diff --git a/examples/efficientnet.py b/examples/efficientnet.py
index cf45261e5d..b6c8f4c37e 100644
--- a/examples/efficientnet.py
+++ b/examples/efficientnet.py
@@ -13,9 +13,7 @@ np.set_printoptions(suppress=True)
 
 from tinygrad.tensor import Tensor
 from tinygrad.utils import fetch
-
-# BatchNorm2D and swish
-from tinygrad.nn import *
+from tinygrad.nn import BatchNorm2D
 
 class MBConvBlock:
   def __init__(self, kernel_size, strides, expand_ratio, input_filters, output_filters, se_ratio):
@@ -47,14 +45,14 @@ class MBConvBlock:
   def __call__(self, inputs):
     x = inputs
     if self._expand_conv:
-      x = swish(self._bn0(x.conv2d(self._expand_conv)))
+      x = self._bn0(x.conv2d(self._expand_conv)).swish()
     x = x.pad2d(padding=self.pad)
     x = x.conv2d(self._depthwise_conv, stride=self.strides, groups=self._depthwise_conv.shape[0])
-    x = swish(self._bn1(x))
+    x = self._bn1(x).swish()
 
     # has_se
     x_squeezed = x.avg_pool2d(kernel_size=x.shape[2:4])
-    x_squeezed = swish(x_squeezed.conv2d(self._se_reduce).add(self._se_reduce_bias.reshape(shape=[1, -1, 1, 1])))
+    x_squeezed = x_squeezed.conv2d(self._se_reduce).add(self._se_reduce_bias.reshape(shape=[1, -1, 1, 1])).swish()
     x_squeezed = x_squeezed.conv2d(self._se_expand).add(self._se_expand_bias.reshape(shape=[1, -1, 1, 1]))
     x = x.mul(x_squeezed.sigmoid())
 
@@ -124,11 +122,11 @@ class EfficientNet:
 
   def forward(self, x):
     x = x.pad2d(padding=(0,1,0,1))
-    x = swish(self._bn0(x.conv2d(self._conv_stem, stride=2)))
+    x = self._bn0(x.conv2d(self._conv_stem, stride=2)).swish()
     for block in self._blocks:
       #print(x.shape)
       x = block(x)
-    x = swish(self._bn1(x.conv2d(self._conv_head)))
+    x = self._bn1(x.conv2d(self._conv_head)).swish()
     x = x.avg_pool2d(kernel_size=x.shape[2:4])
     x = x.reshape(shape=(-1, x.shape[1]))
     #x = x.dropout(0.2)
diff --git a/tinygrad/nn.py b/tinygrad/nn.py
index 1ce179667c..bf0a0cdd6d 100644
--- a/tinygrad/nn.py
+++ b/tinygrad/nn.py
@@ -1,8 +1,5 @@
 from tinygrad.tensor import Tensor
 
-def swish(x):
-  return x.mul(x.sigmoid())
-
 class BatchNorm2D:
   def __init__(self, sz, eps=0.001):
     self.eps = eps
diff --git a/tinygrad/tensor.py b/tinygrad/tensor.py
index dd2cd3a679..8bd7ab6e21 100644
--- a/tinygrad/tensor.py
+++ b/tinygrad/tensor.py
@@ -188,6 +188,9 @@ class Tensor:
     root = Tensor(np.zeros(self.shape, dtype=self.data.dtype)-1, gpu=self.gpu)
     return self.mul(y.pow(root))
 
+  def swish(self):
+    return self.mul(self.sigmoid())
+
 # An instantiation of the Function is the Context
 class Function:
   def __init__(self, *tensors):