add tf.where api.

src/TensorFlowNET.Core/APIs/tf.array.cs

@@ -20,6 +20,13 @@ namespace Tensorflow
         public static Tensor expand_dims(Tensor input, int axis = -1, string name = null, int dim = -1)
             => array_ops.expand_dims(input, axis, name, dim);
 
+        /// <summary>
+        /// Return the elements, either from `x` or `y`, depending on the `condition`.
+        /// </summary>
+        /// <returns></returns>
+        public static Tensor where<Tx, Ty>(Tensor condition, Tx x, Ty y, string name = null)
+            => array_ops.where(condition, x, y, name);
+
         /// <summary>
         /// Transposes `a`. Permutes the dimensions according to `perm`.
         /// </summary>

src/TensorFlowNET.Core/APIs/tf.random.cs

@@ -25,7 +25,7 @@ namespace Tensorflow
 
         public static Tensor random_uniform(int[] shape,
             float minval = 0,
-            float? maxval = null,
+            float maxval = 1,
             TF_DataType dtype = TF_DataType.TF_FLOAT,
             int? seed = null,
             string name = null) => random_ops.random_uniform(shape, minval, maxval, dtype, seed, name);

src/TensorFlowNET.Core/Layers/Layer.cs

@@ -2,6 +2,7 @@
 using System.Collections.Generic;
 using System.Linq;
 using System.Text;
+using static Tensorflow.Python;
 
 namespace Tensorflow.Layers
 {
@@ -50,7 +51,7 @@ namespace Tensorflow.Layers
                     auxiliary_name_scope: false);
             }
 
-            Python.with(scope_context_manager, scope2 => _current_scope = scope2);
+            with(scope_context_manager, scope2 => _current_scope = scope2);
             // Actually call layer
             var outputs = base.__call__(new Tensor[] { inputs }, training: training);
 
@@ -60,6 +61,13 @@ namespace Tensorflow.Layers
             return outputs;
         }
 
+        protected override void _init_set_name(string name, bool zero_based = true)
+        {
+            // Determine layer name (non-unique).
+            base._init_set_name(name, zero_based: zero_based);
+            _base_name = this.name;
+        }
+
         protected virtual void _add_elements_to_collection(Operation[] elements, string[] collection_list)
         {
             foreach(var name in collection_list)
@@ -140,10 +148,18 @@ namespace Tensorflow.Layers
         {
             if (_scope == null)
             {
-                Python.with(tf.variable_scope(scope, default_name: _base_name), captured_scope =>
+                if(_reuse.HasValue && _reuse.Value)
                 {
-                    _scope = captured_scope;
-                });
+                    throw new NotImplementedException("_set_scope _reuse.HasValue");
+                    /*with(tf.variable_scope(scope == null ? _base_name : scope),
+                        captured_scope => _scope = captured_scope);*/
+                }
+                else
+                {
+                    with(tf.variable_scope(scope, default_name: _base_name),
+                        captured_scope => _scope = captured_scope);
+                }
+
             }
         }
     }

src/TensorFlowNET.Core/Operations/array_ops.py.cs

@@ -233,7 +233,7 @@ namespace Tensorflow
             });
         }
 
-        public static Tensor where(Tensor condition, Tensor x = null, Tensor y = null, string name = null)
+        public static Tensor where(Tensor condition, object x = null, object y = null, string name = null)
         {
             if( x == null && y == null)
             {

src/TensorFlowNET.Core/Operations/gen_array_ops.cs

@@ -234,7 +234,7 @@ namespace Tensorflow
             return _op.outputs[0];
         }
 
-        public static Tensor select(Tensor condition, Tensor t, Tensor e, string name = null)
+        public static Tensor select<Tx, Ty>(Tensor condition, Tx t, Ty e, string name = null)
         {
             var _op = _op_def_lib._apply_op_helper("Select", name, new { condition, t, e });
             return _op.outputs[0];

src/TensorFlowNET.Core/Operations/random_ops.py.cs

@@ -49,7 +49,7 @@ namespace Tensorflow
         /// <returns>A tensor of the specified shape filled with random uniform values.</returns>
         public static Tensor random_uniform(int[] shape, 
             float minval = 0,
-            float? maxval = null,
+            float maxval = 1,
             TF_DataType dtype = TF_DataType.TF_FLOAT, 
             int? seed = null, 
             string name = null)

