diff --git a/src/TensorFlowNET.Keras/Engine/Model.Evaluate.cs b/src/TensorFlowNET.Keras/Engine/Model.Evaluate.cs
index b3264429..ec99d7ef 100644
--- a/src/TensorFlowNET.Keras/Engine/Model.Evaluate.cs
+++ b/src/TensorFlowNET.Keras/Engine/Model.Evaluate.cs
@@ -112,7 +112,19 @@ public Dictionary<string, float> evaluate(IDatasetV2 x, int verbose = 1, bool is
Steps = data_handler.Inferredsteps
});
- return evaluate(data_handler, callbacks, is_val, test_function);
+ Func<DataHandler, OwnedIterator, Dictionary<string, float>> testFunction;
+
+ if (data_handler.DataAdapter.GetDataset().structure.Length > 2 ||
+ data_handler.DataAdapter.GetDataset().FirstInputTensorCount > 1)
+ {
+ testFunction = test_step_multi_inputs_function;
+ }
+ else
+ {
+ testFunction = test_function;
+ }
+
+ return evaluate(data_handler, callbacks, is_val, testFunction);
}
/// <summary>
diff --git a/src/TensorFlowNET.Keras/Engine/Model.Fit.cs b/src/TensorFlowNET.Keras/Engine/Model.Fit.cs
index 13a1b63b..e1303513 100644
--- a/src/TensorFlowNET.Keras/Engine/Model.Fit.cs
+++ b/src/TensorFlowNET.Keras/Engine/Model.Fit.cs
@@ -179,9 +179,20 @@ public ICallback fit(IDatasetV2 dataset,
StepsPerExecution = _steps_per_execution
});
+ Func<DataHandler, OwnedIterator, Dictionary<string, float>> trainStepFunction;
+
+ if (data_handler.DataAdapter.GetDataset().structure.Length > 2 ||
+ data_handler.DataAdapter.GetDataset().FirstInputTensorCount > 1)
+ {
+ trainStepFunction = train_step_multi_inputs_function;
+ }
+ else
+ {
+ trainStepFunction = train_step_function;
+ }
return FitInternal(data_handler, epochs, validation_step, verbose, callbacks, validation_data: validation_data,
- train_step_func: train_step_function);
+ train_step_func: trainStepFunction);
}
History FitInternal(DataHandler data_handler, int epochs, int validation_step, int verbose, List<ICallback> callbackList, IDatasetV2 validation_data,
diff --git a/test/TensorFlowNET.Keras.UnitTest/MultiInputModelTest.cs b/test/TensorFlowNET.Keras.UnitTest/MultiInputModelTest.cs
index dd8ef8f9..54b76d41 100644
--- a/test/TensorFlowNET.Keras.UnitTest/MultiInputModelTest.cs
+++ b/test/TensorFlowNET.Keras.UnitTest/MultiInputModelTest.cs
@@ -2,6 +2,7 @@
using System;
using Tensorflow.Keras.Optimizers;
using Tensorflow.NumPy;
+using static Tensorflow.Binding;
using static Tensorflow.KerasApi;
namespace Tensorflow.Keras.UnitTest
@@ -54,10 +55,91 @@ public void LeNetModel()
var x = new NDArray[] { x1, x2 };
model.fit(x, dataset.Train.Labels, batch_size: 8, epochs: 3);
+ x1 = x1["0:8"];
+ x2 = x1;
+
+ x = new NDArray[] { x1, x2 };
+ var y = dataset.Train.Labels["0:8"];
+ (model as Engine.Model).evaluate(x, y);
+
x1 = np.ones((1, 28, 28, 1), TF_DataType.TF_FLOAT);
x2 = np.zeros((1, 28, 28, 1), TF_DataType.TF_FLOAT);
var pred = model.predict((x1, x2));
