Implemented CPU version of tanh

dlib/dnn/cpu_dlib.cpp

@@ -650,8 +650,10 @@ namespace dlib
             const tensor& src
         )
         {
-            // TODO
-            DLIB_CASSERT(false,"");
+            const auto d = dest.host();
+            const auto s = src.host();
+            for (size_t i = 0; i < src.size(); ++i)
+                d[i] = std::tanh(s[i]);
         }
 
         void tanh_gradient (
@@ -660,8 +662,11 @@ namespace dlib
             const tensor& gradient_input
         )
         {
-            // TODO
-            DLIB_CASSERT(false,"");
+            const auto g = grad.host();
+            const auto d = dest.host();
+            const auto in = gradient_input.host();
+            for (size_t i = 0; i < dest.size(); ++i)
+                g[i] = in[i]*(1-d[i]*d[i]);
         }
 
     // ------------------------------------------------------------------------------------

dlib/test/dnn.cpp

@@ -39,6 +39,43 @@ namespace
         return max_error;
     }
 
+// ----------------------------------------------------------------------------------------
+
+    void test_tanh()
+    {
+        print_spinner();
+        resizable_tensor src(5,5), dest(5,5), gradient_input(5,5);
+        src = matrix_cast<float>(gaussian_randm(5,5, 0));
+        dest = matrix_cast<float>(gaussian_randm(5,5, 1));
+        gradient_input = matrix_cast<float>(gaussian_randm(5,5, 2));
+
+
+
+        auto grad_src = [&](long idx) {
+            auto f = [&](float eps) {
+                const float old = src.host()[idx];
+                src.host()[idx] += eps;
+                tanh(dest, src);
+                float result = dot(gradient_input, dest);
+                src.host()[idx] = old;
+                return result;
+            };
+            const float eps = 0.01;
+            return (f(+eps)-f(-eps))/(2*eps);
+        };
+
+        resizable_tensor src_grad;
+        src_grad.copy_size(src);
+        src_grad = 0;
+
+        tanh(dest, src);
+        tanh_gradient(src_grad, dest, gradient_input);
+
+        auto grad_error = compare_gradients(src_grad, grad_src);
+        dlog << LINFO << "src error: " << grad_error;
+        DLIB_TEST(grad_error < 0.001);
+    }
+
     void test_sigmoid()
     {
         print_spinner();
@@ -324,6 +361,7 @@ namespace
         void perform_test (
         )
         {
+            test_tanh();
             test_softmax();
             test_sigmoid();
             test_batch_normalize();