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Oceania2018Oceania2018
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Fixed model.fit return results. SciSharp#927 
1 parent f48ba40 commit c5cdf2c

11 files changed

Lines changed: 258 additions & 63 deletions

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‎src/TensorFlowNET.Core/NumPy/Numpy.Manipulation.cs‎

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@@ -8,6 +8,10 @@ namespace Tensorflow.NumPy
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{
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public partial class np
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{
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[AutoNumPy]
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public static NDArray concatenate((NDArray, NDArray) tuple, int axis = 0)
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=> new NDArray(array_ops.concat(new[] { tuple.Item1, tuple.Item2 }, axis));
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[AutoNumPy]
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public static NDArray concatenate(NDArray[] arrays, int axis = 0) => new NDArray(array_ops.concat(arrays, axis));
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‎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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namespace Tensorflow.Keras.Callbacks
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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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}
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}

‎src/TensorFlowNET.Keras/Callbacks/CallbackParams.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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{
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public class CallbackParams
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{
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public IModel Model { get; set; }
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public int Verbose { get; set; }
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public int Epochs { get; set; }
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public long Steps { get; set; }
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}
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}

‎src/TensorFlowNET.Keras/Callbacks/History.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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namespace Tensorflow.Keras.Callbacks
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{
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public class History : ICallback
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{
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List<int> epochs;
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CallbackParams _parameters;
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public Dictionary<string, List<float>> history { get; set; }
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public History(CallbackParams parameters)
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{
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_parameters = parameters;
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}
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public void on_train_begin()
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{
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epochs = new List<int>();
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history = new Dictionary<string, List<float>>();
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}
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public void on_epoch_begin(int epoch)
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{
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}
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public void on_train_batch_begin(long step)
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{
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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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}
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public void on_epoch_end(int epoch, Dictionary<string, float> epoch_logs)
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{
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epochs.Add(epoch);
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foreach (var log in epoch_logs)
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{
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if (!history.ContainsKey(log.Key))
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{
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history[log.Key] = new List<float>();
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}
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history[log.Key].Add((float)log.Value);
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}
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}
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}
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}

‎src/TensorFlowNET.Keras/Callbacks/ICallback.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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namespace Tensorflow.Keras.Callbacks
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{
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public interface ICallback
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{
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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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}
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}

‎src/TensorFlowNET.Keras/Callbacks/ProgbarLogger.cs‎

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using PureHDF;
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using System;
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using System.Collections.Generic;
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using System.Diagnostics;
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using System.Linq;
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using System.Text;
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namespace Tensorflow.Keras.Callbacks
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{
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public class ProgbarLogger : ICallback
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{
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bool _called_in_fit = false;
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int seen = 0;
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CallbackParams _parameters;
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Stopwatch _sw;
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public ProgbarLogger(CallbackParams parameters)
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{
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_parameters = parameters;
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}
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public void on_train_begin()
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{
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_called_in_fit = true;
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_sw = new Stopwatch();
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}
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public void on_epoch_begin(int epoch)
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{
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_reset_progbar();
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_maybe_init_progbar();
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Binding.tf_output_redirect.WriteLine($"Epoch: {epoch + 1:D3}/{_parameters.Epochs:D3}");
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}
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public void on_train_batch_begin(long step)
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{
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_sw.Restart();
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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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_sw.Stop();
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var elapse = _sw.ElapsedMilliseconds;
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var results = string.Join(" - ", logs.Select(x => $"{x.Key}: {(float)x.Value:F6}"));
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var progress = "";
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var length = 30.0 / _parameters.Steps;
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for (int i = 0; i < Math.Floor(end_step * length - 1); i++)
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progress += "=";
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if (progress.Length < 28)
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progress += ">";
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else
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progress += "=";
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var remaining = "";
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for (int i = 1; i < 30 - progress.Length; i++)
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remaining += ".";
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Binding.tf_output_redirect.Write($"{end_step + 1:D4}/{_parameters.Steps:D4} [{progress}{remaining}] - {elapse}ms/step - {results}");
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if (!Console.IsOutputRedirected)
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{
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Console.CursorLeft = 0;
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}
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}
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66+
public void on_epoch_end(int epoch, Dictionary<string, float> epoch_logs)
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{
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Console.WriteLine();
69+
}
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71+
void _reset_progbar()
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{
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seen = 0;
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}
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void _maybe_init_progbar()
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{
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}
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}
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}

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

Lines changed: 7 additions & 11 deletions
Original file line numberDiff line numberDiff line change
@@ -31,7 +31,7 @@ public void evaluate(NDArray x, NDArray y,
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bool use_multiprocessing = false,
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bool return_dict = false)
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{
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data_handler = new DataHandler(new DataHandlerArgs
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var data_handler = new DataHandler(new DataHandlerArgs
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{
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X = x,
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Y = y,
@@ -46,7 +46,6 @@ public void evaluate(NDArray x, NDArray y,
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StepsPerExecution = _steps_per_execution
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});
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Binding.tf_output_redirect.WriteLine($"Testing...");
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foreach (var (epoch, iterator) in data_handler.enumerate_epochs())
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{
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reset_metrics();
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foreach (var step in data_handler.steps())
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{
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// callbacks.on_train_batch_begin(step)
59-
results = test_function(iterator);
58+
results = test_function(data_handler, iterator);
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}
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Binding.tf_output_redirect.WriteLine($"iterator: {epoch + 1}, " + string.Join(", ", results.Select(x => $"{x.Item1}: {(float)x.Item2}")));
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}
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}
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6563
public KeyValuePair<string, float>[] evaluate(IDatasetV2 x)
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{
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data_handler = new DataHandler(new DataHandlerArgs
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var data_handler = new DataHandler(new DataHandlerArgs
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{
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Dataset = x,
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Model = this,
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StepsPerExecution = _steps_per_execution
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});
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Binding.tf_output_redirect.WriteLine($"Testing...");
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IEnumerable<(string, Tensor)> logs = null;
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foreach (var (epoch, iterator) in data_handler.enumerate_epochs())
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{
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8279
foreach (var step in data_handler.steps())
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{
8481
// callbacks.on_train_batch_begin(step)
85-
logs = test_function(iterator);
82+
logs = test_function(data_handler, iterator);
8683
}
87-
Binding.tf_output_redirect.WriteLine($"iterator: {epoch + 1}, " + string.Join(", ", logs.Select(x => $"{x.Item1}: {(float)x.Item2}")));
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}
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return logs.Select(x => new KeyValuePair<string, float>(x.Item1, (float)x.Item2)).ToArray();
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}
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92-
IEnumerable<(string, Tensor)> test_function(OwnedIterator iterator)
88+
IEnumerable<(string, Tensor)> test_function(DataHandler data_handler, OwnedIterator iterator)
9389
{
9490
var data = iterator.next();
95-
var outputs = test_step(data[0], data[1]);
91+
var outputs = test_step(data_handler, data[0], data[1]);
9692
tf_with(ops.control_dependencies(new object[0]), ctl => _test_counter.assign_add(1));
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return outputs;
9894
}
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100-
List<(string, Tensor)> test_step(Tensor x, Tensor y)
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List<(string, Tensor)> test_step(DataHandler data_handler, Tensor x, Tensor y)
10197
{
10298
(x, y) = data_handler.DataAdapter.Expand1d(x, y);
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var y_pred = Apply(x, training: false);

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