Console.WriteLine(pred);
}
+
+ [TestMethod]
+ public void LeNetModelDataset()
+ {
+ var inputs = keras.Input((28, 28, 1));
+ var conv1 = keras.layers.Conv2D(16, (3, 3), activation: "relu", padding: "same").Apply(inputs);
+ var pool1 = keras.layers.MaxPooling2D((2, 2), 2).Apply(conv1);
+ var conv2 = keras.layers.Conv2D(32, (3, 3), activation: "relu", padding: "same").Apply(pool1);
+ var pool2 = keras.layers.MaxPooling2D((2, 2), 2).Apply(conv2);
+ var flat1 = keras.layers.Flatten().Apply(pool2);
+
+ var inputs_2 = keras.Input((28, 28, 1));
+ var conv1_2 = keras.layers.Conv2D(16, (3, 3), activation: "relu", padding: "same").Apply(inputs_2);
+ var pool1_2 = keras.layers.MaxPooling2D((4, 4), 4).Apply(conv1_2);
+ var conv2_2 = keras.layers.Conv2D(32, (1, 1), activation: "relu", padding: "same").Apply(pool1_2);
+ var pool2_2 = keras.layers.MaxPooling2D((2, 2), 2).Apply(conv2_2);
+ var flat1_2 = keras.layers.Flatten().Apply(pool2_2);
+
+ var concat = keras.layers.Concatenate().Apply((flat1, flat1_2));
+ var dense1 = keras.layers.Dense(512, activation: "relu").Apply(concat);
+ var dense2 = keras.layers.Dense(128, activation: "relu").Apply(dense1);
+ var dense3 = keras.layers.Dense(10, activation: "relu").Apply(dense2);
+ var output = keras.layers.Softmax(-1).Apply(dense3);
+
+ var model = keras.Model((inputs, inputs_2), output);
+ model.summary();
+
+ var data_loader = new MnistModelLoader();
+
+ var dataset = data_loader.LoadAsync(new ModelLoadSetting
+ {
+ TrainDir = "mnist",
+ OneHot = false,
+ ValidationSize = 59900,
+ }).Result;
+
+ var loss = keras.losses.SparseCategoricalCrossentropy();
+ var optimizer = new Adam(0.001f);
+ model.compile(optimizer, loss, new string[] { "accuracy" });
+
+ NDArray x1 = np.reshape(dataset.Train.Data, (dataset.Train.Data.shape[0], 28, 28, 1));
+
+ var multiInputDataset = tf.data.Dataset.zip(
+ tf.data.Dataset.from_tensor_slices(x1),
+ tf.data.Dataset.from_tensor_slices(x1),
+ tf.data.Dataset.from_tensor_slices(dataset.Train.Labels)
+ ).batch(8);
+ multiInputDataset.FirstInputTensorCount = 2;
+
+ model.fit(multiInputDataset, epochs: 3);
+
+ x1 = x1["0:8"];
+
+ multiInputDataset = tf.data.Dataset.zip(
+ tf.data.Dataset.from_tensor_slices(x1),
+ tf.data.Dataset.from_tensor_slices(x1),
+ tf.data.Dataset.from_tensor_slices(dataset.Train.Labels["0:8"])
+ ).batch(8);
+ multiInputDataset.FirstInputTensorCount = 2;
+
+ (model as Engine.Model).evaluate(multiInputDataset);
+
+ x1 = np.ones((1, 28, 28, 1), TF_DataType.TF_FLOAT);
+ var x2 = np.zeros((1, 28, 28, 1), TF_DataType.TF_FLOAT);
+
+ multiInputDataset = tf.data.Dataset.zip(
+ tf.data.Dataset.from_tensor_slices(x1),
+ tf.data.Dataset.from_tensor_slices(x2)
+ ).batch(8);
+ multiInputDataset.FirstInputTensorCount = 2;
+
+ var pred = model.predict(multiInputDataset);
+ Console.WriteLine(pred);
+ }
}
}