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Oceania2018Oceania2018
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ICallback
1 parent 76a964f commit cbf2d81

32 files changed

Lines changed: 269 additions & 216 deletions

‎src/TensorFlowNET.Console/Tensorflow.Console.csproj‎

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</PropertyGroup>
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<ItemGroup>
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<PackageReference Include="SciSharp.TensorFlow.Redist" Version="2.10.0" />
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<PackageReference Include="SciSharp.TensorFlow.Redist" Version="2.11.0" />
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</ItemGroup>
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<ItemGroup>

‎src/TensorFlowNET.Core/Keras/Engine/ICallback.cs‎

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namespace Tensorflow.Keras.Engine;
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public interface ICallback
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{
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Dictionary<string, List<float>> history { get; set; }
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void on_train_begin();
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void on_epoch_begin(int epoch);
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void on_train_batch_begin(long step);
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void on_train_batch_end(long end_step, Dictionary<string, float> logs);
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void on_epoch_end(int epoch, Dictionary<string, float> epoch_logs);
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void on_predict_begin();
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void on_predict_batch_begin(long step);
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void on_predict_batch_end(long end_step, Dictionary<string, Tensors> logs);
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void on_predict_end();
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}

‎src/TensorFlowNET.Core/Keras/Engine/IModel.cs‎

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namespace Tensorflow.Keras.Engine
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using Tensorflow.Functions;
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using Tensorflow.Keras.Losses;
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using Tensorflow.Keras.Saving;
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using Tensorflow.NumPy;
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namespace Tensorflow.Keras.Engine;
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public interface IModel : ILayer
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{
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public interface IModel
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{
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}
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void compile(IOptimizer optimizer = null,
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ILossFunc loss = null,
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string[] metrics = null);
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void compile(string optimizer, string loss, string[] metrics);
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ICallback fit(NDArray x, NDArray y,
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int batch_size = -1,
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int epochs = 1,
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int verbose = 1,
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float validation_split = 0f,
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bool shuffle = true,
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int initial_epoch = 0,
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int max_queue_size = 10,
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int workers = 1,
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bool use_multiprocessing = false);
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void save(string filepath,
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bool overwrite = true,
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bool include_optimizer = true,
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string save_format = "tf",
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SaveOptions? options = null,
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ConcreteFunction? signatures = null,
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bool save_traces = true);
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void save_weights(string filepath,
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bool overwrite = true,
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string save_format = null,
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object options = null);
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void load_weights(string filepath,
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bool by_name = false,
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bool skip_mismatch = false,
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object options = null);
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void evaluate(NDArray x, NDArray y,
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int batch_size = -1,
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int verbose = 1,
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int steps = -1,
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int max_queue_size = 10,
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int workers = 1,
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bool use_multiprocessing = false,
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bool return_dict = false);
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Tensors predict(Tensor x,
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int batch_size = -1,
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int verbose = 0,
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int steps = -1,
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int max_queue_size = 10,
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int workers = 1,
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bool use_multiprocessing = false);
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void summary(int line_length = -1, float[] positions = null);
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IKerasConfig get_config();
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}

‎src/TensorFlowNET.Core/Keras/Engine/IOptimizer.cs‎

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namespace Tensorflow.Keras.Engine;
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public interface IOptimizer
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{
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Tensor[] aggregate_gradients(IEnumerable<(Tensor, IVariableV1)> grads_and_vars);
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Tensor[] clip_gradients(Tensor[] grads);
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void apply_gradients((Tensor, ResourceVariable) grads_and_vars,
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string name = null,
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bool experimental_aggregate_gradients = true);
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void apply_gradients(IEnumerable<(Tensor, ResourceVariable)> grads_and_vars,
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string name = null,
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bool experimental_aggregate_gradients = true);
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}

‎src/TensorFlowNET.Core/Keras/IKerasApi.cs‎

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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 Tensorflow.Keras.Engine;
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using Tensorflow.Keras.Layers;
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using Tensorflow.Keras.Losses;
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using Tensorflow.Keras.Metrics;
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public ILossesApi losses { get; }
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public IMetricsApi metrics { get; }
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public IInitializersApi initializers { get; }
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/// <summary>
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/// `Model` groups layers into an object with training and inference features.
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/// </summary>
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/// <param name="input"></param>
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/// <param name="output"></param>
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/// <returns></returns>
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public IModel Model(Tensors inputs, Tensors outputs, string name = null);
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}
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}

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

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using System.Collections.Generic;
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using Tensorflow.Keras.ArgsDefinition;
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using Tensorflow.Keras.Engine;
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using Tensorflow.Keras.Engine;
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using Tensorflow.Keras.Saving;
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using Tensorflow.Training;
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List<ILayer> Layers { get; }
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List<INode> InboundNodes { get; }
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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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Tensors Apply(Tensors inputs, Tensor state = null, bool training = false);
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List<IVariableV1> TrainableVariables { get; }
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List<IVariableV1> TrainableWeights { get; }
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List<IVariableV1> NonTrainableWeights { get; }