src/TensorFlowNET.Core/tf.cs

@@ -15,7 +15,7 @@ namespace Tensorflow
         public static TF_DataType float16 = TF_DataType.TF_HALF;
         public static TF_DataType float32 = TF_DataType.TF_FLOAT;
         public static TF_DataType float64 = TF_DataType.TF_DOUBLE;
-        public static TF_DataType boolean = TF_DataType.TF_BOOL;
+        public static TF_DataType @bool = TF_DataType.TF_BOOL;
         public static TF_DataType chars = TF_DataType.TF_STRING;
 
         public static Context context = new Context(new ContextOptions(), new Status());

test/TensorFlowNET.Examples/TextProcess/CnnTextClassification.cs

@@ -40,43 +40,157 @@ namespace TensorFlowNET.Examples
         
         protected float loss_value = 0;
         int vocabulary_size = 50000;
+        NDArray train_x, valid_x, train_y, valid_y;
 
         public bool Run()
         {
             PrepareData();
 
-            var graph = tf.Graph().as_default();
-            return with(tf.Session(graph), sess =>
+            Train();
+
+            return true;
+        }
+
+        // TODO: this originally is an SKLearn utility function. it randomizes train and test which we don't do here
+        private (NDArray, NDArray, NDArray, NDArray) train_test_split(NDArray x, NDArray y, float test_size = 0.3f)
+        {
+            Console.WriteLine("Splitting in Training and Testing data...");
+            int len = x.shape[0];
+            //int classes = y.Data<int>().Distinct().Count();
+            //int samples = len / classes;
+            int train_size = (int)Math.Round(len * (1 - test_size));
+            var train_x = x[new Slice(stop: train_size), new Slice()];
+            var valid_x = x[new Slice(start: train_size), new Slice()];
+            var train_y = y[new Slice(stop: train_size)];
+            var valid_y = y[new Slice(start: train_size)];
+            Console.WriteLine("\tDONE");
+            return (train_x, valid_x, train_y, valid_y);
+        }
+
+        private static void FillWithShuffledLabels(int[][] x, int[] y, int[][] shuffled_x, int[] shuffled_y, Random random, Dictionary<int, HashSet<int>> labels)
+        {
+            int i = 0;
+            var label_keys = labels.Keys.ToArray();
+            while (i < shuffled_x.Length)
             {
-                if (IsImportingGraph)
-                    return RunWithImportedGraph(sess, graph);
-                else
-                    return RunWithBuiltGraph(sess, graph);
-            });
+                var key = label_keys[random.Next(label_keys.Length)];
+                var set = labels[key];
+                var index = set.First();
+                if (set.Count == 0)
+                {
+                    labels.Remove(key); // remove the set as it is empty
+                    label_keys = labels.Keys.ToArray();
+                }
+                shuffled_x[i] = x[index];
+                shuffled_y[i] = y[index];
+                i++;
+            }
         }
 
-        protected virtual bool RunWithImportedGraph(Session sess, Graph graph)
+        private IEnumerable<(NDArray, NDArray, int)> batch_iter(NDArray inputs, NDArray outputs, int batch_size, int num_epochs)
         {
-            var stopwatch = Stopwatch.StartNew();
+            var num_batches_per_epoch = (len(inputs) - 1) / batch_size + 1;
+            var total_batches = num_batches_per_epoch * num_epochs;
+            foreach (var epoch in range(num_epochs))
+            {
+                foreach (var batch_num in range(num_batches_per_epoch))
+                {
+                    var start_index = batch_num * batch_size;
+                    var end_index = Math.Min((batch_num + 1) * batch_size, len(inputs));
+                    if (end_index <= start_index)
+                        break;
+                    yield return (inputs[new Slice(start_index, end_index)], outputs[new Slice(start_index, end_index)], total_batches);
+                }
+            }
+        }
+
+        public void PrepareData()
+        {
+            // full dataset https://github.com/le-scientifique/torchDatasets/raw/master/dbpedia_csv.tar.gz
+            var url = "https://raw.githubusercontent.com/SciSharp/TensorFlow.NET/master/data/dbpedia_subset.zip";
+            Web.Download(url, dataDir, "dbpedia_subset.zip");
+            Compress.UnZip(Path.Combine(dataDir, "dbpedia_subset.zip"), Path.Combine(dataDir, "dbpedia_csv"));
+
             Console.WriteLine("Building dataset...");
-            int[][] x = null;
-            int[] y = null;
+
             int alphabet_size = 0;
 
             var word_dict = DataHelpers.build_word_dict(TRAIN_PATH);
-            // vocabulary_size = len(word_dict);
-            (x, y) = DataHelpers.build_word_dataset(TRAIN_PATH, word_dict, WORD_MAX_LEN);
+            vocabulary_size = len(word_dict);
+            var (x, y) = DataHelpers.build_word_dataset(TRAIN_PATH, word_dict, WORD_MAX_LEN);
 
