Add Custom Keras Layer Test

This test is written based on Dueliing DQN' s network structure.

Author: lsylusiyao

test/TensorFlowNET.Keras.UnitTest/Layers/LayersTest.cs

@@ -35,6 +35,72 @@ public void Functional()
             var model = keras.Model(inputs, outputs, name: "mnist_model");
             model.summary();
         }
+        
+        /// <summary>
+        /// Custom layer test, used in Dueling DQN
+        /// </summary>
+        [TestMethod, Ignore]
+        public void FunctionalTest()
+        {
+            var layers = keras.layers;
+            var inputs = layers.Input(shape: 24);
+            var x = layers.Dense(128, activation:"relu").Apply(inputs);
+            var value = layers.Dense(24).Apply(x);
+            var adv = layers.Dense(1).Apply(x);
+            
+            var adv_out = adv - Binding.tf.reduce_mean(adv, axis: 1, keepdims: true); // Here's problem.
+            var outputs = layers.Add().Apply(new Tensors(adv_out, value));
+            var model = keras.Model(inputs, outputs);
+            model.summary();
+            model.compile(optimizer: keras.optimizers.RMSprop(0.001f),
+                          loss: keras.losses.MeanSquaredError(),
+                          metrics: new[] { "acc" });
+            // Here we consider the adv_out is one layer, which is a little different from py's version
+            Assert.AreEqual(model.Layers.Count, 6);
+
+            // py code:
+            //from tensorflow.keras.layers import Input, Dense, Add, Subtract, Lambda
+            //from tensorflow.keras.models import Model
+            //from tensorflow.keras.optimizers import RMSprop
+            //import tensorflow.keras.backend as K
+
+            //inputs = Input(24)
+            //x = Dense(128, activation = "relu")(inputs)
+            //value = Dense(24)(x)
+            //adv = Dense(1)(x)
+            //meam = Lambda(lambda x: K.mean(x, axis = 1, keepdims = True))(adv)
+            //adv = Subtract()([adv, meam])
+            //outputs = Add()([value, adv])
+            //model = Model(inputs, outputs)
+            //model.compile(loss = "mse", optimizer = RMSprop(1e-3))
+            //model.summary()
+
+            //py output:
+            //Model: "functional_3"
+            //__________________________________________________________________________________________________
+            //Layer(type)                    Output Shape         Param #     Connected to
+            //==================================================================================================
+            //input_2 (InputLayer)            [(None, 24)]         0
+            //__________________________________________________________________________________________________
+            //dense_3 (Dense)                 (None, 128)          3200        input_2[0][0]
+            //__________________________________________________________________________________________________
+            //dense_5 (Dense)                 (None, 1)            129         dense_3[0][0]
+            //__________________________________________________________________________________________________
+            //lambda_1 (Lambda)               (None, 1)            0           dense_5[0][0]
+            //__________________________________________________________________________________________________
+            //dense_4 (Dense)                 (None, 24)           3096        dense_3[0][0]
+            //__________________________________________________________________________________________________
+            //subtract_1 (Subtract)           (None, 1)            0           dense_5[0][0]
+            //                                                                 lambda_1[0][0]
+            //__________________________________________________________________________________________________
+            //add_1 (Add)                     (None, 24)           0           dense_4[0][0]
+            //                                                                 subtract_1[0][0]
+            //==================================================================================================
+            //Total params: 6,425
+            //Trainable params: 6,425
+            //Non-trainable params: 0
+            //__________________________________________________________________________________________________
+        }
 
         /// <summary>
         /// https://www.tensorflow.org/api_docs/python/tf/keras/layers/Embedding