refactor model.evaluate to deal with confilict

Wanglongzhi2001 · Wanglongzhi2001 · commit 35d2e107f325 · 2023-06-18T12:15:56.000+08:00
diff --git a/src/TensorFlowNET.Keras/Engine/Model.Evaluate.cs b/src/TensorFlowNET.Keras/Engine/Model.Evaluate.cs
@@ -72,7 +72,7 @@ public Dictionary<string, float> evaluate(IEnumerable<Tensor> x, Tensor y, int v
         {
             var data_handler = new DataHandler(new DataHandlerArgs
             {
-                X = new Tensors(x),
+                X = new Tensors(x.ToArray()),
                 Y = y,
                 Model = this,
                 StepsPerExecution = _steps_per_execution
@@ -168,7 +168,8 @@ Dictionary<string, float> test_function(DataHandler data_handler, Tensor[] data)
         Dictionary<string, float> test_step_multi_inputs_function(DataHandler data_handler, Tensor[] data)
         {
             var x_size = data_handler.DataAdapter.GetDataset().FirstInputTensorCount;
-            var outputs = train_step(data_handler, new Tensors(data.Take(x_size)), new Tensors(data.Skip(x_size)));
+            var outputs = train_step(data_handler, new Tensors(data.Take(x_size).ToArray()), new Tensors(data.Skip(x_size).ToArray()));
+            tf_with(ops.control_dependencies(new object[0]), ctl => _train_counter.assign_add(1));
             return outputs;
         }
     }

Original file line number	Diff line number	Diff line change
`@@ -72,7 +72,7 @@ public Dictionary<string, float> evaluate(IEnumerable<Tensor> x, Tensor y, int v`
`72`	`72`	`{`
`73`	`73`	`var data_handler = new DataHandler(new DataHandlerArgs`
`74`	`74`	`{`
`75`		`- X = new Tensors(x),`
	`75`	`+ X = new Tensors(x.ToArray()),`
`76`	`76`	`Y = y,`
`77`	`77`	`Model = this,`
`78`	`78`	`StepsPerExecution = _steps_per_execution`
`@@ -168,7 +168,8 @@ Dictionary<string, float> test_function(DataHandler data_handler, Tensor[] data)`
`168`	`168`	`Dictionary<string, float> test_step_multi_inputs_function(DataHandler data_handler, Tensor[] data)`
`169`	`169`	`{`
`170`	`170`	`var x_size = data_handler.DataAdapter.GetDataset().FirstInputTensorCount;`
`171`		`- var outputs = train_step(data_handler, new Tensors(data.Take(x_size)), new Tensors(data.Skip(x_size)));`
	`171`	`+ var outputs = train_step(data_handler, new Tensors(data.Take(x_size).ToArray()), new Tensors(data.Skip(x_size).ToArray()));`
	`172`	`+ tf_with(ops.control_dependencies(new object[0]), ctl => _train_counter.assign_add(1));`
`172`	`173`	`return outputs;`
`173`	`174`	`}`
`174`	`175`	`}`