diff --git a/examples/inception_v3/inception_v3.cpp b/examples/inception_v3/inception_v3.cpp
index 9c8e0db..5091df7 100644
--- a/examples/inception_v3/inception_v3.cpp
+++ b/examples/inception_v3/inception_v3.cpp
@@ -14,35 +14,91 @@ int main(int argc, const char *const argv[]) {
 class BasicConv2d : public CUDANet::Module {
   public:
     BasicConv2d(
-        const int inputSize,
-        const int inputChannels,
-        const int outputChannels,
-        const int kernelSize,
-        const int stride,
-        const int padding,
-        const std::string& prefix
+        const int          inputSize,
+        const int          inputChannels,
+        const int          outputChannels,
+        const int          kernelSize,
+        const int          stride,
+        const int          padding,
+        const std::string &prefix
     )
         : inputSize(inputSize),
           inputChannels(inputChannels),
           outputChannels(outputChannels) {
         // Create the convolution layer
         CUDANet::Layers::Conv2d *conv = new CUDANet::Layers::Conv2d(
-            inputSize, inputChannels, kernelSize, stride, outputChannels, padding, CUDANet::Layers::ActivationType::NONE
+            inputSize, inputChannels, kernelSize, stride, outputChannels,
+            padding, CUDANet::Layers::ActivationType::NONE
         );
 
         int batchNormSize = conv->getOutputSize();
 
-        CUDANet::Layers::BatchNorm *batchNorm =
-            new CUDANet::Layers::BatchNorm(
-                batchNormSize, outputChannels, 1e-3f, CUDANet::Layers::ActivationType::RELU
-            );
+        CUDANet::Layers::BatchNorm *batchNorm = new CUDANet::Layers::BatchNorm(
+            batchNormSize, outputChannels, 1e-3f,
+            CUDANet::Layers::ActivationType::RELU
+        );
 
         addLayer(prefix + ".conv", conv);
         addLayer(prefix + ".bn", batchNorm);
     }
 
+    float* forward(const float* d_input) {
+        for (auto& layer : layers) {
+            d_input = layer.second->forward(d_input);
+        }
+        return d_input;
+    }
+
   private:
     int inputSize;
     int inputChannels;
     int outputChannels;
 };
+
+class InceptionA : public CUDANet::Module {
+  public:
+    InceptionA(
+        const int          inputSize,
+        const int          inputChannels,
+        const int          poolFeatures,
+        const std::string &prefix
+    )
+        : inputSize(inputSize),
+          inputChannels(inputChannels),
+          poolFeatures(poolFeatures) {
+        
+        // Branch 1x1
+        CUDANet::Module *branch1x1 = new BasicConv2d(
+            inputSize, inputChannels, 64, 1, 1, 0, prefix + ".branch1x1"
+        );
+
+        // Branch 5x5
+        CUDANet::Module *branch5x5_1 = new BasicConv2d(
+            inputSize, inputChannels, 48, 1, 1, 0, prefix + ".branch5x5_1"
+        );
+        CUDANet::Module *branch5x5_2 = new BasicConv2d(
+            inputSize, 48, 64, 5, 1, 2, prefix + ".branch5x5_2"
+        );
+
+        // Branch 3x3
+        CUDANet::Module *branch3x3_1 = new BasicConv2d(
+            inputSize, inputChannels, 64, 1, 1, 0, prefix + ".branch3x3_1"
+        );
+        CUDANet::Module *branch3x3_2 = new BasicConv2d(
+            inputSize, 64, 96, 3, 1, 1, prefix + ".branch3x3_2"
+        );
+        CUDANet::Module *branch3x3_3 = new BasicConv2d(
+            inputSize, 96, 96, 3, 1, 1, prefix + ".branch3x3_3"
+        );
+
+        // Branch Pool
+        CUDANet::Module *branchPool = new BasicConv2d(
+            inputSize, inputChannels, poolFeatures, 1, 1, 0, prefix + ".branchPool"
+        );
+    }
+
+  private:
+    int inputSize;
+    int inputChannels;
+    int poolFeatures;
+};
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