fix name issue in placeholder.

control_flow_ops.switch.

Author: Oceania2018

src/TensorFlowNET.Core/Framework/smart_module.cs

@@ -0,0 +1,23 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace Tensorflow.Framework
+{
+    public class smart_module
+    {
+        public static object smart_cond(Tensor pred, Action true_fn = null, Action false_fn = null, string name = null)
+        {
+            return control_flow_ops.cond(pred,
+                true_fn: true_fn,
+                false_fn: false_fn,
+                name: name);
+        }
+
+        public static bool smart_constant_value(Tensor pred)
+        {
+            var pred_value = tensor_util.constant_value(pred);
+            return pred_value;
+        }
+    }
+}

src/TensorFlowNET.Core/Graphs/Graph.cs

@@ -20,7 +20,7 @@ public partial class Graph : IPython, IDisposable
         private Dictionary<string, int> _names_in_use;
         public int _version;
         private int _next_id_counter;
-        private List<String> _unfetchable_ops = new List<string>();
+        private List<Operation> _unfetchable_ops = new List<Operation>();
         private List<Tensor> _unfeedable_tensors = new List<Tensor>();
 
         public string _name_stack = "";
@@ -228,13 +228,13 @@ public int _next_id()
 
         public bool is_fetchable<T>(T tensor_or_op)
         {
-            if (tensor_or_op is Tensor)
+            if (tensor_or_op is Tensor tensor)
             {
-                return !_unfetchable_ops.Contains((tensor_or_op as Tensor).name); ;
+                return !_unfetchable_ops.Contains(tensor); ;
             }
-            else if (tensor_or_op is Operation)
+            else if (tensor_or_op is Operation op)
             {
-                return !_unfetchable_ops.Contains((tensor_or_op as Operation).name);
+                return !_unfetchable_ops.Contains(op);
             }
 
             return false;
@@ -372,6 +372,11 @@ public void prevent_feeding(Tensor tensor)
             _unfeedable_tensors.Add(tensor);
         }
 
+        public void prevent_fetching(Operation op)
+        {
+            _unfetchable_ops.Add(op);
+        }
+        
         public void Dispose()
         {
             c_api.TF_DeleteGraph(_handle);

src/TensorFlowNET.Core/Keras/Engine/Layer.cs

@@ -48,6 +48,7 @@ public Layer(bool trainable = true, string name = null, TF_DataType dtype = TF_D
         }
 
         public Tensor __call__(Tensor inputs,
+            Tensor training = null,
             VariableScope scope = null)
         {
             var input_list = new Tensor[] { inputs };
@@ -73,7 +74,7 @@ public Tensor __call__(Tensor inputs,
                     // Symbolic execution on symbolic tensors. We will attempt to build
                     // the corresponding TF subgraph inside `backend.get_graph()`
                     var graph = backend.get_graph();
-                    outputs = call(inputs);
+                    outputs = call(inputs, training: training);
                     _handle_activity_regularization(inputs, outputs);
                     _set_mask_metadata(inputs, outputs, null);
                 }
@@ -100,7 +101,7 @@ private Tensor compute_mask(Tensor inputs, Tensor mask = null)
             return null;
         }
 
-        protected virtual Tensor call(Tensor inputs)
+        protected virtual Tensor call(Tensor inputs, Tensor training = null)
         {
             throw new NotImplementedException("Layer.call");
         }
@@ -143,13 +144,15 @@ protected virtual RefVariable add_weight(string name,
 
         protected virtual void _init_set_name(string name)
         {
-            if (string.IsNullOrEmpty(name))
-                (_name, _base_name) = _make_unique_name();
+            string base_name = name;
+            if (name == null)
+                (_name, base_name) = _make_unique_name();
+            _base_name = base_name;
         }
 
         protected virtual (string, string) _make_unique_name()
         {
-            string base_name = "conv2d";
+            string base_name = generic_utils.to_snake_case(this.GetType().Name);
             string name = base_layer_utils.unique_layer_name(base_name);
             return (name, base_name);
         }

