diff --git a/src/cltest.c b/src/cltest.c
index ac80a68..99bfe75 100644
--- a/src/cltest.c
+++ b/src/cltest.c
@@ -53,22 +53,22 @@ void train(clm_NN nn, unsigned int numElements, clm_Vector *inputs, clm_Vector *
 			currentXs = nn.layers[i].output;
 		}
 
-		clm_Linear lastLayer = nn.layers[nn.numLayers - 1];
+		clm_Linear *lastLayer = &nn.layers[nn.numLayers - 1];
 		for(unsigned int b = 0; b < batchSize; b++) {
 			// Error of last layer = y - yhat
-			clm_matrixCopy(batchOutputs[b], lastLayer.error[b]); // lastLayer.error = y
-			clm_matrixSubtractMatrix(lastLayer.error[b], lastLayer.output[b]); // lastLayer.error -= yhat
+			clm_matrixCopy(batchOutputs[b], lastLayer->error[b]); // lastLayer.error = y
+			clm_matrixSubtractMatrix(lastLayer->error[b], lastLayer->output[b]); // lastLayer.error -= yhat
 		}
 
 		for(int i = nn.numLayers - 1; i >= 0; i--) {
-			clm_Linear layer = nn.layers[i];
+			clm_Linear *layer = &nn.layers[i];
 			clm_Matrix *inputsToThisLayer = i == 0 ? batchInputs : nn.layers[i - 1].output;
-			clm_Matrix *outputsOfThisLayer = layer.output;
-			clm_linearBackprop(&layer, nn.learnRate, batchSize, inputsToThisLayer, outputsOfThisLayer, layer.error, i > 0, i == 0 ? NULL : nn.layers[i - 1].error, layer.weightsError, layer.gradient);
+			clm_Matrix *outputsOfThisLayer = layer->output;
+			clm_linearBackprop(layer, nn.learnRate, batchSize, inputsToThisLayer, outputsOfThisLayer, layer->error, i > 0, i == 0 ? NULL : nn.layers[i - 1].error, layer->weightsError, layer->gradient);
 
 			for(unsigned int b = 0; b < batchSize; b++) {
-				clm_matrixAddMatrix(layer.weights, layer.weightsError[b]);
-				clm_matrixAddMatrix(layer.bias, layer.gradient[b]);
+				clm_matrixAddMatrix(layer->weights, layer->weightsError[b]);
+				clm_matrixAddMatrix(layer->bias, layer->gradient[b]);
 			}
 		}