             Console.WriteLine("\tDONE ");
 
             var (train_x, valid_x, train_y, valid_y) = train_test_split(x, y, test_size: 0.15f);
             Console.WriteLine("Training set size: " + train_x.len);
             Console.WriteLine("Test set size: " + valid_x.len);
+        }
 
-            Console.WriteLine("Import graph...");
+        public Graph ImportGraph()
+        {
+            var graph = tf.Graph().as_default();
+
+            // download graph meta data
             var meta_file = "word_cnn.meta";
+            var meta_path = Path.Combine("graph", meta_file);
+            if (File.GetLastWriteTime(meta_path) < new DateTime(2019, 05, 11))
+            {
+                // delete old cached file which contains errors
+                Console.WriteLine("Discarding cached file: " + meta_path);
+                File.Delete(meta_path);
+            }
+            var url = "https://raw.githubusercontent.com/SciSharp/TensorFlow.NET/master/graph/" + meta_file;
+            Web.Download(url, "graph", meta_file);
+
+            Console.WriteLine("Import graph...");
             tf.train.import_meta_graph(Path.Join("graph", meta_file));
-            Console.WriteLine("\tDONE " + stopwatch.Elapsed);
+            Console.WriteLine("\tDONE ");
+
+            return graph;
+        }
+
+        public Graph BuildGraph()
+        {
+            var graph = tf.Graph().as_default();
+
+            var embedding_size = 128;
+            var learning_rate = 0.001f;
+            var filter_sizes = new int[3, 4, 5];
+            var num_filters = 100;
+            var document_max_len = 100;
+
+            var x = tf.placeholder(tf.int32, new TensorShape(-1, document_max_len), name: "x");
+            var y = tf.placeholder(tf.int32, new TensorShape(-1), name: "y");
+            var is_training = tf.placeholder(tf.@bool, new TensorShape(), name: "is_training");
+            var global_step = tf.Variable(0, trainable: false);
+            var keep_prob = tf.where(is_training, 0.5, 1.0);
+            Tensor x_emb = null;
+
+            with(tf.name_scope("embedding"), scope =>
+            {
+                var init_embeddings = tf.random_uniform(new int[] { vocabulary_size, embedding_size });
+                var embeddings = tf.get_variable("embeddings", initializer: init_embeddings);
+                x_emb = tf.nn.embedding_lookup(embeddings, x);
+                x_emb = tf.expand_dims(x_emb, -1);
+            });
+
+            foreach(var filter_size in filter_sizes)
+            {
+                var conv = tf.layers.conv2d(
+                    x_emb,
+                    filters: num_filters,
+                    kernel_size: new int[] { filter_size, embedding_size },
+                    strides: new int[] { 1, 1 },
+                    padding: "VALID",
+                    activation: tf.nn.relu());
+            }
+
+            return graph;
+        }
+
+        private bool RunWithImportedGraph(Session sess, Graph graph)
+        {
+            var stopwatch = Stopwatch.StartNew();
 
             sess.run(tf.global_variables_initializer());
             var saver = tf.train.Saver(tf.global_variables());
@@ -149,107 +263,12 @@ namespace TensorFlowNET.Examples
             return false;
         }
 
