Add ShellProgressBar for model fitting.

src/TensorFlowNET.Keras/Engine/DataAdapters/DataHandler.cs

@@ -15,6 +15,7 @@ namespace Tensorflow.Keras.Engine.DataAdapters
         public IDataAdapter DataAdapter => _adapter;
         IDatasetV2 _dataset;
         int _inferred_steps;
+        public int Inferredsteps => _inferred_steps;
         int _current_step;
         int _step_increment;
         bool _insufficient_data;

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

@@ -1,4 +1,5 @@
 using NumSharp;
+using ShellProgressBar;
 using System;
 using System.Collections.Generic;
 using System.Linq;
@@ -51,22 +52,7 @@ namespace Tensorflow.Keras.Engine
                 StepsPerExecution = _steps_per_execution
             });
 
-            stop_training = false;
-            _train_counter.assign(0);
-            Console.WriteLine($"Training...");
-            foreach (var (epoch, iterator) in data_handler.enumerate_epochs())
-            {
-                // reset_metrics();
-                // callbacks.on_epoch_begin(epoch)
-                // data_handler.catch_stop_iteration();
-                IEnumerable<(string, Tensor)> results = null;
-                foreach (var step in data_handler.steps())
-                {
-                    // callbacks.on_train_batch_begin(step)
-                    results = step_function(iterator);
-                }
-                Console.WriteLine($"epoch: {epoch + 1}, " + string.Join(", ", results.Select(x => $"{x.Item1}: {(float)x.Item2}")));
-            }
+            FitInternal(epochs);
         }
 
         public void fit(IDatasetV2 dataset, 
@@ -95,21 +81,32 @@ namespace Tensorflow.Keras.Engine
                 StepsPerExecution = _steps_per_execution
             });
 
+            FitInternal(epochs);
+        }
+
+        void FitInternal(int epochs)
+        {
             stop_training = false;
             _train_counter.assign(0);
-            Console.WriteLine($"Training...");
+            var options = new ProgressBarOptions
+            {
+                ProgressCharacter = '.',
+                ProgressBarOnBottom = true
+            };
+
             foreach (var (epoch, iterator) in data_handler.enumerate_epochs())
             {
+                using var pbar = new ProgressBar(data_handler.Inferredsteps, "Training...", options);
                 // reset_metrics();
                 // callbacks.on_epoch_begin(epoch)
                 // data_handler.catch_stop_iteration();
-                IEnumerable<(string, Tensor)> results = null;
                 foreach (var step in data_handler.steps())
                 {
                     // callbacks.on_train_batch_begin(step)
-                    results = step_function(iterator);
+                    var results = step_function(iterator);
+                    var result_pairs = string.Join(", ", results.Select(x => $"{x.Item1}: {(float)x.Item2:F6}"));
+                    pbar.Tick($"[Epoch: {epoch + 1:D3}/{epochs:D3}, Step: {step + 1:D4}/{data_handler.Inferredsteps:D4}, {result_pairs}]");
                 }
-                Console.WriteLine($"epoch: {epoch + 1}, " + string.Join(", ", results.Select(x => $"{x.Item1}: {(float)x.Item2}")));
             }
         }
     }

src/TensorFlowNET.Keras/Tensorflow.Keras.csproj

@@ -47,6 +47,7 @@ Keras is an API designed for human beings, not machines. Keras follows best prac
     <PackageReference Include="MethodBoundaryAspect.Fody" Version="2.0.138" />
     <PackageReference Include="Newtonsoft.Json" Version="12.0.3" />
     <PackageReference Include="SharpZipLib" Version="1.3.1" />
+    <PackageReference Include="ShellProgressBar" Version="5.0.0" />
   </ItemGroup>
 
   <ItemGroup>