From 502ab760335c5e436de627a89ec7eeeb65425397 Mon Sep 17 00:00:00 2001
From: Himanshu Pathak <hpathak336@gmail.com>
Date: Mon, 15 Jul 2024 19:59:36 +0530
Subject: [PATCH] Adding Batch Norm layer to mnist example.

---
 .gitignore                                  | 2 +-
 cpp/neural_networks/mnist_cnn/mnist_cnn.cpp | 8 +++++++-
 2 files changed, 8 insertions(+), 2 deletions(-)

diff --git a/.gitignore b/.gitignore
index 41653f59..d5d51829 100644
--- a/.gitignore
+++ b/.gitignore
@@ -12,7 +12,7 @@ cmake-build-*
 *.so
 data
 plots
-mnist_*
+mnist_t*
 nn_*
 cifar_*
 covertype-rf
diff --git a/cpp/neural_networks/mnist_cnn/mnist_cnn.cpp b/cpp/neural_networks/mnist_cnn/mnist_cnn.cpp
index b5862158..44a371be 100644
--- a/cpp/neural_networks/mnist_cnn/mnist_cnn.cpp
+++ b/cpp/neural_networks/mnist_cnn/mnist_cnn.cpp
@@ -120,6 +120,9 @@ int main()
                         2, // Stride along height.
                         true);
 
+  // Add BatchNorm.
+  model.Add<BatchNorm>();
+
   // Add the second convolution layer.
   model.Add<Convolution>(16, // Number of output activation maps.
                          5,  // Filter width.
@@ -136,6 +139,9 @@ int main()
   // Add the second pooling layer.
   model.Add<MaxPooling>(2, 2, 2, 2, true);
 
+  // Add BatchNorm
+  model.Add<BatchNorm>();
+  
   // Add the final dense layer.
   model.Add<Linear>(10);
   model.Add<LogSoftMax>();
@@ -200,7 +206,7 @@ int main()
   // Get predictions on test data points.
   // The original file could be download from
   // https://www.kaggle.com/c/digit-recognizer/data
-  data::Load("../data/mnist_test.csv", dataset, true);
+  data::Load("../../../data/mnist_test.csv", dataset, true);
   const mat testX = dataset.submat(1, 0, dataset.n_rows - 1, dataset.n_cols - 1)
       / 256.0;
   const mat testY = dataset.row(0);