src/TensorFlowNET.Core/Keras/Layers/BatchNormalization.cs

@@ -2,6 +2,7 @@
 using System.Collections.Generic;
 using System.Linq;
 using System.Text;
+using Tensorflow.Keras.Utils;
 using Tensorflow.Layers;
 
 namespace Tensorflow.Keras.Layers
@@ -25,6 +26,7 @@ public class BatchNormalization : Layer
         private RefVariable gamma;
         private RefVariable beta;
         private RefVariable moving_mean;
+        private RefVariable moving_variance;
 
         public BatchNormalization(int axis = -1,
             float momentum = 0.99f,
@@ -103,7 +105,56 @@ protected override void build(TensorShape input_shape)
 
             moving_mean = add_weight("moving_mean",
                 param_shape,
-                dtype: param_dtype);
+                dtype: param_dtype,
+                initializer: moving_mean_initializer,
+                synchronization: VariableSynchronization.ON_READ,
+                trainable: false,
+                aggregation: VariableAggregation.MEAN);
+
+            moving_variance = add_weight("moving_variance",
+              shape: param_shape,
+              dtype: param_dtype,
+              initializer: moving_variance_initializer,
+              synchronization: VariableSynchronization.ON_READ,
+              trainable: false,
+              aggregation: VariableAggregation.MEAN);
+
+            if (renorm)
+                throw new NotImplementedException("build when renorm is true");
+
+            built = true;
+        }
+
+        protected override Tensor call(Tensor inputs, Tensor training = null)
+        {
+            Tensor outputs = null;
+
+            if (fused)
+            {
+                outputs = _fused_batch_norm(inputs, training: training);
+            }
+
+            throw new NotImplementedException("BatchNormalization call");
+        }
+
+        private Tensor _fused_batch_norm(Tensor inputs, Tensor training)
+        {
+            var beta = this.beta;
+            var gamma = this.gamma;
+
+            Action _fused_batch_norm_training = () =>
+            {
+
+            };
+
+            Action _fused_batch_norm_inference = () =>
+            {
+
+            };
+
+            tf_utils.smart_cond(training, _fused_batch_norm_training, _fused_batch_norm_inference);
+
+            throw new NotImplementedException("_fused_batch_norm");
         }
     }
 }

src/TensorFlowNET.Core/Keras/Layers/Conv.cs

@@ -91,7 +91,7 @@ protected override void build(TensorShape input_shape)
             built = true;
         }
 
-        protected override Tensor call(Tensor inputs)
+        protected override Tensor call(Tensor inputs, Tensor training = null)
         {
             var outputs = _convolution_op.__call__(inputs, kernel);
             if (use_bias)

src/TensorFlowNET.Core/Keras/Utils/generic_utils.cs

@@ -0,0 +1,20 @@
+using System;
+using System.Collections.Generic;
+using System.Linq;
+using System.Text;
+
+namespace Tensorflow.Keras.Utils
+{
+    public class generic_utils
+    {
+        public static string to_snake_case(string name)
+        {
+            return string.Concat(name.Select((x, i) =>
+            {
+                return i > 0 && char.IsUpper(x) && !Char.IsDigit(name[i - 1]) ?
+                    "_" + x.ToString() :
+                    x.ToString();
+            })).ToLower();
+        }
+    }
+}

src/TensorFlowNET.Core/Keras/Utils/tf_utils.cs

@@ -2,6 +2,7 @@
 using System.Collections.Generic;
 using System.Linq;
 using System.Text;
+using Tensorflow.Framework;
 
 namespace Tensorflow.Keras.Utils
 {
@@ -16,5 +17,13 @@ public static bool is_symbolic_tensor(Tensor tensor)
         {
             return true;
         }
+
+        public static object smart_cond(Tensor pred, Action true_fn = null, Action false_fn = null, string name = null)
+        {
+            return smart_module.smart_cond(pred,
+                true_fn: true_fn,
+                false_fn: false_fn,
+                name: name);
+        }
     }
 }

src/TensorFlowNET.Core/Layers/Layer.cs

@@ -29,10 +29,11 @@ public Layer(bool trainable = true,
 
         public virtual Tensor apply(Tensor inputs, Tensor training = null)
         {
-            return __call__(inputs);
+            return __call__(inputs, training: training);
         }
 
         public Tensor __call__(Tensor inputs,
+            Tensor training = null,
             VariableScope scope = null)
         {
             _set_scope(scope);
@@ -51,7 +52,7 @@ public Tensor __call__(Tensor inputs,
 
