started to use MnistModelLoader in Tensorflow.Hub (#330)

.gitignore

@@ -332,3 +332,7 @@ src/TensorFlowNET.Native/bazel-*
 src/TensorFlowNET.Native/c_api.h
 /.vscode
 test/TensorFlowNET.Examples/mnist
+
+
+# training model resources
+.resources

test/TensorFlowNET.Examples/BasicModels/KMeansClustering.cs

@@ -18,7 +18,7 @@ using NumSharp;
 using System;
 using System.Diagnostics;
 using Tensorflow;
-using TensorFlowNET.Examples.Utility;
+using Tensorflow.Hub;
 using static Tensorflow.Python;
 
 namespace TensorFlowNET.Examples
@@ -39,7 +39,7 @@ namespace TensorFlowNET.Examples
         public int? test_size = null;
         public int batch_size = 1024; // The number of samples per batch
 
-        Datasets<DataSetMnist> mnist;
+        Datasets<MnistDataSet> mnist;
         NDArray full_data_x;
         int num_steps = 20; // Total steps to train
         int k = 25; // The number of clusters
@@ -62,19 +62,31 @@ namespace TensorFlowNET.Examples
 
         public void PrepareData()
         {
-            mnist = MNIST.read_data_sets("mnist", one_hot: true, train_size: train_size, validation_size:validation_size, test_size:test_size);
-            full_data_x = mnist.train.data;
+            var loader = new MnistModelLoader();
+
+            var setting = new ModelLoadSetting
+            {
+                TrainDir = ".resources/mnist",
+                OneHot = true,
+                TrainSize = train_size,
+                ValidationSize = validation_size,
+                TestSize = test_size
+            };
+
+            mnist = loader.LoadAsync(setting).Result;
+
+            full_data_x = mnist.Train.Data;
 
             // download graph meta data
             string url = "https://raw.githubusercontent.com/SciSharp/TensorFlow.NET/master/graph/kmeans.meta";
-            Web.Download(url, "graph", "kmeans.meta");
+            loader.DownloadAsync(url, ".resources/graph", "kmeans.meta").Wait();
         }
 
         public Graph ImportGraph()
         {
             var graph = tf.Graph().as_default();
 
-            tf.train.import_meta_graph("graph/kmeans.meta");
+            tf.train.import_meta_graph(".resources/graph/kmeans.meta");
 
             return graph;
         }
@@ -132,7 +144,7 @@ namespace TensorFlowNET.Examples
             sw.Start();
             foreach (var i in range(idx.Length))
             {
-                var x = mnist.train.labels[i];
+                var x = mnist.Train.Labels[i];
                 counts[idx[i]] += x;
             }
 
@@ -153,7 +165,7 @@ namespace TensorFlowNET.Examples
             var accuracy_op = tf.reduce_mean(cast);
 
             // Test Model
-            var (test_x, test_y) = (mnist.test.data, mnist.test.labels);
+            var (test_x, test_y) = (mnist.Test.Data, mnist.Test.Labels);
             result = sess.run(accuracy_op, new FeedItem(X, test_x), new FeedItem(Y, test_y));
             accuray_test = result;
             print($"Test Accuracy: {accuray_test}");

test/TensorFlowNET.Examples/BasicModels/LogisticRegression.cs

@@ -19,7 +19,7 @@ using System;
 using System.Diagnostics;
 using System.IO;
 using Tensorflow;
-using TensorFlowNET.Examples.Utility;
+using Tensorflow.Hub;
 using static Tensorflow.Python;
 
 namespace TensorFlowNET.Examples
@@ -45,7 +45,7 @@ namespace TensorFlowNET.Examples
         private float learning_rate = 0.01f;
         private int display_step = 1;
 