-        protected virtual bool RunWithBuiltGraph(Session session, Graph graph)
-        {
-            Console.WriteLine("Building dataset...");
-            var (x, y, alphabet_size) = DataHelpers.build_char_dataset("train", "word_cnn", CHAR_MAX_LEN, DataLimit);
-
-            var (train_x, valid_x, train_y, valid_y) = train_test_split(x, y, test_size: 0.15f);
-
-            ITextClassificationModel model = null;
-            // todo train the model
-            return false;
-        }
-
-        // TODO: this originally is an SKLearn utility function. it randomizes train and test which we don't do here
-        private (NDArray, NDArray, NDArray, NDArray) train_test_split(NDArray x, NDArray y, float test_size = 0.3f)
-        {
-            Console.WriteLine("Splitting in Training and Testing data...");
-            int len = x.shape[0];
-            //int classes = y.Data<int>().Distinct().Count();
-            //int samples = len / classes;
-            int train_size = (int)Math.Round(len * (1 - test_size));
-            var train_x = x[new Slice(stop: train_size), new Slice()];
-            var valid_x = x[new Slice(start: train_size), new Slice()];
-            var train_y = y[new Slice(stop: train_size)];
-            var valid_y = y[new Slice(start: train_size)];
-            Console.WriteLine("\tDONE");
-            return (train_x, valid_x, train_y, valid_y);
-        }
-
-        private static void FillWithShuffledLabels(int[][] x, int[] y, int[][] shuffled_x, int[] shuffled_y, Random random, Dictionary<int, HashSet<int>> labels)
-        {
-            int i = 0;
-            var label_keys = labels.Keys.ToArray();
-            while (i < shuffled_x.Length)
-            {
-                var key = label_keys[random.Next(label_keys.Length)];
-                var set = labels[key];
-                var index = set.First();
-                if (set.Count == 0)
-                {
-                    labels.Remove(key); // remove the set as it is empty
-                    label_keys = labels.Keys.ToArray();
-                }
-                shuffled_x[i] = x[index];
-                shuffled_y[i] = y[index];
-                i++;
-            }
-        }
-
-        private IEnumerable<(NDArray, NDArray, int)> batch_iter(NDArray inputs, NDArray outputs, int batch_size, int num_epochs)
-        {
-            var num_batches_per_epoch = (len(inputs) - 1) / batch_size + 1;
-            var total_batches = num_batches_per_epoch * num_epochs;
-            foreach (var epoch in range(num_epochs))
-            {
-                foreach (var batch_num in range(num_batches_per_epoch))
-                {
-                    var start_index = batch_num * batch_size;
-                    var end_index = Math.Min((batch_num + 1) * batch_size, len(inputs));
-                    if (end_index <= start_index)
-                        break;
-                    yield return (inputs[new Slice(start_index, end_index)], outputs[new Slice(start_index, end_index)], total_batches);
-                }
-            }
-        }
-
-        public void PrepareData()
-        {
-            // full dataset https://github.com/le-scientifique/torchDatasets/raw/master/dbpedia_csv.tar.gz
-            var url = "https://raw.githubusercontent.com/SciSharp/TensorFlow.NET/master/data/dbpedia_subset.zip";
-            Web.Download(url, dataDir, "dbpedia_subset.zip");
-            Compress.UnZip(Path.Combine(dataDir, "dbpedia_subset.zip"), Path.Combine(dataDir, "dbpedia_csv"));
-
-            if (IsImportingGraph)
-            {
-                // download graph meta data
-                var meta_file = "word_cnn.meta";
-                var meta_path = Path.Combine("graph", meta_file);
-                if (File.GetLastWriteTime(meta_path) < new DateTime(2019, 05, 11))
-                {
-                    // delete old cached file which contains errors
-                    Console.WriteLine("Discarding cached file: " + meta_path);
-                    File.Delete(meta_path);
-                }
-                url = "https://raw.githubusercontent.com/SciSharp/TensorFlow.NET/master/graph/" + meta_file;
-                Web.Download(url, "graph", meta_file);
-            }
-        }
-
-        public Graph ImportGraph()
-        {
-            throw new NotImplementedException();
-        }
-
-        public Graph BuildGraph()
-        {
-            throw new NotImplementedException();
-        }
-
         public bool Train()
         {
-            throw new NotImplementedException();
+            var graph = IsImportingGraph ? ImportGraph() : BuildGraph();
+
+            return with(tf.Session(graph), sess 
+                => RunWithImportedGraph(sess, graph));
         }
 
         public bool Predict()

test/TensorFlowNET.Examples/TextProcess/cnn_models/VdCnn.cs

@@ -40,7 +40,7 @@ namespace TensorFlowNET.Examples.TextClassification
 
             x = tf.placeholder(tf.int32, new TensorShape(-1, document_max_len), name: "x");
             y = tf.placeholder(tf.int32, new TensorShape(-1), name: "y");
-            is_training = tf.placeholder(tf.boolean, new TensorShape(), name: "is_training");
+            is_training = tf.placeholder(tf.@bool, new TensorShape(), name: "is_training");
             global_step = tf.Variable(0, trainable: false);
 
             // Embedding Layer