diff --git a/paddle/math/Matrix.cpp b/paddle/math/Matrix.cpp
index ebbbdfab1d368e83889431e77b4f4c77b71621cc..1ec4336cabbc7d3073b7638b7484bf61e83a2dc5 100644
--- a/paddle/math/Matrix.cpp
+++ b/paddle/math/Matrix.cpp
@@ -28,6 +28,7 @@ limitations under the License. */
 #include "hl_top_k.h"
 #include "paddle/utils/Logging.h"
 
+#include "NEONFunctions.h"
 #include "paddle/function/GemmFunctor.h"
 #include "paddle/utils/ThreadLocal.h"
 
@@ -4165,16 +4166,36 @@ void CpuMatrix::print(std::ostream& os) const {
 void CpuMatrix::paramReluForward(Matrix& data, Matrix& W) {
   real* input = data.getData();
   real* w = W.getData();
+  real* output = data_;
   size_t numElements = data.getWidth();
   size_t numSamples = data.getHeight();
   size_t paraSize = W.getHeight() * W.getWidth();
   CHECK(!(numElements % paraSize));  // this check from ParameterReluLayer::init
+
   size_t partial_sum = numElements / paraSize;
+  if (paraSize == numElements) {
+    for (size_t n = 0; n < numSamples * numElements; ++n) {
+      output[n] = input[n] > 0 ? input[n] : input[n] * w[n % numElements];
+    }
+    return;
+  }
+
+#if defined(__ARM_NEON__) || defined(__ARM_NEON)
+  for (size_t n = 0; n < numSamples; ++n) {
+    for (size_t i = 0; i < paraSize; i++) {
+      neon::prelu(
+          input + i * partial_sum, w[i], output + i * partial_sum, partial_sum);
+    }
+    input = input + numElements;
+    output = output + numElements;
+  }
+#else
   for (size_t n = 0, k = 0; n < numSamples; ++n) {
     for (size_t i = 0; i < numElements; ++i, ++k) {
-      data_[k] = input[k] > 0 ? input[k] : input[k] * w[i / partial_sum];
+      output[k] = input[k] > 0 ? input[k] : input[k] * w[i / partial_sum];
     }
   }
+#endif
 }
 
 void CpuMatrix::paramReluBackwardW(Matrix& oGrad, Matrix& data) {
diff --git a/paddle/math/NEONFunctions.cpp b/paddle/math/NEONFunctions.cpp
index 3bf47901f1069ac228fa1b877e29848d8cc130e8..0f8314942290a71dd327437b8a6da2d64fe48444 100644
--- a/paddle/math/NEONFunctions.cpp
+++ b/paddle/math/NEONFunctions.cpp
@@ -49,6 +49,46 @@ void relu(const float* a, float* b, int len) {
   }
 }
 
+// b[i] = a[i] > 0.0f ? a[i] : a[i] * w
+void prelu(const float* a, float w, float* b, int len) {
+  int offset = len % 16;
+  float32x4_t ma0, ma1, ma2, ma3;
+
+  float32x4_t zero = vdupq_n_f32(0.f);
+  float32x4_t vw = vdupq_n_f32(w);
+
+  for (int k = 0; k < len / 16; k++, a += 16, b += 16) {
+    ma0 = vld1q_f32(a);
+    ma1 = vld1q_f32(a + 4);
+    ma2 = vld1q_f32(a + 8);
+    ma3 = vld1q_f32(a + 12);
+
+    uint32x4_t flag0 = vcgtq_f32(ma0, zero);
+    uint32x4_t flag1 = vcgtq_f32(ma1, zero);
+    uint32x4_t flag2 = vcgtq_f32(ma2, zero);
+    uint32x4_t flag3 = vcgtq_f32(ma3, zero);
+
+    float32x4_t mul0 = vmulq_f32(ma0, vw);
+    float32x4_t mul1 = vmulq_f32(ma1, vw);
+    float32x4_t mul2 = vmulq_f32(ma2, vw);
+    float32x4_t mul3 = vmulq_f32(ma3, vw);
+
+    ma0 = vbslq_f32(flag0, ma0, mul0);
+    ma1 = vbslq_f32(flag1, ma1, mul1);
+    ma2 = vbslq_f32(flag2, ma2, mul2);
+    ma3 = vbslq_f32(flag3, ma3, mul3);
+
+    vst1q_f32(b, ma0);
+    vst1q_f32(b + 4, ma1);
+    vst1q_f32(b + 8, ma2);
+    vst1q_f32(b + 12, ma3);
+  }
+
+  for (int i = 0; i < offset; i++) {
+    b[i] = a[i] > 0.0f ? a[i] : a[i] * w;
+  }
+}
+
 }  // namespace neon
 }  // namespace paddle
 
diff --git a/paddle/math/NEONFunctions.h b/paddle/math/NEONFunctions.h
index 69085e333547a31a341fbfde247f1e30adb957ee..d67b2f47a85a963949d23415e4f6881658203bb7 100644
--- a/paddle/math/NEONFunctions.h
+++ b/paddle/math/NEONFunctions.h
@@ -18,6 +18,7 @@ namespace paddle {
 namespace neon {
 
 void relu(const float* a, float* b, int len);
+void prelu(const float* a, float w, float* b, int len);
 