-        Datasets<DataSetMnist> mnist;
+        Datasets<MnistDataSet> mnist;
 
         public bool Run()
         {
@@ -84,11 +84,11 @@ namespace TensorFlowNET.Examples
                     sw.Start();
 
                     var avg_cost = 0.0f;
-                    var total_batch = mnist.train.num_examples / batch_size;
+                    var total_batch = mnist.Train.NumOfExamples / batch_size;
                     // Loop over all batches
                     foreach (var i in range(total_batch))
                     {
-                        var (batch_xs, batch_ys) = mnist.train.next_batch(batch_size);
+                        var (batch_xs, batch_ys) = mnist.Train.GetNextBatch(batch_size);
                         // Run optimization op (backprop) and cost op (to get loss value)
                         var result = sess.run(new object[] { optimizer, cost },
                             new FeedItem(x, batch_xs),
@@ -115,7 +115,7 @@ namespace TensorFlowNET.Examples
                 var correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1));
                 // Calculate accuracy
                 var accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32));
-                float acc = accuracy.eval(new FeedItem(x, mnist.test.data), new FeedItem(y, mnist.test.labels));
+                float acc = accuracy.eval(new FeedItem(x, mnist.Test.Data), new FeedItem(y, mnist.Test.Labels));
                 print($"Accuracy: {acc.ToString("F4")}");
 
                 return acc > 0.9;
@@ -124,23 +124,23 @@ namespace TensorFlowNET.Examples
 
         public void PrepareData()
         {
-            mnist = MNIST.read_data_sets("mnist", one_hot: true, train_size: train_size, validation_size: validation_size, test_size: test_size);
+            mnist = MnistModelLoader.LoadAsync(".resources/mnist", oneHot: true, trainSize: train_size, validationSize: validation_size, testSize: test_size).Result;
         }
 
         public void SaveModel(Session sess)
         {
             var saver = tf.train.Saver();
-            var save_path = saver.save(sess, "logistic_regression/model.ckpt");
-            tf.train.write_graph(sess.graph, "logistic_regression", "model.pbtxt", as_text: true);
+            var save_path = saver.save(sess, ".resources/logistic_regression/model.ckpt");
+            tf.train.write_graph(sess.graph, ".resources/logistic_regression", "model.pbtxt", as_text: true);
 
-            FreezeGraph.freeze_graph(input_graph: "logistic_regression/model.pbtxt",
+            FreezeGraph.freeze_graph(input_graph: ".resources/logistic_regression/model.pbtxt",
                               input_saver: "",
                               input_binary: false,
-                              input_checkpoint: "logistic_regression/model.ckpt",
+                              input_checkpoint: ".resources/logistic_regression/model.ckpt",
                               output_node_names: "Softmax",
                               restore_op_name: "save/restore_all",
                               filename_tensor_name: "save/Const:0",
-                              output_graph: "logistic_regression/model.pb",
+                              output_graph: ".resources/logistic_regression/model.pb",
                               clear_devices: true,
                               initializer_nodes: "");
         }
@@ -148,7 +148,7 @@ namespace TensorFlowNET.Examples
         public void Predict(Session sess)
         {
             var graph = new Graph().as_default();
-            graph.Import(Path.Join("logistic_regression", "model.pb"));
+            graph.Import(Path.Join(".resources/logistic_regression", "model.pb"));
 
             // restoring the model
             // var saver = tf.train.import_meta_graph("logistic_regression/tensorflowModel.ckpt.meta");
@@ -159,7 +159,7 @@ namespace TensorFlowNET.Examples
             var input = x.outputs[0];
 
             // predict
-            var (batch_xs, batch_ys) = mnist.train.next_batch(10);
+            var (batch_xs, batch_ys) = mnist.Train.GetNextBatch(10);
             var results = sess.run(output, new FeedItem(input, batch_xs[np.arange(1)]));
 
             if (results.argmax() == (batch_ys[0] as NDArray).argmax())

test/TensorFlowNET.Examples/BasicModels/NearestNeighbor.cs

@@ -17,7 +17,7 @@
 using NumSharp;
 using System;
 using Tensorflow;
-using TensorFlowNET.Examples.Utility;
+using Tensorflow.Hub;
 using static Tensorflow.Python;
 
 namespace TensorFlowNET.Examples
@@ -31,7 +31,7 @@ namespace TensorFlowNET.Examples
     {
         public bool Enabled { get; set; } = true;
         public string Name => "Nearest Neighbor";
-        Datasets<DataSetMnist> mnist;
+        Datasets<MnistDataSet> mnist;
         NDArray Xtr, Ytr, Xte, Yte;
         public int? TrainSize = null;
         public int ValidationSize = 5000;
@@ -84,10 +84,10 @@ namespace TensorFlowNET.Examples
 
