diff --git a/src/mnist.c b/src/mnist.c
index 118faa2c2..d7d18f2a8 100644
--- a/src/mnist.c
+++ b/src/mnist.c
@@ -83,7 +83,7 @@ static nn_t network_mnist_create(const long odims[2], const long idims[3], enum
 
 	const struct initializer_s* init = NULL; // fallback to default initializer
 	
-	long pool_size[] = {2, 2, 1};
+	long pool_size[] = { 2, 2, 1 };
 
 	bool conv = false;	// we usecross correlation not convolution, i.e. as usual in deep-learning the convolution kernels are not flipped
 
@@ -119,7 +119,6 @@ static const char help_str[] = "Trains or applies a MNIST network.\nThis network
 
 int main_mnist(int argc, char* argv[argc])
 {
-
 	bool apply = false;
 	bool train = false;
 
@@ -152,11 +151,12 @@ int main_mnist(int argc, char* argv[argc])
 
 		num_init_gpu();
 		cuda_use_global_memory();
-	}
 
-	else
+	} else
 #endif
+	{
 		num_init();
+	}
 
 
 	if (apply && train)
@@ -176,8 +176,8 @@ int main_mnist(int argc, char* argv[argc])
 		long dims_out[NO];
 		complex float* out = load_cfl(filename_out, NO, dims_out);
 
-		long bdims_in[] = { dims_in[0], dims_in[1], Nb};
-		long bdims_out[] = { dims_out[0], Nb};
+		long bdims_in[] = { dims_in[0], dims_in[1], Nb };
+		long bdims_out[] = { dims_out[0], Nb };
 
 		long Nt = dims_out[1];	 //dataset size
 		assert(Nt == dims_in[2]);
@@ -199,8 +199,7 @@ int main_mnist(int argc, char* argv[argc])
 									(const long*[2]){ bdims_out, bdims_in},
 									(const long*[2]){ dims_out, dims_in },
 									(const complex float*[2]){ out, in },
-									0, BATCH_GEN_SHUFFLE_DATA, 123
-								);
+									0, BATCH_GEN_SHUFFLE_DATA, 123);
 
 		//setup for iter algorithm
 		int II = nn_get_nr_in_args(train_op);
@@ -232,6 +231,7 @@ int main_mnist(int argc, char* argv[argc])
 		unmap_cfl(NO, dims_out, out);
 	}
 
+
 	if (apply) {
 
 		long dims_out[] = { 10, dims_in[2] };
@@ -247,11 +247,12 @@ int main_mnist(int argc, char* argv[argc])
 		net = nn_get_wo_weights_F(net, weights, false);	//set inputs corresponding to weights to the loaded weights
 
 		nlop_generic_apply_sameplace(nn_get_nlop(net),
-			1, (int[1]){ 2 }, (const long*[1]){ dims_out } , (complex float* [1]){ out },
-			1, (int[1]){ 3 }, (const long*[1]){ dims_in } , (const complex float*[1]){ in },
+			1, (int[1]){ 2 }, (const long*[1]){ dims_out }, (complex float* [1]){ out },
+			1, (int[1]){ 3 }, (const long*[1]){ dims_in }, (const complex float*[1]){ in },
 			weights->tensors[0]);
 
 		unmap_cfl(NO, dims_out, out);
+
 		nn_weights_free(weights);
 	}