tf.while_loop #348

Author: Oceania2018

src/TensorFlowNET.Core/Device/c_api.device.cs

@@ -0,0 +1,32 @@
+/*****************************************************************************
+   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 System;
+using System.Runtime.InteropServices;
+
+namespace Tensorflow
+{
+    public partial class c_api
+    {
+        /// <summary>
+        /// Specify the device for `desc`.  Defaults to empty, meaning unconstrained.
+        /// </summary>
+        /// <param name="desc"></param>
+        /// <param name="device"></param>
+        [DllImport(TensorFlowLibName)]
+        public static extern void TF_SetDevice(IntPtr desc, string device);
+    }
+}

src/TensorFlowNET.Core/Graphs/_ControlDependenciesController.cs

@@ -69,7 +69,9 @@ public _ControlDependenciesController(Graph graph, List<ITensorOrOperation> cont
                 _new_stack = false;
             }
 
-            _seen_nodes = new List<ITensorOrOperation>();
+            _seen_nodes = new List<ITensorOrOperation>();
+            _old_stack = null;
+            _old_control_flow_context = null;
         }
 
         public void add_op(ITensorOrOperation op)

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

@@ -139,7 +139,7 @@ protected override void build(TensorShape input_shape)
             built = true;
         }
 
-        protected override (Tensor, Tensor) call(Tensor inputs, Tensor training = null)
+        protected override (Tensor, Tensor) call(Tensor inputs, Tensor training = null, Tensor state = null)
         {
             Tensor outputs = null;
 

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

@@ -108,7 +108,7 @@ protected override void build(TensorShape input_shape)
             built = true;
         }
 
-        protected override (Tensor, Tensor) call(Tensor inputs, Tensor training = null)
+        protected override (Tensor, Tensor) call(Tensor inputs, Tensor training = null, Tensor state = null)
         {
             var outputs = _convolution_op.__call__(inputs, kernel);
             if (use_bias)

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

@@ -72,7 +72,7 @@ protected override void build(TensorShape input_shape)
             built = true;
         }
 
-        protected override (Tensor, Tensor) call(Tensor inputs, Tensor training = null)
+        protected override (Tensor, Tensor) call(Tensor inputs, Tensor training = null, Tensor state = null)
         {
             Tensor outputs = null;
             var rank = inputs.rank;

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

@@ -50,7 +50,7 @@ protected override void build(TensorShape input_shape)
             built = true;
         }
 
-        protected override (Tensor, Tensor) call(Tensor inputs, Tensor training = null)
+        protected override (Tensor, Tensor) call(Tensor inputs, Tensor training = null, Tensor state = null)
         {
             var dtype = inputs.dtype;
             if (dtype != tf.int32 && dtype != tf.int64)

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

@@ -52,6 +52,7 @@ public class Layer : AutoTrackable
         protected InputSpec input_spec;
         protected bool supports_masking;
         protected List<VariableV1> _trainable_weights;
+        protected List<VariableV1> _non_trainable_weights;
         private string _name;
         public string name => _name;
         protected string _base_name;
@@ -84,6 +85,7 @@ public Layer(bool trainable = true,
 
             _init_set_name(name);
             _trainable_weights = new List<VariableV1>();
+            _non_trainable_weights = new List<VariableV1>();
             _compute_previous_mask = false;
             _updates = new List<Operation>();
 
@@ -103,6 +105,7 @@ public Layer(bool trainable = true,
 
         public (Tensor, Tensor) __call__(Tensor[] inputs,
             Tensor training = null,
+            Tensor state = null,
             VariableScope scope = null)
         {
             var input_list = inputs;
@@ -139,7 +142,9 @@ public Layer(bool trainable = true,
                     // overridden).
                     _maybe_build(inputs[0]);
 
-                    (input, outputs) = call(inputs[0], training: training);
+                    (input, outputs) = call(inputs[0], 
+                        training: training,
+                        state: state);
                     (input, outputs) = _set_connectivity_metadata_(input, outputs);
                     _handle_activity_regularization(inputs[0], outputs);
                     _set_mask_metadata(inputs[0], outputs, null);
@@ -173,7 +178,7 @@ private Tensor compute_mask(Tensor inputs, Tensor mask = null)
             return null;
         }
 
-        protected virtual (Tensor, Tensor) call(Tensor inputs, Tensor training = null)
+        protected virtual (Tensor, Tensor) call(Tensor inputs, Tensor training = null, Tensor state = null)
         {
             return (inputs, inputs);
         }
@@ -233,7 +238,10 @@ protected virtual VariableV1 add_weight(string name,
                 initializer: initializer,
                 trainable: trainable.Value);
             //backend.track_variable(variable);
-            _trainable_weights.Add(variable);
+            if (trainable == true)
+                _trainable_weights.Add(variable);
+            else
+                _non_trainable_weights.Add(variable);
 
             return variable;
         }

src/TensorFlowNET.Core/Keras/Layers/Pooling2D.cs

@@ -43,7 +43,7 @@ public Pooling2D(IPoolFunction pool_function,
             this.input_spec = new InputSpec(ndim: 4);
         }
 
-        protected override (Tensor, Tensor) call(Tensor inputs, Tensor training = null)
+        protected override (Tensor, Tensor) call(Tensor inputs, Tensor training = null, Tensor state = null)
         {
             int[] pool_shape;
             if (data_format == "channels_last")

src/TensorFlowNET.Core/Layers/Layer.cs

@@ -43,6 +43,7 @@ public Layer(bool trainable = true,
 
             // Avoid an incorrect lint error
             _trainable_weights = new List<VariableV1>();
+            _non_trainable_weights = new List<VariableV1>();
             this.built = false;
             _keras_style = false;
         }
@@ -54,6 +55,7 @@ public virtual (Tensor, Tensor) apply(Tensor inputs, Tensor training = null)
 
         public (Tensor, Tensor) __call__(Tensor inputs,
             Tensor training = null,
+            Tensor state = null,
             VariableScope scope = null)
         {
             _set_scope(scope);
@@ -76,7 +78,9 @@ public virtual (Tensor, Tensor) apply(Tensor inputs, Tensor training = null)
             {
                 _current_scope = scope2;
                 // Actually call layer
-                outputs = base.__call__(new Tensor[] { inputs }, training: training);
+                outputs = base.__call__(new Tensor[] { inputs }, 
+                    training: training,
+                    state: state);
             });
 
 
@@ -121,6 +125,11 @@ protected virtual VariableV1 add_weight(string name,
             Graph init_graph = null;
             VariableV1[] existing_variables = null;
 
+            if (synchronization == VariableSynchronization.OnRead)
+                trainable = false;
+            else if (!trainable.HasValue)
+                trainable = true;
+
             if (default_graph.building_function)
             {
                 throw new NotImplementedException("add_weight");

src/TensorFlowNET.Core/Operations/BasicRNNCell.cs

@@ -66,7 +66,7 @@ protected override void build(TensorShape inputs_shape)
             built = true;
         }
 
-        protected override (Tensor, Tensor) call(Tensor inputs, Tensor state = null)
+        protected override (Tensor, Tensor) call(Tensor inputs, Tensor training = null, Tensor state = null)
         {
             // Most basic RNN: output = new_state = act(W * input + U * state + B).
             var concat = array_ops.concat(new[] { inputs, state }, 1);