         public void PrepareData()
         {
-            mnist = MNIST.read_data_sets("mnist", one_hot: true, train_size: TrainSize, validation_size:ValidationSize, test_size:TestSize);
+            mnist = MnistModelLoader.LoadAsync(".resources/mnist", oneHot: true, trainSize: TrainSize, validationSize: ValidationSize, testSize: TestSize).Result;
             // In this example, we limit mnist data
-            (Xtr, Ytr) = mnist.train.next_batch(TrainSize==null ? 5000 : TrainSize.Value / 100); // 5000 for training (nn candidates)
-            (Xte, Yte) = mnist.test.next_batch(TestSize==null ? 200 : TestSize.Value / 100); // 200 for testing
+            (Xtr, Ytr) = mnist.Train.GetNextBatch(TrainSize == null ? 5000 : TrainSize.Value / 100); // 5000 for training (nn candidates)
+            (Xte, Yte) = mnist.Test.GetNextBatch(TestSize == null ? 200 : TestSize.Value / 100); // 200 for testing
         }
 
         public Graph ImportGraph()

test/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionCNN.cs

@@ -18,7 +18,7 @@ using NumSharp;
 using System;
 using System.Diagnostics;
 using Tensorflow;
-using TensorFlowNET.Examples.Utility;
+using Tensorflow.Hub;
 using static Tensorflow.Python;
 
 namespace TensorFlowNET.Examples.ImageProcess
@@ -46,7 +46,7 @@ namespace TensorFlowNET.Examples.ImageProcess
         int epochs = 5; // accuracy > 98%
         int batch_size = 100;
         float learning_rate = 0.001f;
-        Datasets<DataSetMnist> mnist;
+        Datasets<MnistDataSet> mnist;
 
         // Network configuration
         // 1st Convolutional Layer
@@ -310,14 +310,14 @@ namespace TensorFlowNET.Examples.ImageProcess
             
         public void PrepareData()
         {
-            mnist = MNIST.read_data_sets("mnist", one_hot: true);
-            (x_train, y_train) = Reformat(mnist.train.data, mnist.train.labels);
-            (x_valid, y_valid) = Reformat(mnist.validation.data, mnist.validation.labels);
-            (x_test, y_test) = Reformat(mnist.test.data, mnist.test.labels);
+            mnist = MnistModelLoader.LoadAsync(".resources/mnist", oneHot: true).Result;
+            (x_train, y_train) = Reformat(mnist.Train.Data, mnist.Train.Labels);
+            (x_valid, y_valid) = Reformat(mnist.Validation.Data, mnist.Validation.Labels);
+            (x_test, y_test) = Reformat(mnist.Test.Data, mnist.Test.Labels);
 
             print("Size of:");
-            print($"- Training-set:\t\t{len(mnist.train.data)}");
-            print($"- Validation-set:\t{len(mnist.validation.data)}");
+            print($"- Training-set:\t\t{len(mnist.Train.Data)}");
+            print($"- Validation-set:\t{len(mnist.Validation.Data)}");
         }
 
         /// <summary>

test/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionNN.cs

@@ -17,7 +17,7 @@
 using NumSharp;
 using System;
 using Tensorflow;
-using TensorFlowNET.Examples.Utility;
+using Tensorflow.Hub;
 using static Tensorflow.Python;
 
 namespace TensorFlowNET.Examples.ImageProcess
@@ -44,7 +44,7 @@ namespace TensorFlowNET.Examples.ImageProcess
         int batch_size = 100;
         float learning_rate = 0.001f;
         int h1 = 200; // number of nodes in the 1st hidden layer
-        Datasets<DataSetMnist> mnist;
+        Datasets<MnistDataSet> mnist;
 
