diff --git a/src/validate_mnist.cu b/src/validate_mnist.cu
index f4fb76be..56023304 100644
--- a/src/validate_mnist.cu
+++ b/src/validate_mnist.cu
@@ -12,13 +12,13 @@ int main(int argc, char** argv) {
   
   const size_t IMAGE_SIZE = 784;
   const size_t LABEL_SIZE = 10;
+  int BATCH_SIZE = 10000;
   
   std::cerr << "Loading test set...";
-  std::vector<float> testImages = datasets::mnist::ReadImages("../examples/mnist/t10k-images-idx3-ubyte", numofdata, IMAGE_SIZE);
-  std::vector<float> testLabels = datasets::mnist::ReadLabels("../examples/mnist/t10k-labels-idx1-ubyte", numofdata, LABEL_SIZE);
+  std::vector<float> testImages = datasets::mnist::ReadImages("../examples/mnist/t10k-images-idx3-ubyte", BATCH_SIZE, IMAGE_SIZE);
+  std::vector<float> testLabels = datasets::mnist::ReadLabels("../examples/mnist/t10k-labels-idx1-ubyte", BATCH_SIZE, LABEL_SIZE);
   std::cerr << "Done." << std::endl;
 
-  const size_t BATCH_SIZE = testLabels.size();
   
   std::cerr << "Loading model params...";
   NpzConverter converter("../scripts/test_model/model.npz");
@@ -43,19 +43,18 @@ int main(int argc, char** argv) {
                          axis=1, name="pred");
   auto graph = -mean(sum(y * log(predict), axis=1),
                      axis=0, name="cost");
-  
   std::cerr << "Done." << std::endl;
 
-  Tensor xt({numofdata, IMAGE_SIZE});
-  Tensor yt({numofdata, LABEL_SIZE});
+  Tensor xt({BATCH_SIZE, IMAGE_SIZE});
+  Tensor yt({BATCH_SIZE, LABEL_SIZE});
   
   x = xt << testImages;
   y = yt << testLabels;
   
   graph.forward(BATCH_SIZE);
-  auto results = predict.val();
   graph.backward();
   
+  auto results = predict.val();
   std::vector<float> resultsv(results.size());
   resultsv << results;