 }  // namespace neon
 }  // namespace paddle
diff --git a/paddle/operators/row_conv_op.cu b/paddle/operators/row_conv_op.cu
index 3fc5eabcf51aea5c4237da865341c1d9e896dc3f..56a98ff299e8263179306756631949761e386f70 100644
--- a/paddle/operators/row_conv_op.cu
+++ b/paddle/operators/row_conv_op.cu
@@ -243,7 +243,6 @@ __global__ void RowConvGradFilter(const T *in, const T *dout, int num_sequence,
                                   int block_x, int block_y,
                                   const size_t *batch_indices, T *dfilter) {
   int blx = blockDim.x;
-  int bly = blockDim.y;
   int thx = threadIdx.x;
   int thy = threadIdx.y;
   int gx = blockIdx.x * blx;
diff --git a/python/paddle/v2/fluid/layers.py b/python/paddle/v2/fluid/layers.py
index fd8a2ed18c9690ab55f62aea6c7b9dd7a92e68d5..1f45487902a96856dfb411aca8d66a972abbdf38 100644
--- a/python/paddle/v2/fluid/layers.py
+++ b/python/paddle/v2/fluid/layers.py
@@ -1732,8 +1732,10 @@ def conv2d_transpose(input,
 
         h_in = input.shape[2]
         w_in = input.shape[3]
-        filter_size_h = output_size[0] - (h_in - 1) * stride[0] + 2 * padding[0]
-        filter_size_w = output_size[1] - (w_in - 1) * stride[1] + 2 * padding[1]
+        filter_size_h = output_size[0] - \
+            (h_in - 1) * stride[0] + 2 * padding[0]
+        filter_size_w = output_size[1] - \
+            (w_in - 1) * stride[1] + 2 * padding[1]
         filter_size = [filter_size_h, filter_size_w]
     elif isinstance(filter_size, int):
         filter_size = [filter_size, filter_size]
diff --git a/python/paddle/v2/fluid/nets.py b/python/paddle/v2/fluid/nets.py
index 05728ad75a5bd1e87aa3c75ffcc4eac34b6b956c..7ef524318e637604cc22ba9d8d7cafe1b7505261 100644
--- a/python/paddle/v2/fluid/nets.py
+++ b/python/paddle/v2/fluid/nets.py
@@ -9,6 +9,7 @@ def simple_img_conv_pool(input,
                          pool_size,
                          pool_stride,
                          act,
+                         param_attr=None,
                          pool_type='max',
                          main_program=None,
                          startup_program=None):
@@ -16,6 +17,7 @@ def simple_img_conv_pool(input,
         input=input,
         num_filters=num_filters,
         filter_size=filter_size,
+        param_attr=param_attr,
         act=act,
         main_program=main_program,
         startup_program=startup_program)
@@ -36,6 +38,7 @@ def img_conv_group(input,
                    conv_padding=1,
                    conv_filter_size=3,
                    conv_act=None,
+                   param_attr=None,
                    conv_with_batchnorm=False,
                    conv_batchnorm_drop_rate=None,
                    pool_stride=1,
@@ -57,6 +60,7 @@ def img_conv_group(input,
 
     conv_padding = __extend_list__(conv_padding)
     conv_filter_size = __extend_list__(conv_filter_size)
+    param_attr = __extend_list__(param_attr)
     conv_with_batchnorm = __extend_list__(conv_with_batchnorm)
     conv_batchnorm_drop_rate = __extend_list__(conv_batchnorm_drop_rate)
 
@@ -70,6 +74,7 @@ def img_conv_group(input,
             num_filters=conv_num_filter[i],
             filter_size=conv_filter_size[i],
             padding=conv_padding[i],
+            param_attr=param_attr[i],
             act=local_conv_act,
             main_program=main_program,
             startup_program=startup_program)
@@ -101,6 +106,7 @@ def img_conv_group(input,
 def sequence_conv_pool(input,
                        num_filters,
                        filter_size,
+                       param_attr=None,
                        act="sigmoid",
                        pool_type="max",
                        main_program=None,
@@ -109,6 +115,7 @@ def sequence_conv_pool(input,
         input=input,
         num_filters=num_filters,
         filter_size=filter_size,
+        param_attr=param_attr,
         act=act,
         main_program=main_program,
         startup_program=startup_program)