         Tensor x, y;
         Tensor loss, accuracy;
@@ -121,13 +121,13 @@ namespace TensorFlowNET.Examples.ImageProcess
             
         public void PrepareData()
         {
-            mnist = MNIST.read_data_sets("mnist", one_hot: true);
+            mnist = MnistModelLoader.LoadAsync(".resources/mnist", oneHot: true).Result;
         }
 
         public void Train(Session sess)
         {
             // Number of training iterations in each epoch
-            var num_tr_iter = mnist.train.labels.len / batch_size;
+            var num_tr_iter = mnist.Train.Labels.len / batch_size;
 
             var init = tf.global_variables_initializer();
             sess.run(init);
@@ -139,13 +139,13 @@ namespace TensorFlowNET.Examples.ImageProcess
             {
                 print($"Training epoch: {epoch + 1}");
                 // Randomly shuffle the training data at the beginning of each epoch 
-                var (x_train, y_train) = randomize(mnist.train.data, mnist.train.labels);
+                var (x_train, y_train) = mnist.Randomize(mnist.Train.Data, mnist.Train.Labels);
 
                 foreach (var iteration in range(num_tr_iter))
                 {
                     var start = iteration * batch_size;
                     var end = (iteration + 1) * batch_size;
-                    var (x_batch, y_batch) = get_next_batch(x_train, y_train, start, end);
+                    var (x_batch, y_batch) = mnist.GetNextBatch(x_train, y_train, start, end);
 
                     // Run optimization op (backprop)
                     sess.run(optimizer, new FeedItem(x, x_batch), new FeedItem(y, y_batch));
@@ -161,7 +161,8 @@ namespace TensorFlowNET.Examples.ImageProcess
                 }
 
                 // Run validation after every epoch
-                var results1 = sess.run(new[] { loss, accuracy }, new FeedItem(x, mnist.validation.data), new FeedItem(y, mnist.validation.labels));
+                var results1 = sess.run(new[] { loss, accuracy }, new FeedItem(x, mnist.Validation.Data), new FeedItem(y, mnist.Validation.Labels));
+                
                 loss_val = results1[0];
                 accuracy_val = results1[1];
                 print("---------------------------------------------------------");
@@ -172,35 +173,12 @@ namespace TensorFlowNET.Examples.ImageProcess
 
         public void Test(Session sess)
         {
-            var result = sess.run(new[] { loss, accuracy }, new FeedItem(x, mnist.test.data), new FeedItem(y, mnist.test.labels));
+            var result = sess.run(new[] { loss, accuracy }, new FeedItem(x, mnist.Test.Data), new FeedItem(y, mnist.Test.Labels));
             loss_test = result[0];
             accuracy_test = result[1];
             print("---------------------------------------------------------");
             print($"Test loss: {loss_test.ToString("0.0000")}, test accuracy: {accuracy_test.ToString("P")}");
             print("---------------------------------------------------------");
         }
-
-        private (NDArray, NDArray) randomize(NDArray x, NDArray y)
-        {
-            var perm = np.random.permutation(y.shape[0]);
-
-            np.random.shuffle(perm);
-            return (mnist.train.data[perm], mnist.train.labels[perm]);
-        }
-
-        /// <summary>
-        /// selects a few number of images determined by the batch_size variable (if you don't know why, read about Stochastic Gradient Method)
-        /// </summary>
-        /// <param name="x"></param>
-        /// <param name="y"></param>
-        /// <param name="start"></param>
-        /// <param name="end"></param>
-        /// <returns></returns>
-        private (NDArray, NDArray) get_next_batch(NDArray x, NDArray y, int start, int end)
-        {
-            var x_batch = x[$"{start}:{end}"];
-            var y_batch = y[$"{start}:{end}"];
-            return (x_batch, y_batch);
-        }
     }
 }

test/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionRNN.cs

@@ -17,7 +17,7 @@
 using NumSharp;
 using System;
 using Tensorflow;
-using TensorFlowNET.Examples.Utility;
+using Tensorflow.Hub;
 using static Tensorflow.Python;
 
 namespace TensorFlowNET.Examples.ImageProcess
@@ -45,7 +45,7 @@ namespace TensorFlowNET.Examples.ImageProcess
         int n_inputs = 28;
         int n_outputs = 10;
 
-        Datasets<DataSetMnist> mnist;
+        Datasets<MnistDataSet> mnist;
 