             Python.with(scope_context_manager, scope2 => _current_scope = scope2);
             // Actually call layer
-            var outputs = base.__call__(inputs);
+            var outputs = base.__call__(inputs, training: training);
 
             // Update global default collections.
             //_add_elements_to_collection(updates, ops.GraphKeys.UPDATE_OPS);
@@ -63,7 +64,9 @@ protected virtual RefVariable add_weight(string name,
             int[] shape,
             TF_DataType dtype = TF_DataType.DtInvalid,
             IInitializer initializer = null,
-            bool? trainable = null)
+            bool? trainable = null,
+            VariableSynchronization synchronization = VariableSynchronization.AUTO,
+            VariableAggregation aggregation = VariableAggregation.NONE)
         {
             var default_graph = ops.get_default_graph();
             Graph init_graph = null;

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

@@ -135,5 +135,53 @@ public static Tensor _Identity(Tensor data, string name = null)
             else
                 return gen_array_ops.identity(data, name: name);
         }
+
+        public static (Tensor, Tensor) cond(Tensor pred, 
+            Action true_fn = null, 
+            Action false_fn = null,
+            bool strict = false,
+            string name = null)
+        {
+            return with(ops.name_scope(name, "cond", new { pred }), delegate
+            {
+                // Add the Switch to the graph.
+                var (p_2, p_1) = @switch(pred, pred);
+                var pivot_1 = array_ops.identity(p_1, name: "switch_t");
+                var pivot_2 = array_ops.identity(p_2, name: "switch_f");
+                pred = array_ops.identity(pred, name: "pred_id");
+
+                // Disable the fetching of tensors that are only on one branch of cond.
+                foreach (var tensor in new Tensor[] { p_1, p_2, pivot_1, pivot_2, pred })
+                    tensor.op.graph.prevent_fetching(tensor.op);
+
+                return (p_2, p_1);
+            });
+        }
+
+        /// <summary>
+        /// Forwards `data` to an output determined by `pred`.
+        /// </summary>
+        /// <param name="data"></param>
+        /// <param name="pred"></param>
+        /// <param name="dtype"></param>
+        /// <param name="name"></param>
+        public static (Tensor, Tensor) @switch(Tensor data, 
+            Tensor pred, 
+            TF_DataType dtype = TF_DataType.DtInvalid, 
+            string name = null)
+        {
+            return with(ops.name_scope(name, "Switch", new { data, pred }), scope =>
+            {
+                name = scope;
+                data = ops.internal_convert_to_tensor_or_indexed_slices(data,
+                    dtype: dtype,
+                    name: "data",
+                    as_ref: true);
+
+                pred = ops.convert_to_tensor(pred, name: "pred");
+
+                return gen_control_flow_ops.@switch(data, pred, name: name);
+            });
+        }
     }
 }

src/TensorFlowNET.Core/Operations/gen_array_ops.cs

@@ -42,7 +42,7 @@ public static Tensor less<Tx, Ty>(Tx x, Ty y, string name = null)
 
         public static Tensor placeholder(TF_DataType dtype, TensorShape shape = null, string name = null)
         {
-            var _op = _op_def_lib._apply_op_helper("Placeholder", args: new { dtype, shape });
+            var _op = _op_def_lib._apply_op_helper("Placeholder", name: name, args: new { dtype, shape });
             var _result = _op.outputs;
             var _inputs_flat = _op.inputs;