From 5142ad658cf9233abd2c9fe727c2daeea84a88f6 Mon Sep 17 00:00:00 2001 From: Aleksej Solomatin Date: Sun, 30 Jun 2024 22:06:12 +0300 Subject: [PATCH 1/3] test: Added an `evaluate` method call to a unit test for a multi-input model. --- test/TensorFlowNET.Keras.UnitTest/MultiInputModelTest.cs | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/test/TensorFlowNET.Keras.UnitTest/MultiInputModelTest.cs b/test/TensorFlowNET.Keras.UnitTest/MultiInputModelTest.cs index dd8ef8f91..bb293bd90 100644 --- a/test/TensorFlowNET.Keras.UnitTest/MultiInputModelTest.cs +++ b/test/TensorFlowNET.Keras.UnitTest/MultiInputModelTest.cs @@ -54,6 +54,13 @@ 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)); From f8b7bdeb9b7fa10bf49b888934683f04febfc6e2 Mon Sep 17 00:00:00 2001 From: Aleksej Solomatin Date: Sun, 30 Jun 2024 22:43:01 +0300 Subject: [PATCH 2/3] test: Added a unit test of training a multi-input model using a dataset. --- .../MultiInputModelTest.cs | 75 +++++++++++++++++++ 1 file changed, 75 insertions(+) diff --git a/test/TensorFlowNET.Keras.UnitTest/MultiInputModelTest.cs b/test/TensorFlowNET.Keras.UnitTest/MultiInputModelTest.cs index bb293bd90..54b76d41a 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 @@ -66,5 +67,79 @@ public void LeNetModel() 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); + } } } From 93dda17944b6e34380897ad3480ac2218fb7398e Mon Sep 17 00:00:00 2001 From: Aleksej Solomatin Date: Sun, 30 Jun 2024 22:44:03 +0300 Subject: [PATCH 3/3] fix: Added support for training a multi-input model using a dataset. --- src/TensorFlowNET.Keras/Engine/Model.Evaluate.cs | 14 +++++++++++++- src/TensorFlowNET.Keras/Engine/Model.Fit.cs | 13 ++++++++++++- 2 files changed, 25 insertions(+), 2 deletions(-) diff --git a/src/TensorFlowNET.Keras/Engine/Model.Evaluate.cs b/src/TensorFlowNET.Keras/Engine/Model.Evaluate.cs index b3264429e..ec99d7ef9 100644 --- a/src/TensorFlowNET.Keras/Engine/Model.Evaluate.cs +++ b/src/TensorFlowNET.Keras/Engine/Model.Evaluate.cs @@ -112,7 +112,19 @@ public Dictionary evaluate(IDatasetV2 x, int verbose = 1, bool is Steps = data_handler.Inferredsteps }); - return evaluate(data_handler, callbacks, is_val, test_function); + Func> 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); } /// diff --git a/src/TensorFlowNET.Keras/Engine/Model.Fit.cs b/src/TensorFlowNET.Keras/Engine/Model.Fit.cs index 13a1b63bc..e1303513e 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> 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 callbackList, IDatasetV2 validation_data,