         Tensor x, y;
         Tensor loss, accuracy, cls_prediction;
@@ -143,15 +143,15 @@ namespace TensorFlowNET.Examples.ImageProcess
 
         public void PrepareData()
         {
-            mnist = MNIST.read_data_sets("mnist", one_hot: true);
-            (x_train, y_train) = (mnist.train.data, mnist.train.labels);
-            (x_valid, y_valid) = (mnist.validation.data, mnist.validation.labels);
-            (x_test, y_test) = (mnist.test.data, mnist.test.labels);
+            mnist = MnistModelLoader.LoadAsync(".resources/mnist", oneHot: true).Result;
+            (x_train, y_train) = (mnist.Train.Data, mnist.Train.Labels);
+            (x_valid, y_valid) = (mnist.Validation.Data, mnist.Validation.Labels);
+            (x_test, y_test) = (mnist.Test.Data, mnist.Test.Labels);
 
             print("Size of:");
-            print($"- Training-set:\t\t{len(mnist.train.data)}");
-            print($"- Validation-set:\t{len(mnist.validation.data)}");
-            print($"- Test-set:\t\t{len(mnist.test.data)}");
+            print($"- Training-set:\t\t{len(mnist.Train.Data)}");
+            print($"- Validation-set:\t{len(mnist.Validation.Data)}");
+            print($"- Test-set:\t\t{len(mnist.Test.Data)}");
         }
 
         public Graph ImportGraph() => throw new NotImplementedException();

test/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj

@@ -18,5 +18,6 @@
     <ProjectReference Include="..\..\src\KerasNET.Core\Keras.Core.csproj" />
     <ProjectReference Include="..\..\src\TensorFlowNET.Core\TensorFlowNET.Core.csproj" />
     <ProjectReference Include="..\..\src\TensorFlowText\TensorFlowText.csproj" />
+    <ProjectReference Include="..\..\src\TensorFlowHub\TensorFlowHub.csproj" />
   </ItemGroup>
 </Project>

test/TensorFlowNET.Examples/Utility/DataSetMnist.cs

@@ -1,95 +0,0 @@
-/*****************************************************************************
-   Copyright 2018 The TensorFlow.NET Authors. All Rights Reserved.
-
-   Licensed under the Apache License, Version 2.0 (the "License");
-   you may not use this file except in compliance with the License.
-   You may obtain a copy of the License at
-
-       http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing, software
-   distributed under the License is distributed on an "AS IS" BASIS,
-   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   See the License for the specific language governing permissions and
-   limitations under the License.
-******************************************************************************/
-
-using NumSharp;
-using Tensorflow;
-
-namespace TensorFlowNET.Examples.Utility
-{
-    public class DataSetMnist : IDataSet
-    {
-        public int num_examples { get; }
-
-        public int epochs_completed { get; private set; }
-        public int index_in_epoch { get; private set; }
-        public NDArray data { get; private set; }
-        public NDArray labels { get; private set; }
-
-        public DataSetMnist(NDArray images, NDArray labels, TF_DataType dtype, bool reshape)
-        {
-            num_examples = images.shape[0];
-            images = images.reshape(images.shape[0], images.shape[1] * images.shape[2]);
-            images.astype(dtype.as_numpy_datatype());
-            images = np.multiply(images, 1.0f / 255.0f);
-
-            labels.astype(dtype.as_numpy_datatype());
-
-            data = images;
-            this.labels = labels;
-            epochs_completed = 0;
-            index_in_epoch = 0;
-        }
-
-        public (NDArray, NDArray) next_batch(int batch_size, bool fake_data = false, bool shuffle = true)
-        {
-            var start = index_in_epoch;
-            // Shuffle for the first epoch
-            if(epochs_completed == 0 && start == 0 && shuffle)
-            {
-                var perm0 = np.arange(num_examples);
-                np.random.shuffle(perm0);
-                data = data[perm0];
-                labels = labels[perm0];
-            }
-
-            // Go to the next epoch
-            if (start + batch_size > num_examples)
-            {
-                // Finished epoch
-                epochs_completed += 1;
-
-                // Get the rest examples in this epoch
-                var rest_num_examples = num_examples - start;
-                //var images_rest_part = _images[np.arange(start, _num_examples)];
-                //var labels_rest_part = _labels[np.arange(start, _num_examples)];
-                // Shuffle the data
-                if (shuffle)
-                {
-                    var perm = np.arange(num_examples);
-                    np.random.shuffle(perm);
-                    data = data[perm];
-                    labels = labels[perm];
-                }
-
-                start = 0;
-                index_in_epoch = batch_size - rest_num_examples;
-                var end = index_in_epoch;
-                var images_new_part = data[np.arange(start, end)];
-                var labels_new_part = labels[np.arange(start, end)];
-
-                /*return (np.concatenate(new float[][] { images_rest_part.Data<float>(), images_new_part.Data<float>() }, axis: 0),
-                    np.concatenate(new float[][] { labels_rest_part.Data<float>(), labels_new_part.Data<float>() }, axis: 0));*/
-                return (images_new_part, labels_new_part);
-            }
-            else
-            {
-                index_in_epoch += batch_size;
-                var end = index_in_epoch;
-                return (data[np.arange(start, end)], labels[np.arange(start, end)]);
-            }
-        }
-    }
-}

