diff --git a/src/blas_kernels.cu b/src/blas_kernels.cu
index 657be1de..3fdc742d 100644
--- a/src/blas_kernels.cu
+++ b/src/blas_kernels.cu
@@ -99,7 +99,7 @@ __global__ void dot_kernel(float *output, float scale, int batch, int n, int siz
     int f1 = index / n;
     int f2 = index % n;
     if (f2 <= f1) return;
-    
+
     float sum = 0;
     float norm1 = 0;
     float norm2 = 0;
@@ -140,19 +140,20 @@ void backward_bias_gpu(float *bias_updates, float *delta, int batch, int n, int
     check_error(cudaPeekAtLastError());
 }
 
-
 __global__ void adam_kernel(int N, float *x, float *m, float *v, float B1, float B2, float rate, float eps, int t)
 {
     int index = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
     if (index >= N) return;
-    
-    x[index] = x[index] - (rate * sqrtf(1.F-powf(B2, t)) / (1.F-powf(B1, t)) * m[index] / (sqrtf(v[index]) + eps));
-    //if(index == 0) printf("%f %f %f %f\n", m[index], v[index], (rate * sqrtf(1.F-powf(B2, t)) / (1.F-powf(B1, t)) * m[index] / (sqrt(v[index]) + eps)));
+
+    float mhat = m[index] / (1.f - powf(B1, t));
+    float vhat = v[index] / (1.f - powf(B2, t));
+
+    x[index] = x[index] + rate * mhat / (sqrtf(vhat) + eps);
 }
 
 extern "C" void adam_gpu(int n, float *x, float *m, float *v, float B1, float B2, float rate, float eps, int t)
 {
-    adam_kernel<<<cuda_gridsize(n), BLOCK>>>(n, x, m, v, B1, B2, rate, eps, t);
+    adam_kernel << <cuda_gridsize(n), BLOCK >> >(n, x, m, v, B1, B2, rate, eps, t);
     check_error(cudaPeekAtLastError());
 }
 
@@ -175,7 +176,7 @@ __global__ void normalize_kernel(int N, float *x, float *mean, float *variance,
     int index = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
     if (index >= N) return;
     int f = (index/spatial)%filters;
-    
+
     x[index] = (x[index] - mean[f])/(sqrtf(variance[f]) + .000001f);
 }
 
@@ -184,7 +185,7 @@ __global__ void normalize_delta_kernel(int N, float *x, float *mean, float *vari
     int index = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
     if (index >= N) return;
     int f = (index/spatial)%filters;
-    
+
     delta[index] = delta[index] * 1.F/(sqrtf(variance[f]) + .000001f) + variance_delta[f] * 2. * (x[index] - mean[f]) / (spatial * batch) + mean_delta[f]/(spatial*batch);
 }