
166+
}
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}
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else {
169+
weights.Add(i);
170+
};
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}
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}
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return weights;
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}
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}
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}
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‎src/TensorFlowNET.Keras/Tensorflow.Keras.csproj‎

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@@ -26,7 +26,7 @@
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Keras is an API designed for human beings, not machines. Keras follows best practices for reducing cognitive load: it offers consistent &amp; simple APIs, it minimizes the number of user actions required for common use cases, and it provides clear &amp; actionable error messages.</Description>
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<Company>SciSharp STACK</Company>
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<GeneratePackageOnBuild>true</GeneratePackageOnBuild>
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<PackageTags>tensorflow, keras, deep learning, machine learning, scisharp</PackageTags>
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<PackageTags>tensorflow, keras, deep learning, machine learning</PackageTags>
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<PackageRequireLicenseAcceptance>true</PackageRequireLicenseAcceptance>
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<RepositoryType>Git</RepositoryType>
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<SignAssembly>true</SignAssembly>
@@ -46,15 +46,14 @@ Keras is an API designed for human beings, not machines. Keras follows best prac
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</PropertyGroup>
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4848
<ItemGroup>
49+
<PackageReference Include="HDF.PInvoke.1.10" Version="1.10.500" />
4950
<PackageReference Include="MethodBoundaryAspect.Fody" Version="2.0.138" />
5051
<PackageReference Include="Newtonsoft.Json" Version="12.0.3" />
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<PackageReference Include="NumSharp.Lite" Version="0.1.10" />
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<PackageReference Include="SciSharp.Keras.HDF5" Version="1.1.10.500" />
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<PackageReference Include="SharpZipLib" Version="1.3.1" />
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</ItemGroup>
5356

54-
<ItemGroup>
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<Folder Include="Saving\" />
56-
</ItemGroup>
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5857
<ItemGroup>
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<ProjectReference Include="..\TensorFlowNET.Core\Tensorflow.Binding.csproj" />
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</ItemGroup>

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