test/TensorFlowNET.Examples/Utility/Datasets.cs

@@ -1,46 +0,0 @@
-using NumSharp;
-
-namespace TensorFlowNET.Examples.Utility
-{
-    public class Datasets<T> where T : IDataSet
-    {
-        private T _train;
-        public T train => _train;
-
-        private T _validation;
-        public T validation => _validation;
-
-        private T _test;
-        public T test => _test;
-
-        public Datasets(T train, T validation, T test)
-        {
-            _train = train;
-            _validation = validation;
-            _test = test;
-        }
-
-        public (NDArray, NDArray) Randomize(NDArray x, NDArray y)
-        {
-            var perm = np.random.permutation(y.shape[0]);
-
-            np.random.shuffle(perm);
-            return (x[perm], y[perm]);
-        }
-
-        /// <summary>
-        /// selects a few number of images determined by the batch_size variable (if you don't know why, read about Stochastic Gradient Method)
-        /// </summary>
-        /// <param name="x"></param>
-        /// <param name="y"></param>
-        /// <param name="start"></param>
-        /// <param name="end"></param>
-        /// <returns></returns>
-        public (NDArray, NDArray) GetNextBatch(NDArray x, NDArray y, int start, int end)
-        {
-            var x_batch = x[$"{start}:{end}"];
-            var y_batch = y[$"{start}:{end}"];
-            return (x_batch, y_batch);
-        }
-    }
-}

test/TensorFlowNET.Examples/Utility/IDataSet.cs

@@ -1,10 +0,0 @@
-using NumSharp;
-
-namespace TensorFlowNET.Examples.Utility
-{
-    public interface IDataSet
-    {
-        NDArray data { get; }
-        NDArray labels { get; }
-    }
-}

