diff --git a/include/kernels/activations.cuh b/include/kernels/activations.cuh
index 5f7935b..cbd91a2 100644
--- a/include/kernels/activations.cuh
+++ b/include/kernels/activations.cuh
@@ -10,4 +10,10 @@ relu_kernel(const float* __restrict__ src, float* __restrict__ dst, int len);
 __global__ void
 linear_kernel(const float* __restrict__ src, float* __restrict__ dst, int len);
 
+enum Activation {
+    SIGMOID,
+    RELU,
+    LINEAR
+};
+
 #endif  // ACTIVATIONS_H
\ No newline at end of file
diff --git a/include/layers/dense.cuh b/include/layers/dense.cuh
index b1d7fbf..12be07d 100644
--- a/include/layers/dense.cuh
+++ b/include/layers/dense.cuh
@@ -16,7 +16,7 @@ class Dense : public ILayer {
     Dense(
         int            inputSize,
         int            outputSize,
-        std::string    activation,
+        Activation     activation,
         cublasHandle_t cublasHandle
     );
     ~Dense();
@@ -37,7 +37,7 @@ class Dense : public ILayer {
     std::vector<float> weights;
     std::vector<float> biases;
 
-    std::string activation;
+    Activation activation;
 
     void initializeWeights();
     void initializeBiases();
diff --git a/src/layers/dense.cu b/src/layers/dense.cu
index 227b53b..2bacd59 100644
--- a/src/layers/dense.cu
+++ b/src/layers/dense.cu
@@ -13,7 +13,7 @@
 Layers::Dense::Dense(
     int            inputSize,
     int            outputSize,
-    std::string    activation,
+    Activation     activation,
     cublasHandle_t cublasHandle
 )
     : inputSize(inputSize),
@@ -68,18 +68,24 @@ void Layers::Dense::forward(const float* d_input, float* d_output) {
     int threadsPerBlock = 256;
     int blocksPerGrid   = (outputSize + threadsPerBlock - 1) / threadsPerBlock;
 
-    if (activation == "sigmoid") {
-        sigmoid_kernel<<<blocksPerGrid, threadsPerBlock>>>(
-            d_output, d_output, outputSize
-        );
-    } else if (activation == "relu") {
-        relu_kernel<<<blocksPerGrid, threadsPerBlock>>>(
-            d_output, d_output, outputSize
-        );
-    } else {
-        linear_kernel<<<blocksPerGrid, threadsPerBlock>>>(
-            d_output, d_output, outputSize
-        );
+    switch (activation) {
+        case SIGMOID:
+            sigmoid_kernel<<<blocksPerGrid, threadsPerBlock>>>(
+                d_output, d_output, outputSize
+            );
+            break;
+
+        case RELU:
+            relu_kernel<<<blocksPerGrid, threadsPerBlock>>>(
+                d_output, d_output, outputSize
+            );
+            break;
+
+        default:
+            linear_kernel<<<blocksPerGrid, threadsPerBlock>>>(
+                d_output, d_output, outputSize
+            );
+            break;
     }
 
     CUDA_CHECK(cudaDeviceSynchronize());
diff --git a/test/layers/test_dense.cu b/test/layers/test_dense.cu
index 8221ca1..323125b 100644
--- a/test/layers/test_dense.cu
+++ b/test/layers/test_dense.cu
@@ -17,7 +17,7 @@ class DenseLayerTest : public CublasTestFixture {
         std::vector<float>&              biases,
         float*&                          d_input,
         float*&                          d_output,
-        std::string                      activation
+        Activation                       activation
     ) {
         // Create Dense layer
         Layers::Dense denseLayer(
@@ -63,7 +63,7 @@ TEST_F(DenseLayerTest, Init) {
             // std::cout << "Dense layer: input size = " << inputSize << ",
             // output size = " << outputSize << std::endl;
             Layers::Dense denseLayer(
-                inputSize, outputSize, "sigmoid", cublasHandle
+                inputSize, outputSize, SIGMOID, cublasHandle
             );
         }
     }
@@ -81,7 +81,7 @@ TEST_F(DenseLayerTest, setWeights) {
         {1.3f, 0.5f, 0.0f, 1.7f}
     };
 
-    Layers::Dense denseLayer(inputSize, outputSize, "sigmoid", cublasHandle);
+    Layers::Dense denseLayer(inputSize, outputSize, SIGMOID, cublasHandle);
 
     denseLayer.setWeights(weights);
 }
@@ -108,8 +108,7 @@ TEST_F(DenseLayerTest, ForwardUnitWeightMatrixLinear) {
     float* d_output;
 
     Layers::Dense denseLayer = commonTestSetup(
-        inputSize, outputSize, input, weights, biases, d_input, d_output,
-        "linear"
+        inputSize, outputSize, input, weights, biases, d_input, d_output, LINEAR
     );
     denseLayer.forward(d_input, d_output);
 
@@ -145,7 +144,7 @@ TEST_F(DenseLayerTest, ForwardRandomWeightMatrixRelu) {
     float* d_output;
 
     Layers::Dense denseLayer = commonTestSetup(
-        inputSize, outputSize, input, weights, biases, d_input, d_output, "relu"
+        inputSize, outputSize, input, weights, biases, d_input, d_output, RELU
     );
 
     denseLayer.forward(d_input, d_output);
@@ -188,7 +187,7 @@ TEST_F(DenseLayerTest, ForwardRandomWeightMatrixSigmoid) {
 
     Layers::Dense denseLayer = commonTestSetup(
         inputSize, outputSize, input, weights, biases, d_input, d_output,
-        "sigmoid"
+        SIGMOID
     );
 
     denseLayer.forward(d_input, d_output);