‎src/TensorFlowNET.Core/Tensorflow.Binding.csproj‎

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<AssemblyName>Tensorflow.Binding</AssemblyName>
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<RootNamespace>Tensorflow</RootNamespace>
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<TargetTensorFlow>2.10.0</TargetTensorFlow>
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<Version>0.100.4</Version>
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<Version>1.0.0</Version>
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<LangVersion>10.0</LangVersion>
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<Nullable>enable</Nullable>
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<Authors>Haiping Chen, Meinrad Recheis, Eli Belash</Authors>
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<Description>Google's TensorFlow full binding in .NET Standard.
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Building, training and infering deep learning models.
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https://tensorflownet.readthedocs.io</Description>
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<AssemblyVersion>0.100.4.0</AssemblyVersion>
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<AssemblyVersion>1.0.0.0</AssemblyVersion>
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<PackageReleaseNotes>
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tf.net 0.100.x and above are based on tensorflow native 2.10.0
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tf.net 0.7x.x aligns with TensorFlow v2.7.x native library.
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tf.net 0.10x.x aligns with TensorFlow v2.10.x native library.
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</PackageReleaseNotes>
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<FileVersion>0.100.4.0</FileVersion>
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<FileVersion>1.0.0.0</FileVersion>
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<PackageLicenseFile>LICENSE</PackageLicenseFile>
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<PackageRequireLicenseAcceptance>true</PackageRequireLicenseAcceptance>
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<SignAssembly>true</SignAssembly>

‎src/TensorFlowNET.Keras/Callbacks/CallbackList.cs‎

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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 Tensorflow.Keras.Engine;
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namespace Tensorflow.Keras.Callbacks
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namespace Tensorflow.Keras.Callbacks;
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public class CallbackList
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{
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public class CallbackList
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{
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List<ICallback> callbacks = new List<ICallback>();
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public History History => callbacks[0] as History;
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public CallbackList(CallbackParams parameters)
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{
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callbacks.Add(new History(parameters));
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callbacks.Add(new ProgbarLogger(parameters));
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}
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public void on_train_begin()
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{
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callbacks.ForEach(x => x.on_train_begin());
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}
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public void on_epoch_begin(int epoch)
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{
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callbacks.ForEach(x => x.on_epoch_begin(epoch));
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}
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public void on_train_batch_begin(long step)
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{
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callbacks.ForEach(x => x.on_train_batch_begin(step));
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}
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public void on_train_batch_end(long end_step, Dictionary<string, float> logs)
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{
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callbacks.ForEach(x => x.on_train_batch_end(end_step, logs));
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}
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public void on_epoch_end(int epoch, Dictionary<string, float> epoch_logs)
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{
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callbacks.ForEach(x => x.on_epoch_end(epoch, epoch_logs));
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}
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public void on_predict_begin()
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{
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callbacks.ForEach(x => x.on_predict_begin());
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}
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public void on_predict_batch_begin(long step)
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{
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callbacks.ForEach(x => x.on_predict_batch_begin(step));
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}
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public void on_predict_batch_end(long end_step, Dictionary<string, Tensors> logs)
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{
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callbacks.ForEach(x => x.on_predict_batch_end(end_step, logs));
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}
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public void on_predict_end()
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{
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callbacks.ForEach(x => x.on_predict_end());
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}
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List<ICallback> callbacks = new List<ICallback>();
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public History History => callbacks[0] as History;
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public CallbackList(CallbackParams parameters)
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{
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callbacks.Add(new History(parameters));
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callbacks.Add(new ProgbarLogger(parameters));
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}
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public void on_train_begin()
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{
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callbacks.ForEach(x => x.on_train_begin());
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}
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public void on_epoch_begin(int epoch)
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{
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callbacks.ForEach(x => x.on_epoch_begin(epoch));
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}
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public void on_train_batch_begin(long step)
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{
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callbacks.ForEach(x => x.on_train_batch_begin(step));
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}
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public void on_train_batch_end(long end_step, Dictionary<string, float> logs)
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{
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callbacks.ForEach(x => x.on_train_batch_end(end_step, logs));
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}
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public void on_epoch_end(int epoch, Dictionary<string, float> epoch_logs)
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{
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callbacks.ForEach(x => x.on_epoch_end(epoch, epoch_logs));
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}
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public void on_predict_begin()
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{
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callbacks.ForEach(x => x.on_predict_begin());
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}
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public void on_predict_batch_begin(long step)
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{
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callbacks.ForEach(x => x.on_predict_batch_begin(step));
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}
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public void on_predict_batch_end(long end_step, Dictionary<string, Tensors> logs)
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{
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callbacks.ForEach(x => x.on_predict_batch_end(end_step, logs));
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}
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public void on_predict_end()
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{
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callbacks.ForEach(x => x.on_predict_end());
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}
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}

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