@@ -21,7 +21,7 @@ public TensorLikeDataAdapter(TensorLikeDataAdapterArgs args)
             this.args = args;
             _process_tensorlike();
             num_samples = args.X.shape[0];
-            var batch_size = args.BatchSize;
+            var batch_size = args.BatchSize == -1 ? 32 : args.BatchSize;
             _batch_size = batch_size;
             _size = Convert.ToInt32(Math.Ceiling(num_samples / (batch_size + 0f)));
             num_full_batches = num_samples / batch_size;
 
@@ -0,0 +1,84 @@
+using NumSharp;
+using System;
+using System.Collections.Generic;
+using System.Linq;
+using Tensorflow.Keras.ArgsDefinition;
+using Tensorflow.Keras.Engine.DataAdapters;
+using static Tensorflow.Binding;
+
+namespace Tensorflow.Keras.Engine
+{
+    public partial class Model
+    {
+        /// <summary>
+        /// Returns the loss value & metrics values for the model in test mode.
+        /// </summary>
+        /// <param name="x"></param>
+        /// <param name="y"></param>
+        /// <param name="batch_size"></param>
+        /// <param name="verbose"></param>
+        /// <param name="steps"></param>
+        /// <param name="max_queue_size"></param>
+        /// <param name="workers"></param>
+        /// <param name="use_multiprocessing"></param>
+        /// <param name="return_dict"></param>
+        public void evaluate(NDArray x, NDArray y,
+            int batch_size = -1,
+            int verbose = 1,
+            int steps = -1,
+            int max_queue_size = 10,
+            int workers = 1,
+            bool use_multiprocessing = false,
+            bool return_dict = false)
+        {
+            data_handler = new DataHandler(new DataHandlerArgs
+            {
+                X = x,
+                Y = y,
+                BatchSize = batch_size,
+                StepsPerEpoch = steps,
+                InitialEpoch = 0,
+                Epochs = 1,
+                MaxQueueSize = max_queue_size,
+                Workers = workers,
+                UseMultiprocessing = use_multiprocessing,
+                Model = this,
+                StepsPerExecution = _steps_per_execution
+            });
+
+            Console.WriteLine($"Testing...");
+            foreach (var (epoch, iterator) in data_handler.enumerate_epochs())
+            {
+                // reset_metrics();
+                // callbacks.on_epoch_begin(epoch)
+                // data_handler.catch_stop_iteration();
+                IEnumerable<(string, Tensor)> results = null;
+                foreach (var step in data_handler.steps())
+                {
+                    // callbacks.on_train_batch_begin(step)
+                    results = test_function(iterator);
+                }
+                Console.WriteLine($"iterator: {epoch + 1}, " + string.Join(", ", results.Select(x => $"{x.Item1}: {(float)x.Item2}")));
+            }
+        }
+
+        IEnumerable<(string, Tensor)> test_function(OwnedIterator iterator)
+        {
+            var data = iterator.next();
+            var outputs = test_step(data[0], data[1]);
+            tf_with(ops.control_dependencies(new object[0]), ctl => _test_counter.assign_add(1));
+            return outputs;
+        }
+
+        List<(string, Tensor)> test_step(Tensor x, Tensor y)
+        {
+            (x, y) = data_handler.DataAdapter.Expand1d(x, y);
+            var y_pred = Apply(x, is_training: false);
+            var loss = compiled_loss.Call(y, y_pred);
+
+            compiled_metrics.update_state(y, y_pred);
+
+            return metrics.Select(x => (x.Name, x.result())).ToList();
+        }
+    }
+}
@@ -54,6 +54,7 @@ public void fit(NDArray x, NDArray y,
             stop_training = false;
             _train_counter.assign(0);
             bool first_step = true;
+            Console.WriteLine($"Training...");
             foreach (var (epoch, iterator) in data_handler.enumerate_epochs())
             {
                 // reset_metrics();
 
@@ -0,0 +1,13 @@
+using System.Collections.Generic;
+using Tensorflow.Keras.Metrics;
+
+namespace Tensorflow.Keras.Engine
+{
+    public partial class Model
+    {
+        public void save(string path)
+        {
+          
+        }
+    }
+}
@@ -5,6 +5,8 @@ PM> Install-Package TensorFlow.NET
 PM> Install-Package SciSharp.TensorFlow.Redist
 ```
 
+Add `<RuntimeIdentifier>win-x64</RuntimeIdentifier>` to a `PropertyGroup` in your `.csproj` when targeting `.NET 472`.
+
 ### Run in Linux
 
 Download Linux pre-built library and unzip `libtensorflow.so` and `libtensorflow_framework.so` into current running directory.
Original file line number	Diff line number	Diff line change
@@ -54,6 +54,7 @@ public void fit(NDArray x, NDArray y,
54	54	stop_training = false;
55	55	_train_counter.assign(0);
56	56	bool first_step = true;
	57	+ Console.WriteLine($"Training...");
57	58	foreach (var (epoch, iterator) in data_handler.enumerate_epochs())
58	59	{
59	60	// reset_metrics();