test/TensorFlowNET.Examples/Utility/MNIST.cs

@@ -1,131 +0,0 @@
-/*****************************************************************************
-   Copyright 2018 The TensorFlow.NET Authors. All Rights Reserved.
-
-   Licensed under the Apache License, Version 2.0 (the "License");
-   you may not use this file except in compliance with the License.
-   You may obtain a copy of the License at
-
-       http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing, software
-   distributed under the License is distributed on an "AS IS" BASIS,
-   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   See the License for the specific language governing permissions and
-   limitations under the License.
-******************************************************************************/
-
-using NumSharp;
-using System;
-using System.IO;
-using Tensorflow;
-
-namespace TensorFlowNET.Examples.Utility
-{
-    public class MNIST
-    {
-        private const string DEFAULT_SOURCE_URL = "https://storage.googleapis.com/cvdf-datasets/mnist/";
-        private const string TRAIN_IMAGES = "train-images-idx3-ubyte.gz";
-        private const string TRAIN_LABELS = "train-labels-idx1-ubyte.gz";
-        private const string TEST_IMAGES = "t10k-images-idx3-ubyte.gz";
-        private const string TEST_LABELS = "t10k-labels-idx1-ubyte.gz";
-        public static Datasets<DataSetMnist> read_data_sets(string train_dir, 
-            bool one_hot = false,
-            TF_DataType dtype = TF_DataType.TF_FLOAT,
-            bool reshape = true,
-            int validation_size = 5000,
-            int? train_size = null,
-            int? test_size = null,
-            string source_url = DEFAULT_SOURCE_URL)
-        {
-            if (train_size!=null && validation_size >= train_size)
-                throw new ArgumentException("Validation set should be smaller than training set");
-
-            Web.Download(source_url + TRAIN_IMAGES, train_dir, TRAIN_IMAGES);
-            Compress.ExtractGZip(Path.Join(train_dir, TRAIN_IMAGES), train_dir);
-            var train_images = extract_images(Path.Join(train_dir, TRAIN_IMAGES.Split('.')[0]), limit: train_size);
-
-            Web.Download(source_url + TRAIN_LABELS, train_dir, TRAIN_LABELS);
-            Compress.ExtractGZip(Path.Join(train_dir, TRAIN_LABELS), train_dir);
-            var train_labels = extract_labels(Path.Join(train_dir, TRAIN_LABELS.Split('.')[0]), one_hot: one_hot, limit: train_size);
-
-            Web.Download(source_url + TEST_IMAGES, train_dir, TEST_IMAGES);
-            Compress.ExtractGZip(Path.Join(train_dir, TEST_IMAGES), train_dir);
-            var test_images = extract_images(Path.Join(train_dir, TEST_IMAGES.Split('.')[0]), limit: test_size);
-
-            Web.Download(source_url + TEST_LABELS, train_dir, TEST_LABELS);
-            Compress.ExtractGZip(Path.Join(train_dir, TEST_LABELS), train_dir);
-            var test_labels = extract_labels(Path.Join(train_dir, TEST_LABELS.Split('.')[0]), one_hot: one_hot, limit:test_size);
-
-            int end = train_images.shape[0];
-            var validation_images = train_images[np.arange(validation_size)];
-            var validation_labels = train_labels[np.arange(validation_size)];
-            train_images = train_images[np.arange(validation_size, end)];
-            train_labels = train_labels[np.arange(validation_size, end)];
-
-            var train = new DataSetMnist(train_images, train_labels, dtype, reshape);
-            var validation = new DataSetMnist(validation_images, validation_labels, dtype, reshape);
-            var test = new DataSetMnist(test_images, test_labels, dtype, reshape);
-
-            return new Datasets<DataSetMnist>(train, validation, test);
-        }
-
-        public static NDArray extract_images(string file, int? limit=null)
-        {
-            using (var bytestream = new FileStream(file, FileMode.Open))
-            {
-                var magic = _read32(bytestream);
-                if (magic != 2051)
-                    throw new ValueError($"Invalid magic number {magic} in MNIST image file: {file}");
-                var num_images =  _read32(bytestream);
-                num_images = limit == null ? num_images : Math.Min(num_images, (uint)limit);
-                var rows = _read32(bytestream);
-                var cols = _read32(bytestream);
-                var buf = new byte[rows * cols * num_images];
-                bytestream.Read(buf, 0, buf.Length);
-                var data = np.frombuffer(buf, np.uint8);
-                data = data.reshape((int)num_images, (int)rows, (int)cols, 1);
-                return data;
-            }
-        }
-
-        public static NDArray extract_labels(string file, bool one_hot = false, int num_classes = 10, int? limit = null)
-        {
-            using (var bytestream = new FileStream(file, FileMode.Open))
-            {
-                var magic = _read32(bytestream);
-                if (magic != 2049)
-                    throw new ValueError($"Invalid magic number {magic} in MNIST label file: {file}");
-                var num_items = _read32(bytestream);
-                num_items = limit == null ? num_items : Math.Min(num_items,(uint) limit);
-                var buf = new byte[num_items];
-                bytestream.Read(buf, 0, buf.Length);
-                var labels = np.frombuffer(buf, np.uint8);
-                if (one_hot)
-                    return dense_to_one_hot(labels, num_classes);
-                return labels;
-            }
-        }
-
-        private static NDArray dense_to_one_hot(NDArray labels_dense, int num_classes)
-        {
-            var num_labels = labels_dense.shape[0];
-            var index_offset = np.arange(num_labels) * num_classes;
-            var labels_one_hot = np.zeros(num_labels, num_classes);
-
-            for(int row = 0; row < num_labels; row++)
-            {
-                var col = labels_dense.Data<byte>(row);
-                labels_one_hot.SetData(1.0, row, col);
-            }
-
-            return labels_one_hot;
-        }
-
-        private static uint _read32(FileStream bytestream)
-        {
-            var buffer = new byte[sizeof(uint)];
-            var count = bytestream.Read(buffer, 0, 4);
-            return np.frombuffer(buffer, ">u4").Data<uint>(0);
-        }
-    }
-}