Finish SimpleRNNCell and add test

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

@@ -206,7 +206,9 @@ namespace Tensorflow.Keras.Layers
             bool use_bias = true,
             string kernel_initializer = "glorot_uniform",
             string recurrent_initializer = "orthogonal",
-            string bias_initializer = "zeros");
+            string bias_initializer = "zeros",
+            float dropout = 0f,
+            float recurrent_dropout = 0f);
 
         public ILayer Subtract();
     }

src/TensorFlowNET.Core/Operations/gen_math_ops.cs

@@ -4633,8 +4633,9 @@ public static class gen_math_ops
                 var _fast_path_result = tf.Runner.TFE_FastPathExecute(new FastPathOpExecInfo(_ctx, "MatMul", name) { args = new object[] { a, b }, attrs = new Dictionary<string, object>() { ["transpose_a"] = transpose_a, ["transpose_b"] = transpose_b } });
                 return _fast_path_result[0];
             }
-            catch (Exception)
+            catch (ArgumentException)
             {
+                throw new ArgumentException("In[0] and In[1] has diffrent ndims!");
             }
             try
             {

src/TensorFlowNET.Keras/Layers/LayersApi.cs

@@ -715,7 +715,9 @@ namespace Tensorflow.Keras.Layers
             bool use_bias = true,
             string kernel_initializer = "glorot_uniform",
             string recurrent_initializer = "orthogonal",
-            string bias_initializer = "zeros")
+            string bias_initializer = "zeros",
+            float dropout = 0f,
+            float recurrent_dropout = 0f)
                 => new SimpleRNNCell(new SimpleRNNArgs
                 {
                     Units = units,
@@ -723,6 +725,8 @@ namespace Tensorflow.Keras.Layers
                     UseBias = use_bias,
                     KernelInitializer = GetInitializerByName(kernel_initializer),
                     RecurrentInitializer = GetInitializerByName(recurrent_initializer),
+                    Dropout = dropout,
+                    RecurrentDropout = recurrent_dropout
                 }
                 );
 

src/TensorFlowNET.Keras/Layers/Rnn/DropOutRNNCellMixin.cs

@@ -13,9 +13,10 @@ namespace Tensorflow.Keras.Layers.Rnn
         public float dropout;
         public float recurrent_dropout;
         // Get the dropout mask for RNN cell's input.
-        public Tensors get_dropout_maskcell_for_cell(Tensors input, bool training, int count = 1)
+        public Tensors? get_dropout_maskcell_for_cell(Tensors input, bool training, int count = 1)
         {
-
+            if (dropout == 0f)
+                return null;
             return _generate_dropout_mask(
                 tf.ones_like(input),
                 dropout,
@@ -24,8 +25,10 @@ namespace Tensorflow.Keras.Layers.Rnn
         }
 
         // Get the recurrent dropout mask for RNN cell.
-        public Tensors get_recurrent_dropout_maskcell_for_cell(Tensors input, bool training, int count = 1)
+        public Tensors? get_recurrent_dropout_maskcell_for_cell(Tensors input, bool training, int count = 1)
         {
+            if (dropout == 0f)
+                return null;
             return _generate_dropout_mask(
                 tf.ones_like(input),
                 recurrent_dropout,

src/TensorFlowNET.Keras/Layers/Rnn/SimpleRNNCell.cs

@@ -58,10 +58,7 @@ namespace Tensorflow.Keras.Layers.Rnn
 
         protected override Tensors Call(Tensors inputs, Tensor mask = null, bool? training = null, Tensors initial_state = null, Tensors constants = null)
         {
-            Console.WriteLine($"shape of input: {inputs.shape}");
             Tensor states = initial_state[0];
-            Console.WriteLine($"shape of initial_state: {states.shape}");
-
             var prev_output = nest.is_nested(states) ? states[0] : states;
             var dp_mask = DRCMixin.get_dropout_maskcell_for_cell(inputs, training.Value);
             var rec_dp_mask = DRCMixin.get_recurrent_dropout_maskcell_for_cell(prev_output, training.Value);
@@ -72,11 +69,12 @@ namespace Tensorflow.Keras.Layers.Rnn
             {
                 if (ranks > 2)
                 {
-                    h = tf.linalg.tensordot(tf.multiply(inputs, dp_mask), kernel.AsTensor(), new[,] { { ranks - 1 }, { 0 } });
+                    // 因为multiply函数会自动添加第一个维度，所以加上下标0
+                    h = tf.linalg.tensordot(math_ops.multiply(inputs, dp_mask)[0], kernel.AsTensor(), new[,] { { ranks - 1 }, { 0 } });
                 }
                 else
                 {
-                    h = math_ops.matmul(tf.multiply(inputs, dp_mask), kernel.AsTensor());
+                    h = math_ops.matmul(math_ops.multiply(inputs, dp_mask)[0], kernel.AsTensor());
                 }
             }
             else
@@ -98,22 +96,18 @@ namespace Tensorflow.Keras.Layers.Rnn
 
             if (rec_dp_mask != null)
             {
-                prev_output = tf.multiply(prev_output, rec_dp_mask);
+                prev_output = math_ops.multiply(prev_output, rec_dp_mask)[0];
             }
 
             ranks = prev_output.rank;
-            Console.WriteLine($"shape of h: {h.shape}");
-
             Tensor output;
             if (ranks > 2)
             {
-                var tmp = tf.linalg.tensordot(prev_output, recurrent_kernel.AsTensor(), new[,] { { ranks - 1 }, { 0 } });
-                output = h + tf.linalg.tensordot(prev_output, recurrent_kernel.AsTensor(), new[,] { { ranks - 1 }, { 0 } })[0];
+                output = h + tf.linalg.tensordot(prev_output[0], recurrent_kernel.AsTensor(), new[,] { { ranks - 1 }, { 0 } });
             }
             else
             {
-                output = h + math_ops.matmul(prev_output, recurrent_kernel.AsTensor())[0];
-
+                output = h + math_ops.matmul(prev_output, recurrent_kernel.AsTensor());
             }
             Console.WriteLine($"shape of output: {output.shape}");
 

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

@@ -147,13 +147,15 @@ namespace Tensorflow.Keras.UnitTest.Layers
         [TestMethod]
         public void SimpleRNNCell()
         {
+            var cell = keras.layers.SimpleRNNCell(64, dropout:0.5f, recurrent_dropout:0.5f);
             var h0 = new Tensors { tf.zeros(new Shape(4, 64)) };
-            var x = tf.random.normal(new Shape(4, 100));
-            var cell = keras.layers.SimpleRNNCell(64);
-            var (y, h1) = cell.Apply(inputs:x, state:h0);
+            var x = tf.random.normal((4, 100));
+            var (y, h1) = cell.Apply(inputs: x, state: h0);
+            // TODO(Wanglongzhi2001)，因为SimpleRNNCell需要返回一个Tensor和一个Tensors，只用一个Tensors的话
+            // hold不住，所以自行在外面将h强制转换成Tensors
+            var h2 = (Tensors)h1;
             Assert.AreEqual((4, 64), y.shape);
-            // this test now cannot pass, need to deal with SimpleRNNCell's Call method
-            //Assert.AreEqual((4, 64), h1[0].shape);
+            Assert.AreEqual((4, 64), h2[0].shape);
         }
 
         [TestMethod, Ignore("WIP")]